From bba3fd12d01122e3809ae514b5b3931a588b9d6a Mon Sep 17 00:00:00 2001 From: bch0w Date: Wed, 1 Jun 2022 14:43:08 -0800 Subject: [PATCH 001/195] fixed failing seisflows and config tests. updated test parameter files --- seisflows/config.py | 2 +- .../tests/test_data/test_conf_parameters.yaml | 15 +++++++++------ .../tests/test_data/test_filled_parameters.yaml | 15 +++++++++------ .../tests/test_data/test_setup_parameters.yaml | 2 +- seisflows/tests/test_seisflows.py | 7 +++++-- 5 files changed, 25 insertions(+), 16 deletions(-) diff --git a/seisflows/config.py b/seisflows/config.py index 4119df7a..5347d534 100644 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -210,7 +210,7 @@ def config_logger(level="DEBUG", filename=None, filemode="a", verbose=True): """ # Make sure that we don't already have handlers described, which may happen # if this function gets run multiple times, and leads to duplicate logs - while logger.hasHandlers(): + while logger.hasHandlers() and logger.handlers: logger.removeHandler(logger.handlers[0]) # Two levels of verbosity on log level, triggered with PAR.VERBOSE diff --git a/seisflows/tests/test_data/test_conf_parameters.yaml b/seisflows/tests/test_data/test_conf_parameters.yaml index f387edaf..0b08e2d0 100644 --- a/seisflows/tests/test_data/test_conf_parameters.yaml +++ b/seisflows/tests/test_data/test_conf_parameters.yaml @@ -9,7 +9,7 @@ # # .. rubric:: # - To determine available options for modules listed below, run: -# > seisflows print module +# > seisflows print modules # - To auto-fill with docstrings and default values (recommended), run: # > seisflows configure # - To set values as NoneType, use: null @@ -78,10 +78,10 @@ NPROC: 1 # seisflows.plugins.adjoint # NORMALIZE (list): # Data normalization parameters used to normalize the amplitudes of -# waveforms. Choose from two sets:ENORML1: normalize per event by L1 of -# traces; ORENORML2: normalize per event by L2 of traces; ANDTNORML1: -# normalize per trace by L1 of itself; ORTNORML2: normalize per trace by L2 -# of itself +# waveforms. Choose from two sets: ENORML1: normalize per event by L1 of +# traces; OR ENORML2: normalize per event by L2 of traces; AND TNORML1: +# normalize per trace by L1 of itself; OR TNORML2: normalize per trace by +# L2 of itself # FILTER (str): # Data filtering type, available options are:BANDPASS (req. MIN/MAX # PERIOD/FREQ);LOWPASS (req. MAX_FREQ or MIN_PERIOD); HIGHPASS (req. @@ -260,6 +260,8 @@ END: !!! REQUIRED PARAMETER !!! # path to local data to be used during workflow # LOGFILE: # the main output log file where all processes will track their status +# PREPROCESS: +# scratch path to store any preprocessing outputs # SOLVER: # scratch path to hold solver working directories # SPECFEM_BIN: @@ -290,6 +292,7 @@ PATHS: SYSTEM: scratch/system LOCAL: LOGFILE: output_sf.txt + PREPROCESS: scratch/preprocess SOLVER: scratch/solver SPECFEM_BIN: !!! REQUIRED PATH !!! SPECFEM_DATA: !!! REQUIRED PATH !!! @@ -298,6 +301,6 @@ PATHS: OPTIMIZE: scratch/optimize MODEL_INIT: !!! REQUIRED PATH !!! MODEL_TRUE: - FUNC: scratch/scratch + FUNC: scratch/evalfunc GRAD: scratch/evalgrad HESS: scratch/evalhess diff --git a/seisflows/tests/test_data/test_filled_parameters.yaml b/seisflows/tests/test_data/test_filled_parameters.yaml index 56e7717b..a33448c0 100644 --- a/seisflows/tests/test_data/test_filled_parameters.yaml +++ b/seisflows/tests/test_data/test_filled_parameters.yaml @@ -9,7 +9,7 @@ # # .. rubric:: # - To determine available options for modules listed below, run: -# > seisflows print module +# > seisflows print modules # - To auto-fill with docstrings and default values (recommended), run: # > seisflows configure # - To set values as NoneType, use: null @@ -78,10 +78,10 @@ NPROC: 1 # seisflows.plugins.adjoint # NORMALIZE (list): # Data normalization parameters used to normalize the amplitudes of -# waveforms. Choose from two sets:ENORML1: normalize per event by L1 of -# traces; ORENORML2: normalize per event by L2 of traces; ANDTNORML1: -# normalize per trace by L1 of itself; ORTNORML2: normalize per trace by L2 -# of itself +# waveforms. Choose from two sets: ENORML1: normalize per event by L1 of +# traces; OR ENORML2: normalize per event by L2 of traces; AND TNORML1: +# normalize per trace by L1 of itself; OR TNORML2: normalize per trace by +# L2 of itself # FILTER (str): # Data filtering type, available options are:BANDPASS (req. MIN/MAX # PERIOD/FREQ);LOWPASS (req. MAX_FREQ or MIN_PERIOD); HIGHPASS (req. @@ -260,6 +260,8 @@ END: 1 # path to local data to be used during workflow # LOGFILE: # the main output log file where all processes will track their status +# PREPROCESS: +# scratch path to store any preprocessing outputs # SOLVER: # scratch path to hold solver working directories # SPECFEM_BIN: @@ -290,6 +292,7 @@ PATHS: SYSTEM: scratch/system LOCAL: LOGFILE: output_sf.txt + PREPROCESS: scratch/preprocess SOLVER: scratch/solver SPECFEM_BIN: ./bin SPECFEM_DATA: ./DATA @@ -298,6 +301,6 @@ PATHS: OPTIMIZE: scratch/optimize MODEL_INIT: ./MODEL_INIT MODEL_TRUE: ./MODEL_TRUE - FUNC: scratch/scratch + FUNC: scratch/evalfunc GRAD: scratch/evalgrad HESS: scratch/evalhess diff --git a/seisflows/tests/test_data/test_setup_parameters.yaml b/seisflows/tests/test_data/test_setup_parameters.yaml index 08fbbae8..303931d1 100644 --- a/seisflows/tests/test_data/test_setup_parameters.yaml +++ b/seisflows/tests/test_data/test_setup_parameters.yaml @@ -9,7 +9,7 @@ # # .. rubric:: # - To determine available options for modules listed below, run: -# > seisflows print module +# > seisflows print modules # - To auto-fill with docstrings and default values (recommended), run: # > seisflows configure # - To set values as NoneType, use: null diff --git a/seisflows/tests/test_seisflows.py b/seisflows/tests/test_seisflows.py index 098c9ac6..196a7a8b 100644 --- a/seisflows/tests/test_seisflows.py +++ b/seisflows/tests/test_seisflows.py @@ -15,7 +15,8 @@ from seisflows import logger from seisflows.seisflows import sfparser, SeisFlows -from seisflows.config import save, Dict, ROOT_DIR, NAMES, CFGPATHS +from seisflows.config import (save, Dict, ROOT_DIR, NAMES, CFGPATHS, + config_logger) from seisflows.tools.wrappers import loadyaml TEST_DIR = os.path.join(ROOT_DIR, "tests") @@ -239,6 +240,8 @@ def test_cmd_clean(tmpdir): def test_config_logging(tmpdir, copy_par_file): """ Test logging configuration to make sure we can print to file + + TODO move this to test_config.py? :param tmpdir: :return: """ @@ -250,7 +253,7 @@ def test_config_logging(tmpdir, copy_par_file): with patch.object(sys, "argv", ["seisflows"]): sf = SeisFlows() sf._register(force=True) - sf._config_logging() + config_logger(filename=CFGPATHS.LOGFILE) logger.debug(msg) # Check that we created the log file and wrote the message in From 16adfd9a45b2c65a0650717717dd9103fd06d2f5 Mon Sep 17 00:00:00 2001 From: bch0w Date: Wed, 1 Jun 2022 16:34:27 -0800 Subject: [PATCH 002/195] working on solver tests, making solver definitions more explicit by adding arguments and not just *args --- seisflows/config.py | 2 +- seisflows/solver/base.py | 38 ++++++++++++++-------------------- seisflows/solver/specfem2d.py | 38 +++++++--------------------------- seisflows/tests/test_solver.py | 1 + 4 files changed, 26 insertions(+), 53 deletions(-) diff --git a/seisflows/config.py b/seisflows/config.py index 5347d534..ac67f5f0 100644 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -32,7 +32,7 @@ !!! WARNING !!! The following constants are (some of the only) hardwired components -of the pacakge. The naming, order, case, etc., of each constant may be +of the package. The naming, order, case, etc., of each constant may be important, and any changes to these will more-than-likely break the underlying mechanics of the package. Do not touch unless you know what you're doing! """ diff --git a/seisflows/solver/base.py b/seisflows/solver/base.py index f1db5e62..3edf36e3 100644 --- a/seisflows/solver/base.py +++ b/seisflows/solver/base.py @@ -172,10 +172,6 @@ def check(self, validate=True): assert (required_parameter in PAR), \ f"Solver requires {required_parameter}" - # Important to reset parameters to a blank list and let the check - # statements fill it. If not, each time workflow is resumed, parameters - # list will append redundant parameters and things stop working - available_materials = ["ELASTIC", "ACOUSTIC", # specfem2d, specfem3d "ISOTROPIC", "ANISOTROPIC"] # specfem3d_globe assert(PAR.MATERIALS.upper() in available_materials), \ @@ -186,6 +182,9 @@ def check(self, validate=True): f"DENSITY must be in {acceptable_densities}" # Internal parameter list based on user-input material choices + # Important to reset parameters to a blank list and let the check + # statements fill it. If not, each time workflow is resumed, parameters + # list will append redundant parameters and things stop working self.parameters = [] if PAR.MATERIALS.upper() == "ELASTIC": assert(PAR.SOLVER.lower() in ["specfem2d", "specfem3d"]) @@ -225,15 +224,20 @@ def setup(self): # Clean up for new inversion unix.rm(self.cwd) self.initialize_solver_directories() + self.check_solver_parameter_files() # Determine where observation data will come from if PAR.CASE.upper() == "SYNTHETIC" and PATH.MODEL_TRUE is not None: if self.taskid == 0: self.logger.info("generating 'data' with MODEL_TRUE synthetics") # Generate synthetic data on the fly using the true model - self.generate_data(model_path=PATH.MODEL_TRUE, + self.generate_mesh(model_path=PATH.MODEL_TRUE, model_name="model_true", model_type="gll") + self.forward(path=os.path.join("traces", "obs")) + if PAR.SAVETRACES: + self.export_traces(os.path.join(PATH.OUTPUT, "traces", "obs")) + elif PATH.DATA is not None and os.path.exists(PATH.DATA): # If Data provided by user, copy directly into the solver directory unix.cp(src=glob(os.path.join(PATH.DATA, self.source_name, "*")), @@ -259,26 +263,18 @@ def clean(self): unix.rm("OUTPUT_FILES") unix.mkdir("OUTPUT_FILES") - def generate_data(self, *args, **kwargs): - """ - Generates data based on a given model - - !!! Must be implemented by subclass !!! - """ - raise NotImplementedError - - def generate_mesh(self, *args, **kwargs): + def generate_mesh(self, model_path, model_name, model_type): """ - Performs meshing and database generation + Performs meshing and database generation. - !!! Must be implemented by subclass !!! + This function is Solver specific and is responsible for generating + the mesh using the external numerical solver. """ - raise NotImplementedError + raise NotImplementedError("function 'solver.generate_mesh()' must be" + "implemented by a Solver sub class") def eval_func(self, path, write_residuals=True): """ - High level solver interface - Performs forward simulations and evaluates the misfit function :type path: str @@ -359,7 +355,7 @@ def apply_hess(self, path): self.adjoint() self.export_kernels(path) - def forward(self): + def forward(self, path): """ Low level solver interface @@ -794,8 +790,6 @@ def initialize_solver_directories(self): dst = os.path.join(self.cwd, "OUTPUT_FILES", "DATABASES_MPI") unix.cp(src, dst) - self.check_solver_parameter_files() - def initialize_adjoint_traces(self): """ Setup utility: Creates the "adjoint traces" expected by SPECFEM. diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index c41a0dc1..994aa727 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -140,33 +140,6 @@ def check_solver_parameter_files(self): else: setpar(key="absorbtop", val=".true.", file="DATA/Par_file") - def generate_data(self, **model_kwargs): - """ - Generates data using the True model, exports traces to `traces/obs` - - :param model_kwargs: keyword arguments to pass to `generate_mesh` - """ - self.generate_mesh(**model_kwargs) - - unix.cd(self.cwd) - setpar(key="SIMULATION_TYPE", val="1", file="DATA/Par_file") - setpar(key="SAVE_FORWARD", val=".true.", file="DATA/Par_file") - - call_solver(PAR.MPIEXEC, "bin/xmeshfem2D", output="mesher.log") - call_solver(PAR.MPIEXEC, "bin/xspecfem2D", output="solver.log") - - if PAR.FORMAT.upper() == "SU": - # Work around SPECFEM2D's version dependent file names - for tag in ["d", "v", "a", "p"]: - unix.rename(old=f"single_{tag}.su", new="single.su", - names=glob(os.path.join("OUTPUT_FILES", "*.su"))) - - unix.mv(src=glob(os.path.join("OUTPUT_FILES", self.data_wildcard)), - dst=os.path.join("traces", "obs")) - - if PAR.SAVETRACES: - self.export_traces(os.path.join(PATH.OUTPUT, "traces", "obs")) - def initialize_adjoint_traces(self): """ Setup utility: Creates the "adjoint traces" expected by SPECFEM. @@ -247,18 +220,23 @@ def generate_mesh(self, model_path, model_name, model_type='gll'): if self.taskid == 0: self.export_model(os.path.join(PATH.OUTPUT, model_name)) - def forward(self, path='traces/syn'): + def forward(self, path="traces/syn"): """ Calls SPECFEM2D forward solver, exports solver outputs to traces dir :type path: str :param path: path to export traces to after completion of simulation + relatiev to the solver cwd """ + unix.cd(self.cwd) + setpar(key="SIMULATION_TYPE", val="1", file="DATA/Par_file") setpar(key="SAVE_FORWARD", val=".true.", file="DATA/Par_file") - call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xmeshfem2D") - call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xspecfem2D") + call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xmeshfem2D", + output="mesher.log") + call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xspecfem2D", + output="solver.log") if PAR.FORMAT.upper() == "SU": # Work around SPECFEM2D's version dependent file names diff --git a/seisflows/tests/test_solver.py b/seisflows/tests/test_solver.py index c2facb2d..a7bbc198 100644 --- a/seisflows/tests/test_solver.py +++ b/seisflows/tests/test_solver.py @@ -130,3 +130,4 @@ def test_required_functions_exist(sfinit, modules): # MODULE AND FUNCTION SPECIFIC TESTS TO FOLLOW # ============================================================================== +def test \ No newline at end of file From 1237df34996181bc51e7e1dc267513aa9db072e5 Mon Sep 17 00:00:00 2001 From: bch0w Date: Wed, 1 Jun 2022 18:02:11 -0800 Subject: [PATCH 003/195] cleaning up some solver calls to remove redundant code --- seisflows/solver/base.py | 29 ++++------------------------ seisflows/solver/specfem2d.py | 36 ++++++++++++++++++++++++++++++++++- seisflows/solver/specfem3d.py | 1 - 3 files changed, 39 insertions(+), 27 deletions(-) diff --git a/seisflows/solver/base.py b/seisflows/solver/base.py index 3edf36e3..25cfec73 100644 --- a/seisflows/solver/base.py +++ b/seisflows/solver/base.py @@ -225,31 +225,8 @@ def setup(self): unix.rm(self.cwd) self.initialize_solver_directories() self.check_solver_parameter_files() - - # Determine where observation data will come from - if PAR.CASE.upper() == "SYNTHETIC" and PATH.MODEL_TRUE is not None: - if self.taskid == 0: - self.logger.info("generating 'data' with MODEL_TRUE synthetics") - # Generate synthetic data on the fly using the true model - self.generate_mesh(model_path=PATH.MODEL_TRUE, - model_name="model_true", - model_type="gll") - self.forward(path=os.path.join("traces", "obs")) - if PAR.SAVETRACES: - self.export_traces(os.path.join(PATH.OUTPUT, "traces", "obs")) - - elif PATH.DATA is not None and os.path.exists(PATH.DATA): - # If Data provided by user, copy directly into the solver directory - unix.cp(src=glob(os.path.join(PATH.DATA, self.source_name, "*")), - dst=os.path.join("traces", "obs") - ) - - # Prepare initial model - if self.taskid == 0: - self.logger.info("running mesh generation for MODEL_INIT") - self.generate_mesh(model_path=PATH.MODEL_INIT, - model_name="model_init", - model_type="gll") + self.generate_data() + self.generate_mesh(model_name="init", model_type="gll") # Create blank adjoint traces to be overwritten self.initialize_adjoint_traces() @@ -823,6 +800,8 @@ def check_mesh_properties(self, path=None): """ Determine if Mesh properties are okay for workflow + TODO fix or rewrite this function + :type path: str :param path: path to the mesh file """ diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index 994aa727..69dfa4f7 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -187,7 +187,31 @@ def initialize_adjoint_traces(self): if not exists(fid_check): unix.cp(fid, fid_check) - def generate_mesh(self, model_path, model_name, model_type='gll'): + def generate_data(self): + """ + Generates observation data to be compared to synthetics. This must + only be run once. If `PAR.CASE`=='data', then real data will be copied + over. If `PAR.CASE`=='synthetic' then the external solver will use the + True model to generate 'observed' synthetics. Finally exports traces to + 'cwd/traces/obs' + """ + # If synthetic inversion, generate 'data' with solver + if PAR.CASE.upper() == "SYNTHETIC": + if PATH.MODEL_TRUE is not None: + if self.taskid == 0: + self.logger.info("generating 'data' with MODEL_TRUE") + # Generate synthetic data on the fly using the true model + self.generate_mesh(model_name="true", model_type="gll") + self.forward(path=os.path.join("traces", "obs")) + # If Data provided by user, copy directly into the solver directory + elif PATH.DATA is not None and os.path.exists(PATH.DATA): + unix.cp(src=glob(os.path.join(PATH.DATA, self.source_name, "*")), + dst=os.path.join("traces", "obs") + ) + if PAR.SAVETRACES: + self.export_traces(path=os.path.join(PATH.OUTPUT, "traces", "obs")) + + def generate_mesh(self, model_name, model_type="gll"): """ Performs meshing with internal mesher Meshfem2D and database generation @@ -199,8 +223,18 @@ def generate_mesh(self, model_path, model_name, model_type='gll'): :param model_type: available model types to be passed to the Specfem3D Par_file. See Specfem3D Par_file for available options. """ + if model_name.upper() == "INIT": + model_path = PATH.MODEL_INIT + elif model_name.upper() == "TRUE": + model_path = PATH.MODEL_TRUE + else: + raise ValueError(f"model name must be 'INIT' or 'TRUE'") assert(exists(model_path)), f"model {model_path} does not exist" + if self.taskid == 0: + self.logger.info(f"running mesh generation for " + f"MODEL_{model_name.upper()}") + available_model_types = ["gll"] assert(model_type in available_model_types), \ f"{model_type} not in available types {available_model_types}" diff --git a/seisflows/solver/specfem3d.py b/seisflows/solver/specfem3d.py index 5f247f71..0512035c 100644 --- a/seisflows/solver/specfem3d.py +++ b/seisflows/solver/specfem3d.py @@ -123,7 +123,6 @@ def generate_mesh(self, model_path, model_name, model_type=None): Par_file. See Specfem3D Par_file for available options. """ available_model_types = ["gll"] - assert(exists(model_path)), f"model {model_path} does not exist" model_type = model_type or getpar(key="MODEL", file="DATA/Par_file") From a0a9e161db3543d6b4e68fd84ad1858e06558521 Mon Sep 17 00:00:00 2001 From: bch0w Date: Tue, 14 Jun 2022 10:09:10 -0800 Subject: [PATCH 004/195] fixing typos and docstrings in optimization module --- seisflows/optimize/LBFGS.py | 1 - seisflows/optimize/NLCG.py | 6 ++++-- seisflows/optimize/base.py | 5 ++--- seisflows/solver/specfem2d.py | 4 ++-- 4 files changed, 8 insertions(+), 8 deletions(-) diff --git a/seisflows/optimize/LBFGS.py b/seisflows/optimize/LBFGS.py index 29f04b2f..0ac5db07 100644 --- a/seisflows/optimize/LBFGS.py +++ b/seisflows/optimize/LBFGS.py @@ -10,7 +10,6 @@ from seisflows.tools import unix from seisflows.tools.msg import DEG -from seisflows.tools.wrappers import exists from seisflows.tools.math import angle from seisflows.config import custom_import, SeisFlowsPathsParameters diff --git a/seisflows/optimize/NLCG.py b/seisflows/optimize/NLCG.py index e060947c..c08e9862 100644 --- a/seisflows/optimize/NLCG.py +++ b/seisflows/optimize/NLCG.py @@ -7,6 +7,7 @@ import logging from seisflows.config import custom_import, SeisFlowsPathsParameters +from seisflows.tools import unix PAR = sys.modules['seisflows_parameters'] PATH = sys.modules['seisflows_paths'] @@ -113,8 +114,9 @@ def compute_direction(self): # CASE 2: Force restart if the iterations have surpassed the maximum # number of allowable iter elif self.NLCG_iter > PAR.NLCGMAX: - logger.info("restarting NLCG due to periodic restart condition. " - "setting search direction as inverse gradient") + self.logger.info("restarting NLCG due to periodic restart " + "condition. setting search direction as inverse " + "gradient") self.restart() p_new = -g_new restarted = 1 diff --git a/seisflows/optimize/base.py b/seisflows/optimize/base.py index 1cf59f44..94c9436a 100644 --- a/seisflows/optimize/base.py +++ b/seisflows/optimize/base.py @@ -54,8 +54,7 @@ class Base: def __init__(self): """ - These parameters should not be set by __init__! - Attributes are just initialized as NoneTypes for clarity and docstrings + Initialize internally used variables for optimization workflow :type iter: int :param iter: the current iteration of the workflow @@ -174,7 +173,7 @@ def setup(self): """ Sets up nonlinear optimization machinery """ - # All ptimization statistics text files will be written to path_stats + # All optimization statistics text files will be written to path_stats path_stats = os.path.join(PATH.WORKDIR, CFGPATHS.STATSDIR) unix.mkdir(path_stats) diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index 69dfa4f7..18602c26 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -223,6 +223,8 @@ def generate_mesh(self, model_name, model_type="gll"): :param model_type: available model types to be passed to the Specfem3D Par_file. See Specfem3D Par_file for available options. """ + unix.cd(self.cwd) + if model_name.upper() == "INIT": model_path = PATH.MODEL_INIT elif model_name.upper() == "TRUE": @@ -239,8 +241,6 @@ def generate_mesh(self, model_name, model_type="gll"): assert(model_type in available_model_types), \ f"{model_type} not in available types {available_model_types}" - unix.cd(self.cwd) - # Run mesh generation if model_type == "gll": self.check_mesh_properties(model_path) From 7f351f450eab35bb59fc811bc94de11efd23bbd3 Mon Sep 17 00:00:00 2001 From: bch0w Date: Tue, 14 Jun 2022 15:25:33 -0800 Subject: [PATCH 005/195] cleaning up more docstrings --- seisflows/plugins/solver_io/fortran_binary.py | 5 + seisflows/preprocess/base.py | 1 + seisflows/solver/base.py | 35 ++--- seisflows/solver/specfem2d.py | 9 -- seisflows/solver/specfem3d.py | 33 +---- seisflows/system/cluster.py | 2 +- seisflows/tests/test_modules.py | 42 +++--- seisflows/tests/test_preprocess.py | 44 ++---- seisflows/tests/test_solver.py | 133 ------------------ 9 files changed, 60 insertions(+), 244 deletions(-) delete mode 100644 seisflows/tests/test_solver.py diff --git a/seisflows/plugins/solver_io/fortran_binary.py b/seisflows/plugins/solver_io/fortran_binary.py index 1543ab89..3428525b 100644 --- a/seisflows/plugins/solver_io/fortran_binary.py +++ b/seisflows/plugins/solver_io/fortran_binary.py @@ -85,6 +85,11 @@ def _write(v, filename): """ Writes Fortran style binary files Data are written as single precision floating point numbers + + .. note:: + FORTRAN unformatted binaries are bounded by an INT*4 byte count. This + function mimics that behavior by tacking on the boundary data. + https://docs.oracle.com/cd/E19957-01/805-4939/6j4m0vnc4/index.html """ n = np.array([4 * len(v)], dtype='int32') v = np.array(v, dtype='float32') diff --git a/seisflows/preprocess/base.py b/seisflows/preprocess/base.py index 80a7f8b3..92ac58df 100644 --- a/seisflows/preprocess/base.py +++ b/seisflows/preprocess/base.py @@ -447,4 +447,5 @@ def _apply_normalize(self, st): w = np.linalg.norm(tr.data, ord=2) if w > 0: tr.data /= w + return st_out diff --git a/seisflows/solver/base.py b/seisflows/solver/base.py index 25cfec73..db5d7510 100644 --- a/seisflows/solver/base.py +++ b/seisflows/solver/base.py @@ -221,14 +221,11 @@ def setup(self): In the former case, a value for PATH.DATA must be supplied; in the latter case, a value for PATH.MODEL_TRUE must be provided. """ - # Clean up for new inversion unix.rm(self.cwd) self.initialize_solver_directories() self.check_solver_parameter_files() self.generate_data() self.generate_mesh(model_name="init", model_type="gll") - - # Create blank adjoint traces to be overwritten self.initialize_adjoint_traces() def clean(self): @@ -247,8 +244,13 @@ def generate_mesh(self, model_path, model_name, model_type): This function is Solver specific and is responsible for generating the mesh using the external numerical solver. """ - raise NotImplementedError("function 'solver.generate_mesh()' must be" - "implemented by a Solver sub class") + raise NotImplementedError("must be implemented by solver subclass") + + def generate_data(self): + """ + Performs meshing and data generation for "true" data. + """ + raise NotImplementedError("must be implemented by solver subclass") def eval_func(self, path, write_residuals=True): """ @@ -321,7 +323,7 @@ def apply_hess(self, path): :type path: str :param path: directory to which output files are exported """ - raise NotImplementedError + raise NotImplementedError("must be implemented by solver subclass") unix.cd(self.cwd) self.import_model(path) @@ -340,7 +342,7 @@ def forward(self, path): !!! Must be implemented by subclass !!! """ - raise NotImplementedError + raise NotImplementedError("must be implemented by solver subclass") def adjoint(self): """ @@ -350,7 +352,7 @@ def adjoint(self): !!! Must be implemented by subclass !!! """ - raise NotImplementedError + raise NotImplementedError("must be implemented by solver subclass") @property def io(self): @@ -574,14 +576,6 @@ def smooth(self, input_path, output_path, parameters=None, span_h=0., files = glob(os.path.join(output_path, "*")) unix.rename(old="_smooth", new="", names=files) - def combine_vol_data_vtk(self): - """ - Postprocessing wrapper for xcombine_vol_data_vtk - - !!! must be implemented by subclass !!! - """ - pass - def import_model(self, path): """ File transfer utility. Import the model into the workflow. @@ -709,7 +703,7 @@ def rename_data(self, path): !!! Can be implemented by subclass !!! """ - pass + raise NotImplementedError("must be implemented by solver subclass") def initialize_solver_directories(self): """ @@ -962,10 +956,11 @@ def kernel_databases(self): @property def source_prefix(self): """ - Template filenames for accessing sources + Preferred source prefix - !!! Must be implemented by subclass !!! + :rtype: str + :return: source prefix """ - return NotImplementedError + return PAR.SOURCE_PREFIX.upper() diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index 18602c26..37cdca33 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -433,13 +433,4 @@ def data_wildcard(self, comp="?"): elif PAR.FORMAT.upper() == "ASCII": return f"*.?X{comp}.sem?" - @property - def source_prefix(self): - """ - Specfem2D's preferred source prefix - - :rtype: str - :return: source prefix - """ - return PAR.SOURCE_PREFIX.upper() diff --git a/seisflows/solver/specfem3d.py b/seisflows/solver/specfem3d.py index 0512035c..1c88ed48 100644 --- a/seisflows/solver/specfem3d.py +++ b/seisflows/solver/specfem3d.py @@ -236,7 +236,7 @@ def initialize_adjoint_traces(self): """ Setup utility: Creates the "adjoint traces" expected by SPECFEM - Note: + .. note:: Adjoint traces are initialized by writing zeros for all channels. Channels actually in use during an inversion or migration will be overwritten with nonzero values later on. @@ -271,37 +271,6 @@ def rename_data(self): files = glob(os.path.join(self.cwd, "traces", "adj", "*SU")) unix.rename(old='_SU', new='_SU.adj', names=files) - def write_parameters(self): - """ - Write a set of parameters - - !!! This calls on plugins.solver.specfem3d.write_parameters() - but that function doesn't exist !!! - """ - unix.cd(self.cwd) - solvertools.write_parameters(vars(PAR)) - - def write_receivers(self): - """ - Write a list of receivers into a text file - - !!! This calls on plugins.solver.specfem3d.write_receivers() - but incorrect number of parameters is forwarded !!! - """ - unix.cd(self.cwd) - setpar(key="use_existing_STATIONS", val=".true", file="DATA/Par_file") - - _, h = preprocess.load("traces/obs") - solvertools.write_receivers(h.nr, h.rx, h.rz) - - def write_sources(self): - """ - Write sources to text file - """ - unix.cd(self.cwd) - _, h = preprocess.load(dir="traces/obs") - solvertools.write_sources(PAR=vars(PAR), h=h) - @property def data_wildcard(self): """ diff --git a/seisflows/system/cluster.py b/seisflows/system/cluster.py index 27ce1f44..c3e51eff 100644 --- a/seisflows/system/cluster.py +++ b/seisflows/system/cluster.py @@ -22,7 +22,7 @@ class Cluster(custom_import("system", "base")): compute systems such as HPC clusters. """ # Class-specific logger accessed using self.logger - logger = logging.getLogger(__name__).getChild(_nrpo_qualname__) + logger = logging.getLogger(__name__).getChild(__qualname__) @property def required(self): diff --git a/seisflows/tests/test_modules.py b/seisflows/tests/test_modules.py index 9b8dad04..957e9384 100644 --- a/seisflows/tests/test_modules.py +++ b/seisflows/tests/test_modules.py @@ -140,9 +140,10 @@ def test_required_functions_exist(sfinit): f"{name}.{module}" -def test_setup(sfinit, modules): +@pytest.mark.skip("test not working as expected") +def test_setup(sfinit): """ - Test the expected behavior of each of the rqeuired functions. + Test the expected behavior of each of the required functions. Setup: make sure that setup creates the necessary directory structure @@ -150,28 +151,29 @@ def test_setup(sfinit, modules): :param modules: :return: """ - return - sf = sfinit + sfinit PATH = sys.modules["seisflows_paths"] SETUP_CREATES = [PATH.SCRATCH, PATH.SYSTEM, PATH.OUTPUT] - for package, module_list in modules.items(): - for module in module_list: - loaded_module = config.custom_import(MODULE, module)() + for name in config.NAMES: + for package, module_list in return_modules()[name].items(): + for module in module_list: + loaded_module = config.custom_import(name, module)() - # Make sure these don't already exist - for path_ in SETUP_CREATES: - assert(not os.path.exists(path_)) + # Make sure these don't already exist + for path_ in SETUP_CREATES: + assert(not os.path.exists(path_)) - loaded_module.setup() + loaded_module.setup() - # Check that the minimum required directories were created - for path_ in SETUP_CREATES: - assert(os.path.exists(path_)) + # Check that the minimum required directories were created + for path_ in SETUP_CREATES: + pytest.set_trace() + assert(os.path.exists(path_)) - # Remove created paths so we can check the next module - for path_ in SETUP_CREATES: - if os.path.isdir(path_): - shutil.rmtree(path_) - else: - os.remove(path_) \ No newline at end of file + # Remove created paths so we can check the next module + for path_ in SETUP_CREATES: + if os.path.isdir(path_): + shutil.rmtree(path_) + else: + os.remove(path_) \ No newline at end of file diff --git a/seisflows/tests/test_preprocess.py b/seisflows/tests/test_preprocess.py index 7fb418d1..cb0a36da 100644 --- a/seisflows/tests/test_preprocess.py +++ b/seisflows/tests/test_preprocess.py @@ -11,14 +11,6 @@ from seisflows.seisflows import SeisFlows, return_modules -# The module that we're testing, allows for copy-pasting these test suites -MODULE = "preprocess" - -# Ensures that these parameters are always defined, even when using subclasses -REQUIRED_PARAMETERS = [] -REQUIRED_FUNCTIONS = ["required", "check", "setup", "prepare_eval_grad", - "sum_residuals", "finalize"] - # Define some re-used paths TEST_DIR = os.path.join(config.ROOT_DIR, "tests") REPO_DIR = os.path.abspath(os.path.join(config.ROOT_DIR, "..")) @@ -36,14 +28,6 @@ def copy_par_file(tmpdir): shutil.copy(src, dst) -@pytest.fixture -def modules(): - """ - Return a list of subclasses that falls under the System module - """ - return return_modules()[MODULE] - - @pytest.fixture def sfinit(tmpdir, copy_par_file): """ @@ -138,16 +122,18 @@ def test_default_setup(sfinit): assert(preprocess.writer.__name__ == io_name) -# def test_default_prepare_eval_grad(tmpdir, sfinit): -# """ -# Ensure that prepare_eval_grad writes out adjoint traces and auxiliary files -# """ -# sfinit -# PAR = sys.modules["seisflows_parameters"] -# preprocess = sys.modules["seisflows_preprocess"] -# -# cwd = tmpdir -# taskid = 0 -# filenames = [] -# preprocess.prepare_eval_grad(cwd=cwd, taskid=taskid, filenames=filenames) -# pytest.set_trace() +def test_default_prepare_eval_grad(tmpdir, sfinit): + """ + Ensure that prepare_eval_grad writes out adjoint traces and auxiliary files + """ + sfinit + PAR = sys.modules["seisflows_parameters"] + preprocess = sys.modules["seisflows_preprocess"] + + cwd = tmpdir + taskid = 0 + filenames = [] + preprocess.prepare_eval_grad(cwd=cwd, taskid=taskid, filenames=filenames) + pytest.set_trace() + + diff --git a/seisflows/tests/test_solver.py b/seisflows/tests/test_solver.py deleted file mode 100644 index a7bbc198..00000000 --- a/seisflows/tests/test_solver.py +++ /dev/null @@ -1,133 +0,0 @@ -""" -Test suite for the SeisFlows system module, which controls interaction with -various compute systems -""" -import os -import sys -import shutil -import pytest -from unittest.mock import patch -from seisflows import config -from seisflows.seisflows import SeisFlows, return_modules - - -# The module that we're testing, allows for copy-pasting these test suites -MODULE = "solver" - -# Ensures that these parameters are always defined, even when using subclasses -REQUIRED_PARAMETERS = ["MATERIALS", "DENSITY", "ATTENUATION"] -# !!! TODO Figure out what solver functions are called from other modules -REQUIRED_FUNCTIONS = [] - -# Define some re-used paths -TEST_DIR = os.path.join(config.ROOT_DIR, "tests") -REPO_DIR = os.path.abspath(os.path.join(config.ROOT_DIR, "..")) - - -@pytest.fixture -def copy_par_file(tmpdir): - """ - Copy the template parameter file into the temporary test directory - :rtype: str - :return: location of the parameter file - """ - src = os.path.join(TEST_DIR, "test_data", "test_filled_parameters.yaml") - dst = os.path.join(tmpdir, "parameters.yaml") - shutil.copy(src, dst) - - -@pytest.fixture -def modules(): - """ - Return a list of subclasses that falls under the System module - """ - return return_modules()[MODULE] - - -@pytest.fixture -def sfinit(tmpdir, copy_par_file): - """ - Re-used function that will initate a SeisFlows working environment in - sys modules - :return: - """ - copy_par_file - os.chdir(tmpdir) - with patch.object(sys, "argv", ["seisflows"]): - sf = SeisFlows() - sf._register(force=True) - config.init_seisflows(check=False) - - return sf - - -def test_import(sfinit, modules): - """ - Test code by importing all available classes for this module. - If any of these fails then the module itself has some code error - (e.g., syntax errors, inheritance errors). - """ - sfinit - for package, module_list in modules.items(): - for module in module_list: - config.custom_import(MODULE, module)() - - -def test_validate(sfinit, modules): - """ - Test out path and parameter validation, essentially checking that all - the paths and parameters are set properly - - .. note:: - This doesn't work because we have required parameters that are not - set in the default parameter file. We can run configure beforehand - but does that make sense? - :return: - """ - return - sfinit - for package, module_list in modules.items(): - for module in module_list: - loaded_module = config.custom_import(MODULE, module)() - from IPython import embed;embed() - loaded_module.required.validate() - - -def test_required_parameters_exist(sfinit, modules): - """ - Ensure that the required parameters are set in all the classes/subclasses - That is, that the parameters defined above in REQUIRED_PARAMETERS have been - defined by each SYSTEM class - """ - sfinit - for package, module_list in modules.items(): - for module in module_list: - loaded_module = config.custom_import(MODULE, module)() - sf_pp = loaded_module.required - # Check that required parameters are set - for req_par in REQUIRED_PARAMETERS: - assert(req_par in sf_pp.parameters.keys()), \ - f"{req_par} is a required parameter for module {MODULE}" - - -def test_required_functions_exist(sfinit, modules): - """ - Make sure that the named, required functions exist within the class - Do not execute just make sure they're defined, because they will be - expected by other modules - """ - sfinit - for package, module_list in modules.items(): - for module in module_list: - loaded_module = config.custom_import(MODULE, module)() - for func in REQUIRED_FUNCTIONS: - assert(func in dir(loaded_module)), \ - f"'{func}' is a required function in module: " \ - f"{MODULE}.{module}" - - -# ============================================================================== -# MODULE AND FUNCTION SPECIFIC TESTS TO FOLLOW -# ============================================================================== - -def test \ No newline at end of file From 1054492fc5d49bfbf507351152c4739d67b821ae Mon Sep 17 00:00:00 2001 From: bch0w Date: Wed, 15 Jun 2022 12:59:41 -0800 Subject: [PATCH 006/195] moved 'call_solver' into solver.base and OUT of the specfem tool as it makes more sense to have it be a solver function --- seisflows/solver/base.py | 67 ++++++++++++++++++++++++++++------- seisflows/solver/specfem2d.py | 16 ++++----- seisflows/solver/specfem3d.py | 18 +++++----- seisflows/tools/specfem.py | 40 --------------------- 4 files changed, 71 insertions(+), 70 deletions(-) diff --git a/seisflows/solver/base.py b/seisflows/solver/base.py index db5d7510..79804f42 100644 --- a/seisflows/solver/base.py +++ b/seisflows/solver/base.py @@ -8,13 +8,14 @@ import os import sys import logging +import subprocess import numpy as np from glob import glob from functools import partial from seisflows.plugins import solver_io from seisflows.tools import msg, unix -from seisflows.tools.specfem import Container, call_solver +from seisflows.tools.specfem import Container from seisflows.tools.wrappers import Struct, diff, exists from seisflows.config import SeisFlowsPathsParameters @@ -354,6 +355,45 @@ def adjoint(self): """ raise NotImplementedError("must be implemented by solver subclass") + def call_solver(self, executable, output="solver.log"): + """ + Calls MPI solver executable to run solver binaries, used by individual + processes to run the solver on system. If the external solver returns a + non-zero exit code (failure), this function will return a negative boolean. + + :type mpiexec: str + :param mpiexec: call to mpi. If None (e.g., serial run, defaults to ./) + :type executable: str + :param executable: executable function to call + :type output: str + :param output: where to redirect stdout + """ + # mpiexec is None when running in serial mode, so e.g., ./xmeshfem2D + if PAR.SYSTEM in ["workstation"]: + exc_cmd = f"./{executable}" + # Otherwise mpiexec is system dependent (e.g., srun, mpirun) + else: + exc_cmd = f"{PAR.MPIEXEC} {executable}" + + try: + # Write solver stdout (log files) to text file + f = open(output, "w") + subprocess.run(exc_cmd, shell=True, check=True, stdout=f) + except (subprocess.CalledProcessError, OSError) as e: + print(msg.cli("The external numerical solver has returned a nonzero " + "exit code (failure). Consider stopping any currently " + "running jobs to avoid wasted computational resources. " + f"Check 'scratch/solver/mainsolver/{output}' for the " + f"solvers stdout log message. " + f"The failing command and error message are: ", + items=[f"exc: {exc_cmd}", f"err: {e}"], + header="external solver error", + border="=") + ) + sys.exit(-1) + finally: + f.close() + @property def io(self): """ @@ -517,10 +557,11 @@ def combine(self, input_path, output_path, parameters=None): # Call on xcombine_sem to combine kernels into a single file for name in self.parameters: # e.g.: mpiexec ./bin/xcombine_sem alpha_kernel kernel_paths output - call_solver(mpiexec=PAR.MPIEXEC, - executable=" ".join([f"bin/xcombine_sem", - f"{name}_kernel", "kernel_paths", - output_path]) + self.call_solver(mpiexec=PAR.MPIEXEC, + executable=" ".join([ + f"bin/xcombine_sem", f"{name}_kernel", + "kernel_paths", output_path] + ) ) def smooth(self, input_path, output_path, parameters=None, span_h=0., @@ -562,14 +603,14 @@ def smooth(self, input_path, output_path, parameters=None, span_h=0., # mpiexec ./bin/xsmooth_sem SMOOTH_H SMOOTH_V name input output use_gpu for name in parameters: - call_solver(mpiexec=PAR.MPIEXEC, - executable=" ".join(["bin/xsmooth_sem", - str(span_h), str(span_v), - f"{name}_kernel", - os.path.join(input_path, ""), - os.path.join(output_path, ""), - ".false"]), - output=output + self.call_solver(mpiexec=PAR.MPIEXEC, + executable=" ".join(["bin/xsmooth_sem", + str(span_h), str(span_v), + f"{name}_kernel", + os.path.join(input_path, ""), + os.path.join(output_path, ""), + ".false"]), + output=output ) # Rename output files diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index 37cdca33..825c1c81 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -13,7 +13,7 @@ from seisflows.tools import unix, msg from seisflows.tools.wrappers import exists from seisflows.config import custom_import, SeisFlowsPathsParameters -from seisflows.tools.specfem import call_solver, getpar, setpar +from seisflows.tools.specfem import getpar, setpar PAR = sys.modules['seisflows_parameters'] @@ -62,7 +62,7 @@ def required(self): sf.par("F0", required=True, par_type=float, docstr="Dominant source frequency") - sf.par("FORMAT", required=True, par_type=float, + sf.par("FORMAT", required=False, par_type=float, default="ASCII", docstr="Format of synthetic waveforms used during workflow, " "available options: ['ascii', 'su']") @@ -267,10 +267,10 @@ def forward(self, path="traces/syn"): setpar(key="SIMULATION_TYPE", val="1", file="DATA/Par_file") setpar(key="SAVE_FORWARD", val=".true.", file="DATA/Par_file") - call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xmeshfem2D", - output="mesher.log") - call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xspecfem2D", - output="solver.log") + self.call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xmeshfem2D", + output="mesher.log") + self.call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xspecfem2D", + output="solver.log") if PAR.FORMAT.upper() == "SU": # Work around SPECFEM2D's version dependent file names @@ -298,8 +298,8 @@ def adjoint(self): unix.rename(old=".su", new=".su.adj", names=glob(os.path.join("traces", "adj", "*.su"))) - call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xmeshfem2D") - call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xspecfem2D") + self.call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xmeshfem2D") + self.call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xspecfem2D") def smooth(self, input_path, **kwargs): """ diff --git a/seisflows/solver/specfem3d.py b/seisflows/solver/specfem3d.py index 1c88ed48..37c9dcc3 100644 --- a/seisflows/solver/specfem3d.py +++ b/seisflows/solver/specfem3d.py @@ -14,7 +14,7 @@ from seisflows.tools import unix, msg from seisflows.tools.wrappers import exists from seisflows.config import custom_import, SeisFlowsPathsParameters -from seisflows.tools.specfem import call_solver, getpar, setpar +from seisflows.tools.specfem import getpar, setpar # Seisflows configuration @@ -101,7 +101,7 @@ def generate_data(self, **model_kwargs): else: setpar(key="ATTENUATION", val=".false.", file="DATA/Par_file") - call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xspecfem3D") + self.call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xspecfem3D") unix.mv(src=glob(os.path.join("OUTPUT_FILES", self.data_wildcard)), dst=os.path.join("traces", "obs")) @@ -139,9 +139,9 @@ def generate_mesh(self, model_path, model_name, model_type=None): dst = self.model_databases unix.cp(src, dst) - call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xmeshfem3D") - call_solver(mpiexec=PAR.MPIEXEC, - executable="bin/xgenerate_databases") + self.call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xmeshfem3D") + self.call_solver(mpiexec=PAR.MPIEXEC, + executable="bin/xgenerate_databases") # Export the model for future use in the workflow if self.taskid == 0: @@ -171,9 +171,9 @@ def forward(self, path="traces/syn"): else: setpar(key="ATTENUATION", val=".false`.", file="DATA/Par_file") - call_solver(mpiexec=PAR.MPIEXEC, - executable="bin/xgenerate_databases") - call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xspecfem3D") + self.call_solver(mpiexec=PAR.MPIEXEC, + executable="bin/xgenerate_databases") + self.call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xspecfem3D") # Find and move output traces, by default to synthetic traces dir unix.mv(src=glob(os.path.join("OUTPUT_FILES", self.data_wildcard)), @@ -191,7 +191,7 @@ def adjoint(self): unix.rm("SEM") unix.ln("traces/adj", "SEM") - call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xspecfem3D") + self.call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xspecfem3D") def check_solver_parameter_files(self): """ diff --git a/seisflows/tools/specfem.py b/seisflows/tools/specfem.py index df518bbc..5800c6f2 100644 --- a/seisflows/tools/specfem.py +++ b/seisflows/tools/specfem.py @@ -40,46 +40,6 @@ def __init__(self): self.minmax = Minmax() -def call_solver(mpiexec, executable, output="solver.log"): - """ - Calls MPI solver executable to run solver binaries, used by individual - processes to run the solver on system. If the external solver returns a - non-zero exit code (failure), this function will return a negative boolean. - - :type mpiexec: str - :param mpiexec: call to mpi. If None (e.g., serial run, defaults to ./) - :type executable: str - :param executable: executable function to call - :type output: str - :param output: where to redirect stdout - """ - # mpiexec is None when running in serial mode, so e.g., ./xmeshfem2D - if mpiexec is None: - exc_cmd = f"./{executable}" - # Otherwise mpiexec is system dependent (e.g., srun, mpirun) - else: - exc_cmd = f"{mpiexec} {executable}" - - try: - # Write solver stdout (log files) to text file - f = open(output, "w") - subprocess.run(exc_cmd, shell=True, check=True, stdout=f) - except (subprocess.CalledProcessError, OSError) as e: - print(msg.cli("The external numerical solver has returned a nonzero " - "exit code (failure). Consider stopping any currently " - "running jobs to avoid wasted computational resources. " - f"Check 'scratch/solver/mainsolver/{output}' for the " - f"solvers stdout log message. " - f"The failing command and error message are: ", - items=[f"exc: {exc_cmd}", f"err: {e}"], - header="external solver error", - border="=") - ) - sys.exit(-1) - finally: - f.close() - - def getpar(key, file, delim="=", match_partial=False): """ Reads and returns parameters from a SPECFEM or SeisFlows parameter file From 9bcb66d559481c83b5d8f08b981945bf7e2d35c2 Mon Sep 17 00:00:00 2001 From: bch0w Date: Wed, 15 Jun 2022 13:00:11 -0800 Subject: [PATCH 007/195] working on test framework which runs some simple functions to test out module capabilities --- seisflows/config.py | 6 +- seisflows/preprocess/base.py | 2 +- seisflows/preprocess/pyatoa.py | 2 +- seisflows/system/slurm.py | 47 +- .../work/traces/obs/AA.S0001.BXY.semd | 5000 +++++++++++++++++ .../work/traces/syn/AA.S0001.BXY.semd | 5000 +++++++++++++++++ .../tests/test_data/workdir/error_sf3.txt | 0 .../tests/test_data/workdir/output_sf3.txt | 0 .../tests/test_data/workdir/parameters.yaml | 0 seisflows/workflow/base.py | 2 +- seisflows/workflow/test.py | 63 +- 11 files changed, 10088 insertions(+), 34 deletions(-) create mode 100644 seisflows/tests/test_data/work/traces/obs/AA.S0001.BXY.semd create mode 100644 seisflows/tests/test_data/work/traces/syn/AA.S0001.BXY.semd delete mode 100644 seisflows/tests/test_data/workdir/error_sf3.txt delete mode 100644 seisflows/tests/test_data/workdir/output_sf3.txt delete mode 100644 seisflows/tests/test_data/workdir/parameters.yaml diff --git a/seisflows/config.py b/seisflows/config.py index ac67f5f0..8a6f6d70 100644 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -60,8 +60,8 @@ SCRATCHDIR="scratch", # SeisFlows internal working directory STATSDIR="stats", # Optimization module log file output OUTPUTDIR="output", # Permanent disk storage for state and outputs - LOGFILE="output_sf.txt", # Log files for all system log - ERRLOGFILE="error_sf.txt", # StdErr dump site for crash messages + LOGFILE="sfoutput.txt", # Log files for all system log + ERRLOGFILE="sferror.txt", # StdErr dump site for crash messages LOGDIR="logs", # Dump site for previously created log files ) """ @@ -279,7 +279,7 @@ def __getattr__(self, key): try: return self.__dict__[key] except KeyError: - raise AttributeError(key) + raise AttributeError(f"{key} not found in Dict") def __getitem__(self, key): """.get() like access of the internal dictionary attributes """ diff --git a/seisflows/preprocess/base.py b/seisflows/preprocess/base.py index 92ac58df..d553b79f 100644 --- a/seisflows/preprocess/base.py +++ b/seisflows/preprocess/base.py @@ -269,7 +269,7 @@ def prepare_eval_grad(self, cwd, taskid, filenames, **kwargs): raise NotImplementedError self._write_adjoint_traces(path=os.path.join(cwd, "traces", "adj"), - syn=syn, obs=obs, filename=filename_out) + syn=syn, obs=obs, filename=filename_out) # Copy over the STATIONS file to STATIONS_ADJOINT required by Specfem # ASSUMING that all stations are used in adjoint simulation diff --git a/seisflows/preprocess/pyatoa.py b/seisflows/preprocess/pyatoa.py index 3bc08553..2af5d6ab 100644 --- a/seisflows/preprocess/pyatoa.py +++ b/seisflows/preprocess/pyatoa.py @@ -187,7 +187,7 @@ def setup(self): """ unix.mkdir(PATH.PREPROCESS) - def prepare_eval_grad(self, cwd, source_name, taskid, **kwargs): + def prepare_eval_grad(self, cwd, taskid, source_name, **kwargs): """ Prepare the gradient evaluation by gathering, preprocessing waveforms, and measuring misfit between observations and synthetics using Pyatoa. diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index e8da4afc..4c83116c 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -150,26 +150,15 @@ def run(self, classname, method, single=False, run_call=None, **kwargs): if single: self.logger.info("replacing parts of sbatch run call for single " "process job") - for part in run_call.split(" "): - if "--array" in part: - run_call.replace(part, "--array=0-0") - elif "--ntasks" in part: - run_call.replace(part, "--ntasks=1") - # Append taskid to environment variable, deal with the case where - # PAR.ENVIRONS is an empty string - task_id_str = "SEISFLOWS_TASKID=0" - if not run_call.strip().endswith("--environment"): - task_id_str = f",{task_id_str}" # appending to the list of vars - run_call += task_id_str - self.logger.debug(run_call) + run_call = _modify_run_call_single_proc(run_call) # The standard response from SLURM when submitting jobs # is something like 'Submitted batch job 441636', we want job number stdout = subprocess.run(run_call, stdout=subprocess.PIPE, text=True, shell=True).stdout - job_ids = job_id_list(stdout, single) - # Contiously check for job completion on ALL running array jobs + # Continuously check for job completion on ALL running array jobs + job_ids = job_id_list(stdout, single) is_done = False count = 0 bad_states = ["TIMEOUT", "FAILED", "NODE_FAIL", "OUT_OF_MEMORY", @@ -191,8 +180,8 @@ def run(self, classname, method, single=False, run_call=None, **kwargs): f"LOG: logs/{job_ids[i]}", f"SBATCH: {run_call}"], header="slurm run error", border="=")) - sys.exit(-1) - # WAIT CONDITION: if sacct is not working, we'll get stuck in a loop + sys.exit(-1) + # WAIT CONDITION: if sacct is not working, we'll get stuck in a loop if "UNDEFINED" in states: count += 1 # Every 10 counts, warn the user this is unexpected behavior @@ -321,7 +310,6 @@ def check_job_state(job_id): # Undefined status will be retured if we cannot match the job id with # the sacct output - # TODO should undefined state throw an error? state = "UNDEFINED" lines = stdout.strip().split("\n") for line in lines: @@ -338,3 +326,28 @@ def check_job_state(job_id): return state +def _modify_run_call_single_proc(run_call): + """ + Modifies a SLURM SBATCH command to use only 1 processor as a single run + + :type run_call: str + :param run_call: The SBATCH command to modify + :rtype: str + :return: a modified SBATCH command that should only run on 1 processor + """ + 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a/seisflows/tests/test_data/workdir/parameters.yaml b/seisflows/tests/test_data/workdir/parameters.yaml deleted file mode 100644 index e69de29b..00000000 diff --git a/seisflows/workflow/base.py b/seisflows/workflow/base.py index ede9639f..3548835e 100644 --- a/seisflows/workflow/base.py +++ b/seisflows/workflow/base.py @@ -37,7 +37,7 @@ def required(self): """ sf = SeisFlowsPathsParameters() - sf.par("CASE", required=True, par_type=str, + sf.par("CASE", required=False, default="data", par_type=str, docstr="Type of inversion, available: " "['data': real data inversion, " "'synthetic': synthetic-synthetic inversion]") diff --git a/seisflows/workflow/test.py b/seisflows/workflow/test.py index 3f96fc96..e479bb89 100644 --- a/seisflows/workflow/test.py +++ b/seisflows/workflow/test.py @@ -9,7 +9,8 @@ import time import logging from seisflows.tools import msg -from seisflows.config import SeisFlowsPathsParameters, custom_import +from seisflows.config import SeisFlowsPathsParameters, custom_import, ROOT_DIR +from seisflows.tools.specfem import call_solver # Required SeisFlows configuration PAR = sys.modules['seisflows_parameters'] @@ -45,6 +46,12 @@ def required(self): """ sf = SeisFlowsPathsParameters(super().required) + sf.path("TEST_DATA", required=False, + default=os.path.join(ROOT_DIR, "tests", "test_data", + "work"), + docstr="Example data for test system" + ) + return sf def check(self, validate=True): @@ -56,9 +63,6 @@ def check(self, validate=True): :type validate: bool :param validate: set required paths and parameters into sys.modules """ - # The validate statement is used internally to set required paths - # and parameters into sys.modules. Default values are stored for - # optional terms if validate: self.required.validate() @@ -66,7 +70,9 @@ def main(self, return_flow=False): """ This controls the main testing workflow """ - FLOW = [self.test_system] + FLOW = [self.test_system, + self.test_preprocess + ] if return_flow: return FLOW @@ -77,17 +83,52 @@ def test_function(self): """ A simple function that can be called by system.run() """ - print(f"Hello world, from taskid {system.taskid()}") + check_value = 1234.5 + print(f"Hello world, from taskid {system.taskid()}. " + f"Check: {check_value}") def test_system(self): """ - This is an example test function which can take any number of args - or kwargs. The base class is responsible for setting all of the - necessary functions + Test the system by submitting a simple print statement using the + run() and run(single) functions. """ system.run(classname="workflow", method="test_function") - # Wait a bit for system to catch up - time.sleep(3) + time.sleep(3) # wait a bit for system to catch up system.run(classname="workflow", method="test_function", single=True) + def test_preprocess(self): + """ + Test the exposed 'prepare_eval_grad()' preprocessing function + """ + cwd = PATH.TEST_DATA + taskid = 0 + filenames = ["AA.S0001.BXY.semd"] + source_name = "001" + preprocess.prepare_eval_grad(cwd=cwd, taskid=taskid, + filenames=filenames, + source_name=source_name + ) + + def test_solver(self): + """ + Simply test that the solver binaries can be called, which is what the + solver directory is responsible for + """ + assert(os.path.exists(PATH.SPECFEM_BIN)), ( + f"SPECFEM_BIN {PATH.SPECFEM_BIN} directory does not exist" + ) + + + + def test_postprocess(self): + """ + + """ + pass + + def test_workflow(self): + """ + + """ + pass From d1288e2251d63ba33fa46de795ed4635c5031ede Mon Sep 17 00:00:00 2001 From: bch0w Date: Wed, 15 Jun 2022 15:08:28 -0800 Subject: [PATCH 008/195] workstation now writes function run outputs to log files rather than to stdout --- seisflows/config.py | 1 - seisflows/preprocess/base.py | 10 +++--- seisflows/solver/base.py | 7 +++- seisflows/system/base.py | 2 -- seisflows/system/workstation.py | 19 ++++++++-- seisflows/workflow/test.py | 61 +++++++++++++++++++++------------ 6 files changed, 69 insertions(+), 31 deletions(-) diff --git a/seisflows/config.py b/seisflows/config.py index 8a6f6d70..e6cd94aa 100644 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -131,7 +131,6 @@ def save(): """ Export the current session to disk """ - logger.info("exporting current working environment to disk") output = sys.modules[PATH]["OUTPUT"] unix.mkdir(output) diff --git a/seisflows/preprocess/base.py b/seisflows/preprocess/base.py index d553b79f..365939dd 100644 --- a/seisflows/preprocess/base.py +++ b/seisflows/preprocess/base.py @@ -182,8 +182,10 @@ def check(self, validate=True): # Assert that readers and writers available # TODO | This is a bit vague as dir contains imported modules and hidden # TODO | variables (e.g., np, __name__) - assert(PAR.FORMAT in dir(readers)), f"Reader {PAR.FORMAT} not found" - assert(PAR.FORMAT in dir(writers)), f"Writer {PAR.FORMAT} not found" + assert(PAR.FORMAT.lower() in dir(readers)), ( + f"Reader {PAR.FORMAT} not found") + assert(PAR.FORMAT.lower() in dir(writers)), ( + f"Writer {PAR.FORMAT} not found") # Assert that either misfit or backproject exists if PAR.WORKFLOW.upper() == "INVERSION": @@ -207,8 +209,8 @@ def setup(self): self.adjoint = getattr(adjoint, PAR.BACKPROJECT.lower()) # Define seismic data reader and writer - self.reader = getattr(readers, PAR.FORMAT) - self.writer = getattr(writers, PAR.FORMAT) + self.reader = getattr(readers, PAR.FORMAT.lower()) + self.writer = getattr(writers, PAR.FORMAT.lower()) def prepare_eval_grad(self, cwd, taskid, filenames, **kwargs): """ diff --git a/seisflows/solver/base.py b/seisflows/solver/base.py index 79804f42..4a08daeb 100644 --- a/seisflows/solver/base.py +++ b/seisflows/solver/base.py @@ -129,7 +129,7 @@ def required(self): "['CONSTANT': Do not update density, " "'VARIABLE': Update density]") - sf.par("ATTENUATION", required=True, par_type=str, + sf.par("ATTENUATION", required=True, par_type=bool, docstr="If True, turn on attenuation during forward " "simulations, otherwise set attenuation off. Attenuation " "is always off for adjoint simulations.") @@ -368,6 +368,11 @@ def call_solver(self, executable, output="solver.log"): :type output: str :param output: where to redirect stdout """ + if not os.path.exists(executable): + print(msg.cli(f"solver executable {executable} does not exist", + header="external solver error", border="=")) + sys.exit(-1) + # mpiexec is None when running in serial mode, so e.g., ./xmeshfem2D if PAR.SYSTEM in ["workstation"]: exc_cmd = f"./{executable}" diff --git a/seisflows/system/base.py b/seisflows/system/base.py index 2a252898..f55290a5 100644 --- a/seisflows/system/base.py +++ b/seisflows/system/base.py @@ -203,8 +203,6 @@ def checkpoint(self, path, classname, method, kwargs): :type kwargs: dict :param kwargs: dictionary to pass to object saving """ - self.logger.debug("checkpointing working environment to disk") - argspath = os.path.join(path, "kwargs") argsfile = os.path.join(argspath, f"{classname}_{method}.p") unix.mkdir(argspath) diff --git a/seisflows/system/workstation.py b/seisflows/system/workstation.py index 91baa5d7..73f89896 100644 --- a/seisflows/system/workstation.py +++ b/seisflows/system/workstation.py @@ -6,9 +6,10 @@ import os import sys import logging +from contextlib import redirect_stdout from seisflows.tools import unix, msg -from seisflows.config import custom_import, SeisFlowsPathsParameters +from seisflows.config import custom_import, SeisFlowsPathsParameters, CFGPATHS PAR = sys.modules["seisflows_parameters"] PATH = sys.modules["seisflows_paths"] @@ -84,6 +85,7 @@ def run(self, classname, method, single=False, **kwargs): # 4}_{taskid:0>2}.log") + if os.path.exists(log_file): + idx += 1 + else: + break + if taskid == 0: self.logger.info(f"running task {classname}.{method} " f"{PAR.NTASK} times") - function(**kwargs) + # Redirect output to a log file to mimic cluster runs + with open(log_file, "w") as f: + with redirect_stdout(f): + function(**kwargs) def taskid(self): """ diff --git a/seisflows/workflow/test.py b/seisflows/workflow/test.py index e479bb89..18cac91e 100644 --- a/seisflows/workflow/test.py +++ b/seisflows/workflow/test.py @@ -8,15 +8,14 @@ import sys import time import logging +from glob import glob from seisflows.tools import msg -from seisflows.config import SeisFlowsPathsParameters, custom_import, ROOT_DIR -from seisflows.tools.specfem import call_solver +from seisflows.config import (SeisFlowsPathsParameters, custom_import, ROOT_DIR, + CFGPATHS) -# Required SeisFlows configuration -PAR = sys.modules['seisflows_parameters'] -PATH = sys.modules['seisflows_paths'] +PAR = sys.modules["seisflows_parameters"] +PATH = sys.modules["seisflows_paths"] -# The number of loaded modules depends on the module this Base class belongs to system = sys.modules["seisflows_system"] solver = sys.modules["seisflows_solver"] optimize = sys.modules["seisflows_optimize"] @@ -70,8 +69,9 @@ def main(self, return_flow=False): """ This controls the main testing workflow """ - FLOW = [self.test_system, - self.test_preprocess + FLOW = [#self.test_system, + # self.test_preprocess, + self.test_solver, ] if return_flow: return FLOW @@ -79,11 +79,10 @@ def main(self, return_flow=False): for func in FLOW: func() - def test_function(self): + def test_function(self, check_value): """ A simple function that can be called by system.run() """ - check_value = 1234.5 print(f"Hello world, from taskid {system.taskid()}. " f"Check: {check_value}") @@ -91,10 +90,27 @@ def test_system(self): """ Test the system by submitting a simple print statement using the run() and run(single) functions. + + Check that these functions perform as expected by passing in a random + value and checking that this value gets logged back """ - system.run(classname="workflow", method="test_function") + check_value_1 = 1234.5 + system.run(classname="workflow", method="test_function", + check_value=check_value_1) + time.sleep(3) # wait a bit for system to catch up - system.run(classname="workflow", method="test_function", single=True) + + check_value_2 = 5432.1 + system.run(classname="workflow", method="test_function", single=True, + check_value=check_value_2) + + for fid, check in zip( + sorted(glob(os.path.join(CFGPATHS.LOGDIR, "*.log"))), + [check_value_1, check_value_2] + ): + with open(fid, "r") as f: + line = f.readlines()[0] + assert(float(line.strip().split(" ")[-1]) == check) def test_preprocess(self): """ @@ -112,12 +128,21 @@ def test_preprocess(self): def test_solver(self): """ Simply test that the solver binaries can be called, which is what the - solver directory is responsible for + solver module is ultimately responsible for """ - assert(os.path.exists(PATH.SPECFEM_BIN)), ( + assert(PATH.SPECFEM_BIN is not None and + os.path.exists(PATH.SPECFEM_BIN)), ( f"SPECFEM_BIN {PATH.SPECFEM_BIN} directory does not exist" ) - + # SPECFEM2D won't have this executable + try: + solver.call_solver( + executable=f"{PATH.SPECFEM_BIN}/xcombine_sem", + output=os.path.join(PATH.TEST_DATA, "test_solver.log") + ) + # We expect this to throw a system exit because + except SystemExit: + pass def test_postprocess(self): @@ -126,9 +151,3 @@ def test_postprocess(self): """ pass - def test_workflow(self): - """ - - """ - pass - From 2010f3cbf4fd80200a218c00a94317d026a18d43 Mon Sep 17 00:00:00 2001 From: bch0w Date: Wed, 15 Jun 2022 15:21:50 -0800 Subject: [PATCH 009/195] removing unused tool functions --- seisflows/tools/specfem.py | 37 +------------------------------------ seisflows/workflow/test.py | 12 +++--------- 2 files changed, 4 insertions(+), 45 deletions(-) diff --git a/seisflows/tools/specfem.py b/seisflows/tools/specfem.py index 5800c6f2..95609907 100644 --- a/seisflows/tools/specfem.py +++ b/seisflows/tools/specfem.py @@ -21,7 +21,7 @@ def __init__(self): super(Minmax, self).__init__(lambda: [+np.inf, -np.inf]) def update(self, keys, vals): - for key, val in _zip(keys, vals): + for key, val in zip(iterable(keys), iterable(vals)): if min(val) < self.dict[key][0]: self.dict[key][0] = min(val) if max(val) > self.dict[key][1]: @@ -258,38 +258,3 @@ def check_poissons_ratio(vp, vs, min_val=-1., max_val=0.5): ) sys.exit(-1) return poissons - - -def _split(string, sep): - """ - Utility function to split a string by a given separation character or str - - :type string: str - :param string: string to split - :type sep: str - :param sep: substring to split by - """ - n = string.find(sep) - if n >= 0: - return string[:n], string[n + len(sep):] - else: - return string, '' - - -def _merge(*parts): - """ - Utility function to merge various strings together with no breaks - """ - return ' '.join(parts) - - -def _zip(keys, vals): - """ - Zip together keys and vals - - :type keys: dict_keys - :param keys: keys - :type vals: dict_values - :param vals: values - """ - return zip(iterable(keys), iterable(vals)) diff --git a/seisflows/workflow/test.py b/seisflows/workflow/test.py index 18cac91e..cfd280fd 100644 --- a/seisflows/workflow/test.py +++ b/seisflows/workflow/test.py @@ -70,8 +70,8 @@ def main(self, return_flow=False): This controls the main testing workflow """ FLOW = [#self.test_system, - # self.test_preprocess, - self.test_solver, + self.test_preprocess, + # self.test_solver, ] if return_flow: return FLOW @@ -120,6 +120,7 @@ def test_preprocess(self): taskid = 0 filenames = ["AA.S0001.BXY.semd"] source_name = "001" + import pdb;pdb.set_trace() preprocess.prepare_eval_grad(cwd=cwd, taskid=taskid, filenames=filenames, source_name=source_name @@ -144,10 +145,3 @@ def test_solver(self): except SystemExit: pass - - def test_postprocess(self): - """ - - """ - pass - From cceea64c13b101d9d937ff0c8b5e736544567968 Mon Sep 17 00:00:00 2001 From: bch0w Date: Thu, 16 Jun 2022 11:58:50 -0800 Subject: [PATCH 010/195] working on adding rosenbrock optimization test --- .../{work => test_solver}/traces/obs/AA.S0001.BXY.semd | 0 .../{work => test_solver}/traces/syn/AA.S0001.BXY.semd | 0 seisflows/workflow/test.py | 5 +++++ 3 files changed, 5 insertions(+) rename seisflows/tests/test_data/{work => test_solver}/traces/obs/AA.S0001.BXY.semd (100%) rename seisflows/tests/test_data/{work => test_solver}/traces/syn/AA.S0001.BXY.semd (100%) diff --git a/seisflows/tests/test_data/work/traces/obs/AA.S0001.BXY.semd b/seisflows/tests/test_data/test_solver/traces/obs/AA.S0001.BXY.semd similarity index 100% rename from seisflows/tests/test_data/work/traces/obs/AA.S0001.BXY.semd rename to seisflows/tests/test_data/test_solver/traces/obs/AA.S0001.BXY.semd diff --git a/seisflows/tests/test_data/work/traces/syn/AA.S0001.BXY.semd b/seisflows/tests/test_data/test_solver/traces/syn/AA.S0001.BXY.semd similarity index 100% rename from seisflows/tests/test_data/work/traces/syn/AA.S0001.BXY.semd rename to seisflows/tests/test_data/test_solver/traces/syn/AA.S0001.BXY.semd diff --git a/seisflows/workflow/test.py b/seisflows/workflow/test.py index cfd280fd..2da41968 100644 --- a/seisflows/workflow/test.py +++ b/seisflows/workflow/test.py @@ -145,3 +145,8 @@ def test_solver(self): except SystemExit: pass + def test_optimize(self): + """ + Test optimization module with a simple rosenbrock function + """ + optimize.setup() From adbd6642e19526c6bea3a71f9817b734fa72509f Mon Sep 17 00:00:00 2001 From: bch0w Date: Thu, 16 Jun 2022 12:31:43 -0800 Subject: [PATCH 011/195] removed seisflows-super, moved all super modules back into main package. all future development will just take place in seisflows directory to avoid confusion removed 'edit' command from CLI tool --- seisflows-super/__init__.py | 0 seisflows-super/optimize/__init__.py | 0 seisflows-super/postprocess/__init__.py | 0 seisflows-super/preprocess/__init__.py | 0 seisflows-super/solver/__init__.py | 0 seisflows-super/solver/specfem3d_maui.py | 293 ------------------ seisflows-super/system/__init__.py | 0 seisflows-super/workflow/__init__.py | 0 seisflows-super/workflow/thrifty_maui.py | 84 ----- seisflows/config.py | 13 +- seisflows/seisflows.py | 78 +---- .../system/chinook.py | 0 {seisflows-super => seisflows}/system/maui.py | 0 .../workflow/thrifty_inversion.py | 0 14 files changed, 5 insertions(+), 463 deletions(-) delete mode 100644 seisflows-super/__init__.py delete mode 100644 seisflows-super/optimize/__init__.py delete mode 100644 seisflows-super/postprocess/__init__.py delete mode 100644 seisflows-super/preprocess/__init__.py delete mode 100644 seisflows-super/solver/__init__.py delete mode 100644 seisflows-super/solver/specfem3d_maui.py delete mode 100644 seisflows-super/system/__init__.py delete mode 100644 seisflows-super/workflow/__init__.py delete mode 100644 seisflows-super/workflow/thrifty_maui.py rename {seisflows-super => seisflows}/system/chinook.py (100%) rename {seisflows-super => seisflows}/system/maui.py (100%) rename {seisflows-super => seisflows}/workflow/thrifty_inversion.py (100%) diff --git a/seisflows-super/__init__.py b/seisflows-super/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/seisflows-super/optimize/__init__.py b/seisflows-super/optimize/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/seisflows-super/postprocess/__init__.py b/seisflows-super/postprocess/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/seisflows-super/preprocess/__init__.py b/seisflows-super/preprocess/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/seisflows-super/solver/__init__.py b/seisflows-super/solver/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/seisflows-super/solver/specfem3d_maui.py b/seisflows-super/solver/specfem3d_maui.py deleted file mode 100644 index 882f2c53..00000000 --- a/seisflows-super/solver/specfem3d_maui.py +++ /dev/null @@ -1,293 +0,0 @@ -#!/usr/bin/env python3 -""" -This is the subclass seisflows.solver.Specfem3DMaui - -This class is almost the same as Specfem3D, except the setup step is run as -a serial task. This is useful as HPC job queues are long on Maui, so it saves -on job queue time by replacing it with a long-winded serial task. - -Additionally, misfit quantification is split off from the forward simulation, -because Anaconda is not available on the main cluster, so jobs need to be -submitted to an auxiliary cluster. This is paired with a new evaluate_function() -function defined by the InversionMaui workflow class. -""" -import os -import sys -import warnings - -from glob import glob -from seisflows.tools import unix -from seisflows.tools.wrappers import exists -from seisflows.config import custom_import, SeisFlowsPathsParameters -from seisflows.tools.specfem import call_solver, getpar, setpar - - -# Seisflows configuration -PAR = sys.modules['seisflows_parameters'] -PATH = sys.modules['seisflows_paths'] - -system = sys.modules['seisflows_system'] -preprocess = sys.modules['seisflows_preprocess'] - - -class Specfem3DMaui(custom_import("solver", "specfem3d")): - """ - Python interface to Specfem3D Cartesian. This subclass inherits functions - from seisflows.solver.specfem3d - - !!! See base class for method descriptions !!! - """ - @property - def required(self): - """ - A hard definition of paths and parameters required by this class, - alongside their necessity for the class and their string explanations. - """ - sf = SeisFlowsPathsParameters(super().required) - - return sf - - def check(self, validate=True): - """ - Checks parameters and paths - """ - if validate: - self.required.validate() - super().check(validate=False) - - def setup(self, model): - """ - Overload of solver.base.setup(), should be run as a single instance - - :type model: str - :param model: "init", "true", generates the mesh to be used for workflow - "true" used for synthetic-synthetic cases - "init" for initial model, default - :type model: str - :param model: model to setup, either 'true' or 'init' - """ - # Choice of model will determine which mesh to generate - self.generate_mesh(model_path=getattr(PATH, f"MODEL_{model.upper()}"), - model_name=f"model_{model.lower()}", - model_type="gll") - - self.distribute_databases() - - def generate_data(self): - """ - Overload seisflows.solver.base.generate_data. To be run in parallel - - Not used if PAR.CASE == "Data" - - Generates data in the synthetic-synthetic comparison case. - Automatically calls generate mesh for the true model, rather than - passing them in as kwargs. - - Also turns on attenuation for the forward model - !!! attenuation could be moved into parameters.yaml? !!! - """ - unix.cd(self.cwd) - - setpar(key="SIMULATION_TYPE", val="1", file="DATA/Par_file") - setpar(key="SAVE_FORWARD", val=".true.", file="DATA/Par_file") - if PAR.ATTENUATION: - setpar(key="ATTENUATION ", val=".true.", file="DATA/Par_file") - else: - setpar(key="ATTENUATION ", val=".false.", file="DATA/Par_file") - - call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xspecfem3D") - - # move ASCII .sem? files into appropriate directory - unix.mv(src=glob(os.path.join("OUTPUT_FILES", self.data_wildcard)), - dst=os.path.join("traces", "obs")) - - # Export traces to permanent storage on disk - if PAR.SAVETRACES: - self.export_traces(os.path.join(PATH.OUTPUT, "traces", "obs")) - - def generate_mesh(self, model_path, model_name, model_type='gll'): - """ - Performs meshing and database generation as a serial task. Differs - slightly from specfem3d class as it only creates database files for - the main solver, which are then copied in serial by the function - distribute_databases() - - :type model_path: str - :param model_path: path to the model to be used for mesh generation - :type model_name: str - :param model_name: name of the model to be used as identification - :type model_type: str - :param model_type: available model types to be passed to the Specfem3D - Par_file. See Specfem3D Par_file for available options. - """ - available_model_types = ["gll"] - - assert(exists(model_path)), f"model {model_path} does not exist" - - model_type = model_type or getpar(key="MODEL", file="DATA/Par_file") - assert(model_type in available_model_types), \ - f"{model_type} not in available types {available_model_types}" - - # Ensure that we're running on the main solver only - assert(self.taskid == 0) - - unix.cd(self.cwd) - - # Check that the model parameter falls into the acceptable types - par = getpar("MODEL").strip() - assert(par in available_model_types), \ - f"Par_file {par} not in available types {available_model_types}" - - if par == "gll": - self.check_mesh_properties(model_path) - - # Copy model files and then run xgenerate databases - src = glob(os.path.join(model_path, "*")) - dst = self.model_databases - unix.cp(src, dst) - - call_solver(mpiexec=PAR.MPIEXEC, - executable="bin/xgenerate_databases") - - self.export_model(os.path.join(PATH.OUTPUT, model_name)) - - def eval_misfit(self, path='', export_traces=False): - """ - Performs function evaluation only, that is, the misfit quantification. - Forward simulations are performed in a separate function - - :type path: str - :param path: path in the scratch directory to use for I/O - :type export_traces: bool - :param export_traces: option to save the observation traces to disk - :return: - """ - preprocess.prepare_eval_grad(cwd=self.cwd, taskid=self.taskid, - source_name=self.source_name, - filenames=self.data_filenames - ) - if export_traces: - self.export_residuals(path) - - def eval_fwd(self, path=''): - """ - High level solver interface - - Performans forward simulations only, function evaluation is split off - into its own function - - :type path: str - :param path: path in the scratch directory to use for I/O - """ - unix.cd(self.cwd) - self.import_model(path) - self.forward() - - def distribute_databases(self): - """ - A serial task to distrubute the database files outputted by - xgenerate_databases from main solver to all other solver directories - """ - # Copy the database files but ignore any vt? files - src_db = glob(os.path.join(PATH.SOLVER, self.mainsolver, - "OUTPUT_FILES", "DATABASES_MPI", "*")) - for extension in [".vtu", ".vtk"]: - src_db = [_ for _ in src_db if extension not in _] - - # Copy the .h files from the mesher, Specfem needs these as well - src_h = glob(os.path.join(PATH.SOLVER, self.mainsolver, - "OUTPUT_FILES", "*.h")) - - for source_name in self.source_names: - # Ensure main solver is skipped - if source_name == self.mainsolver: - continue - # Copy database files to each of the other source directories - dst_db = os.path.join(PATH.SOLVER, source_name, - "OUTPUT_FILES", "DATABASES_MPI", "") - unix.cp(src_db, dst_db) - - # Copy mesher h files into the overlying directory - dst_h = os.path.join(PATH.SOLVER, source_name, "OUTPUT_FILES", "") - unix.cp(src_h, dst_h) - - def initialize_solver_directories(self): - """ - Creates solver directories in serial using a single node. - Should only be run by master job. - - Differs from Base initialize_solver_directories() as this serial task - will create directory structures for each source, rather than having - each source create its own. However the internal dir structure is the - same. - """ - for source_name in self.source_names: - cwd = os.path.join(PATH.SOLVER, source_name) - # Remove any existing scratch directory - unix.rm(cwd) - - # Create internal directory structure, change into directory to make - # all actions RELATIVE path actions - unix.mkdir(cwd) - unix.cd(cwd) - for cwd_dir in ["bin", "DATA", "OUTPUT_FILES/DATABASES_MPI", - "traces/obs", "traces/syn", "traces/adj"]: - unix.mkdir(cwd_dir) - - # Copy exectuables - src = glob(os.path.join(PATH.SPECFEM_BIN, "*")) - dst = os.path.join("bin", "") - unix.cp(src, dst) - - # Copy all input files except source files - src = glob(os.path.join(PATH.SPECFEM_DATA, "*")) - src = [_ for _ in src if self.source_prefix not in _] - dst = os.path.join("DATA", "") - unix.cp(src, dst) - - # symlink event source specifically - src = os.path.join(PATH.SPECFEM_DATA, - f"{self.source_prefix}_{source_name}") - dst = os.path.join("DATA", self.source_prefix) - unix.ln(src, dst) - - if source_name == self.mainsolver: - # Symlink taskid_0 as mainsolver in solver directory - unix.ln(source_name, os.path.join(PATH.SOLVER, "mainsolver")) - # Only check the solver parameters once - self.check_solver_parameter_files() - - def check_solver_parameter_files(self): - """ - Checks solver parameters. Only slightly different to Specfem3D as it - is run by the main task, not be an array process, so no need to check - task_id - """ - nt = getpar(key="NSTEP", cast=int) - dt = getpar(key="DT", cast=float) - - if nt != PAR.NT: - warnings.warn("Specfem3D NSTEP != PAR.NT\n" - "overwriting Specfem3D with Seisflows parameter" - ) - setpar(key="NSTEP", val=PAR.NT) - - if dt != PAR.DT: - warnings.warn("Specfem3D DT != PAR.DT\n" - "overwriting Specfem3D with Seisflows parameter" - ) - setpar(key="DT", val=PAR.DT) - - if self.mesh_properties.nproc != PAR.NPROC: - warnings.warn("Specfem3D mesh nproc != PAR.NPROC") - - if "MULTIPLES" in PAR: - raise NotImplementedError - - @property - def mainsolver(self): - """ - Ensure that the main solver has a consistent reference inside Solver - """ - return self.source_names[0] - diff --git a/seisflows-super/system/__init__.py b/seisflows-super/system/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/seisflows-super/workflow/__init__.py b/seisflows-super/workflow/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/seisflows-super/workflow/thrifty_maui.py b/seisflows-super/workflow/thrifty_maui.py deleted file mode 100644 index c76c2c0c..00000000 --- a/seisflows-super/workflow/thrifty_maui.py +++ /dev/null @@ -1,84 +0,0 @@ -#!/usr/bin/env python3 -""" -This is a subclass seisflows.workflow.InversionMaui -""" -import sys - -from seisflows.config import custom_import -from seisflows.tools.err import ParameterError - -PAR = sys.modules["seisflows_parameters"] -PATH = sys.modules["seisflows_paths"] - -system = sys.modules["seisflows_system"] -solver = sys.modules["seisflows_solver"] -optimize = sys.modules["seisflows_optimize"] -preprocess = sys.modules["seisflows_preprocess"] -postprocess = sys.modules["seisflows_postprocess"] - - -class ThriftyMaui(custom_import("workflow", "thrifty_inversion")): - """ - Waveform thrify inversion class specifically for running jobs on the - New Zealand HPC cluster Maui. - - On Maui, Anaconda is only available on an ancillary cluster, Maui_ancil, - so jobs involving the preprocessing module must be called through a - separate system run call. - """ - def check(self): - """ - Ensure that the correct submodules are specified, otherwise - this workflow won't function properly. - """ - super().check() - - if "MAUI" not in PAR.SYSTEM.upper(): - raise ParameterError() - - if "MAUI" not in PAR.SOLVER.upper(): - raise ParameterError() - - def setup(self): - """ - Lays groundwork for inversion by running setup() functions for the - involved sub-modules, and generating synthetic true data if necessary, - and generating the pre-requisite database files. Should only be run once - at the iteration 1 - """ - # Set up all the requisite modules - print("SETUP") - preprocess.setup() - postprocess.setup() - optimize.setup() - - # Run the setup in serial to reduce unnecessary job submissions - # Needs to be split up into multiple system calls - solver.initialize_solver_directories() - - if PAR.CASE.upper() == "SYNTHETIC": - system.run("solver", "setup", single=True, model="true") - system.run("solver", "generate_data") - - system.run("solver", "setup", single=True, model="init") - - def evaluate_function(self, path, suffix): - """ - Performs forward simulation, and evaluates the objective function. - - Differs from Inversion.evaluate_function() as it splits the forward - problem and misfit quantification into two separate system calls, - rather than a single system call. - - :type path: str - :param path: path in the scratch directory to use for I/O - :type suffix: str - :param suffix: suffix to use for I/O - """ - print("EVALUATE FUNCTION\n\tRunning forward simulation") - self.write_model(path=path, suffix=suffix) - system.run("solver", "eval_fwd", path=path) - print("\tEvaluating misfit") - system.run_ancil("solver", "eval_misfit", path=path) - self.write_misfit(path=path, suffix=suffix) - diff --git a/seisflows/config.py b/seisflows/config.py index e6cd94aa..ec166ed2 100644 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -42,9 +42,6 @@ NAMES = ["system", "preprocess", "solver", "postprocess", "optimize", "workflow"] -# Packages that define the source code, used to search for base- and subclasses -PACKAGES = ["seisflows", "seisflows-super"] - # These define the sys.modules names where parameter values and paths are stored PAR = "seisflows_parameters" PATH = "seisflows_paths" @@ -72,7 +69,7 @@ def init_seisflows(check=True): """ Instantiates SeisFlows objects and makes them globally accessible by - registering them in sys.modules + registering them in sys.modules. This must be run anytime seisflows is run. :type check: bool :param check: Run parameter and path checking, defined in the module.check() @@ -496,12 +493,8 @@ class 'Inversion'. # Check if modules exist, otherwise raise custom exception _exists = False - for package in PACKAGES: - full_dotted_name = ".".join([package, name, module]) - if module_exists(full_dotted_name): - _exists = True - break - if not _exists: + full_dotted_name = ".".join(["seisflows", name, module]) + if not module_exists(full_dotted_name): print(msg.cli(f"The following module was not found within the package: " f"seisflows.{name}.{module}", header="custom import error", border="=") diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 3ead0c87..e77143bd 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -33,7 +33,7 @@ from seisflows.tools.wrappers import loadyaml from seisflows.config import (init_seisflows, format_paths, config_logger, Dict, custom_import, SeisFlowsPathsParameters, - NAMES, PACKAGES, ROOT_DIR, CFGPATHS) + NAMES, ROOT_DIR, CFGPATHS) def sfparser(): @@ -280,24 +280,6 @@ def _format_action(self, action): made during debugging. """) # ========================================================================= - edit = subparser.add_parser( - "edit", help="Open source code file in text editor", - description="""Directly edit source code files in your favorite - terminal text editor. Simply a shortcut to avoid having to root around - in the repository. Any saved edits will directly affect the SeisFlows - source code and any code errors may lead to failure of the package; - e.g. 'seisflows edit solver base'""" - ) - edit.add_argument("name", type=str, nargs="?", default=None, - help="Name of module to search for source file in") - edit.add_argument("module", type=str, nargs="?", default=None, - help="Name of specific module file to open, extension " - "not required") - edit.add_argument("-e", "--editor", type=str, nargs="?", default=None, - help="Chosen text editor, defaults to $EDITOR env var") - edit.add_argument("-d", "--dont_open", action="store_true", - help="Dont open the text editor, just list full pathname") - # ========================================================================= examples = subparser.add_parser( "examples", help="Look at and run pre-configured example problems", description="""Lists out available example problems and allows the @@ -313,7 +295,7 @@ def _format_action(self, action): # ========================================================================= # Defines all arguments/functions that expect a sub-argument subparser_dict = {"check": check, "par": par, "inspect": inspect, - "edit": edit, "sempar": sempar, "clean": clean, + "sempar": sempar, "clean": clean, "restart": restart, "print": print_, "reset": reset, "examples": examples} if parser.parse_args().command in subparser_dict: @@ -999,62 +981,6 @@ def _par_required(self): if check in line: print(f"\t{line.split(':')[0].strip()}") - def edit(self, name, module, editor=None, **kwargs): - """ - Directly edit the SeisFlows source code matching the given name - and module using the chosen text editor. - - USAGE - - seisflows edit [name] [module] [editor] - - To edit the base Solver class using vim, one would run: - - seisflows edit solver base vim - - To simply find the location of the inversion workflow source code: - - seisflows edit workflow inversion q - - :type name: str - :param name: name of module, must match seisflows.config.NAMES - :type module: str - :param module: the module name contained under the SeisFlows namespace - :type editor: str - :param editor: optional chosen text editor to open the file. - * If NoneType: defaults to system environment $EDITOR - * If 'q': For quit, does not open an editor, simply prints fid - """ - if name is None: - self._subparser.print_help() - sys.exit(0) - - editor = editor or os.environ.get("EDITOR") - if editor is None: - print(msg.cli("$EDITOR environment variable is not set, please " - "set manually with the following call structure: " - "seisflows edit [name] [module] [editor]")) - sys.exit(-1) - - REPO_DIR = os.path.abspath(os.path.join(ROOT_DIR, "..")) - if name not in NAMES: - print(msg.cli(f"{name} not in SeisFlows names: {NAMES}")) - sys.exit(-1) - - for package in PACKAGES: - fid_try = os.path.join(REPO_DIR, package, name, f"{module}.py") - if os.path.exists(fid_try): - if self._args.dont_open: - print(msg.cli(text=fid_try)) - sys.exit(0) - else: - subprocess.call([editor, fid_try]) - print(msg.cli(f"Edited file: {fid_try}")) - sys.exit(0) - else: - print(msg.cli(f"seisflows.{name}.{module} not found")) - sys.exit(-1) - def examples(self, run=None, choice=None, **kwargs): """ List or run a SeisFlows example problem diff --git a/seisflows-super/system/chinook.py b/seisflows/system/chinook.py similarity index 100% rename from seisflows-super/system/chinook.py rename to seisflows/system/chinook.py diff --git a/seisflows-super/system/maui.py b/seisflows/system/maui.py similarity index 100% rename from seisflows-super/system/maui.py rename to seisflows/system/maui.py diff --git a/seisflows-super/workflow/thrifty_inversion.py b/seisflows/workflow/thrifty_inversion.py similarity index 100% rename from seisflows-super/workflow/thrifty_inversion.py rename to seisflows/workflow/thrifty_inversion.py From fc8c19b9e0eeeae2dad706549197358bd0a3c9d9 Mon Sep 17 00:00:00 2001 From: bch0w Date: Thu, 16 Jun 2022 13:54:52 -0800 Subject: [PATCH 012/195] cleaning up unused functions and docstrings --- seisflows/seisflows.py | 36 ++++++++------------ seisflows/solver/base.py | 30 ++++++----------- seisflows/solver/specfem2d.py | 9 ++--- seisflows/solver/specfem3d.py | 18 +++++----- seisflows/system/slurm.py | 43 ++++++++++++++++------- seisflows/workflow/migration.py | 1 - seisflows/workflow/test.py | 45 +++++++++++++++++++++++-- seisflows/workflow/thrifty_inversion.py | 3 -- 8 files changed, 111 insertions(+), 74 deletions(-) diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index e77143bd..3448c543 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -26,7 +26,6 @@ from copy import copy from IPython import embed -from seisflows import logger from seisflows.tools import unix, msg from seisflows.tools.specfem import (getpar, setpar, getpar_vel_model, setpar_vel_model) @@ -1272,17 +1271,14 @@ def _print_modules(self, name=None, package=None, **kwargs): :param package: specify an indivdual package to search """ items = [] - module_list = return_modules() - for name_, package_dict in module_list.items(): - if name is not None and name != name_: + module_dict = return_modules() + + for module_, module_list in module_dict.items(): + if package is not None and module_ != package: continue - items.append(f"+ {name_.upper()}") - for package_, module_list in package_dict.items(): - if package is not None and package_ != package: - continue - items.append(f"\t- {package_}".expandtabs(tabsize=4)) - for module_ in module_list: - items.append(f"\t\t* {module_}".expandtabs(tabsize=4)) + items.append(f"- {module_}".expandtabs(tabsize=4)) + for module_ in module_list: + items.append(f"\t* {module_}".expandtabs(tabsize=4)) print(msg.cli("'+': package, '-': module, '*': class", items=items, header="seisflows modules")) @@ -1429,19 +1425,15 @@ def return_modules(): :return: a dict with keys matching names and values as dicts for each package. nested list contains all the avaialble modules """ - REPO_DIR = os.path.abspath(os.path.join(ROOT_DIR, "..")) - module_dict = {} for NAME in NAMES: - module_dict[NAME] = {} - for PACKAGE in PACKAGES: - module_dict[NAME][PACKAGE] = [] - mod_dir = os.path.join(REPO_DIR, PACKAGE, NAME) - for pyfile in sorted(glob(os.path.join(mod_dir, "*.py"))): - stripped_pyfile = os.path.basename(pyfile) - stripped_pyfile = os.path.splitext(stripped_pyfile)[0] - if not stripped_pyfile.startswith("_"): - module_dict[NAME][PACKAGE].append(stripped_pyfile) + module_dict[NAME] = [] + mod_dir = os.path.join(ROOT_DIR, NAME) + for pyfile in sorted(glob(os.path.join(mod_dir, "*.py"))): + stripped_pyfile = os.path.basename(pyfile) + stripped_pyfile = os.path.splitext(stripped_pyfile)[0] + if not stripped_pyfile.startswith("_"): + module_dict[NAME].append(stripped_pyfile) return module_dict diff --git a/seisflows/solver/base.py b/seisflows/solver/base.py index 4a08daeb..9c5ee54f 100644 --- a/seisflows/solver/base.py +++ b/seisflows/solver/base.py @@ -229,15 +229,6 @@ def setup(self): self.generate_mesh(model_name="init", model_type="gll") self.initialize_adjoint_traces() - def clean(self): - """ - Clean up solver-dependent run directory by removing the OUTPUT_FILES/ - directory - """ - unix.cd(self.cwd) - unix.rm("OUTPUT_FILES") - unix.mkdir("OUTPUT_FILES") - def generate_mesh(self, model_path, model_name, model_type): """ Performs meshing and database generation. @@ -380,10 +371,10 @@ def call_solver(self, executable, output="solver.log"): else: exc_cmd = f"{PAR.MPIEXEC} {executable}" + # Run solver. Write solver stdout (log files) to text file try: - # Write solver stdout (log files) to text file - f = open(output, "w") - subprocess.run(exc_cmd, shell=True, check=True, stdout=f) + with open(output, "w") as f: + subprocess.run(exc_cmd, shell=True, check=True, stdout=f) except (subprocess.CalledProcessError, OSError) as e: print(msg.cli("The external numerical solver has returned a nonzero " "exit code (failure). Consider stopping any currently " @@ -396,8 +387,7 @@ def call_solver(self, executable, output="solver.log"): border="=") ) sys.exit(-1) - finally: - f.close() + @property def io(self): @@ -560,14 +550,14 @@ def combine(self, input_path, output_path, parameters=None): ) # Call on xcombine_sem to combine kernels into a single file - for name in self.parameters: + for name in parameters: # e.g.: mpiexec ./bin/xcombine_sem alpha_kernel kernel_paths output - self.call_solver(mpiexec=PAR.MPIEXEC, - executable=" ".join([ - f"bin/xcombine_sem", f"{name}_kernel", - "kernel_paths", output_path] + self.call_solver(executable=" ".join([f"bin/xcombine_sem", + f"{name}_kernel", + "kernel_paths", + output_path] + ) ) - ) def smooth(self, input_path, output_path, parameters=None, span_h=0., span_v=0., output="solver.log"): diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index 825c1c81..1e846211 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -267,10 +267,8 @@ def forward(self, path="traces/syn"): setpar(key="SIMULATION_TYPE", val="1", file="DATA/Par_file") setpar(key="SAVE_FORWARD", val=".true.", file="DATA/Par_file") - self.call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xmeshfem2D", - output="mesher.log") - self.call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xspecfem2D", - output="solver.log") + self.call_solver(executable="bin/xmeshfem2D", output="fwd_mesher.log") + self.call_solver(executable="bin/xspecfem2D", output="fwd_solver.log") if PAR.FORMAT.upper() == "SU": # Work around SPECFEM2D's version dependent file names @@ -298,8 +296,7 @@ def adjoint(self): unix.rename(old=".su", new=".su.adj", names=glob(os.path.join("traces", "adj", "*.su"))) - self.call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xmeshfem2D") - self.call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xspecfem2D") + self.call_solver(executable="bin/xspecfem2D", output="adj_solver.log") def smooth(self, input_path, **kwargs): """ diff --git a/seisflows/solver/specfem3d.py b/seisflows/solver/specfem3d.py index 37c9dcc3..906a03db 100644 --- a/seisflows/solver/specfem3d.py +++ b/seisflows/solver/specfem3d.py @@ -101,7 +101,7 @@ def generate_data(self, **model_kwargs): else: setpar(key="ATTENUATION", val=".false.", file="DATA/Par_file") - self.call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xspecfem3D") + self.call_solver(executable="bin/xspecfem3D", output="true_solver.log") unix.mv(src=glob(os.path.join("OUTPUT_FILES", self.data_wildcard)), dst=os.path.join("traces", "obs")) @@ -139,9 +139,10 @@ def generate_mesh(self, model_path, model_name, model_type=None): dst = self.model_databases unix.cp(src, dst) - self.call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xmeshfem3D") - self.call_solver(mpiexec=PAR.MPIEXEC, - executable="bin/xgenerate_databases") + self.call_solver(executable="bin/xmeshfem3D", + output="true_mesher.log") + self.call_solver(executable="bin/xgenerate_databases", + output="true_solver.log") # Export the model for future use in the workflow if self.taskid == 0: @@ -171,9 +172,10 @@ def forward(self, path="traces/syn"): else: setpar(key="ATTENUATION", val=".false`.", file="DATA/Par_file") - self.call_solver(mpiexec=PAR.MPIEXEC, - executable="bin/xgenerate_databases") - self.call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xspecfem3D") + self.call_solver(executable="bin/xgenerate_databases", + output="fwd_mesher.log") + self.call_solver(executable="bin/xmeshfem3D", output="fwd_solver.log") + # Find and move output traces, by default to synthetic traces dir unix.mv(src=glob(os.path.join("OUTPUT_FILES", self.data_wildcard)), @@ -191,7 +193,7 @@ def adjoint(self): unix.rm("SEM") unix.ln("traces/adj", "SEM") - self.call_solver(mpiexec=PAR.MPIEXEC, executable="bin/xspecfem3D") + self.call_solver(executable="bin/xmeshfem3D", output="adj_solver.log") def check_solver_parameter_files(self): """ diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index 4c83116c..cc3efab0 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -159,13 +159,37 @@ def run(self, classname, method, single=False, run_call=None, **kwargs): # Continuously check for job completion on ALL running array jobs job_ids = job_id_list(stdout, single) + job_id, status = self.check_job_status(job_ids) + if status is not "OKAY": + print(msg.cli((f"Stopping workflow for {status} job. " + f"Please check log file for details."), + items=[f"TASK: {classname}.{method}", + f"TASK ID: {job_id}", + f"LOG: logs/{job_id}", + f"SBATCH: {run_call}"], + header="slurm run error", border="=")) + sys.exit(-1) + + self.logger.info(f"Task {classname}.{method} finished successfully") + + def check_job_status(self, job_ids): + """ + Repeatedly check the status of a currently running job using 'sacct'. + If the job goes into a bad state like 'FAILED', return the failing + job's id and the state. If all jobs complete nominally, + return state=="OKAY" + + :type job_ids: list + :param job_ids: list of running jobs to check using SACCT + :rtype: tuple (int, str) + :return: (job_id, state) state=="OKAY" if all jobs complete, else it + will be a bad state. + """ is_done = False count = 0 bad_states = ["TIMEOUT", "FAILED", "NODE_FAIL", "OUT_OF_MEMORY", "CANCELLED"] while not is_done: - # Wait a bit to avoid rapidly querying sacct - time.sleep(5) is_done, states = job_array_status(job_ids) # EXIT CONDITION: if any of the jobs provide job failure codes if not is_done: @@ -173,15 +197,8 @@ def run(self, classname, method, single=False, run_call=None, **kwargs): # Sometimes states can be something like 'CANCELLED+', so # we can't do exact string matching, check partial matches if any([check in state for check in bad_states]): - print(msg.cli((f"Stopping workflow for {state} job. " - f"Please check log file for details."), - items=[f"TASK: {classname}.{method}", - f"TASK ID: {job_ids[i]}", - f"LOG: logs/{job_ids[i]}", - f"SBATCH: {run_call}"], - header="slurm run error", border="=")) - sys.exit(-1) - # WAIT CONDITION: if sacct is not working, we'll get stuck in a loop + return job_id, state + # WAIT CONDITION: if sacct is not working, we'll get stuck in a loop if "UNDEFINED" in states: count += 1 # Every 10 counts, warn the user this is unexpected behavior @@ -192,8 +209,10 @@ def run(self, classname, method, single=False, run_call=None, **kwargs): f"times. This job may have failed " f"unexpectedly. Consider checking " f"manually") + # Wait a bit to avoid rapidly querying sacct + time.sleep(5) - self.logger.info(f"Task {classname}.{method} finished successfully") + return None, "OKAY" def taskid(self): """ diff --git a/seisflows/workflow/migration.py b/seisflows/workflow/migration.py index 04798159..49fabc7b 100644 --- a/seisflows/workflow/migration.py +++ b/seisflows/workflow/migration.py @@ -9,7 +9,6 @@ import logging from seisflows.tools import unix, msg -from seisflows.tools.wrappers import exists from seisflows.config import custom_import, SeisFlowsPathsParameters diff --git a/seisflows/workflow/test.py b/seisflows/workflow/test.py index 2da41968..36c1719f 100644 --- a/seisflows/workflow/test.py +++ b/seisflows/workflow/test.py @@ -8,6 +8,9 @@ import sys import time import logging +import subprocess +import numpy as np + from glob import glob from seisflows.tools import msg from seisflows.config import (SeisFlowsPathsParameters, custom_import, ROOT_DIR, @@ -94,6 +97,7 @@ def test_system(self): Check that these functions perform as expected by passing in a random value and checking that this value gets logged back """ + # Run a very simple test function using system.run() check_value_1 = 1234.5 system.run(classname="workflow", method="test_function", check_value=check_value_1) @@ -104,6 +108,7 @@ def test_system(self): system.run(classname="workflow", method="test_function", single=True, check_value=check_value_2) + # Check the output log files to match the check values for fid, check in zip( sorted(glob(os.path.join(CFGPATHS.LOGDIR, "*.log"))), [check_value_1, check_value_2] @@ -112,6 +117,11 @@ def test_system(self): line = f.readlines()[0] assert(float(line.strip().split(" ")[-1]) == check) + # Check that MPI Exec works + assert("MPIEXEC" in PAR), f"MPIEXEC is not defined for this system" + stdout = subprocess.run(PAR.MPIEXEC, shell=True, check=True, + stdout=subprocess.PIPE) + def test_preprocess(self): """ Test the exposed 'prepare_eval_grad()' preprocessing function @@ -135,13 +145,13 @@ def test_solver(self): os.path.exists(PATH.SPECFEM_BIN)), ( f"SPECFEM_BIN {PATH.SPECFEM_BIN} directory does not exist" ) - # SPECFEM2D won't have this executable try: solver.call_solver( executable=f"{PATH.SPECFEM_BIN}/xcombine_sem", output=os.path.join(PATH.TEST_DATA, "test_solver.log") ) - # We expect this to throw a system exit because + # We expect this to throw a system exit because we are not running with + # MPI except SystemExit: pass @@ -150,3 +160,34 @@ def test_optimize(self): Test optimization module with a simple rosenbrock function """ optimize.setup() + + def rosenbrock(self, n=1E8): + """ + Rosenbrock test for optimization library + """ + model_init = 0.1 * np.ones(n) + model_true = np.ones(n) + + def func(x): + """ + Rosenbrock function + """ + return sum(100 * (x[:-1]**2. - x[1:])**2. + (x[:-1] - 1.)**2) + + def grad(x): + """ + Gradient for Rosenbrock function + """ + g = np.zeros(n) + g[1:-1] = -200 * (x[:-2] ** 2. - x[1:-1]) + \ + 400. * x[1:-1] * (x[1:-1] ** 2. - x[2:]) + \ + 2. * (x[1:-1] - 1.) + + g[0] = 400. * x[0] * (x[0] ** 2. - x[1]) + \ + 2. * (x[0] - 1) + + g[-1] = -200. * (x[-2] ** 2. - x[-1]) + + return g + + return model_init, model_true, func, grad \ No newline at end of file diff --git a/seisflows/workflow/thrifty_inversion.py b/seisflows/workflow/thrifty_inversion.py index 202a8755..11f9336d 100644 --- a/seisflows/workflow/thrifty_inversion.py +++ b/seisflows/workflow/thrifty_inversion.py @@ -100,6 +100,3 @@ def update_status(self): self.thrifty = thrifty - - - From f1ae1c8ec8727710f082bf03e9bdfd4a7ff8add1 Mon Sep 17 00:00:00 2001 From: bch0w Date: Fri, 17 Jun 2022 09:42:22 -0800 Subject: [PATCH 013/195] removed 'base' line search, bracketing line search is now the base. avoids confusion about having incomplete 'base' modules which aren't useful on their own --- seisflows/optimize/base.py | 30 +-- seisflows/plugins/line_search/backtrack.py | 25 +- seisflows/plugins/line_search/base.py | 239 ------------------- seisflows/plugins/line_search/bracket.py | 252 ++++++++++++++++----- 4 files changed, 205 insertions(+), 341 deletions(-) delete mode 100644 seisflows/plugins/line_search/base.py diff --git a/seisflows/optimize/base.py b/seisflows/optimize/base.py index 94c9436a..524efb89 100644 --- a/seisflows/optimize/base.py +++ b/seisflows/optimize/base.py @@ -173,35 +173,17 @@ def setup(self): """ Sets up nonlinear optimization machinery """ - # All optimization statistics text files will be written to path_stats - path_stats = os.path.join(PATH.WORKDIR, CFGPATHS.STATSDIR) - unix.mkdir(path_stats) + unix.mkdir(PATH.OPTIMIZE) # Line search machinery is defined externally as a plugin class - self.line_search = getattr(line_search, PAR.LINESEARCH)( - step_count_max=PAR.STEPCOUNTMAX, step_len_max=PAR.STEPLENMAX, - log_file=os.path.join(path_stats, f"{self.line_search_log}.txt"), - ) - + if PAR.LINESEARCH: + self.line_search = getattr(line_search, PAR.LINESEARCH)() if PAR.PRECOND: self.precond = getattr(preconds, PAR.PRECOND)() - else: - self.precond = None - - # Instantiate all log files in stats/ directory as empty text files - # OVERWRITES any existing stats/ log files that may already be there - for key, val in vars(self).items(): - if "log_" in key: - self.write_stats(val) - # Ensure that line search step count starts at 0 (workflow.intialize) - self.write_stats(self.log_step_count, 0) - - unix.mkdir(PATH.OPTIMIZE) - if "MODEL_INIT" in PATH: - m_new = solver.merge(solver.load(PATH.MODEL_INIT)) - self.save(self.m_new, m_new) - self.check_model(m_new, self.m_new) + m_new = solver.merge(solver.load(PATH.MODEL_INIT)) + self.save(self.m_new, m_new) + self.check_model(m_new, self.m_new) @property def eval_str(self): diff --git a/seisflows/plugins/line_search/backtrack.py b/seisflows/plugins/line_search/backtrack.py index 3be1c19e..94a2df14 100644 --- a/seisflows/plugins/line_search/backtrack.py +++ b/seisflows/plugins/line_search/backtrack.py @@ -35,16 +35,11 @@ class Backtrack(Bracket): # Class-specific logger accessed using self.logger logger = logging.getLogger(__name__).getChild(__qualname__) - def __init__(self, **kwargs): - """ - These parameters should not be set by the user. - Attributes are initialized as NoneTypes for clarity and docstrings. - """ - super().__init__(**kwargs) def calculate_step(self): """ - Determines step length and search status + Determines step length and search status. Overloads the bracketing + line search """ # Determine the line search history x, f, gtg, gtp, step_count, update_count = self.search_history() @@ -69,7 +64,7 @@ def calculate_step(self): alpha = min(1., self.step_len_max) status = 0 # Pass if misfit is reduced - elif self._check_decrease(x, f): + elif f.min() < f[0]: self.logger.info("misfit decrease, pass") alpha = x[f.argmin()] status = 1 @@ -88,17 +83,3 @@ def calculate_step(self): return alpha, status - @staticmethod - def _check_decrease(step_lens, func_vals, c=1.e-4): - """ - Checks for sufficient decrease by comparing the current functional value - with the smallest functional value in the list. - - !!! What's with the unused value of 'c'?, also 'x' isn't used - """ - x, f = step_lens, func_vals - if f.min() < f[0]: - return 1 - else: - return 0 - diff --git a/seisflows/plugins/line_search/base.py b/seisflows/plugins/line_search/base.py deleted file mode 100644 index 0f77772d..00000000 --- a/seisflows/plugins/line_search/base.py +++ /dev/null @@ -1,239 +0,0 @@ -#!/usr/bin/env python3 -""" -This is the Base class for seisflows.plugins.line_search - -Line search is called on by the optimization procedure and should not really -have any agency (i.e. it should not be able to iterate its own step count etc., -this should be completely left to the optimization algorithm to keep everything -in one place) -""" -import os -import logging -import numpy as np - -from seisflows.tools.array import count_zeros - - -class Base: - """ - Abstract base class for line search - - Variables Descriptions: - x: list of step lenths from current line search - f: correpsonding list of function values - m: number of step lengths in current line search - n: number of model updates in optimization problem - gtg: dot product of gradient with itself - gtp: dot product of gradient and search direction - - Status codes - status > 0 : finished - status == 0 : not finished - status < 0 : failed - """ - # Class-specific logger accessed using self.logger - logger = logging.getLogger(__name__).getChild(__qualname__) - - def __init__(self, step_count_max, step_len_max, log_file): - """ - - :type step_count_max: int - :param step_count_max: maximum number of step counts before changing - line search behavior. set by PAR.STEP_COUNT_MAX - :type step_len_max: int - :param step_len_max: maximum length of the step, defaults to infinity, - that is unbounded step length. set by PAR.STEP_LEN_MAX - :type log_file: str - :param log_file: path to write line search stats to. set by optimize.setup() - """ - # Set maximum number of trial steps - self.step_count_max = step_count_max - - # Optional maximum step length safeguard - if step_len_max is None: - self.step_len_max = np.inf - else: - self.step_len_max = step_len_max - - # Write header information to line search file - self.log = log_file - self.write_log() - - # Prepare lists for line search history - self.func_vals = [] - self.step_lens = [] - self.gtg = [] - self.gtp = [] - self.step_count = 0 - - def initialize(self, iter, step_len, func_val, gtg, gtp): - """ - Initialize a new search from step count 0 and calculate the step - direction and length - - :type iter: int - :param iter: current iteration defined by OPTIMIZE.iter - :type step_len: float - :param step_len: initial step length determined by optimization - :type func_val: float - :param func_val: current evaluation of the objective function - :type gtg: float - :param gtg: dot product of the gradient with itself - :type gtp: float - :param gtp: dot product of gradient `g` with search direction `p` - :rtype alpha: float - :return alpha: the calculated trial step length - :rtype status: int - :return status: current status of the line search - """ - self.step_count = 0 - self.step_lens += [step_len] - self.func_vals += [func_val] - self.gtg += [gtg] - self.gtp += [gtp] - - # Write the current misfit evaluation to disk - self.write_log(iter=iter, step_len=step_len, func_val=func_val) - - # Call calculate step, must be implemented by subclass - alpha, status = self.calculate_step() - - return alpha, status - - def update(self, iter, step_len, func_val): - """ - Update search history by appending internal attributes, writing the - current list of step lengths and function evaluations, and calculating a - new step length - - :type iter: int - :param iter: current iteration defined by OPTIMIZE.iter - :type step_len: float - :param step_len: step length determined by optimization - :type func_val: float - :param func_val: current evaluation of the objective function - :rtype alpha: float - :return alpha: the calculated rial step length - :rtype status: int - :return status: current status of the line search - """ - # This has been moved into workflow.line_search() - # self.step_count += 1 - self.step_lens += [step_len] - self.func_vals += [func_val] - - # Write the current misfit evaluation to disk - self.write_log(iter=iter, step_len=step_len, func_val=func_val) - - # Call calcuate step, must be implemented by subclass - alpha, status = self.calculate_step() - - return alpha, status - - def clear_history(self): - """ - Clears internal line search history - """ - self.func_vals = [] - self.step_lens = [] - self.gtg = [] - self.gtp = [] - self.step_count = 0 - - def reset(self): - """ - If a line search fails mid-search, and the User wants to resume from - the line search function. Initialize will be called again. This function - undos the progress made by the previous line search so that a new line - search can be called without problem. - - output.optim needs to have its lines cleared manually - """ - # First step treated differently - if len(self.step_lens) <= 1: - self.clear_history() - else: - # Wind back dot products by one - self.gtg = self.gtg[:-1] - self.gtp = self.gtp[:-1] - - # Move step lens and function evaluations by number of step count - original_idx = -1 * self.step_count - 1 - self.step_lens = self.step_lens[:original_idx] - self.func_vals = self.func_vals[:original_idx] - - def write_log(self, iter=None, step_len=None, func_val=None): - """ - Write the line search history into a formatted text file (self.log) - that looks something like this: - - ITER STEPLEN MISFIT - ======== ========== ========== - 1 0 1 - - :type iter: int - :param iter: the current iteration defined by OPTIMIZATION.iter. - :type step_len: float - :param step_len: Current step length of the line search, also known - as 'alpha' in the optimization algorithm - :type func_val: float - :param func_val: the function evaluation, i.e., the misfit, associated - with the given step length (alpha) - """ - if (iter is None) or (not os.path.exists(self.log)): - # Write out the header of the file to a NEW FILE - self.logger.info(f"writing line search history file:\n{self.log}") - with open(self.log, "w") as f: - f.write(f"{'ITER':>10} {'STEPLEN':>10} {'MISFIT':>10}\n") - f.write(f"{'='*10} {'='*10} {'='*10}\n") - else: - with open(self.log, "a") as f: - # Aesthetic choice, don't repeat iteration numbers in the file - if (step_len is not None) and (step_len > 0): - iter = "" - f.write(f"{iter:>10} {step_len:10.3e} {func_val:10.3e}\n") - - def search_history(self, sort=True): - """ - A convenience function, collects information based on the current - evaluation of the line search, needed to determine search status and - calculate step length. From the full collection of the search history, - only returns values relevant to the current line search. - - :type sort: bool - :param sort: sort the search history by step length - :rtype x: np.array - :return x: list of step lenths from current line search - :rtype f: np.array - :return f: correpsonding list of function values - :rtype gtg: list - :return gtg: dot product dot product of gradient with itself - :rtype gtp: list - :return gtp: dot product of gradient and search direction - :rtype i: int - :return i: step_count - :rtype j: int - :return j: number of iterations corresponding to 0 step length - """ - i = self.step_count - j = count_zeros(self.step_lens) - 1 - k = len(self.step_lens) - x = np.array(self.step_lens[k - i - 1:k]) - f = np.array(self.func_vals[k - i - 1:k]) - - # Sort by step length - if sort: - f = f[abs(x).argsort()] - x = x[abs(x).argsort()] - - return x, f, self.gtg, self.gtp, i, j - - def calculate_step(self): - """ - Determines step length and search status - - !!! Must be implemented by subclass !!! - """ - raise NotImplementedError("Must be implemented by subclass") - - diff --git a/seisflows/plugins/line_search/bracket.py b/seisflows/plugins/line_search/bracket.py index 943c791b..68511a4f 100644 --- a/seisflows/plugins/line_search/bracket.py +++ b/seisflows/plugins/line_search/bracket.py @@ -1,25 +1,28 @@ #!/usr/bin/env python3 """ -This is the subclass class for seisflows.plugins.line_search.bracket +This is the Bracketing line search class for seisflows + +Line search is called on by the optimization procedure and should not really +have any agency (i.e. it should not be able to iterate its own step count etc., +this should be completely left to the optimization algorithm to keep everything +in one place) """ import logging import numpy as np -from seisflows.tools import msg -from seisflows.plugins.line_search.base import Base +from seisflows.tools.array import count_zeros from seisflows.tools.math import parabolic_backtrack, polynomial_fit -class Bracket(Base): +class Bracket: """ - Implements bracketing line search, which attempts to find a step length - corresponding to misfit reduction, and a misfit corresponding to misfit - increase, so that the optimization procedure can scale the step length - in future iterations. + Abstract base class for line search Variables Descriptions: x: list of step lenths from current line search f: correpsonding list of function values + m: number of step lengths in current line search + n: number of model updates in optimization problem gtg: dot product of gradient with itself gtp: dot product of gradient and search direction @@ -31,12 +34,149 @@ class Bracket(Base): # Class-specific logger accessed using self.logger logger = logging.getLogger(__name__).getChild(__qualname__) - def __init__(self, **kwargs): + def __init__(self): + """ + + :type step_count_max: int + :param step_count_max: maximum number of step counts before changing + line search behavior. set by PAR.STEP_COUNT_MAX + :type step_len_max: int + :param step_len_max: maximum length of the step, defaults to infinity, + that is unbounded step length. set by PAR.STEP_LEN_MAX + :type log_file: str + :param log_file: path to write line search stats to. set by optimize.setup() + """ + # Prepare lists for line search history + self.func_vals = [] + self.step_lens = [] + self.gtg = [] + self.gtp = [] + self.step_count = 0 + + def initialize(self, step_len, func_val, gtg, gtp): + """ + Initialize a new search from step count 0 and calculate the step + direction and length + + :type iter: int + :param iter: current iteration defined by OPTIMIZE.iter + :type step_len: float + :param step_len: initial step length determined by optimization + :type func_val: float + :param func_val: current evaluation of the objective function + :type gtg: float + :param gtg: dot product of the gradient with itself + :type gtp: float + :param gtp: dot product of gradient `g` with search direction `p` + :rtype alpha: float + :return alpha: the calculated trial step length + :rtype status: int + :return status: current status of the line search """ - These parameters should not be set by the user. - Attributes are initialized as NoneTypes for clarity and docstrings. + self.step_count = 0 + self.step_lens += [step_len] + self.func_vals += [func_val] + self.gtg += [gtg] + self.gtp += [gtp] + + # Call calculate step, must be implemented by subclass + alpha, status = self.calculate_step() + + return alpha, status + + def update(self, step_len, func_val): """ - super().__init__(**kwargs) + Update search history by appending internal attributes, writing the + current list of step lengths and function evaluations, and calculating a + new step length + + :type iter: int + :param iter: current iteration defined by OPTIMIZE.iter + :type step_len: float + :param step_len: step length determined by optimization + :type func_val: float + :param func_val: current evaluation of the objective function + :rtype alpha: float + :return alpha: the calculated rial step length + :rtype status: int + :return status: current status of the line search + """ + # This has been moved into workflow.line_search() + # self.step_count += 1 + self.step_lens += [step_len] + self.func_vals += [func_val] + + # Call calcuate step, must be implemented by subclass + alpha, status = self.calculate_step() + + return alpha, status + + def clear_history(self): + """ + Clears internal line search history + """ + self.func_vals = [] + self.step_lens = [] + self.gtg = [] + self.gtp = [] + self.step_count = 0 + + def reset(self): + """ + If a line search fails mid-search, and the User wants to resume from + the line search function. Initialize will be called again. This function + undos the progress made by the previous line search so that a new line + search can be called without problem. + + output.optim needs to have its lines cleared manually + """ + # First step treated differently + if len(self.step_lens) <= 1: + self.clear_history() + else: + # Wind back dot products by one + self.gtg = self.gtg[:-1] + self.gtp = self.gtp[:-1] + + # Move step lens and function evaluations by number of step count + original_idx = -1 * self.step_count - 1 + self.step_lens = self.step_lens[:original_idx] + self.func_vals = self.func_vals[:original_idx] + + def search_history(self, sort=True): + """ + A convenience function, collects information based on the current + evaluation of the line search, needed to determine search status and + calculate step length. From the full collection of the search history, + only returns values relevant to the current line search. + + :type sort: bool + :param sort: sort the search history by step length + :rtype x: np.array + :return x: list of step lenths from current line search + :rtype f: np.array + :return f: correpsonding list of function values + :rtype gtg: list + :return gtg: dot product dot product of gradient with itself + :rtype gtp: list + :return gtp: dot product of gradient and search direction + :rtype i: int + :return i: step_count + :rtype j: int + :return j: number of iterations corresponding to 0 step length + """ + i = self.step_count + j = count_zeros(self.step_lens) - 1 + k = len(self.step_lens) + x = np.array(self.step_lens[k - i - 1:k]) + f = np.array(self.func_vals[k - i - 1:k]) + + # Sort by step length + if sort: + f = f[abs(x).argsort()] + x = x[abs(x).argsort()] + + return x, f, self.gtg, self.gtp, i, j def calculate_step(self): """ @@ -51,7 +191,7 @@ def calculate_step(self): f_str = ", ".join([f"{_:.2E}" for _ in f]) self.logger.debug(f"step length(s) = {x_str}") self.logger.debug(f"misfit val(s) = {f_str}") - + # For the first inversion and initial step, set alpha manually if step_count == 0 and update_count == 0: # Based on idea from Dennis and Schnabel @@ -66,12 +206,12 @@ def calculate_step(self): self.logger.info(f"first step, setting scaled step length") status = 0 # If misfit is reduced and then increased, we've bracketed. Pass - elif self._check_bracket(x, f) and self._good_enough(x,f): + elif _check_bracket(x, f) and _good_enough(x, f): alpha = x[f.argmin()] self.logger.info(f"bracket okay, step length reasonable, pass") status = 1 # If misfit is reduced but not close, set to quadratic fit - elif self._check_bracket(x, f): + elif _check_bracket(x, f): alpha = polynomial_fit(x, f) self.logger.info(f"bracket okay, step length unreasonable, " f"manual step") @@ -111,52 +251,52 @@ def calculate_step(self): return alpha, status - @staticmethod - def _check_bracket(step_lens, func_vals): - """ - Checks if minimum has been bracketed - Looks at the minimum of the misfit values calculated through eval func - to see if the misfit has been reduced w.r.t the initial misfit +def _check_bracket(step_lens, func_vals): + """ + Checks if minimum has been bracketed - :type step_lens: numpy.array - :param step_lens: an array of the step lengths taken during iteration - :type func_vals: numpy.array - :param func_vals: array of misfit values from eval func function - :rtype: bool - :return: status of function as a bool - """ - x, f = step_lens, func_vals - imin, fmin = f.argmin(), f.min() - if (fmin < f[0]) and any(f[imin:] > fmin): + Looks at the minimum of the misfit values calculated through eval func + to see if the misfit has been reduced w.r.t the initial misfit + + :type step_lens: numpy.array + :param step_lens: an array of the step lengths taken during iteration + :type func_vals: numpy.array + :param func_vals: array of misfit values from eval func function + :rtype: bool + :return: status of function as a bool + """ + x, f = step_lens, func_vals + imin, fmin = f.argmin(), f.min() + if (fmin < f[0]) and any(f[imin:] > fmin): + okay = True + else: + okay = False + return okay + +def _good_enough(step_lens, func_vals, thresh=np.log10(1.2)): + """ + Checks if step length is reasonably close to quadratic estimate + + :type step_lens: np.array + :param step_lens: an array of the step lengths taken during iteration + :type func_vals: np.array + :param func_vals: array of misfit values from eval func function + :type thresh: numpy.float64 + :param thresh: threshold value for comparison against quadratic estimate + :rtype: bool + :return: status of function as a bool + """ + x, f = step_lens, func_vals + if not _check_bracket(x, f): + okay = False + else: + x0 = polynomial_fit(x, f) + if any(np.abs(np.log10(x[1:] / x0)) < thresh): okay = True else: okay = False - return okay - - def _good_enough(self, step_lens, func_vals, thresh=np.log10(1.2)): - """ - Checks if step length is reasonably close to quadratic estimate - - :type step_lens: np.array - :param step_lens: an array of the step lengths taken during iteration - :type func_vals: np.array - :param func_vals: array of misfit values from eval func function - :type thresh: numpy.float64 - :param thresh: threshold value for comparison against quadratic estimate - :rtype: bool - :return: status of function as a bool - """ - x, f = step_lens, func_vals - if not self._check_bracket(x, f): - okay = False - else: - x0 = polynomial_fit(x, f) - if any(np.abs(np.log10(x[1:] / x0)) < thresh): - okay = True - else: - okay = False - return okay + return okay From b14fc3b8ba8d27d7383ac330877071b518180150 Mon Sep 17 00:00:00 2001 From: bch0w Date: Fri, 17 Jun 2022 11:08:38 -0800 Subject: [PATCH 014/195] rearranging config and seisflows cli functions for better organization. no api changes, just more concise source code and functions residing in more logical locations. Also replaced the Dict object defined in config with a simpler dictionary replacement --- seisflows/config.py | 152 +++++-------------------------- seisflows/seisflows.py | 199 +++++++++++++++++++++++++++-------------- 2 files changed, 154 insertions(+), 197 deletions(-) diff --git a/seisflows/config.py b/seisflows/config.py index ec166ed2..c62763b4 100644 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -66,87 +66,36 @@ """ -def init_seisflows(check=True): - """ - Instantiates SeisFlows objects and makes them globally accessible by - registering them in sys.modules. This must be run anytime seisflows is run. - - :type check: bool - :param check: Run parameter and path checking, defined in the module.check() - functions. By default should be True, to ensure that paths and - parameters are set correctly. It should only be set False for debug - and testing purposes when we need to force our way past this safeguard. - """ - logger.info("initializing SeisFlows in sys.modules") - - # Parameters and paths must already be loaded (normally done by submit) - assert(PAR in sys.modules) - assert(PATH in sys.modules) - - # Check if objects already exist on disk, exit so as to not overwrite - if "OUTPUT" in sys.modules[PATH] and \ - os.path.exists(sys.modules[PATH]["OUTPUT"]): - print(msg.cli("Data from previous workflow found in working directory.", - items=["> seisflows restart: delete data and start new " - "workflow", - "> seisflows resume: resume existing workflow"], - header="warning", border="=") - ) - sys.exit(-1) - # Instantiate and register objects - for name in NAMES: - sys.modules[f"seisflows_{name}"] = custom_import(name)() - - # Parameter import error checking, missing or improperly set parameters will - # throw assertion errors - if check: - errors = [] - for name in NAMES: - try: - sys.modules[f"seisflows_{name}"].check() - except AssertionError as e: - errors.append(f"{name}: {e}") - if errors: - print(msg.cli("seisflows.config module check failed with:", - items=errors, header="module check error", - border="=")) - sys.exit(-1) - - # Bare minimum module requirements for SeisFlows - req_modules = ["WORKFLOW", "SYSTEM"] - for req in req_modules: - if not hasattr(sys.modules[PAR], req): - print(msg.cli(f"SeisFlows requires defining: {req_modules}." - "Please specify these in the parameter file. Use " - "'seisflows print module' to determine suitable " - "choices.", header="error", border="=")) - sys.exit(-1) - - -def save(): +def save(path): """ - Export the current session to disk + Export the current Python environment to disk as Pickle and JSON files, + which allows us to checkpoint a current workflow and resume without + loss of information. + + :type path: str + :param path: path to save the current session """ - output = sys.modules[PATH]["OUTPUT"] - unix.mkdir(output) + if not os.path.exists(path): + unix.mkdir(path) # Save the paths and parameters into a JSON file for name in [PAR, PATH]: - fullfile = os.path.join(output, f"{name}.json") + fullfile = os.path.join(path, f"{name}.json") with open(fullfile, "w") as f: json.dump(sys.modules[name].__dict__, f, sort_keys=True, indent=4) # Save the current workflow as pickle objects for name in NAMES: - fullfile = os.path.join(output, f"seisflows_{name}.p") + fullfile = os.path.join(path, f"seisflows_{name}.p") with open(fullfile, "wb") as f: pickle.dump(sys.modules[f"seisflows_{name}"], f) def load(path): """ - Imports a previously saved session from disk + Imports a previously saved session from disk by reading in JSON and + Pickle files which define a saved Python environment :type path: str :param path: path to the previously saved session @@ -235,30 +184,17 @@ def config_logger(level="DEBUG", filename=None, filemode="a", verbose=True): file_handler.setFormatter(formatter) logger.addHandler(file_handler) - -class Dict(object): +class Dict(dict): """ - A barebones dictionary-like object for holding parameters or paths. - - Allows for easier access of dictionary items, does not allow resets of - attributes once defined, nor does it allow deleting attributes once defined. - This helps keep a workflow on rails by preventing the User from editing - paths and parameters during a workflow. - - TODO | Does it make sense to have this inherit dict() rather than defining - TODO | an entirely new object? + A dictionary replacement which allows for easier parameter access through + getting and setting attributes. Also has some functionality to make string + printing prettier """ - def __init__(self, newdict): - """Internal dictionary can ONLY be updated by updating the ENTIRE set - at once, usually done by reading in a new parameter file at the start - of a workflow""" - super(Dict, self).__setattr__('__dict__', newdict) - def __str__(self): """Pretty print dictionaries and first level nested dictionaries""" str_ = "" - longest_key = max([len(_) for _ in self.__dict__.keys()]) - for key, val in self.__dict__.items(): + longest_key = max([len(_) for _ in self.keys()]) + for key, val in self.items(): str_ += f"{key:<{longest_key}}: {val}\n" return str_ @@ -266,44 +202,17 @@ def __repr__(self): """Pretty print when calling an instance of this object""" return(self.__str__()) - def __iter__(self): - """Return an iterable list of sorted keys""" - return iter(sorted(self.__dict__.keys())) - def __getattr__(self, key): """Attribute-like access of the internal dictionary attributes""" try: - return self.__dict__[key] + return self[key] except KeyError: raise AttributeError(f"{key} not found in Dict") - def __getitem__(self, key): - """.get() like access of the internal dictionary attributes """ - return self.__dict__[key] - def __setattr__(self, key, val): """Setting attributes can only be performed one time""" - if key in self.__dict__: - raise TypeError("Once defined, parameters cannot be changed.") - self.__dict__[key] = val - - def __delattr__(self, key): - """Attributes cannot be deleted once set to avoid editing a parameter - set during an active workflow""" - if key in self.__dict__: - raise TypeError("Once defined, parameters cannot be deleted.") - raise KeyError - - def force_set(self, key, val): - """Force-set variables even though the intended behavior of this class - is to not allow deleting or replacing already set variables. - This should be used for check() functions and testing purposes only""" self.__dict__[key] = val - def values(self): - """Return values from the internal dictionary""" - return self.__dict__.values() - class Null(object): """ @@ -502,7 +411,7 @@ class 'Inversion'. sys.exit(-1) # If importing the module doesn't work, throw an error. Usually this happens - # when am external dependency isn't available, e.g., Pyatoa + # when an external dependency isn't available, e.g., Pyatoa try: module = import_module(full_dotted_name) except Exception as e: @@ -520,25 +429,6 @@ class 'Inversion'. sys.exit(-1) -def format_paths(mydict): - """ - Ensure that paths have a standardized format before being allowed into - an active working environment. - Expands tilde character (~) in path strings and expands absolute paths - - :type mydict: dict - :param mydict: dictionary of paths to be expanded - :rtype: dict - :return: formatted path dictionary - """ - for key, val in mydict.items(): - try: - mydict[key] = os.path.expanduser(os.path.abspath(val)) - except TypeError: - continue - return mydict - - def _pickle_method(method): """ The following code changes how instance methods are handled by pickle. diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 3448c543..5453e07b 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -28,11 +28,11 @@ from seisflows.tools import unix, msg from seisflows.tools.specfem import (getpar, setpar, getpar_vel_model, - setpar_vel_model) -from seisflows.tools.wrappers import loadyaml -from seisflows.config import (init_seisflows, format_paths, config_logger, - Dict, custom_import, SeisFlowsPathsParameters, - NAMES, ROOT_DIR, CFGPATHS) + setpar_vel_model) +from seisflows.tools.wrappers import loadyaml, format_paths +from seisflows.config import (config_logger, Dict, custom_import, + SeisFlowsPathsParameters, NAMES, ROOT_DIR, + CFGPATHS) def sfparser(): @@ -98,8 +98,6 @@ def _format_action(self, action): directory, a prompt will appear asking the user if they want to overwrite.""" ) - setup.add_argument("-s", "--symlink", action="store_true", - help="symlink source code into the working directory") setup.add_argument("-f", "--force", action="store_true", help="automatically overwrites existing parameter file") # ========================================================================= @@ -387,7 +385,7 @@ def _public_methods(self): """ return [_ for _ in dir(self) if not _.startswith("_")] - def _register(self, force=True): + def _register_parameters(self, force=True): """ Load the paths and parameters from file into sys.modules, set the default parameters if they are missing from the file, and expand all @@ -443,28 +441,100 @@ def _register(self, force=True): # For submit() and resume(), provide a dialogue to stdout requiring a # visual pre-check of parameters before submitting workflow if not force and parameters["PRECHECK"]: - items = [] - for p in parameters["PRECHECK"]: - try: - items.append(f"{p.upper()}: {parameters[p.upper()]}") - except KeyError: - items.append(f"{p.upper()}: !!! PARAMETER NOT FOUND !!!") - print(msg.cli("Please ensure that the parameters listed below " - "are set correctly. You can edit this list with " - "the PRECHECK parameter.", items=items, - header="seisflows precheck", border="=")) - check = input("Continue? (y/[n])\n") - if check != "y": - sys.exit(-1) + self._precheck_parameters(parameters) # Expand all paths to be absolute on the filesystem - paths = format_paths(paths) + for key, val in paths.items(): + paths[key] = os.path.expanduser(os.path.abspath(val)) - # Register parameters to sys, ensure they meet standards of the package + # Register parameters to sys and internally sys.modules["seisflows_parameters"] = Dict(parameters) sys.modules["seisflows_paths"] = Dict(paths) - self._paths = paths - self._parameters = parameters + self._paths = Dict(paths) + self._parameters = Dict(parameters) + + def _precheck_parameters(self, parameters): + """ + Visually display a list of user-chosen parameters to the User before + proceeding with the _register_parameters command. Allows the User to quickly + determine if workflow parameters have been set correctly + + :type parameters: dict + :param parameters: parameters read in from the YAML parameter file + """ + items = [] + for p in parameters["PRECHECK"]: + try: + items.append(f"{p.upper()}: {parameters[p.upper()]}") + except KeyError: + items.append(f"{p.upper()}: !!! PARAMETER NOT FOUND !!!") + print(msg.cli("Please ensure that the parameters listed below " + "are set correctly. You can edit this list with " + "the PRECHECK parameter.", items=items, + header="seisflows precheck", border="=")) + check = input("Continue? (y/[n])\n") + if check != "y": + sys.exit(-1) + + def _register_modules(self, check=True): + """ + First time setup procedure which loads in the user-chosen modules + and registers them into sys.modules so that they are globally accessible + to the program. + + :type check: bool + :param check: run the check() function for each of the instantiated + modules, which essentially checks the validity of all the + user-defined parameters. This is typically wanted, but sometimes + you don't want to check, e.g., during testing when you know + some parameters are set incorrectly + """ + assert(self._paths is not None), ( + f"seisflows._register_parameters() must be run before " + f"_register_modules()" + ) + assert(self._parameters is not None), ( + f"seisflows._register_parameters() must be run before " + f"_register_modules()" + ) + # Check if current workflow exists on disk, exit so as to not overwrite + if "OUTPUT" in self._paths and os.path.exists(self._paths.OUTPUT): + print(msg.cli( + "Data from previous workflow found in working directory.", + items=["> seisflows restart: delete data, start new workflow", + "> seisflows resume: resume existing workflow"], + header="warning", border="=") + ) + sys.exit(-1) + + # Instantiate and register objects + for name in NAMES: + sys.modules[f"seisflows_{name}"] = custom_import(name)() + + # Parameter import error checking, missing or improperly set parameters + # will throw assertion errors + if check: + errors = [] + for name in NAMES: + try: + sys.modules[f"seisflows_{name}"].check() + except AssertionError as e: + errors.append(f"{name}: {e}") + if errors: + print(msg.cli("seisflows.config module check failed with:", + items=errors, header="module check error", + border="=")) + sys.exit(-1) + + # Bare minimum Module requirements for SeisFlows + req_modules = ["WORKFLOW", "SYSTEM"] + for req in req_modules: + if not hasattr(self._parameters, req): + print(msg.cli(f"SeisFlows requires modules: {req_modules}." + "Please specify these in the parameter file. Use " + "'seisflows print module' to determine suitable " + "choices.", header="error", border="=")) + sys.exit(-1) def _load_modules(self): """ @@ -495,10 +565,13 @@ def _load_modules(self): for NAME in NAMES: sys.modules[f"seisflows_{NAME}"].check() - def setup(self, symlink=False, force=False, **kwargs): + def setup(self, force=False, **kwargs): """ - Initiate a SeisFlows working directory from scratch; establish a - template parameter file and symlink the source code for easy access + Initiate a SeisFlows working directory from scratch by establishing a + template parameter file. + + .. note:: + Future working directory setup functions can be placed here :type symlink: bool :param symlink: flag to turn on source code symlinking @@ -506,7 +579,6 @@ def setup(self, symlink=False, force=False, **kwargs): :param force: flag to force parameter file overwriting """ PAR_FILE = os.path.join(ROOT_DIR, "templates", "parameters.yaml") - REPO_DIR = os.path.abspath(os.path.join(ROOT_DIR, "..")) if os.path.exists(self._args.parameter_file): if force: @@ -517,7 +589,6 @@ def setup(self, symlink=False, force=False, **kwargs): f"({self._args.parameter_file}) found. Do you " f"wish to overwrite with a blank file? (y/[n])" )) - if check == "y": unix.rm(self._args.parameter_file) else: @@ -525,13 +596,6 @@ def setup(self, symlink=False, force=False, **kwargs): unix.cp(PAR_FILE, self._args.workdir) print(msg.cli(f"creating parameter file: {self._args.parameter_file}")) - # Symlink the source code for easy access to repo - if symlink: - src_code = os.path.join(self._args.workdir, "source_code") - if not os.path.exists(src_code): - unix.mkdir(src_code) - for package in PACKAGES: - unix.ln(os.path.join(REPO_DIR, package), src_code) def configure(self, relative_paths=False, **kwargs): """ @@ -544,7 +608,7 @@ def configure(self, relative_paths=False, **kwargs): else if False, use path names relative to the working directory. """ print(msg.cli(f"filling {self._args.parameter_file} w/ default values")) - self._register(force=True) + self._register_parameters(force=True) # Check if the User set turn off any modules (if None, dont instantiate) names = copy(NAMES) @@ -606,12 +670,11 @@ def init(self, **kwargs): pickle files to the OUTPUT directory for User inspection and debug purposes. """ - self._register(force=True) - unix.mkdir(self._args.workdir) unix.cd(self._args.workdir) - init_seisflows() + self._register_parameters(force=True) + self._register_modules(check=True) workflow = sys.modules["seisflows_workflow"] workflow.checkpoint() @@ -640,17 +703,35 @@ def submit(self, stop_after=None, force=False, **kwargs): :param force: if True, turns off the parameter precheck and simply submits the workflow """ - # Ensure that the 'RESUME_FROM' parameter is not set, incase of restart - self.par(parameter="resume_from", value="", skip_print=True) + unix.mkdir(self._args.workdir) + unix.cd(self._args.workdir) + # Ensure that the 'RESUME_FROM' parameter is not set, in case of restart + self.par(parameter="resume_from", value="", skip_print=True) if stop_after is not None: self.par(parameter="stop_after", value=stop_after, skip_print=True) - self._register(force=force) + # Read in the Parameter file and set parameters into sys.modules. + self._register_parameters(force=force) + self._check_required_paths() + self._register_modules(check=True) + + # Set logger to print to stdout and write to a file + config_logger(level=self._parameters.LOG_LEVEL, + verbose=self._parameters.VERBOSE, + filename=self._paths.LOGFILE) + + # Submit workflow.main() to the system + system = sys.modules["seisflows_system"] + system.submit() + def _check_required_paths(self): + """ + If the User provides certain paths to the program, they MUST exist. + This function simply checks these required paths and throws a sys exit + if any of them does not exist + """ # A list of paths that need to exist if provided by user - # !!! TODO Move this required paths somewhere more visible? config? - # !!! TODO or is it even necessary? REQ_PATHS = ["SPECFEM_BIN", "SPECFEM_DATA", "MODEL_INIT", "MODEL_TRUE", "DATA", "LOCAL", "MASK"] @@ -667,20 +748,6 @@ def submit(self, stop_after=None, force=False, **kwargs): border="=")) sys.exit(-1) - unix.mkdir(self._args.workdir) - unix.cd(self._args.workdir) - - # Set logger to print to stdout and write to a file - PATH = sys.modules["seisflows_paths"] - PAR = sys.modules["seisflows_parameters"] - config_logger(level=PAR.LOG_LEVEL, verbose=PAR.VERBOSE, - filename=PATH.LOGFILE) - - # Submit workflow.main() to the system - init_seisflows() - system = sys.modules["seisflows_system"] - system.submit() - def clean(self, force=False, **kwargs): """ Clean the SeisFlows working directory except for the parameter file. @@ -747,7 +814,7 @@ def resume(self, stop_after=None, resume_from=None, force=False, self.par(parameter="RESUME_FROM", value=resume_from, skip_print=True) - self._register(force=force) + self._register_parameters(force=force) self._load_modules() # Set logger to print to stdout and write to a file @@ -777,7 +844,7 @@ def debug(self, **kwargs): interactive environment allowing exploration of the package space. Does not allow stepping through of code (not a breakpoint). """ - self._register(force=True) + self._register_parameters(force=True) self._load_modules() # Distribute modules to common names for easy access during debug mode @@ -1067,7 +1134,7 @@ def check(self, choice=None, **kwargs): self._subparser.print_help() sys.exit(0) - self._register(force=True) + self._register_parameters(force=True) self._load_modules() acceptable_args[choice](*self._args.args, **kwargs) @@ -1101,7 +1168,7 @@ def reset(self, choice=None, **kwargs): self._subparser.print_help() sys.exit(0) - self._register(force=True) + self._register_parameters(force=True) self._load_modules() acceptable_args[choice](*self._args.args, **kwargs) @@ -1291,7 +1358,7 @@ def _print_flow(self, **kwargs): .. rubric:: $ seisflows print flow """ - self._register(force=True) + self._register_parameters(force=True) self._load_modules() workflow = custom_import("workflow")() @@ -1324,7 +1391,7 @@ def _print_inheritance(self, name=None, func=None, **kwargs): seisflows inspect solver eval_func """ - self._register(force=True) + self._register_parameters(force=True) self._load_modules() if func is None: self._inspect_module_hierarchy(name, **kwargs) From b1a43437d498e3b8ac0e346fc16a9c45f3d9d481 Mon Sep 17 00:00:00 2001 From: bch0w Date: Fri, 17 Jun 2022 15:00:18 -0800 Subject: [PATCH 015/195] removing some agency from line search and giving it to optimize to keep things simpler cleaning up docstrings and code formatting around --- seisflows/config.py | 18 +- seisflows/optimize/LBFGS.py | 11 +- seisflows/optimize/NLCG.py | 12 +- seisflows/optimize/{base.py => gradient.py} | 273 ++++++++------------ seisflows/plugins/line_search/__init__.py | 1 - seisflows/plugins/line_search/backtrack.py | 1 + seisflows/plugins/line_search/bracket.py | 21 +- seisflows/seisflows.py | 57 ++-- seisflows/solver/base.py | 19 +- seisflows/solver/specfem2d.py | 1 + seisflows/solver/specfem3d.py | 1 - seisflows/system/base.py | 4 +- seisflows/tools/specfem.py | 2 - seisflows/tools/wrappers.py | 4 - seisflows/workflow/base.py | 2 +- seisflows/workflow/inversion.py | 18 +- 16 files changed, 176 insertions(+), 269 deletions(-) rename seisflows/optimize/{base.py => gradient.py} (64%) diff --git a/seisflows/config.py b/seisflows/config.py index c62763b4..9441284b 100644 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -55,7 +55,6 @@ CFGPATHS = dict( PAR_FILE="parameters.yaml", # Default SeisFlows parameter file SCRATCHDIR="scratch", # SeisFlows internal working directory - STATSDIR="stats", # Optimization module log file output OUTPUTDIR="output", # Permanent disk storage for state and outputs LOGFILE="sfoutput.txt", # Log files for all system log ERRLOGFILE="sferror.txt", # StdErr dump site for crash messages @@ -66,7 +65,6 @@ """ - def save(path): """ Export the current Python environment to disk as Pickle and JSON files, @@ -83,7 +81,7 @@ def save(path): for name in [PAR, PATH]: fullfile = os.path.join(path, f"{name}.json") with open(fullfile, "w") as f: - json.dump(sys.modules[name].__dict__, f, sort_keys=True, indent=4) + json.dump(sys.modules[name], f, sort_keys=True, indent=4) # Save the current workflow as pickle objects for name in NAMES: @@ -100,8 +98,6 @@ def load(path): :type path: str :param path: path to the previously saved session """ - logger.info("loading current working environment from disk") - # Load parameters and paths from a JSON file for name in [PAR, PATH]: fullfile = os.path.join(os.path.abspath(path), f"{name}.json") @@ -184,6 +180,7 @@ def config_logger(level="DEBUG", filename=None, filemode="a", verbose=True): file_handler.setFormatter(formatter) logger.addHandler(file_handler) + class Dict(dict): """ A dictionary replacement which allows for easier parameter access through @@ -193,9 +190,12 @@ class Dict(dict): def __str__(self): """Pretty print dictionaries and first level nested dictionaries""" str_ = "" - longest_key = max([len(_) for _ in self.keys()]) - for key, val in self.items(): - str_ += f"{key:<{longest_key}}: {val}\n" + try: + longest_key = max([len(_) for _ in self.keys()]) + for key, val in self.items(): + str_ += f"{key:<{longest_key}}: {val}\n" + except ValueError: + pass return str_ def __repr__(self): @@ -214,7 +214,7 @@ def __setattr__(self, key, val): self.__dict__[key] = val -class Null(object): +class Null: """ A null object that always and reliably does nothing """ diff --git a/seisflows/optimize/LBFGS.py b/seisflows/optimize/LBFGS.py index 0ac5db07..7bd3ed01 100644 --- a/seisflows/optimize/LBFGS.py +++ b/seisflows/optimize/LBFGS.py @@ -17,7 +17,7 @@ PATH = sys.modules["seisflows_paths"] -class LBFGS(custom_import("optimize", "base")): +class LBFGS(custom_import("optimize", "gradient")): """ The Limited memory BFGS algorithm Calls upon seisflows.plugin.optimize.LBFGS to accomplish LBFGS algorithm @@ -80,6 +80,7 @@ def __init__(self): i.e., `m_new - m_old` """ super().__init__() + self.LBFGS_iter = 0 self.memory_used = 0 self.LBFGS_dir = "LBFGS" @@ -155,7 +156,7 @@ def compute_direction(self): # Load the current gradient direction, which is the L-BFGS search # direction if this is the first iteration - g = self.load(self.g_new) + g = np.load(self.g_new) if self.LBFGS_iter == 1: self.logger.info("first L-BFGS iteration, setting search direction " "as inverse gradient") @@ -192,7 +193,7 @@ def compute_direction(self): restarted = 1 # Save values to disk and memory - self.save(self.p_new, p_new) + np.save(self.p_new, p_new) self.restarted = restarted def restart(self): @@ -235,8 +236,8 @@ def update(self): unix.cd(PATH.OPTIMIZE) # Determine the iterates for model m and gradient g - s_k = self.load(self.m_new) - self.load(self.m_old) - y_k = self.load(self.g_new) - self.load(self.g_old) + s_k = np.load(self.m_new) - np.load(self.m_old) + y_k = np.load(self.g_new) - np.load(self.g_old) # Determine the shape of the memory map (length of model, length of mem) m = len(s_k) diff --git a/seisflows/optimize/NLCG.py b/seisflows/optimize/NLCG.py index c08e9862..019f64d4 100644 --- a/seisflows/optimize/NLCG.py +++ b/seisflows/optimize/NLCG.py @@ -5,15 +5,17 @@ """ import sys import logging +import numpy as np from seisflows.config import custom_import, SeisFlowsPathsParameters from seisflows.tools import unix +from seisflows.tools.math import dot PAR = sys.modules['seisflows_parameters'] PATH = sys.modules['seisflows_paths'] -class NLCG(custom_import("optimize", "base")): +class NLCG(custom_import("optimize", "gradient")): """ Nonlinear conjugate gradient method @@ -103,7 +105,7 @@ def compute_direction(self): unix.cd(PATH.OPTIMIZE) # Load the current gradient direction - g_new = self.load(self.g_new) + g_new = np.load(self.g_new) # CASE 1: If first iteration, search direction is the current gradient if self.NLCG_iter == 1: @@ -123,8 +125,8 @@ def compute_direction(self): # Normal NLCG direction compuitation else: # Compute search direction - g_old = self.load(self.g_old) - p_old = self.load(self.p_old) + g_old = np.load(self.g_old) + p_old = np.load(self.p_old) # Apply preconditioner and calc. scale factor for search dir. (beta) if self.precond: @@ -150,7 +152,7 @@ def compute_direction(self): restarted = 0 # Save values to disk and memory - self.save(self.p_new, p_new) + np.save(self.p_new, p_new) self.restarted = restarted def restart(self): diff --git a/seisflows/optimize/base.py b/seisflows/optimize/gradient.py similarity index 64% rename from seisflows/optimize/base.py rename to seisflows/optimize/gradient.py index 524efb89..56b2ac06 100644 --- a/seisflows/optimize/base.py +++ b/seisflows/optimize/gradient.py @@ -16,18 +16,17 @@ from seisflows.tools.math import angle, dot from seisflows.plugins import line_search, preconds from seisflows.tools.specfem import check_poissons_ratio -from seisflows.config import SeisFlowsPathsParameters, CFGPATHS +from seisflows.config import SeisFlowsPathsParameters, CFGPATHS, Dict PAR = sys.modules["seisflows_parameters"] PATH = sys.modules["seisflows_paths"] solver = sys.modules["seisflows_solver"] -class Base: +class Gradient: """ - Nonlinear optimization abstract base class. - - This base class provides a steepest descent optimization algorithm. + Nonlinear optimization abstract base class poviding a gradient/steepest + descent optimization algorithm. Nonlinear conjugate, quasi-Newton and Newton methods can be implemented on top of this base class. @@ -35,7 +34,7 @@ class Base: .. note:: To reduce memory overhead, vectors are read from disk rather than passed from calling routines. For example, at the beginning of - compute_direction the current gradient is read from 'g_new' and the + compute_direction the current gradient is read from 'g_new' and the resulting search direction is written to 'p_new'. As the inversion progresses, other information is stored as well. @@ -84,34 +83,29 @@ def __init__(self): self.precond = None self.restarted = False - # Define the names of output stats logs to keep all paths in one place - # Line search log is named differently so that optimize doesn't - # overwrite this log file when intiating the stats directory - self.line_search_log = "line_search" - self.log_factor = "factor" - self.log_gradient_norm_L1 = "gradient_norm_L1" - self.log_gradient_norm_L2 = "gradient_norm_L2" - self.log_misfit = "misfit" - self.log_restarted = "restarted" - self.log_slope = "slope" - self.log_step_count = "step_count" - self.log_step_length = "step_length" - self.log_theta = "theta" - - # Define the names of variables used to keep track of models etc. so - # that we don't have multiple strings floating around defining the same - # thing - self.m_new = "m_new.npy" - self.m_old = "m_old.npy" - self.m_try = "m_try.npy" - self.f_new = "f_new.txt" - self.f_old = "f_old.txt" - self.f_try = "f_try.txt" - self.g_new = "g_new.npy" - self.g_old = "g_old.npy" - self.p_new = "p_new.npy" - self.p_old = "p_old.npy" - self.alpha = "alpha.npy" + # Models + self.m_new = os.path.join(PATH.OPTIMIZE, "m_new.npy") + self.m_old = os.path.join(PATH.OPTIMIZE, "m_old.npy") + self.m_try = os.path.join(PATH.OPTIMIZE, "m_try.npy") + + # Gradients + self.g_new = os.path.join(PATH.OPTIMIZE, "g_new.npy") + self.g_old = os.path.join(PATH.OPTIMIZE, "g_old.npy") + self.g_try = os.path.join(PATH.OPTIMIZE, "g_try.npy") + + # Search directions + self.p_new = os.path.join(PATH.OPTIMIZE, "p_new.npy") + self.p_old = os.path.join(PATH.OPTIMIZE, "p_old.npy") + self.p_try = os.path.join(PATH.OPTIMIZE, "p_try.npy") + + # Misfits + self.f_new = os.path.join(PATH.OPTIMIZE, "f_new.npy") + self.f_old = os.path.join(PATH.OPTIMIZE, "f_old.npy") + self.f_try = os.path.join(PATH.OPTIMIZE, "f_try.npy") + + # Current search direction + self.alpha = os.path.join(PATH.OPTIMIZE, "alpha.npy") + @property def required(self): @@ -177,13 +171,17 @@ def setup(self): # Line search machinery is defined externally as a plugin class if PAR.LINESEARCH: - self.line_search = getattr(line_search, PAR.LINESEARCH)() + self.line_search = getattr(line_search, PAR.LINESEARCH)( + step_count_max=PAR.STEPCOUNTMAX, + step_len_max=PAR.STEPLENMAX, + ) if PAR.PRECOND: self.precond = getattr(preconds, PAR.PRECOND)() + # Read in initial model as a vector and ensure it is a valid model m_new = solver.merge(solver.load(PATH.MODEL_INIT)) - self.save(self.m_new, m_new) - self.check_model(m_new, self.m_new) + np.save(self.m_new, m_new) + self.check_model(m_new) @property def eval_str(self): @@ -208,24 +206,26 @@ def compute_direction(self): """ self.logger.info(f"computing search direction with {PAR.OPTIMIZE}") - g_new = self.load(self.g_new) + g_new = np.load(self.g_new) if self.precond is not None: p_new = -1 * self.precond(g_new) else: p_new = -1 * g_new - self.save(self.p_new, p_new) + np.save(self.p_new, p_new) def initialize_search(self): """ Initialize the plugin line search machinery. Should only be run at the beginning of line search, by the main workflow module. """ - m = self.load(self.m_new) - g = self.load(self.g_new) - p = self.load(self.p_new) - f = self.loadtxt(self.f_new) + m = np.load(self.m_new) + g = np.load(self.g_new) + p = np.load(self.p_new) + f = np.load(self.f_new) + norm_m = max(abs(m)) norm_p = max(abs(p)) + gtg = dot(g, g) gtp = dot(g, p) @@ -239,23 +239,21 @@ def initialize_search(self): self.logger.debug(f"max step length safeguard is: " f"{self.line_search.step_len_max:.2E}") - # Alpha defines the trial step length - alpha, _ = self.line_search.initialize(iter=self.iter, step_len=0., - func_val=f, gtg=gtg, gtp=gtp - ) + self.line_search.initialize(step_len=0., func_val=f, gtg=gtg, gtp=gtp) + alpha, _ = self.line_search.calculate_step() - # Optional initial step length override + # Alpha defines the trial step length. Optional step length override if PAR.STEPLENINIT and len(self.line_search.step_lens) <= 1: alpha = PAR.STEPLENINIT * norm_m / norm_p - self.logger.debug(f"manually set initial step length: {alpha:.2E}") + self.logger.debug(f"overwrite initial step length: {alpha:.2E}") # The new model is the old model, scaled by the step direction and # gradient threshold to remove any outlier values m_try = m + alpha * p - self.save(self.m_try, m_try) - self.savetxt(self.alpha, alpha) - self.check_model(m_try, self.m_try) + np.save(self.m_try, m_try) + np.save(self.alpha, alpha) + self.check_model(m_try) def update_search(self): """ @@ -267,19 +265,19 @@ def update_search(self): status == 0 : not finished status == -1 : failed """ - alpha, status = self.line_search.update( - iter=self.iter, step_len=self.loadtxt(self.alpha), - func_val=self.loadtxt(self.f_try) - ) + self.line_search.update(step_len=np.load(self.alpha), + func_val=np.load(self.f_try)) + alpha, status = self.line_search.calculate_step() # New search direction needs to be searchable on disk if status in [0, 1]: - m = self.load(self.m_new) - p = self.load(self.p_new) - self.savetxt(self.alpha, alpha) + m = np.load(self.m_new) + p = np.load(self.p_new) + np.save(self.alpha, alpha) + m_try = m + alpha * p - self.save(self.m_try, m_try) - self.check_model(m_try, self.m_try) + np.save(self.m_try, m_try) + self.check_model(m_try) return status @@ -290,15 +288,8 @@ def finalize_search(self): Removes old model/search parameters, moves current parameters to old, sets up new current parameters and writes statistic outputs """ - self.logger.info(msg.sub("FINALIZING LINE SEARCH")) - - g = self.load(self.g_new) - p = self.load(self.p_new) - x = self.line_search.search_history()[0] - f = self.line_search.search_history()[1] - - # Clean scratch directory unix.cd(PATH.OPTIMIZE) + self.logger.info(msg.sub("FINALIZING LINE SEARCH")) # Remove the old model parameters if self.iter > 1: @@ -314,23 +305,12 @@ def finalize_search(self): self.logger.info("setting accepted line search model as current model") unix.mv(self.m_try, self.m_new) - self.savetxt(self.f_new, f.min()) + + f = self.line_search.search_history()[1] + np.save(self.f_new, f.min()) self.logger.info(f"current misfit is {self.f_new}={f.min():.3E}") - # !!! TODO Describe what stats are being written here - self.logger.info(f"writing optimization stats to: {CFGPATHS.STATSDIR}") - self.write_stats(self.log_factor, value= - -dot(g, g) ** -0.5 * (f[1] - f[0]) / (x[1] - x[0]) - ) - self.write_stats(self.log_gradient_norm_L1, value=np.linalg.norm(g, 1)) - self.write_stats(self.log_gradient_norm_L2, value=np.linalg.norm(g, 2)) - self.write_stats(self.log_misfit, value=f[0]) - self.write_stats(self.log_restarted, value=self.restarted) - self.write_stats(self.log_slope, value=(f[1] - f[0]) / (x[1] - x[0])) - self.write_stats(self.log_step_count, value=self.line_search.step_count) - self.write_stats(self.log_step_length, value=x[f.argmin()]) - self.write_stats(self.log_theta, - value=180. * np.pi ** -1 * angle(p, -g)) + self.write_stats() self.logger.info("resetting line search step count to 0") self.line_search.step_count = 0 @@ -341,8 +321,8 @@ def retry_status(self): by checking, in effect, if the search direction was the same as gradient direction """ - g = self.load(self.g_new) - p = self.load(self.p_new) + g = np.load(self.g_new) + p = np.load(self.p_new) theta = angle(p, -g) self.logger.debug(f"theta: {theta:6.3f}") @@ -363,25 +343,20 @@ def restart(self): numerical stagnation. """ # Steepest descent (base) does not need to be restarted - if PAR.OPTIMIZE != "base": - g = self.load(self.g_new) - self.save(self.p_new, -g) + if PAR.OPTIMIZE.capitalize() != "Gradient": + g = np.load(self.g_new) + np.save(self.p_new, -g) + self.line_search.clear_history() self.restarted = 1 - def write_stats(self, log, value=None, format="18.6E"): + def write_stats(self): """ - Simplified write function to append values to text files in the - STATSDIR. Used because stats line search information can be overwritten + Simplified write function to append values to text files. + Used because stats line search information can be overwritten by subsequent iterations so we need to append values to text files if they should be retained. - Log files will look something like: - - ITER FACTOR - ==== ====== - 1 0.0 - :type log: str :param log: name of the file to write to. Will append .txt to it :type value: float @@ -389,20 +364,42 @@ def write_stats(self, log, value=None, format="18.6E"): :type format: str :param format: string formatter for value """ - fid = os.path.join(PATH.WORKDIR, CFGPATHS.STATSDIR, f"{log}.txt") + self.logger.info(f"writing optimization stats") + fid = os.path.join(PATH.OUTPUT, f"optim_stats.txt") - # If no value is given, assuming we are being run from setup() and - # writing to new files. Will OVERWRITE any existing files - if value is None: + # First time, write header information + if not os.path.exists(fid): with open(fid, "w") as f: - f.write(f"{'ITER':>4} {log.upper():>18}\n") - f.write(f"{'='*4} {'='*18}\n") + f.write(f"{'ITER':>4}") + for log in ["FACTOR", "GRAD_NORM_L1", "GRAD_NORM_L2", + "MISFIT", "RESTART", "SLOPE", "STEP", "LENGTH", + "THETA"]: + f.write(f"{log.upper()},") + f.write("\n") + + g = np.load(self.g_new) + p = np.load(self.p_new) + x = self.line_search.search_history()[0] + f = self.line_search.search_history()[1] - else: - with open(fid, "a") as f: - f.write(f"{self.iter:>4} {value:{format}}\n") + # Calculated stats factors + factor = (g, g) ** -0.5 * (f[1] - f[0]) / (x[1] - x[0]) + grad_norm_L1 = np.linalg.norm(g, 1) + grad_norm_L2 = np.linalg.norm(g, 2) + misfit = f[0] + restarted = self.restarted + slope = (f[1] - f[0]) / (x[1] - x[0]) + step_count = self.line_search.step_count + step_length = x[f.argmin()] + theta = 180. * np.pi ** -1 * angle(p, -g) - def check_model(self, m, tag): + with open(fid, "a") as f: + f.write(f"{self.iter},{factor},{grad_norm_L1},{grad_norm_L2}," + f"{misfit},{restarted},{slope},{step_count},{step_length}," + f"{theta}\n") + + + def check_model(self, m): """ Check to ensure that the model parameters fall within the guidelines of the solver. Print off min/max model parameters for the User. @@ -419,77 +416,21 @@ def check_model(self, m, tag): # Check Poisson's ratio, which will error our SPECFEM if outside limits if (pars["vp"] is not None) and (pars["vs"] is not None): - self.logger.debug(f"checking poissons ratio for: '{tag}'") + self.logger.debug(f"checking poissons ratio") pars["pr"] = check_poissons_ratio(vp=pars["vp"], vs=pars["vs"]) if pars["pr"].min() < 0: self.logger.warning("minimum poisson's ratio is negative") # Tell the User min and max values of the updated model - self.logger.info(f"model parameters ({tag} {self.eval_str}):") + self.logger.info(f"model parameters") parts = "{minval:.2f} <= {key} <= {maxval:.2f}" for key, vals in pars.items(): self.logger.info(parts.format(minval=vals.min(), key=key, maxval=vals.max()) ) - @staticmethod - def load(filename): - """ - Convenience function to reads vectors from disk as Numpy files, - reads directly from PATH.OPTIMIZE. Works around Numpy's behavior of - appending '.npy' to files that it saves. - - :type filename: str - :param filename: filename to read from - :rtype: np.array - :return: vector read from disk - """ - fid = os.path.join(PATH.OPTIMIZE, filename) - if not os.path.exists(fid): - fid += ".npy" - return np.load(fid) - @staticmethod - def save(filename, array): - """ - Convenience function to write vectors to disk as numpy files. - Reads directly from PATH.OPTIMIZE - :type filename: str - :param filename: filename to read from - :type array: np.array - :param array: array to be saved - """ - np.save(os.path.join(PATH.OPTIMIZE, filename), array) - @staticmethod - def loadtxt(filename): - """ - Reads scalars from optimize directory on disk, - accounts for savetxt() appending file extension - - :type filename: str - :param filename: filename to read from - :rtype: float - :return: scalar read from disk - """ - if not os.path.splitext(filename)[1]: - filename += ".txt" - return float(np.loadtxt(os.path.join(PATH.OPTIMIZE, filename))) - - @staticmethod - def savetxt(filename, scalar): - """ - Writes scalars to disk with a specific format, appends .txt to the - filename to make it clear that these are text files. - - :type filename: str - :param filename: filename to read from - :type scalar: float - :param scalar: value to write to disk - """ - if not os.path.splitext(filename)[1]: - filename += ".txt" - np.savetxt(os.path.join(PATH.OPTIMIZE, filename), [scalar], "%11.6e") diff --git a/seisflows/plugins/line_search/__init__.py b/seisflows/plugins/line_search/__init__.py index c3c93388..056d49ea 100644 --- a/seisflows/plugins/line_search/__init__.py +++ b/seisflows/plugins/line_search/__init__.py @@ -1,4 +1,3 @@ -from .base import Base from .bracket import Bracket from .backtrack import Backtrack diff --git a/seisflows/plugins/line_search/backtrack.py b/seisflows/plugins/line_search/backtrack.py index 94a2df14..a844232f 100644 --- a/seisflows/plugins/line_search/backtrack.py +++ b/seisflows/plugins/line_search/backtrack.py @@ -70,6 +70,7 @@ def calculate_step(self): status = 1 # If misfit continually increases, decrease step length elif step_count <= self.step_count_max: + import pdb;pdb.set_trace() self.logger.info("misfit increase, decreasing step length") slope = gtp[-1] / gtg[-1] alpha = parabolic_backtrack(f0=f[0], g0=slope, x1=x[1], diff --git a/seisflows/plugins/line_search/bracket.py b/seisflows/plugins/line_search/bracket.py index 68511a4f..a81fb344 100644 --- a/seisflows/plugins/line_search/bracket.py +++ b/seisflows/plugins/line_search/bracket.py @@ -10,6 +10,7 @@ import logging import numpy as np +from seisflows.tools import msg from seisflows.tools.array import count_zeros from seisflows.tools.math import parabolic_backtrack, polynomial_fit @@ -34,8 +35,9 @@ class Bracket: # Class-specific logger accessed using self.logger logger = logging.getLogger(__name__).getChild(__qualname__) - def __init__(self): + def __init__(self, step_count_max, step_len_max): """ + Initiate the line search machinery :type step_count_max: int :param step_count_max: maximum number of step counts before changing @@ -43,10 +45,9 @@ def __init__(self): :type step_len_max: int :param step_len_max: maximum length of the step, defaults to infinity, that is unbounded step length. set by PAR.STEP_LEN_MAX - :type log_file: str - :param log_file: path to write line search stats to. set by optimize.setup() """ - # Prepare lists for line search history + self.step_count_max = step_count_max + self.step_len_max = step_len_max self.func_vals = [] self.step_lens = [] self.gtg = [] @@ -79,11 +80,6 @@ def initialize(self, step_len, func_val, gtg, gtp): self.gtg += [gtg] self.gtp += [gtp] - # Call calculate step, must be implemented by subclass - alpha, status = self.calculate_step() - - return alpha, status - def update(self, step_len, func_val): """ Update search history by appending internal attributes, writing the @@ -101,16 +97,9 @@ def update(self, step_len, func_val): :rtype status: int :return status: current status of the line search """ - # This has been moved into workflow.line_search() - # self.step_count += 1 self.step_lens += [step_len] self.func_vals += [func_val] - # Call calcuate step, must be implemented by subclass - alpha, status = self.calculate_step() - - return alpha, status - def clear_history(self): """ Clears internal line search history diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 5453e07b..dc5cff97 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -29,8 +29,8 @@ from seisflows.tools import unix, msg from seisflows.tools.specfem import (getpar, setpar, getpar_vel_model, setpar_vel_model) -from seisflows.tools.wrappers import loadyaml, format_paths -from seisflows.config import (config_logger, Dict, custom_import, +from seisflows.tools.wrappers import loadyaml +from seisflows.config import (config_logger, Dict, custom_import, load, SeisFlowsPathsParameters, NAMES, ROOT_DIR, CFGPATHS) @@ -445,7 +445,10 @@ def _register_parameters(self, force=True): # Expand all paths to be absolute on the filesystem for key, val in paths.items(): - paths[key] = os.path.expanduser(os.path.abspath(val)) + try: + paths[key] = os.path.expanduser(os.path.abspath(val)) + except TypeError: + continue # Register parameters to sys and internally sys.modules["seisflows_parameters"] = Dict(parameters) @@ -541,24 +544,25 @@ def _load_modules(self): A function to load and check each of the SeisFlows modules, re-initiating the SeisFlows environment. All modules are reliant on one another so any access to SeisFlows requires loading everything - simultaneously and in correct order - """ - # Working directory should already have been created by submit() - unix.cd(self._args.workdir) + simultaneously and in correct order. - # Reload objects from Pickle files + .. note:: + This is similar to config.load() except it doesn't load paths + and parameters. This allows the User to OVERLOAD the currently + defined paths and parameters anytime they call 'seisflows resume' + """ for NAME in NAMES: - fullfile = os.path.join(self._args.workdir, CFGPATHS.OUTPUTDIR, - f"seisflows_{NAME}.p") + fid = os.path.join(self._paths.OUTPUT, f"seisflows_{NAME}.p") - if not os.path.exists(fullfile): + if not os.path.exists(fid): print(msg.cli("Not a SeisFlows working directory (no state " "files found). Run 'seisflows init' or " "'seisflows submit' to instantiate a working " "directory.") ) sys.exit(-1) - with open(fullfile, "rb") as f: + + with open(fid, "rb") as f: sys.modules[f"seisflows_{NAME}"] = pickle.load(f) # Check parameters so that default values are present @@ -794,6 +798,7 @@ def resume(self, stop_after=None, resume_from=None, force=False, """ Resume a previously started workflow by loading the module pickle files and submitting the workflow from where it left off. + :type stop_after: str :param stop_after: allow the function to overwrite the 'STOP_AFTER' parameter in the parameter file, which dictates how far the workflow @@ -818,10 +823,9 @@ def resume(self, stop_after=None, resume_from=None, force=False, self._load_modules() # Set logger to print to stdout and write to a file - PATH = sys.modules["seisflows_paths"] - PAR = sys.modules["seisflows_parameters"] - config_logger(level=PAR.LOG_LEVEL, verbose=PAR.VERBOSE, - filename=PATH.LOGFILE) + config_logger(level=self._parameters.LOG_LEVEL, + verbose=self._parameters.VERBOSE, + filename=self._paths.LOGFILE) system = sys.modules["seisflows_system"] system.submit() @@ -1009,7 +1013,7 @@ def par(self, parameter, value=None, skip_print=False, required=False, # SeisFlows parameter file dictates upper-case parameters parameter = parameter.upper() if isinstance(value, str) and value.lower() == "none": - warnings.warn("to set values to nonetype, use 'null' not 'none'", + warnings.warn("to set values to NoneType, use 'null' not 'none'", UserWarning) # Use the specfem tool to grab related information @@ -1208,25 +1212,6 @@ def convert(self, name, path=None, **kwargs): solver.save(solver.split(optimize.load(name)), path=path, **kwargs ) - def validate(self, module=None, name=None): - """ - Ensure that all the modules (and their respective subclasses) meet some - necessary requirements such as having specific functions and parameters. - Not a full replacement for running the test suite, but useful for - checking newly written subclasses. - - USAGE - - To validate a specific subclass: - - seisflows validate workflow inversion - - To validate the entire codebase - - seisflows validate - """ - raise NotImplementedError - @staticmethod def _inspect_class_that_defined_method(name, func, **kwargs): """ diff --git a/seisflows/solver/base.py b/seisflows/solver/base.py index 9c5ee54f..d6ea3746 100644 --- a/seisflows/solver/base.py +++ b/seisflows/solver/base.py @@ -350,7 +350,8 @@ def call_solver(self, executable, output="solver.log"): """ Calls MPI solver executable to run solver binaries, used by individual processes to run the solver on system. If the external solver returns a - non-zero exit code (failure), this function will return a negative boolean. + non-zero exit code (failure), this function will return a negative + boolean. :type mpiexec: str :param mpiexec: call to mpi. If None (e.g., serial run, defaults to ./) @@ -359,7 +360,9 @@ def call_solver(self, executable, output="solver.log"): :type output: str :param output: where to redirect stdout """ - if not os.path.exists(executable): + # Executable may come with additional sub arguments, we only need to + # check that the actually executable exists + if not os.path.exists(executable.split(" ")[0]): print(msg.cli(f"solver executable {executable} does not exist", header="external solver error", border="=")) sys.exit(-1) @@ -534,14 +537,14 @@ def combine(self, input_path, output_path, parameters=None): :param parameters: optional list of parameters, defaults to `self.parameters` """ + unix.cd(self.cwd) + if parameters is None: parameters = self.parameters if not exists(output_path): unix.mkdir(output_path) - unix.cd(self.cwd) - # Write the source names into the kernel paths file for SEM/ directory with open("kernel_paths", "w") as f: f.writelines( @@ -587,19 +590,17 @@ def smooth(self, input_path, output_path, parameters=None, span_h=0., :type output: str :param output: file to output stdout to """ + unix.cd(self.cwd) + if parameters is None: parameters = self.parameters if not exists(output_path): unix.mkdir(output_path) - # Apply smoothing operator inside scratch/solver/* - unix.cd(self.cwd) - # mpiexec ./bin/xsmooth_sem SMOOTH_H SMOOTH_V name input output use_gpu for name in parameters: - self.call_solver(mpiexec=PAR.MPIEXEC, - executable=" ".join(["bin/xsmooth_sem", + self.call_solver(executable=" ".join(["bin/xsmooth_sem", str(span_h), str(span_v), f"{name}_kernel", os.path.join(input_path, ""), diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index 1e846211..02ec78e9 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -203,6 +203,7 @@ def generate_data(self): # Generate synthetic data on the fly using the true model self.generate_mesh(model_name="true", model_type="gll") self.forward(path=os.path.join("traces", "obs")) + # If Data provided by user, copy directly into the solver directory elif PATH.DATA is not None and os.path.exists(PATH.DATA): unix.cp(src=glob(os.path.join(PATH.DATA, self.source_name, "*")), diff --git a/seisflows/solver/specfem3d.py b/seisflows/solver/specfem3d.py index 906a03db..43e868c4 100644 --- a/seisflows/solver/specfem3d.py +++ b/seisflows/solver/specfem3d.py @@ -10,7 +10,6 @@ import logging from glob import glob -import seisflows.plugins.solver.specfem3d as solvertools from seisflows.tools import unix, msg from seisflows.tools.wrappers import exists from seisflows.config import custom_import, SeisFlowsPathsParameters diff --git a/seisflows/system/base.py b/seisflows/system/base.py index f55290a5..04b740c9 100644 --- a/seisflows/system/base.py +++ b/seisflows/system/base.py @@ -14,7 +14,6 @@ from seisflows.config import save, SeisFlowsPathsParameters, CFGPATHS - PAR = sys.modules["seisflows_parameters"] PATH = sys.modules["seisflows_paths"] @@ -205,8 +204,9 @@ def checkpoint(self, path, classname, method, kwargs): """ argspath = os.path.join(path, "kwargs") argsfile = os.path.join(argspath, f"{classname}_{method}.p") + unix.mkdir(argspath) with open(argsfile, "wb") as f: pickle.dump(kwargs, f) - save() + save(path=PATH.OUTPUT) diff --git a/seisflows/tools/specfem.py b/seisflows/tools/specfem.py index 95609907..2d39f544 100644 --- a/seisflows/tools/specfem.py +++ b/seisflows/tools/specfem.py @@ -2,10 +2,8 @@ Utilities to interact with, manipulate or call on the external solver, i.e., SPECFEM2D/3D/3D_GLOBE """ -import os import sys import numpy as np -import subprocess from collections import defaultdict from seisflows.tools import msg diff --git a/seisflows/tools/wrappers.py b/seisflows/tools/wrappers.py index ded1c204..6b8051d2 100644 --- a/seisflows/tools/wrappers.py +++ b/seisflows/tools/wrappers.py @@ -7,15 +7,11 @@ import re import time import yaml -import json -import pickle import subprocess import numpy as np from importlib import import_module from pkgutil import find_loader -from seisflows.tools import msg - class Struct(dict): """ diff --git a/seisflows/workflow/base.py b/seisflows/workflow/base.py index 3548835e..ecf52309 100644 --- a/seisflows/workflow/base.py +++ b/seisflows/workflow/base.py @@ -222,6 +222,6 @@ def checkpoint(): """ Writes information to disk so workflow can be resumed following a break """ - save() + save(path=PATH.OUTPUT) diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index 441fbcb5..fcbceca9 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -308,12 +308,6 @@ def clean(self): unix.mkdir(PATH.GRAD) unix.mkdir(PATH.FUNC) - def checkpoint(self): - """ - Writes information to disk so workflow can be resumed following a break - """ - save() - def write_model(self, path, tag): """ Writes model in format expected by solver @@ -328,7 +322,7 @@ def write_model(self, path, tag): src = tag dst = os.path.join(path, "model") self.logger.debug(f"saving model '{src}' to:\n{dst}") - solver.save(solver.split(optimize.load(src)), dst) + solver.save(solver.split(np.load(src)), dst) def write_gradient(self): """ @@ -337,12 +331,12 @@ def write_gradient(self): """ self.logger.info(msg.mnr("POSTPROCESSING KERNELS")) src = os.path.join(PATH.GRAD, "gradient") - dst = f"g_new" + dst = optimize.g_new postprocess.write_gradient(PATH.GRAD) parts = solver.load(src, suffix="_kernel") - optimize.save(dst, solver.merge(parts)) + np.save(dst, solver.merge(parts)) def write_misfit(self, path, tag): """ @@ -363,7 +357,7 @@ def write_misfit(self, path, tag): total_misfit = preprocess.sum_residuals(src) self.logger.debug(f"saving misfit {total_misfit:.3E} to tag '{dst}'") - optimize.savetxt(dst, total_misfit) + np.save(dst, total_misfit) def save_gradient(self): """ @@ -398,9 +392,9 @@ def save_model(self): self.logger.debug(f"saving model '{src}' to path:\n{dst}") if PAR.SAVEAS in ["binary", "both"]: - solver.save(solver.split(optimize.load(src)), dst) + solver.save(solver.split(np.load(src)), dst) if PAR.SAVEAS in ["vector", "both"]: - np.save(file=dst, arr=optimize.load(src)) + np.save(file=dst, arr=np.load(src)) def save_kernels(self): """ From 36a6c399a169647aee79ac3559331c2fd9f56439 Mon Sep 17 00:00:00 2001 From: bch0w Date: Fri, 17 Jun 2022 16:44:49 -0800 Subject: [PATCH 016/195] fixing tests after refactor, specfem2d example also works --- seisflows/optimize/gradient.py | 34 +++++---- seisflows/plugins/line_search/bracket.py | 14 ++-- seisflows/preprocess/base.py | 8 +-- seisflows/seisflows.py | 5 +- seisflows/solver/base.py | 42 ++++-------- seisflows/tests/test_config.py | 21 +++--- seisflows/tests/test_modules.py | 87 ++++++++++++------------ seisflows/tests/test_preprocess.py | 20 +++--- seisflows/tests/test_seisflows.py | 57 ++-------------- 9 files changed, 115 insertions(+), 173 deletions(-) diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index 56b2ac06..a067b5ac 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -1,5 +1,8 @@ #!/usr/bin/env python3 """ +Gradient descent nonlinear optimization algorithm. Acts as the Base class for +optimization. + The Optimization library contains classes and methods used to solve nonlinear optimization problems, i.e., misfit minimization. Various subclasses implement different optimization algorithms. @@ -297,6 +300,9 @@ def finalize_search(self): for fid in [self.m_old, self.f_old, self.g_old, self.p_old]: unix.rm(fid) + # Needs to be run before shifting model in next step + self.write_stats() + self.logger.info("shifting current model (new) to previous model (old)") unix.mv(self.m_new, self.m_old) unix.mv(self.f_new, self.f_old) @@ -310,8 +316,6 @@ def finalize_search(self): np.save(self.f_new, f.min()) self.logger.info(f"current misfit is {self.f_new}={f.min():.3E}") - self.write_stats() - self.logger.info("resetting line search step count to 0") self.line_search.step_count = 0 @@ -370,11 +374,10 @@ def write_stats(self): # First time, write header information if not os.path.exists(fid): with open(fid, "w") as f: - f.write(f"{'ITER':>4}") - for log in ["FACTOR", "GRAD_NORM_L1", "GRAD_NORM_L2", - "MISFIT", "RESTART", "SLOPE", "STEP", "LENGTH", - "THETA"]: - f.write(f"{log.upper()},") + for header in ["ITER", "FACTOR", "GRAD_NORM_L1", "GRAD_NORM_L2", + "MISFIT", "RESTART", "SLOPE", "STEP", "LENGTH", + "THETA"]: + f.write(f"{header.upper()},") f.write("\n") g = np.load(self.g_new) @@ -383,7 +386,7 @@ def write_stats(self): f = self.line_search.search_history()[1] # Calculated stats factors - factor = (g, g) ** -0.5 * (f[1] - f[0]) / (x[1] - x[0]) + factor = -dot(g, g) ** -0.5 * (f[1] - f[0]) / (x[1] - x[0]) grad_norm_L1 = np.linalg.norm(g, 1) grad_norm_L2 = np.linalg.norm(g, 2) misfit = f[0] @@ -394,10 +397,17 @@ def write_stats(self): theta = 180. * np.pi ** -1 * angle(p, -g) with open(fid, "a") as f: - f.write(f"{self.iter},{factor},{grad_norm_L1},{grad_norm_L2}," - f"{misfit},{restarted},{slope},{step_count},{step_length}," - f"{theta}\n") - + f.write(f"{self.iter:0>2}," + f"{factor:6.3E}," + f"{grad_norm_L1:6.3E}," + f"{grad_norm_L2:6.3E}," + f"{misfit:6.3E}," + f"{restarted:6.3E}," + f"{slope:6.3E}," + f"{step_count:0>2}," + f"{step_length:6.3E}," + f"{theta:6.3E}\n" + ) def check_model(self, m): """ diff --git a/seisflows/plugins/line_search/bracket.py b/seisflows/plugins/line_search/bracket.py index a81fb344..8ea85fd5 100644 --- a/seisflows/plugins/line_search/bracket.py +++ b/seisflows/plugins/line_search/bracket.py @@ -75,10 +75,10 @@ def initialize(self, step_len, func_val, gtg, gtp): :return status: current status of the line search """ self.step_count = 0 - self.step_lens += [step_len] - self.func_vals += [func_val] - self.gtg += [gtg] - self.gtp += [gtp] + self.step_lens.append(step_len) + self.func_vals.append(func_val) + self.gtg.append(gtg) + self.gtp.append(gtp) def update(self, step_len, func_val): """ @@ -97,12 +97,12 @@ def update(self, step_len, func_val): :rtype status: int :return status: current status of the line search """ - self.step_lens += [step_len] - self.func_vals += [func_val] + self.step_lens.append(step_len) + self.func_vals.append(func_val) def clear_history(self): """ - Clears internal line search history + Clears internal line search history for a new line search attempt """ self.func_vals = [] self.step_lens = [] diff --git a/seisflows/preprocess/base.py b/seisflows/preprocess/base.py index 365939dd..ac704470 100644 --- a/seisflows/preprocess/base.py +++ b/seisflows/preprocess/base.py @@ -152,14 +152,14 @@ def check(self, validate=True): # Set the min/max frequencies and periods, frequency takes priority if PAR.MIN_FREQ is not None: - PAR.force_set("MAX_PERIOD", 1 / PAR.MIN_FREQ) + PAR.MAX_PERIOD = 1 / PAR.MIN_FREQ elif PAR.MAX_PERIOD is not None: - PAR.force_set("MIN_FREQ", 1 / PAR.MAX_PERIOD) + PAR.MIN_FREQ = 1 / PAR.MAX_PERIOD if PAR.MAX_FREQ is not None: - PAR.force_set("MIN_PERIOD", 1 / PAR.MAX_FREQ) + PAR.MIN_PERIOD = 1 / PAR.MAX_FREQ elif PAR.MIN_PERIOD is not None: - PAR.force_set("MAX_FREQ", 1 / PAR.MIN_PERIOD) + PAR.MAX_FREQ = 1 / PAR.MIN_PERIOD # Check that the correct filter bounds have been set if PAR.FILTER.upper() == "BANDPASS": diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index dc5cff97..642551ec 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -30,7 +30,7 @@ from seisflows.tools.specfem import (getpar, setpar, getpar_vel_model, setpar_vel_model) from seisflows.tools.wrappers import loadyaml -from seisflows.config import (config_logger, Dict, custom_import, load, +from seisflows.config import (config_logger, Dict, custom_import, save, SeisFlowsPathsParameters, NAMES, ROOT_DIR, CFGPATHS) @@ -680,8 +680,7 @@ def init(self, **kwargs): self._register_parameters(force=True) self._register_modules(check=True) - workflow = sys.modules["seisflows_workflow"] - workflow.checkpoint() + save(path=self._paths.OUTPUT) # Ensure that all parameters and paths that need to be instantiated # are present in sys modules diff --git a/seisflows/solver/base.py b/seisflows/solver/base.py index d6ea3746..53e2f63d 100644 --- a/seisflows/solver/base.py +++ b/seisflows/solver/base.py @@ -11,13 +11,12 @@ import subprocess import numpy as np from glob import glob -from functools import partial from seisflows.plugins import solver_io from seisflows.tools import msg, unix from seisflows.tools.specfem import Container -from seisflows.tools.wrappers import Struct, diff, exists -from seisflows.config import SeisFlowsPathsParameters +from seisflows.tools.wrappers import diff, exists +from seisflows.config import SeisFlowsPathsParameters, Dict PAR = sys.modules['seisflows_parameters'] @@ -97,7 +96,7 @@ def __init__(self): :type parameters: list of str :param parameters: a list detailing the parameters to be used to define the model, available: ['vp', 'vs', 'rho'] - :type _mesh_properties: seisflows.tools.wrappers.Struct + :type _mesh_properties: Dict :param _mesh_properties: hidden attribute, a dictionary of mesh properties, including the ngll points, nprocs, and mesh coordinates :type _source_names: hidden attribute, @@ -829,9 +828,7 @@ def initialize_adjoint_traces(self): def check_mesh_properties(self, path=None): """ - Determine if Mesh properties are okay for workflow - - TODO fix or rewrite this function + Determine if Mesh properties are okay for workflow. :type path: str :param path: path to the mesh file @@ -845,33 +842,18 @@ def check_mesh_properties(self, path=None): items=[path], header="solver error", border="=")) sys.exit(-1) - # Count slices and grid points + # Count the number of .bin files and the number of grid points key = self.parameters[0] - iproc = 0 + bin_files = glob(os.path.join(path, f"proc*_{key}.bin")) + nproc = len(bin_files) ngll = [] - while True: - dummy = self.io.read_slice(path=path, parameters=key, - iproc=iproc)[0] - ngll += [len(dummy)] - iproc += 1 - if not exists(os.path.join(path, - f"proc{int(iproc):06d}_{key}.bin")): - break - nproc = iproc - - # Create coordinate pointers - # !!! This partial is incorrectly defined and does not execute when - # !!! called. What is the point of that? - coords = Struct() - for key in ['x', 'y', 'z']: - coords[key] = partial(self.io.read_slice, self, path, key) + for i in range(0, len(bin_files)): + ngll.append( + len(self.io.read_slice(path=path, parameters=key, iproc=i)[0]) + ) # Define internal mesh properties - self._mesh_properties = Struct([["nproc", nproc], - ["ngll", ngll], - ["path", path], - ["coords", coords]] - ) + self._mesh_properties = Dict(nproc=nproc, ngll=ngll, path=path) def check_source_names(self): """ diff --git a/seisflows/tests/test_config.py b/seisflows/tests/test_config.py index 32fbb3dc..b3eb7d60 100644 --- a/seisflows/tests/test_config.py +++ b/seisflows/tests/test_config.py @@ -38,8 +38,8 @@ def sfinit(tmpdir, copy_par_file): os.chdir(tmpdir) with patch.object(sys, "argv", ["seisflows"]): sf = SeisFlows() - sf._register(force=True) - config.init_seisflows() + sf._register_parameters(force=True) + sf._register_modules(check=True) return sf @@ -54,18 +54,15 @@ def test_seisflows_constants(): names_check = ["system", "preprocess", "solver", "postprocess", "optimize", "workflow"] - packages_check = ["seisflows", "seisflows-super"] - root_dir_check = os.path.join( os.path.dirname(os.path.abspath(__file__)), ".." ) assert(config.NAMES == names_check) - assert(config.PACKAGES == packages_check) assert(os.path.samefile(config.ROOT_DIR, root_dir_check)) -def test_init_seisflows(sfinit): +def test_register_modules(sfinit): """ Make sure that initiation of the modular approach of seisflows works as expected. That is, that system-wide accessible modules are @@ -88,8 +85,8 @@ def test_save_and_load(sfinit): :return: """ # Instantiate sys modules and save to disk - sf = sfinit - config.save() + sfinit + config.save(path="./output") # Now remove seisflows sys modules so we can try load them back for name in config.NAMES: sys.modules.pop(f"seisflows_{name}") @@ -103,7 +100,7 @@ def test_seisflows_paths_parameters(sfinit): Test the class that makes inputting and checking paths and parameters easier Recreates the required() function at the top of each class. """ - sf = sfinit + sfinit sfpp = config.SeisFlowsPathsParameters() # All of these parameters are defined in the test parameter file @@ -138,8 +135,8 @@ def test_custom_import(sfinit): assert(module.__module__ == "seisflows.optimize.LBFGS") # Check one more to be safe - module = config.custom_import(name="optimize", module="base") - assert(module.__name__ == "Base") - assert(module.__module__ == "seisflows.optimize.base") + module = config.custom_import(name="preprocess", module="pyatoa") + assert(module.__name__ == "Pyatoa") + assert(module.__module__ == "seisflows.preprocess.pyatoa") diff --git a/seisflows/tests/test_modules.py b/seisflows/tests/test_modules.py index 957e9384..16a0dc2b 100644 --- a/seisflows/tests/test_modules.py +++ b/seisflows/tests/test_modules.py @@ -44,7 +44,7 @@ "functions": ["setup", "check", "compute_direction", "initialize_search", "update_search", "finalize_search", "retry_status", - "restart", "save", "load"] + "restart"] }, "workflow": { "parameters": ["CASE"], @@ -83,8 +83,8 @@ def sfinit(tmpdir, copy_par_file): os.chdir(tmpdir) with patch.object(sys, "argv", ["seisflows"]): sf = SeisFlows() - sf._register(force=True) - config.init_seisflows(check=False) + sf._register_parameters(force=True) + sf._register_modules(check=True) return sf @@ -98,9 +98,8 @@ def test_import(sfinit): sfinit for name in config.NAMES: modules = return_modules()[name] - for package, module_list in modules.items(): - for module in module_list: - config.custom_import(name, module)() + for module in modules: + config.custom_import(name, module)() def test_required_parameters_exist(sfinit): @@ -111,15 +110,15 @@ def test_required_parameters_exist(sfinit): """ sfinit for name in config.NAMES: - for package, module_list in return_modules()[name].items(): - for module in module_list: - loaded_module = config.custom_import(name, module)() - sf_pp = loaded_module.required - # Check that required parameters are set - for req_par in required_structure[name]["parameters"]: - assert(req_par in sf_pp.parameters.keys()), \ - f"{req_par} is a required parameter for module: " \ - f"{name}.{module}" + modules = return_modules()[name] + for module in modules: + loaded_module = config.custom_import(name, module)() + sf_pp = loaded_module.required + # Check that required parameters are set + for req_par in required_structure[name]["parameters"]: + assert(req_par in sf_pp.parameters.keys()), \ + f"{req_par} is a required parameter for module: " \ + f"{name}.{module}" def test_required_functions_exist(sfinit): @@ -130,14 +129,14 @@ def test_required_functions_exist(sfinit): """ sfinit for name in config.NAMES: - for package, module_list in return_modules()[name].items(): - for module in module_list: - loaded_module = config.custom_import(name, module)() - # Check that required parameters are set - for func in required_structure[name]["functions"]: - assert(func in dir(loaded_module)), \ - f"{func} is a required function for module: " \ - f"{name}.{module}" + modules = return_modules()[name] + for module in modules: + loaded_module = config.custom_import(name, module)() + # Check that required parameters are set + for func in required_structure[name]["functions"]: + assert(func in dir(loaded_module)), \ + f"{func} is a required function for module: " \ + f"{name}.{module}" @pytest.mark.skip("test not working as expected") @@ -156,24 +155,24 @@ def test_setup(sfinit): SETUP_CREATES = [PATH.SCRATCH, PATH.SYSTEM, PATH.OUTPUT] for name in config.NAMES: - for package, module_list in return_modules()[name].items(): - for module in module_list: - loaded_module = config.custom_import(name, module)() - - # Make sure these don't already exist - for path_ in SETUP_CREATES: - assert(not os.path.exists(path_)) - - loaded_module.setup() - - # Check that the minimum required directories were created - for path_ in SETUP_CREATES: - pytest.set_trace() - assert(os.path.exists(path_)) - - # Remove created paths so we can check the next module - for path_ in SETUP_CREATES: - if os.path.isdir(path_): - shutil.rmtree(path_) - else: - os.remove(path_) \ No newline at end of file + modules = return_modules()[name] + for module in modules: + loaded_module = config.custom_import(name, module)() + + # Make sure these don't already exist + for path_ in SETUP_CREATES: + assert(not os.path.exists(path_)) + + loaded_module.setup() + + # Check that the minimum required directories were created + for path_ in SETUP_CREATES: + pytest.set_trace() + assert(os.path.exists(path_)) + + # Remove created paths so we can check the next module + for path_ in SETUP_CREATES: + if os.path.isdir(path_): + shutil.rmtree(path_) + else: + os.remove(path_) \ No newline at end of file diff --git a/seisflows/tests/test_preprocess.py b/seisflows/tests/test_preprocess.py index cb0a36da..e4a4507c 100644 --- a/seisflows/tests/test_preprocess.py +++ b/seisflows/tests/test_preprocess.py @@ -42,8 +42,8 @@ def sfinit(tmpdir, copy_par_file): os.chdir(tmpdir) with patch.object(sys, "argv", ["seisflows"]): sf = SeisFlows() - sf._register(force=True) - config.init_seisflows(check=False) + sf._register_parameters(force=True) + sf._register_modules(check=False) return sf @@ -71,9 +71,10 @@ def test_default_check(sfinit): og_val = PAR[key] print(key) with pytest.raises(AssertionError): - PAR.force_set(key, val) + PAR[key] = val preprocess.check() - PAR.force_set(key, og_val) + + PAR[key] = og_val # Make sure that parameters set to inappropriate values throw assertions correct_parameters = { @@ -82,7 +83,7 @@ def test_default_check(sfinit): "MIN_FREQ": 1, } for key, val in correct_parameters.items(): - PAR.force_set(key, val) + PAR[key] = val incorrect_values = { "MAX_FREQ": -1, @@ -90,9 +91,9 @@ def test_default_check(sfinit): for key, val in incorrect_values.items(): og_val = PAR[key] with pytest.raises(AssertionError): - PAR.force_set(key, val) + PAR[key] = val preprocess.check() - PAR.force_set(key, og_val) + PAR[key] = og_val def test_default_setup(sfinit): @@ -112,8 +113,8 @@ def test_default_setup(sfinit): # Set some default parameters to run setup misfit_name = "waveform" io_name = "ascii" - PAR.force_set("MISFIT", misfit_name) - PAR.force_set("FORMAT", io_name) + PAR["MISFIT"] = misfit_name + PAR["FORMAT"] = io_name preprocess.setup() assert(preprocess.misfit.__name__ == misfit_name) @@ -134,6 +135,5 @@ def test_default_prepare_eval_grad(tmpdir, sfinit): taskid = 0 filenames = [] preprocess.prepare_eval_grad(cwd=cwd, taskid=taskid, filenames=filenames) - pytest.set_trace() diff --git a/seisflows/tests/test_seisflows.py b/seisflows/tests/test_seisflows.py index 196a7a8b..5ff8b91f 100644 --- a/seisflows/tests/test_seisflows.py +++ b/seisflows/tests/test_seisflows.py @@ -14,9 +14,9 @@ from unittest.mock import patch from seisflows import logger -from seisflows.seisflows import sfparser, SeisFlows -from seisflows.config import (save, Dict, ROOT_DIR, NAMES, CFGPATHS, - config_logger) +from seisflows.seisflows import SeisFlows +from seisflows.config import (Dict, ROOT_DIR, NAMES, CFGPATHS, + config_logger, flush) from seisflows.tools.wrappers import loadyaml TEST_DIR = os.path.join(ROOT_DIR, "tests") @@ -126,7 +126,7 @@ def test_register(tmpdir, par_file_dict, copy_par_file): sf = SeisFlows() assert(sf._paths is None) assert(sf._parameters is None) - sf._register(force=True) + sf._register_parameters(force=True) # Check that paths and parameters have been set in sys.modules paths = sys.modules["seisflows_paths"] @@ -156,9 +156,7 @@ def test_cmd_setup(tmpdir): # With symlinking sf.setup(symlink=True, overwrite=False) assert(os.path.exists(par_file)) - assert(os.path.exists( - os.path.join(tmpdir, "source_code", "seisflows")) - ) + # Edit the current par file in a noticeable way so we can check # if overwriting works in the next step test_phrase = "well this is rather unexpected...\n" @@ -252,7 +250,7 @@ def test_config_logging(tmpdir, copy_par_file): msg = "This is an example log that will be checked for test purposes" with patch.object(sys, "argv", ["seisflows"]): sf = SeisFlows() - sf._register(force=True) + sf._register_parameters(force=True) config_logger(filename=CFGPATHS.LOGFILE) logger.debug(msg) @@ -393,46 +391,3 @@ def test_cmd_par(tmpdir, copy_par_file): assert(par.upper() == parameter.upper()) assert(int(val) == int(new_val)) -# def test_cmd_sempar(tmpdir): -# """ -# -# :param tmpdir: -# :return: -# """ -# pass -# -# -# def test_cmd_check(tmpdir): -# """ -# Very simple -# :param tmpdir: -# :return: -# """ -# pass -# -# -# def test_cmd_print(tmpdir): -# """ -# -# :param tmpdir: -# :return: -# """ -# pass -# -# -# def test_cmd_convert(tmpdir): -# """ -# -# :param tmpdir: -# :return: -# """ -# pass -# -# -# def test_cmd_validate(tmpdir): -# """ -# -# :param tmpdir: -# :return: -# """ -# pass From e84121802e86cb9b22a948781e7b57bd6488313b Mon Sep 17 00:00:00 2001 From: bch0w Date: Mon, 20 Jun 2022 13:36:42 -0800 Subject: [PATCH 017/195] bug fix optimize was trying to load PATH.OPTIMIZE before it was instantiated. reworked init statement to avoid this --- seisflows/optimize/LBFGS.py | 6 +- seisflows/optimize/NLCG.py | 6 +- seisflows/optimize/gradient.py | 125 ++++++++++++++-------------- seisflows/templates/parameters.yaml | 2 +- seisflows/workflow/inversion.py | 11 ++- 5 files changed, 74 insertions(+), 76 deletions(-) diff --git a/seisflows/optimize/LBFGS.py b/seisflows/optimize/LBFGS.py index 7bd3ed01..82defde6 100644 --- a/seisflows/optimize/LBFGS.py +++ b/seisflows/optimize/LBFGS.py @@ -156,7 +156,7 @@ def compute_direction(self): # Load the current gradient direction, which is the L-BFGS search # direction if this is the first iteration - g = np.load(self.g_new) + g = np.load(self.vectors("g_new")) if self.LBFGS_iter == 1: self.logger.info("first L-BFGS iteration, setting search direction " "as inverse gradient") @@ -236,8 +236,8 @@ def update(self): unix.cd(PATH.OPTIMIZE) # Determine the iterates for model m and gradient g - s_k = np.load(self.m_new) - np.load(self.m_old) - y_k = np.load(self.g_new) - np.load(self.g_old) + s_k = np.load(self.vectors("m_new")) - np.load(self.vectors("m_old")) + y_k = np.load(self.vectors("g_new")) - np.load(self.vectors("g_old")) # Determine the shape of the memory map (length of model, length of mem) m = len(s_k) diff --git a/seisflows/optimize/NLCG.py b/seisflows/optimize/NLCG.py index 019f64d4..35a72460 100644 --- a/seisflows/optimize/NLCG.py +++ b/seisflows/optimize/NLCG.py @@ -105,7 +105,7 @@ def compute_direction(self): unix.cd(PATH.OPTIMIZE) # Load the current gradient direction - g_new = np.load(self.g_new) + g_new = np.load(self.vectors("g_new")) # CASE 1: If first iteration, search direction is the current gradient if self.NLCG_iter == 1: @@ -125,8 +125,8 @@ def compute_direction(self): # Normal NLCG direction compuitation else: # Compute search direction - g_old = np.load(self.g_old) - p_old = np.load(self.p_old) + g_old = np.load(self.vectors("g_old")) + p_old = np.load(self.vectors("p_old")) # Apply preconditioner and calc. scale factor for search dir. (beta) if self.precond: diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index a067b5ac..0866897d 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -70,46 +70,12 @@ def __init__(self): :param restarted: a flag signalling if the optimization algorithm has been restarted recently - :param m_new: current model - :param m_old: previous model - :param m_try: line search model - :param f_new: current objective function value - :param f_old: previous objective function value - :param f_try: line search function value - :param g_new: current gradient direction - :param g_old: previous gradient direction - :param p_new: current search direction - :param p_old: previous search direction """ self.iter = 1 self.line_search = None self.precond = None self.restarted = False - # Models - self.m_new = os.path.join(PATH.OPTIMIZE, "m_new.npy") - self.m_old = os.path.join(PATH.OPTIMIZE, "m_old.npy") - self.m_try = os.path.join(PATH.OPTIMIZE, "m_try.npy") - - # Gradients - self.g_new = os.path.join(PATH.OPTIMIZE, "g_new.npy") - self.g_old = os.path.join(PATH.OPTIMIZE, "g_old.npy") - self.g_try = os.path.join(PATH.OPTIMIZE, "g_try.npy") - - # Search directions - self.p_new = os.path.join(PATH.OPTIMIZE, "p_new.npy") - self.p_old = os.path.join(PATH.OPTIMIZE, "p_old.npy") - self.p_try = os.path.join(PATH.OPTIMIZE, "p_try.npy") - - # Misfits - self.f_new = os.path.join(PATH.OPTIMIZE, "f_new.npy") - self.f_old = os.path.join(PATH.OPTIMIZE, "f_old.npy") - self.f_try = os.path.join(PATH.OPTIMIZE, "f_try.npy") - - # Current search direction - self.alpha = os.path.join(PATH.OPTIMIZE, "alpha.npy") - - @property def required(self): """ @@ -146,6 +112,38 @@ def required(self): return sf + + def vectors(self, name): + """ + Convenience function to access the full paths of model and gradient + vectors that are saved to disk + + .. note:: the available options that can be created + m_new: current model + m_old: previous model + m_try: line search model + f_new: current objective function value + f_old: previous objective function value + f_try: line search function value + g_new: current gradient direction + g_old: previous gradient direction + p_new: current search direction + p_old: previous search direction + alpha: trial search direction (aka p_try) + + :type name: str + :param name: name of the vector, acceptable: m, g, p, f, alpha + + """ + # Set model and gradient filenames as attributes + acceptable_names = ["m_new", "m_old", "m_try", + "g_new", "g_old", "g_try", + "p_new", "p_old", "alpha", + "f_new", "f_old", "f_try"] + assert(name in acceptable_names) + return os.path.join(PATH.OPTIMIZE, f"{name}.npy") + + def check(self, validate=True): """ Checks parameters, paths, and dependencies @@ -183,7 +181,7 @@ def setup(self): # Read in initial model as a vector and ensure it is a valid model m_new = solver.merge(solver.load(PATH.MODEL_INIT)) - np.save(self.m_new, m_new) + np.save(self.vectors("m_new"), m_new) self.check_model(m_new) @property @@ -209,22 +207,22 @@ def compute_direction(self): """ self.logger.info(f"computing search direction with {PAR.OPTIMIZE}") - g_new = np.load(self.g_new) + g_new = np.load(self.vectors("g_new")) if self.precond is not None: p_new = -1 * self.precond(g_new) else: p_new = -1 * g_new - np.save(self.p_new, p_new) + np.save(self.vectors("p_new"), p_new) def initialize_search(self): """ Initialize the plugin line search machinery. Should only be run at the beginning of line search, by the main workflow module. """ - m = np.load(self.m_new) - g = np.load(self.g_new) - p = np.load(self.p_new) - f = np.load(self.f_new) + m = np.load(self.vectors("m_new")) + g = np.load(self.vectors("g_new")) + p = np.load(self.vectors("p_new")) + f = np.load(self.vectors("f_new")) norm_m = max(abs(m)) norm_p = max(abs(p)) @@ -254,8 +252,8 @@ def initialize_search(self): # gradient threshold to remove any outlier values m_try = m + alpha * p - np.save(self.m_try, m_try) - np.save(self.alpha, alpha) + np.save(self.vectors("m_try"), m_try) + np.save(self.vectors("alpha"), alpha) self.check_model(m_try) def update_search(self): @@ -268,18 +266,18 @@ def update_search(self): status == 0 : not finished status == -1 : failed """ - self.line_search.update(step_len=np.load(self.alpha), - func_val=np.load(self.f_try)) + self.line_search.update(step_len=np.load(self.vectors("alpha")), + func_val=np.load(self.vectors("f_try"))) alpha, status = self.line_search.calculate_step() # New search direction needs to be searchable on disk if status in [0, 1]: - m = np.load(self.m_new) - p = np.load(self.p_new) - np.save(self.alpha, alpha) + m = np.load(self.vectors("m_new")) + p = np.load(self.vectors("p_new")) + np.save(self.vectors("alpha"), alpha) m_try = m + alpha * p - np.save(self.m_try, m_try) + np.save(self.vectors("m_try"), m_try) self.check_model(m_try) return status @@ -297,24 +295,25 @@ def finalize_search(self): # Remove the old model parameters if self.iter > 1: self.logger.info("removing previously accepted model files (old)") - for fid in [self.m_old, self.f_old, self.g_old, self.p_old]: + for fid in [self.vectors("m_old"), self.vectors("f_old"), + self.vectors("g_old"), self.vectors("p_old")]: unix.rm(fid) # Needs to be run before shifting model in next step self.write_stats() self.logger.info("shifting current model (new) to previous model (old)") - unix.mv(self.m_new, self.m_old) - unix.mv(self.f_new, self.f_old) - unix.mv(self.g_new, self.g_old) - unix.mv(self.p_new, self.p_old) + unix.mv(self.vectors("m_new"), self.vectors("m_old")) + unix.mv(self.vectors("f_new"), self.vectors("f_old")) + unix.mv(self.vectors("g_new"), self.vectors("g_old")) + unix.mv(self.vectors("p_new"), self.vectors("p_old")) self.logger.info("setting accepted line search model as current model") - unix.mv(self.m_try, self.m_new) + unix.mv(self.vectors("m_try"), self.vectors("m_new")) f = self.line_search.search_history()[1] - np.save(self.f_new, f.min()) - self.logger.info(f"current misfit is {self.f_new}={f.min():.3E}") + np.save(self.vectors("f_new"), f.min()) + self.logger.info(f"current misfit is {f.min():.3E}") self.logger.info("resetting line search step count to 0") self.line_search.step_count = 0 @@ -325,8 +324,8 @@ def retry_status(self): by checking, in effect, if the search direction was the same as gradient direction """ - g = np.load(self.g_new) - p = np.load(self.p_new) + g = np.load(self.vectors("g_new")) + p = np.load(self.vectors("p_new")) theta = angle(p, -g) self.logger.debug(f"theta: {theta:6.3f}") @@ -348,8 +347,8 @@ def restart(self): """ # Steepest descent (base) does not need to be restarted if PAR.OPTIMIZE.capitalize() != "Gradient": - g = np.load(self.g_new) - np.save(self.p_new, -g) + g = np.load(self.vectors("g_new")) + np.save(self.vectors("p_new"), -g) self.line_search.clear_history() self.restarted = 1 @@ -380,8 +379,8 @@ def write_stats(self): f.write(f"{header.upper()},") f.write("\n") - g = np.load(self.g_new) - p = np.load(self.p_new) + g = np.load(self.vectors("g_new")) + p = np.load(self.vectors("p_new")) x = self.line_search.search_history()[0] f = self.line_search.search_history()[1] diff --git a/seisflows/templates/parameters.yaml b/seisflows/templates/parameters.yaml index 303931d1..ed38b460 100644 --- a/seisflows/templates/parameters.yaml +++ b/seisflows/templates/parameters.yaml @@ -27,6 +27,6 @@ WORKFLOW: inversion SOLVER: specfem2d SYSTEM: workstation -OPTIMIZE: LBFGS +OPTIMIZE: gradient PREPROCESS: base POSTPROCESS: base diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index fcbceca9..23e6c717 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -247,9 +247,8 @@ def evaluate_function(self, path, suffix): """ self.logger.info(msg.sub("EVALUATE OBJECTIVE FUNCTION")) - # Ensure that we are referencing the same tags as defined in OPTIMIZE - model_tag = getattr(optimize, f"m_{suffix}") - misfit_tag = getattr(optimize, f"f_{suffix}") + model_tag = optimize.vectors(f"m_{suffix}") + misfit_tag = optimize.vectors(f"f_{suffix}") self.write_model(path=path, tag=model_tag) @@ -331,7 +330,7 @@ def write_gradient(self): """ self.logger.info(msg.mnr("POSTPROCESSING KERNELS")) src = os.path.join(PATH.GRAD, "gradient") - dst = optimize.g_new + dst = optimize.vectors("g_new") postprocess.write_gradient(PATH.GRAD) parts = solver.load(src, suffix="_kernel") @@ -373,7 +372,7 @@ def save_gradient(self): src = os.path.join(PATH.GRAD, "gradient") unix.mv(src, dst) if PAR.SAVEAS in ["vector", "both"]: - src = os.path.join(PATH.OPTIMIZE, optimize.g_old) + src = optimize.vectors("g_old") unix.cp(src, dst + ".npy") self.logger.debug(f"saving gradient to path:\n{dst}") @@ -386,7 +385,7 @@ def save_model(self): Saving as a vector saves on file count, but requires numpy and seisflows functions to read """ - src = optimize.m_new + src = optimize.vectors("m_new") dst = os.path.join(PATH.OUTPUT, f"model_{optimize.iter:04d}") self.logger.debug(f"saving model '{src}' to path:\n{dst}") From 9e9c64507f06fc3616f08fae3034ec9d23a68d0b Mon Sep 17 00:00:00 2001 From: bch0w Date: Mon, 20 Jun 2022 16:22:23 -0800 Subject: [PATCH 018/195] bug fix syntax comparison in slurm system --- seisflows/system/slurm.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index cc3efab0..1c3beb3a 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -160,7 +160,7 @@ def run(self, classname, method, single=False, run_call=None, **kwargs): # Continuously check for job completion on ALL running array jobs job_ids = job_id_list(stdout, single) job_id, status = self.check_job_status(job_ids) - if status is not "OKAY": + if status != "OKAY": print(msg.cli((f"Stopping workflow for {status} job. " f"Please check log file for details."), items=[f"TASK: {classname}.{method}", From 1afa661ddcca463cf03659eadc13e219a05517c3 Mon Sep 17 00:00:00 2001 From: bch0w Date: Mon, 20 Jun 2022 21:43:28 -0800 Subject: [PATCH 019/195] working on rosenbrock optimization test problem --- seisflows/optimize/gradient.py | 73 ++++++++++++++-------------- seisflows/workflow/test.py | 88 +++++++++++++++++++++++++--------- 2 files changed, 104 insertions(+), 57 deletions(-) diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index 0866897d..89c8949a 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -112,38 +112,6 @@ def required(self): return sf - - def vectors(self, name): - """ - Convenience function to access the full paths of model and gradient - vectors that are saved to disk - - .. note:: the available options that can be created - m_new: current model - m_old: previous model - m_try: line search model - f_new: current objective function value - f_old: previous objective function value - f_try: line search function value - g_new: current gradient direction - g_old: previous gradient direction - p_new: current search direction - p_old: previous search direction - alpha: trial search direction (aka p_try) - - :type name: str - :param name: name of the vector, acceptable: m, g, p, f, alpha - - """ - # Set model and gradient filenames as attributes - acceptable_names = ["m_new", "m_old", "m_try", - "g_new", "g_old", "g_try", - "p_new", "p_old", "alpha", - "f_new", "f_old", "f_try"] - assert(name in acceptable_names) - return os.path.join(PATH.OPTIMIZE, f"{name}.npy") - - def check(self, validate=True): """ Checks parameters, paths, and dependencies @@ -180,9 +148,14 @@ def setup(self): self.precond = getattr(preconds, PAR.PRECOND)() # Read in initial model as a vector and ensure it is a valid model - m_new = solver.merge(solver.load(PATH.MODEL_INIT)) - np.save(self.vectors("m_new"), m_new) - self.check_model(m_new) + if PATH.MODEL_INIT is not None: + m_new = solver.merge(solver.load(PATH.MODEL_INIT)) + np.save(self.vectors("m_new"), m_new) + self.check_model(m_new) + else: + self.logger.warning("PATH.MODEL_INIT not found. Optimization " + "library expects a model vector " + f"{self.vectors('m_new')}") @property def eval_str(self): @@ -197,6 +170,36 @@ def eval_str(self): step = self.line_search.step_count return f"i{iter_:0>2}s{step:0>2}" + def vectors(self, name): + """ + Convenience function to access the full paths of model and gradient + vectors that are saved to disk + + .. note:: the available options that can be created + m_new: current model + m_old: previous model + m_try: line search model + f_new: current objective function value + f_old: previous objective function value + f_try: line search function value + g_new: current gradient direction + g_old: previous gradient direction + p_new: current search direction + p_old: previous search direction + alpha: trial search direction (aka p_try) + + :type name: str + :param name: name of the vector, acceptable: m, g, p, f, alpha + + """ + # Set model and gradient filenames as attributes + acceptable_names = ["m_new", "m_old", "m_try", + "g_new", "g_old", "g_try", + "p_new", "p_old", "alpha", + "f_new", "f_old", "f_try"] + assert(name in acceptable_names) + return os.path.join(PATH.OPTIMIZE, f"{name}.npy") + def compute_direction(self): """ Computes a steepest descent search direction (inverse gradient) diff --git a/seisflows/workflow/test.py b/seisflows/workflow/test.py index 36c1719f..9b4de68c 100644 --- a/seisflows/workflow/test.py +++ b/seisflows/workflow/test.py @@ -73,8 +73,9 @@ def main(self, return_flow=False): This controls the main testing workflow """ FLOW = [#self.test_system, - self.test_preprocess, + # self.test_preprocess, # self.test_solver, + self.test_optimize ] if return_flow: return FLOW @@ -130,7 +131,6 @@ def test_preprocess(self): taskid = 0 filenames = ["AA.S0001.BXY.semd"] source_name = "001" - import pdb;pdb.set_trace() preprocess.prepare_eval_grad(cwd=cwd, taskid=taskid, filenames=filenames, source_name=source_name @@ -161,33 +161,77 @@ def test_optimize(self): """ optimize.setup() - def rosenbrock(self, n=1E8): - """ - Rosenbrock test for optimization library - """ - model_init = 0.1 * np.ones(n) - model_true = np.ones(n) + m_new, objective_function, gradient = rosenbrock() - def func(x): + def evaluate_function(): """ - Rosenbrock function + Evalaute the misfit function of a given model """ - return sum(100 * (x[:-1]**2. - x[1:])**2. + (x[:-1] - 1.)**2) + m_new = np.load(optimize.vectors("m_new")) + f_try = objective_function(m_new) + np.save(optimize.vectors("f_try"), f_try) - def grad(x): + def evaluate_gradient(): """ - Gradient for Rosenbrock function + Evaluate the gradient of a given model """ - g = np.zeros(n) - g[1:-1] = -200 * (x[:-2] ** 2. - x[1:-1]) + \ - 400. * x[1:-1] * (x[1:-1] ** 2. - x[2:]) + \ - 2. * (x[1:-1] - 1.) + m_new = np.load(optimize.vectors("m_new")) + f_new = objective_function(m_new) + g_new = gradient(m_new) + np.save(optimize.vectors("f_new"), f_new) + np.save(optimize.vectors("g_new"), g_new) + + + # Set up the initial model on disk + np.save(optimize.vectors("m_new"), m_new) + + import pdb;pdb.set_trace() + for iteration in range(1, 200): + evaluate_gradient() + optimize.compute_direction() + # Perform line search + while True: + optimize.initialize_search() + evaluate_function() + status = optimize.update_search() + if status > 0: + optimize.finalize_search() + break + elif status == 0: + continue + elif status < 0: + if optimize.retry_status(): + optimize.restart() + else: + sys.exit() + + +def rosenbrock(n=1E8): + """ + Rosenbrock test for optimization library testing + """ + model_init = 0.1 * np.ones(int(n)) + + def objective_function(x): + """ + Rosenbrock function + """ + return sum(100 * (x[:-1]**2. - x[1:])**2. + (x[:-1] - 1.)**2) + + def gradient(x): + """ + Gradient for Rosenbrock function + """ + g = np.zeros(int(n)) + g[1:-1] = -200 * (x[:-2] ** 2. - x[1:-1]) + \ + 400. * x[1:-1] * (x[1:-1] ** 2. - x[2:]) + \ + 2. * (x[1:-1] - 1.) - g[0] = 400. * x[0] * (x[0] ** 2. - x[1]) + \ - 2. * (x[0] - 1) + g[0] = 400. * x[0] * (x[0] ** 2. - x[1]) + \ + 2. * (x[0] - 1) - g[-1] = -200. * (x[-2] ** 2. - x[-1]) + g[-1] = -200. * (x[-2] ** 2. - x[-1]) - return g + return g - return model_init, model_true, func, grad \ No newline at end of file + return model_init, objective_function, gradient From 9e21c5bc63eb927f4647a0799add98798c88a567 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Tue, 21 Jun 2022 09:57:58 -0800 Subject: [PATCH 020/195] setting default solver values to reduce the number of required parameters for a freshly created parameter file --- docs/index.rst | 5 ++--- docs/start_here.rst | 35 +++++++++++++++++++++++++++++++++++ seisflows/seisflows.py | 2 +- seisflows/solver/base.py | 6 +++--- 4 files changed, 41 insertions(+), 7 deletions(-) diff --git a/docs/index.rst b/docs/index.rst index aec6e479..4301aba4 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -51,7 +51,7 @@ installed in development mode, allowing source code changes to be immediately acccessible to Python. The `devel` branch houses the most up-to-date codebase. We recommend installing -SeisFlows within a virtual environment (e.g., Conda) to preserve your root +SeisFlows within a virtual environment (e.g., Conda) to not affect your root environment. .. code:: bash @@ -70,8 +70,7 @@ Requirements In most production-scale workflows, SeisFlows must be run on a cluster, or high performance computing system. However, serially run example problems -making use of 2D solvers like SPECFEM2D are available for small problems and -workflow tutorials. +making use of 2D solvers like SPECFEM2D are available. SeisFlows + Pyatoa -------------------- diff --git a/docs/start_here.rst b/docs/start_here.rst index 196ff578..9c3d8ca5 100644 --- a/docs/start_here.rst +++ b/docs/start_here.rst @@ -19,6 +19,41 @@ Each sub-argument has it's own help message to further explain what it does. For more information on the SeisFlows command line tool, see the `command line tool `__ docs page. +Running tests +~~~~~~~~~~~~~ + +SeisFlows has some unit tests that ensure the capabilities of the command line +tool and package organization are working as intended. To run the tests: + +.. parsed-literal:: + + cd seisflows + cd tests + pytest + +If developing SeisFlows, please ensure that you run these tests before and after +any changes are made to ensure that your changes do not break intended package +functionality. + +Running the test problem +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +SeisFlows comes with a small test problem that is intended to not only test +the workflow capabilities of the package, but also for users to rapidly develop +new modules without needing to run large, potentially expensive, workflows. + +In order to set up the test problem: + +.. parsed-literal:: + + cd path/to/working/directory # ideally this directory is empty + seisflows setup # this will create a template parameters.yaml file + seisflows par workflow test + seisflows configure + +At this stage, you will have a full SeisFlows parameter file. Certain system or +module specific + Running an example problem ~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 642551ec..e0fd198c 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -601,7 +601,7 @@ def setup(self, force=False, **kwargs): unix.cp(PAR_FILE, self._args.workdir) print(msg.cli(f"creating parameter file: {self._args.parameter_file}")) - def configure(self, relative_paths=False, **kwargs): + def configure(self, relative_paths=True, **kwargs): """ Dynamically generate the parameter file by writing out docstrings and default values for each of the SeisFlows module parameters. diff --git a/seisflows/solver/base.py b/seisflows/solver/base.py index 53e2f63d..74ed8dbf 100644 --- a/seisflows/solver/base.py +++ b/seisflows/solver/base.py @@ -118,17 +118,17 @@ def required(self): """ sf = SeisFlowsPathsParameters() - sf.par("MATERIALS", required=True, par_type=str, + sf.par("MATERIALS", required=False, par_type=str, default="ELASTIC", docstr="Material parameters used to define model. Available: " "['ELASTIC': Vp, Vs, 'ACOUSTIC': Vp, 'ISOTROPIC', " "'ANISOTROPIC']") - sf.par("DENSITY", required=True, par_type=str, + sf.par("DENSITY", required=False, par_type=str, default="CONSTANT", docstr="How to treat density during inversion. Available: " "['CONSTANT': Do not update density, " "'VARIABLE': Update density]") - sf.par("ATTENUATION", required=True, par_type=bool, + sf.par("ATTENUATION", required=False, par_type=bool, default=False, docstr="If True, turn on attenuation during forward " "simulations, otherwise set attenuation off. Attenuation " "is always off for adjoint simulations.") From 351566cf18cda8efe8ea122aedc7a3557db71377 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Tue, 21 Jun 2022 13:27:56 -0800 Subject: [PATCH 021/195] removed PAR.NT, PAR.DT and PAR.F0 from the list of required solver parameters, these can just be read from the SPECFEM Par_file which is assumed to be correct. Also removed the checks related to these and adjusted the way misfit is calculated so that it doesnt explicitely need to be passed this information combined some of the solver parameters which were redundant optimization rosenbrock test running but not working as intended --- seisflows/optimize/gradient.py | 43 ++++-- seisflows/preprocess/base.py | 18 ++- seisflows/preprocess/pyatoa.py | 14 +- ...pecfem2d_workstation_inversion_w_pyatoa.py | 3 - seisflows/scripts/examples/sfexample2d.py | 3 - seisflows/seisflows.py | 144 +++++++++--------- seisflows/solver/base.py | 73 +++++---- seisflows/solver/specfem2d.py | 80 ++-------- seisflows/solver/specfem3d.py | 56 +------ seisflows/tools/specfem.py | 30 ---- seisflows/workflow/base.py | 11 +- seisflows/workflow/test.py | 52 ++++--- 12 files changed, 216 insertions(+), 311 deletions(-) diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index 89c8949a..2ae776d9 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -18,8 +18,8 @@ from seisflows.tools import msg, unix from seisflows.tools.math import angle, dot from seisflows.plugins import line_search, preconds -from seisflows.tools.specfem import check_poissons_ratio -from seisflows.config import SeisFlowsPathsParameters, CFGPATHS, Dict +from seisflows.tools.math import poissons_ratio +from seisflows.config import SeisFlowsPathsParameters PAR = sys.modules["seisflows_parameters"] PATH = sys.modules["seisflows_paths"] @@ -69,7 +69,6 @@ def __init__(self): :type restarted: bool :param restarted: a flag signalling if the optimization algorithm has been restarted recently - """ self.iter = 1 self.line_search = None @@ -132,9 +131,13 @@ def check(self, validate=True): assert PAR.STEPLENINIT < PAR.STEPLENMAX, \ f"STEPLENINIT must be < STEPLENMAX" - def setup(self): + def setup(self, m_new=None): """ Sets up nonlinear optimization machinery + + :type m_new: np.array + :param m_new: Initial model vector which can be user-provided. Otherwise + the initial model will be read from PATH.MODEL_INIT """ unix.mkdir(PATH.OPTIMIZE) @@ -148,14 +151,14 @@ def setup(self): self.precond = getattr(preconds, PAR.PRECOND)() # Read in initial model as a vector and ensure it is a valid model - if PATH.MODEL_INIT is not None: + if m_new is None: + assert(os.path.exists(PATH.MODEL_INIT)), ( + "optimization library requires that PATH.MODEL_INIT exists" + ) m_new = solver.merge(solver.load(PATH.MODEL_INIT)) - np.save(self.vectors("m_new"), m_new) - self.check_model(m_new) - else: - self.logger.warning("PATH.MODEL_INIT not found. Optimization " - "library expects a model vector " - f"{self.vectors('m_new')}") + + np.save(self.vectors("m_new"), m_new) + self.check_model(m_new) @property def eval_str(self): @@ -411,15 +414,19 @@ def write_stats(self): f"{theta:6.3E}\n" ) - def check_model(self, m): + def check_model(self, m, min_pr=-1., max_pr=0.5): """ Check to ensure that the model parameters fall within the guidelines of the solver. Print off min/max model parameters for the User. :type m: np.array :param m: model to check parameters of - :type tag: str - :param tag: tag of the model to be used for more specific error msgs + :type min_pr: float + :param min_pr: minimum allowable Poisson's ratio value dictated by + SPECFEM + :type max_pr: float + :param max_pr: maximum allowable Poisson's ratio value dictated by + SPECFEM """ # Dynamic way to split up the model based on number of params pars = {} @@ -429,9 +436,15 @@ def check_model(self, m): # Check Poisson's ratio, which will error our SPECFEM if outside limits if (pars["vp"] is not None) and (pars["vs"] is not None): self.logger.debug(f"checking poissons ratio") - pars["pr"] = check_poissons_ratio(vp=pars["vp"], vs=pars["vs"]) + pars["pr"] = poissons_ratio(vp=pars["vp"], vs=pars["vs"]) if pars["pr"].min() < 0: self.logger.warning("minimum poisson's ratio is negative") + if pars["pr"].min() < min_pr: + self.logger.warning(f"minimum poisson's ratio out of bounds: " + f"{pars['pr'].max()} > {max_pr}") + if pars["pr"].max() > max_pr: + self.logger.warning(f"maximum poisson's ratio out of bounds: " + f"{pars['pr'].min()} < {min_pr}") # Tell the User min and max values of the updated model self.logger.info(f"model parameters") diff --git a/seisflows/preprocess/base.py b/seisflows/preprocess/base.py index ac704470..2e6b603c 100644 --- a/seisflows/preprocess/base.py +++ b/seisflows/preprocess/base.py @@ -13,7 +13,6 @@ from seisflows.tools import msg from seisflows.tools import signal, unix -from seisflows.tools.wrappers import exists from seisflows.plugins.preprocess import adjoint, misfit, readers, writers from seisflows.config import SeisFlowsPathsParameters @@ -320,11 +319,15 @@ def _write_residuals(self, path, syn, obs): :param syn: observed data """ residuals = [] - for obs_, syn_ in zip(obs, syn): - residuals.append(self.misfit(syn_.data, obs_.data, PAR.NT, PAR.DT)) + for tr_obs, tr_syn in zip(obs, syn): + residual = self.misfit(syn=tr_syn.data, obs=tr_obs.data, + nt=tr_syn.stats.npts, + dt=tr_syn.stats.delta + ) + residuals.append(residual) filename = os.path.join(path, "residuals") - if exists(filename): + if os.path.exists(filename): residuals = np.append(residuals, np.loadtxt(filename)) np.savetxt(filename, residuals) @@ -344,8 +347,11 @@ def _write_adjoint_traces(self, path, syn, obs, filename): """ # Use the synthetics as a template for the adjoint sources adj = syn.copy() - for adj_, obs_, syn_ in zip(adj, obs, syn): - adj.data = self.adjoint(syn_.data, obs_.data, PAR.NT, PAR.DT) + for tr_adj, tr_obs, tr_syn in zip(adj, obs, syn): + tr_adj.data = self.adjoint(syn=tr_syn.data, obs=tr_obs.data, + nt=tr_syn.stats.npts, + dt=tr_syn.stats.delta + ) self.writer(adj, path, filename) diff --git a/seisflows/preprocess/pyatoa.py b/seisflows/preprocess/pyatoa.py index 2af5d6ab..565d980e 100644 --- a/seisflows/preprocess/pyatoa.py +++ b/seisflows/preprocess/pyatoa.py @@ -171,11 +171,11 @@ def check(self, validate=True): assert(PAR.FORMAT.upper() == "ASCII"), \ "Pyatoa preprocess requires PAR.FORMAT=='ASCII'" - assert((PAR.DT * PAR.NT) <= (PAR.START_PAD + PAR.END_PAD)), \ - ("Pyatoa preprocess must have (PAR.START_PAD + PAR.END_PAD) >= " - "(PAR.DT * PAR.NT), current values will not provide sufficiently " - f"long data traces (DT*NT={PAR.DT * PAR.NT}; " - f"START+END={PAR.START_PAD + PAR.END_PAD}") + # assert((PAR.DT * PAR.NT) <= (PAR.START_PAD + PAR.END_PAD)), \ + # ("Pyatoa preprocess must have (PAR.START_PAD + PAR.END_PAD) >= " + # "(PAR.DT * PAR.NT), current values will not provide sufficiently " + # f"long data traces (DT*NT={PAR.DT * PAR.NT}; " + # f"START+END={PAR.START_PAD + PAR.END_PAD}") def setup(self): """ @@ -219,7 +219,7 @@ def prepare_eval_grad(self, cwd, taskid, source_name, **kwargs): "Pyaflowa") # Process all the stations for a given event using Pyaflowa - pyaflowa = self.setup_event_pyaflowa(source_name) + pyaflowa = self._setup_event_pyaflowa(source_name) scaled_misfit = pyaflowa.process(nproc=PAR.NPROC) if scaled_misfit is None: @@ -234,7 +234,7 @@ def prepare_eval_grad(self, cwd, taskid, source_name, **kwargs): # Event misfit defined by Tape et al. (2010) written to solver dir. self.write_residuals(path=cwd, scaled_misfit=scaled_misfit) - def setup_event_pyaflowa(self, source_name=None): + def _setup_event_pyaflowa(self, source_name=None): """ A convenience function to set up a Pyaflowa processing instance for a specific event. diff --git a/seisflows/scripts/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py b/seisflows/scripts/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py index 7741a0dd..812fcfbe 100644 --- a/seisflows/scripts/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py +++ b/seisflows/scripts/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py @@ -96,9 +96,6 @@ def setup_seisflows_working_directory(self): self.sf.par("ntask", self.ntask) # 3 sources for this example self.sf.par("materials", "elastic") # how velocity model parameterized self.sf.par("density", "constant") # update density or keep constant - self.sf.par("nt", 5000) # set by SPECFEM2D Par_file - self.sf.par("dt", .06) # set by SPECFEM2D Par_file - self.sf.par("f0", 0.084) # set by SOURCE file self.sf.par("format", "ascii") # how to output synthetic seismograms self.sf.par("case", "synthetic") # synthetic-synthetic inversion self.sf.par("attenuation", False) diff --git a/seisflows/scripts/examples/sfexample2d.py b/seisflows/scripts/examples/sfexample2d.py index 145b7da8..e2057537 100644 --- a/seisflows/scripts/examples/sfexample2d.py +++ b/seisflows/scripts/examples/sfexample2d.py @@ -293,9 +293,6 @@ def setup_seisflows_working_directory(self): self.sf.par("ntask", self.ntask) # default 3 sources for this example self.sf.par("materials", "elastic") # how velocity model parameterized self.sf.par("density", "constant") # update density or keep constant - self.sf.par("nt", 5000) # set by SPECFEM2D Par_file - self.sf.par("dt", .06) # set by SPECFEM2D Par_file - self.sf.par("f0", 0.084) # set by SOURCE file self.sf.par("format", "ascii") # how to output synthetic seismograms self.sf.par("begin", 1) # first iteration self.sf.par("end", self.niter) # final iteration -- we will run 2 diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index e0fd198c..d2478711 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -112,7 +112,7 @@ def _format_action(self, action): marked appropriately. Required parameters must be set before a workflow can be submitted.""" ) - configure.add_argument("-r", "--relative_paths", action="store_true", + configure.add_argument("-a", "--absolute_paths", action="store_true", help="Set default paths relative to cwd") # ========================================================================= init = subparser.add_parser( @@ -440,8 +440,8 @@ def _register_parameters(self, force=True): # For submit() and resume(), provide a dialogue to stdout requiring a # visual pre-check of parameters before submitting workflow - if not force and parameters["PRECHECK"]: - self._precheck_parameters(parameters) + # if not force and parameters["PRECHECK"]: + # self._precheck_parameters(parameters) # Expand all paths to be absolute on the filesystem for key, val in paths.items(): @@ -456,28 +456,28 @@ def _register_parameters(self, force=True): self._paths = Dict(paths) self._parameters = Dict(parameters) - def _precheck_parameters(self, parameters): - """ - Visually display a list of user-chosen parameters to the User before - proceeding with the _register_parameters command. Allows the User to quickly - determine if workflow parameters have been set correctly - - :type parameters: dict - :param parameters: parameters read in from the YAML parameter file - """ - items = [] - for p in parameters["PRECHECK"]: - try: - items.append(f"{p.upper()}: {parameters[p.upper()]}") - except KeyError: - items.append(f"{p.upper()}: !!! PARAMETER NOT FOUND !!!") - print(msg.cli("Please ensure that the parameters listed below " - "are set correctly. You can edit this list with " - "the PRECHECK parameter.", items=items, - header="seisflows precheck", border="=")) - check = input("Continue? (y/[n])\n") - if check != "y": - sys.exit(-1) + # def _precheck_parameters(self, parameters): + # """ + # Visually display a list of user-chosen parameters to the User before + # proceeding with the _register_parameters command. Allows the User to quickly + # determine if workflow parameters have been set correctly + # + # :type parameters: dict + # :param parameters: parameters read in from the YAML parameter file + # """ + # items = [] + # for p in parameters["PRECHECK"]: + # try: + # items.append(f"{p.upper()}: {parameters[p.upper()]}") + # except KeyError: + # items.append(f"{p.upper()}: !!! PARAMETER NOT FOUND !!!") + # print(msg.cli("Please ensure that the parameters listed below " + # "are set correctly. You can edit this list with " + # "the PRECHECK parameter.", items=items, + # header="seisflows precheck", border="=")) + # check = input("Continue? (y/[n])\n") + # if check != "y": + # sys.exit(-1) def _register_modules(self, check=True): """ @@ -514,20 +514,8 @@ def _register_modules(self, check=True): for name in NAMES: sys.modules[f"seisflows_{name}"] = custom_import(name)() - # Parameter import error checking, missing or improperly set parameters - # will throw assertion errors if check: - errors = [] - for name in NAMES: - try: - sys.modules[f"seisflows_{name}"].check() - except AssertionError as e: - errors.append(f"{name}: {e}") - if errors: - print(msg.cli("seisflows.config module check failed with:", - items=errors, header="module check error", - border="=")) - sys.exit(-1) + self._check_modules() # Bare minimum Module requirements for SeisFlows req_modules = ["WORKFLOW", "SYSTEM"] @@ -539,6 +527,24 @@ def _register_modules(self, check=True): "choices.", header="error", border="=")) sys.exit(-1) + def _check_modules(self): + """ + Runs the .check() function on each of the modules, which validates the + given parameters in a parameter file to ensure that a workflow will not + break unexpectedly + """ + errors = [] + for name in NAMES: + try: + sys.modules[f"seisflows_{name}"].check() + except AssertionError as e: + errors.append(f"{name}: {e}") + if errors: + print(msg.cli("seisflows.config module check failed with:", + items=errors, header="module check error", + border="=")) + sys.exit(-1) + def _load_modules(self): """ A function to load and check each of the SeisFlows modules, @@ -564,10 +570,7 @@ def _load_modules(self): with open(fid, "rb") as f: sys.modules[f"seisflows_{NAME}"] = pickle.load(f) - - # Check parameters so that default values are present - for NAME in NAMES: - sys.modules[f"seisflows_{NAME}"].check() + self._check_modules() def setup(self, force=False, **kwargs): """ @@ -601,15 +604,16 @@ def setup(self, force=False, **kwargs): unix.cp(PAR_FILE, self._args.workdir) print(msg.cli(f"creating parameter file: {self._args.parameter_file}")) - def configure(self, relative_paths=True, **kwargs): + def configure(self, absolute_paths=False, **kwargs): """ Dynamically generate the parameter file by writing out docstrings and default values for each of the SeisFlows module parameters. This function writes files manually, consistent with the .yaml format. - :type relative_paths: bool - :param relative_paths: if True, expand pathnames to absolute paths, + :type absolute_paths: bool + :param absolute_paths: if True, expand pathnames to absolute paths, else if False, use path names relative to the working directory. + Defaults to False, uses relative paths. """ print(msg.cli(f"filling {self._args.parameter_file} w/ default values")) self._register_parameters(force=True) @@ -650,8 +654,8 @@ def configure(self, relative_paths=True, **kwargs): msg.write_par_file_header(f, seisflows_paths, name="PATHS") f.write("PATHS:\n") - if relative_paths: - # If requested, set the paths relative to the current dir + # If requested, set the paths relative to the current dir + if not absolute_paths: for key, attrs in seisflows_paths.items(): if attrs["default"] is not None: seisflows_paths[key]["default"] = os.path.relpath( @@ -716,7 +720,7 @@ def submit(self, stop_after=None, force=False, **kwargs): # Read in the Parameter file and set parameters into sys.modules. self._register_parameters(force=force) - self._check_required_paths() + # self._check_required_paths() self._register_modules(check=True) # Set logger to print to stdout and write to a file @@ -728,28 +732,28 @@ def submit(self, stop_after=None, force=False, **kwargs): system = sys.modules["seisflows_system"] system.submit() - def _check_required_paths(self): - """ - If the User provides certain paths to the program, they MUST exist. - This function simply checks these required paths and throws a sys exit - if any of them does not exist - """ - # A list of paths that need to exist if provided by user - REQ_PATHS = ["SPECFEM_BIN", "SPECFEM_DATA", "MODEL_INIT", "MODEL_TRUE", - "DATA", "LOCAL", "MASK"] - - # Check that all required paths exist before submitting workflow - paths_dont_exist = [] - for key in REQ_PATHS: - if key in self._paths: - # If a required path is given (not None) and doesnt exist, exit - if self._paths[key] and not os.path.exists(self._paths[key]): - paths_dont_exist.append(f"{key}: {self._paths[key]}") - if paths_dont_exist: - print(msg.cli("The following paths do not exist but need to:", - items=paths_dont_exist, header="path error", - border="=")) - sys.exit(-1) + # def _check_required_paths(self): + # """ + # If the User provides certain paths to the program, they MUST exist. + # This function simply checks these required paths and throws a sys exit + # if any of them does not exist + # """ + # # A list of paths that need to exist if provided by user + # REQ_PATHS = ["SPECFEM_BIN", "SPECFEM_DATA", "MODEL_INIT", "MODEL_TRUE", + # "DATA", "LOCAL", "MASK"] + # + # # Check that all required paths exist before submitting workflow + # paths_dont_exist = [] + # for key in REQ_PATHS: + # if key in self._paths: + # # If a required path is given (not None) and doesnt exist, exit + # if self._paths[key] and not os.path.exists(self._paths[key]): + # paths_dont_exist.append(f"{key}: {self._paths[key]}") + # if paths_dont_exist: + # print(msg.cli("The following paths do not exist but need to:", + # items=paths_dont_exist, header="path error", + # border="=")) + # sys.exit(-1) def clean(self, force=False, **kwargs): """ diff --git a/seisflows/solver/base.py b/seisflows/solver/base.py index 74ed8dbf..da1bf9ac 100644 --- a/seisflows/solver/base.py +++ b/seisflows/solver/base.py @@ -14,8 +14,8 @@ from seisflows.plugins import solver_io from seisflows.tools import msg, unix -from seisflows.tools.specfem import Container -from seisflows.tools.wrappers import diff, exists +from seisflows.tools.specfem import Container, getpar +from seisflows.tools.wrappers import diff from seisflows.config import SeisFlowsPathsParameters, Dict @@ -107,6 +107,8 @@ def __init__(self): from this logger will be tagged by its specific module/classname """ self.parameters = [] + self.nt = None + self.dt = None self._mesh_properties = None self._source_names = None @@ -133,6 +135,10 @@ def required(self): "simulations, otherwise set attenuation off. Attenuation " "is always off for adjoint simulations.") + sf.par("FORMAT", required=False, par_type=float, default="ASCII", + docstr="Format of synthetic waveforms used during workflow, " + "available options: ['ascii', 'su']") + sf.par("COMPONENTS", required=False, default="ZNE", par_type=str, docstr="Components used to generate data, formatted as a single " "string, e.g. ZNE or NZ or E") @@ -146,18 +152,20 @@ def required(self): default=os.path.join(PATH.SCRATCH, "solver"), docstr="scratch path to hold solver working directories") - sf.path("SPECFEM_BIN", required=True, + sf.path("DATA", required=False, + docstr="path to a directory containing any external data " + "required by the workflow. Catch all directory that " + "can be accessed by all modules") + + sf.path("SPECFEM_BIN", required=False, + default=os.path.join(PATH.WORKDIR, "specfem", "bin"), docstr="path to the SPECFEM binary executables") - sf.path("SPECFEM_DATA", required=True, + sf.path("SPECFEM_DATA", required=False, + default=os.path.join(PATH.WORKDIR, "specfem", "DATA"), docstr="path to the SPECFEM DATA/ directory containing the " "'Par_file', 'STATIONS' file and 'CMTSOLUTION' files") - sf.path("DATA", required=False, - docstr="path to a directory containing any external data " - "required by the workflow. Catch all directory that " - "can be accessed by all modules") - return sf def check(self, validate=True): @@ -181,6 +189,10 @@ def check(self, validate=True): assert(PAR.DENSITY.upper() in acceptable_densities), \ f"DENSITY must be in {acceptable_densities}" + acceptable_formats = ["SU", "ASCII"] + if PAR.FORMAT.upper() not in acceptable_formats: + raise Exception(f"'FORMAT' must be {acceptable_formats}") + # Internal parameter list based on user-input material choices # Important to reset parameters to a blank list and let the check # statements fill it. If not, each time workflow is resumed, parameters @@ -221,13 +233,16 @@ def setup(self): In the former case, a value for PATH.DATA must be supplied; in the latter case, a value for PATH.MODEL_TRUE must be provided. """ - unix.rm(self.cwd) self.initialize_solver_directories() - self.check_solver_parameter_files() self.generate_data() self.generate_mesh(model_name="init", model_type="gll") self.initialize_adjoint_traces() + # Assuming that NT and DT are set correctly in the Par_file + unix.cd(self.cwd) + self.nt = getpar(key="NSTEP", file="DATA/Par_file")[1] + self.dt = getpar(key="DT", file="DATA/Par_file")[1] + def generate_mesh(self, model_path, model_name, model_type): """ Performs meshing and database generation. @@ -390,7 +405,6 @@ def call_solver(self, executable, output="solver.log"): ) sys.exit(-1) - @property def io(self): """ @@ -541,7 +555,7 @@ def combine(self, input_path, output_path, parameters=None): if parameters is None: parameters = self.parameters - if not exists(output_path): + if not os.path.exists(output_path): unix.mkdir(output_path) # Write the source names into the kernel paths file for SEM/ directory @@ -755,6 +769,7 @@ def initialize_solver_directories(self): if self.taskid == 0: self.logger.info(f"initializing {PAR.NTASK} solver directories") + unix.rm(self.cwd) unix.mkdir(self.cwd) unix.cd(self.cwd) @@ -837,23 +852,23 @@ def check_mesh_properties(self, path=None): if path is None: path = PATH.MODEL_INIT - if not exists(path): - print(msg.cli(f"The following mesh path does not exist but should", - items=[path], header="solver error", border="=")) - sys.exit(-1) - - # Count the number of .bin files and the number of grid points - key = self.parameters[0] - bin_files = glob(os.path.join(path, f"proc*_{key}.bin")) - nproc = len(bin_files) - ngll = [] - for i in range(0, len(bin_files)): - ngll.append( - len(self.io.read_slice(path=path, parameters=key, iproc=i)[0]) - ) + if os.path.exists(path): + # Count the number of .bin files and the number of grid points + key = self.parameters[0] + bin_files = glob(os.path.join(path, f"proc*_{key}.bin")) + nproc = len(bin_files) + ngll = [] + for i in range(0, len(bin_files)): + ngll.append( + len(self.io.read_slice(path=path, + parameters=key, iproc=i)[0]) + ) - # Define internal mesh properties - self._mesh_properties = Dict(nproc=nproc, ngll=ngll, path=path) + # Define internal mesh properties + self._mesh_properties = Dict(nproc=nproc, ngll=ngll, path=path) + else: + self.logger.warning("solver cannot find mesh and will not have " + "access to mesh properties") def check_source_names(self): """ diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index 02ec78e9..5bbc1b54 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -37,13 +37,11 @@ def __init__(self): """ These parameters should not be set by the user. Attributes are initialized as NoneTypes for clarity and docstrings. - - :type logger: Logger - :param logger: Class-specific logging module, log statements pushed - from this logger will be tagged by its specific module/classname """ super().__init__() + self.f0 = None + @property def required(self): """ @@ -52,20 +50,6 @@ def required(self): """ sf = SeisFlowsPathsParameters(super().required) - # Define the Parameters required by this module - sf.par("NT", required=True, par_type=float, - docstr="Number of time steps set in the SPECFEM Par_file") - - sf.par("DT", required=True, par_type=float, - docstr="Time step or delta set in the SPECFEM Par_file") - - sf.par("F0", required=True, par_type=float, - docstr="Dominant source frequency") - - sf.par("FORMAT", required=False, par_type=float, default="ASCII", - docstr="Format of synthetic waveforms used during workflow, " - "available options: ['ascii', 'su']") - sf.par("SOURCE_PREFIX", required=False, default="SOURCE", par_type=str, docstr="Prefix of SOURCE files in path SPECFEM_DATA. By " @@ -82,57 +66,13 @@ def check(self, validate=True): super().check(validate=False) - acceptable_formats = ["SU", "ASCII"] - assert(PAR.FORMAT.upper() in acceptable_formats), \ - f"FORMAT must be {acceptable_formats}" - - def check_solver_parameter_files(self): - """ - Checks SPECFEM2D Par_file for acceptable parameters and matches with - the internally set parameters - """ - # Check the number of steps in the SPECFEM2D Par_file - nt_str, nt, nt_i = getpar(key="NSTEP", file="DATA/Par_file") - if int(nt) != PAR.NT: - if self.taskid == 0: - print(msg.cli(f"SPECFEM2D {nt_str}=={nt} is not equal " - f"SeisFlows PAR.NT=={PAR.NT}. Please ensure " - f"that these values match in both files.", - header="parameter match error", border="=") - ) - sys.exit(-1) - - dt_str, dt, dt_i = getpar(key="DT", file="DATA/Par_file") - if float(dt) != PAR.DT: - if self.taskid == 0: - print(msg.cli(f"SPECFEM2D {dt_str}=={dt} is not equal " - f"SeisFlows PAR.DT=={PAR.DT}. Please ensure " - f"that these values match in both files.", - header="parameter match error", border="=") - ) - sys.exit(-1) - - # Check the central frequency in the SPECFEM2D SOURCE file - f0_str, f0, f0_i = getpar(key="f0", file="DATA/SOURCE") - if float(f0) != PAR.F0: - if self.taskid == 0: - print(msg.cli(f"SPECFEM2D {f0_str}=={f0} is not equal " - f"SeisFlows PAR.F0=={PAR.F0}. Please ensure " - f"that these values match the DATA/SOURCE file.", - header="parameter match error", border="=") - ) - sys.exit(-1) - - # Ensure that NPROC matches the MESH values - nproc = self.mesh_properties.nproc - if nproc != PAR.NPROC: - if self.taskid == 0: - print(msg.cli(f"SPECFEM2D mesh NPROC=={nproc} is not equal" - f"SeisFlows PAR.NPROC=={PAR.NPROC}. " - f"Please check that your mesh matches this val.", - header="parameter match error", border="=") - ) - sys.exit(-1) + def setup(self): + """ + Additional SPECFEM2D setup steps + """ + super().setup() + self.f0 = getpar(key="f0", file=os.path.join(self.cwd, + "DATA/SOURCE"))[1] if "MULTIPLES" in PAR: if PAR.MULTIPLES: @@ -285,6 +225,8 @@ def adjoint(self): Calls SPECFEM2D adjoint solver, creates the `SEM` folder with adjoint traces which is required by the adjoint solver """ + unix.cd(self.cwd) + setpar(key="SIMULATION_TYPE", val="3", file="DATA/Par_file") setpar(key="SAVE_FORWARD", val=".false.", file="DATA/Par_file") diff --git a/seisflows/solver/specfem3d.py b/seisflows/solver/specfem3d.py index 43e868c4..8ef68ad5 100644 --- a/seisflows/solver/specfem3d.py +++ b/seisflows/solver/specfem3d.py @@ -52,17 +52,6 @@ def required(self): """ sf = SeisFlowsPathsParameters(super().required) - # Define the Parameters required by this module - sf.par("NT", required=True, par_type=float, - docstr="Number of time steps set in the SPECFEM Par_file") - - sf.par("DT", required=True, par_type=float, - docstr="Time step or delta set in the SPECFEM Par_file") - - sf.par("FORMAT", required=True, par_type=float, - docstr="Format of synthetic waveforms used during workflow, " - "available options: ['ascii', 'su']") - sf.par("SOURCE_PREFIX", required=False, default="CMTSOLUTION", par_type=str, docstr="Prefix of SOURCE files in path SPECFEM_DATA. Available " @@ -76,11 +65,8 @@ def check(self, validate=True): """ if validate: self.required.validate() - super().check(validate=False) - acceptable_formats = ["SU", "ASCII"] - if PAR.FORMAT.upper() not in acceptable_formats: - raise Exception(f"'FORMAT' must be {acceptable_formats}") + super().check(validate=False) def generate_data(self, **model_kwargs): """ @@ -175,7 +161,6 @@ def forward(self, path="traces/syn"): output="fwd_mesher.log") self.call_solver(executable="bin/xmeshfem3D", output="fwd_solver.log") - # Find and move output traces, by default to synthetic traces dir unix.mv(src=glob(os.path.join("OUTPUT_FILES", self.data_wildcard)), dst=path) @@ -194,45 +179,6 @@ def adjoint(self): self.call_solver(executable="bin/xmeshfem3D", output="adj_solver.log") - def check_solver_parameter_files(self): - """ - Checks solver parameters - """ - # Check the number of steps in the SPECFEM2D Par_file - nt_str, nt, nt_i = getpar(key="NSTEP", file="DATA/Par_file") - if int(nt) != PAR.NT: - if self.taskid == 0: - print(msg.cli(f"SPECFEM3D {nt_str}=={nt} is not equal " - f"SeisFlows PAR.NT=={PAR.NT}. Please ensure " - f"that these values match in both files.", - header="parameter match error", border="=") - ) - sys.exit(-1) - - dt_str, dt, dt_i = getpar(key="DT", file="DATA/Par_file") - if float(dt) != PAR.DT: - if self.taskid == 0: - print(msg.cli(f"SPECFEM3D {dt_str}=={dt} is not equal " - f"SeisFlows PAR.DT=={PAR.DT}. Please ensure " - f"that these values match in both files.", - header="parameter match error", border="=") - ) - sys.exit(-1) - - # Ensure that NPROC matches the MESH values - nproc = self.mesh_properties.nproc - if nproc != PAR.NPROC: - if self.taskid == 0: - print(msg.cli(f"SPECFEM3D mesh NPROC=={nproc} is not equal " - f"SeisFlows PAR.NPROC=={PAR.NPROC}. " - f"Please check that your mesh matches this val.", - header="parameter match error", border="=") - ) - sys.exit(-1) - - if "MULTIPLES" in PAR: - raise NotImplementedError - def initialize_adjoint_traces(self): """ Setup utility: Creates the "adjoint traces" expected by SPECFEM diff --git a/seisflows/tools/specfem.py b/seisflows/tools/specfem.py index 2d39f544..bfc17db3 100644 --- a/seisflows/tools/specfem.py +++ b/seisflows/tools/specfem.py @@ -226,33 +226,3 @@ def setpar_vel_model(file, model): # Set nbmodels to the correct value setpar(key="nbmodels", val=len(model), file=file) - -def check_poissons_ratio(vp, vs, min_val=-1., max_val=0.5): - """ - Check Poisson's ratio based on Vp and Vs model vectors. Exit SeisFlows if - Poisson's ratio is outside `min_val` or `max_val` which by default are - set internally by SPECFEM. Otherwise return the value - - :type vp: np.array - :param vp: P-wave velocity model vector - :type vs: np.array - :param vp: S-wave velocity model vector - :type min_val: float - :param min_val: minimum model-wide acceptable value for poissons ratio - :type max_val: float - :param max_val: maximum model-wide acceptable value for poissons ratio - :return: - """ - poissons = poissons_ratio(vp=vp, vs=vs) - pmin = poissons.min() - pmax = poissons.max() - if (pmin < min_val) or (pmax > max_val): - print(msg.cli(f"The Poisson's ratio of the given model is out of " - f"bounds with respect to the defined range " - f"({min_val}, {max_val}). " - f"The model bounds were found to be:", - items=[f"{pmin:.2f} < PR < {pmax:.2f}"], border="=", - header="Poisson's Ratio Error") - ) - sys.exit(-1) - return poissons diff --git a/seisflows/workflow/base.py b/seisflows/workflow/base.py index ecf52309..d16bb406 100644 --- a/seisflows/workflow/base.py +++ b/seisflows/workflow/base.py @@ -3,6 +3,7 @@ This is the Base class for seisflows.workflow. It contains mandatory functions that must be called by subclasses """ +import os import sys import logging @@ -70,15 +71,17 @@ def required(self): "'vector': save files as NumPy .npy files, " "'both': save as both binary and vectors]") - sf.path("MODEL_INIT", required=True, + sf.path("DATA", required=False, default=None, + docstr="path to data available to workflow") + + sf.path("MODEL_INIT", required=False, + default=os.path.join(PATH.WORKDIR, "specfem", "MODEL_INIT"), docstr="location of the initial model to be used for workflow") sf.path("MODEL_TRUE", required=False, + default=os.path.join(PATH.WORKDIR, "specfem", "MODEL_TRUE"), docstr="Target model to be used for PAR.CASE == 'synthetic'") - sf.path("DATA", required=False, default=None, - docstr="path to data available to workflow") - return sf def check(self, validate=True): diff --git a/seisflows/workflow/test.py b/seisflows/workflow/test.py index 9b4de68c..4341fd4c 100644 --- a/seisflows/workflow/test.py +++ b/seisflows/workflow/test.py @@ -159,14 +159,14 @@ def test_optimize(self): """ Test optimization module with a simple rosenbrock function """ - optimize.setup() - m_new, objective_function, gradient = rosenbrock() + optimize.setup(m_new=m_new) def evaluate_function(): """ Evalaute the misfit function of a given model """ + self.logger.info("evaluating objective function") m_new = np.load(optimize.vectors("m_new")) f_try = objective_function(m_new) np.save(optimize.vectors("f_try"), f_try) @@ -175,52 +175,64 @@ def evaluate_gradient(): """ Evaluate the gradient of a given model """ + self.logger.info("evaluating gradient") m_new = np.load(optimize.vectors("m_new")) f_new = objective_function(m_new) g_new = gradient(m_new) np.save(optimize.vectors("f_new"), f_new) np.save(optimize.vectors("g_new"), g_new) - - # Set up the initial model on disk - np.save(optimize.vectors("m_new"), m_new) - - import pdb;pdb.set_trace() - for iteration in range(1, 200): - evaluate_gradient() - optimize.compute_direction() - # Perform line search + def line_search(): + """ + Run a line search until a suitable model has been found + """ while True: - optimize.initialize_search() evaluate_function() status = optimize.update_search() - if status > 0: + if status == 1: + self.logger.info("finalizing line search") optimize.finalize_search() - break + return elif status == 0: + self.logger.info("continuing line search") continue - elif status < 0: + elif status == -1: if optimize.retry_status(): + self.logger.info("restarting line search") optimize.restart() + # Recursively run the line search after restart + line_search() else: - sys.exit() + sys.exit(-1) -def rosenbrock(n=1E8): + self.logger.info("testing optimization library with Rosenbrock problem") + for iteration in range(1, 200): + self.logger.info("iteration {iteration}") + evaluate_gradient() + optimize.compute_direction() + optimize.initialize_search() + line_search() + optimize.iter += 1 + + +def rosenbrock(n=1E5): """ - Rosenbrock test for optimization library testing + Rosenbrock test problem for optimization library testing + + https://en.wikipedia.org/wiki/Rosenbrock_function """ model_init = 0.1 * np.ones(int(n)) def objective_function(x): """ - Rosenbrock function + Rosenbrock objective function """ return sum(100 * (x[:-1]**2. - x[1:])**2. + (x[:-1] - 1.)**2) def gradient(x): """ - Gradient for Rosenbrock function + Gradient of the objective function for Rosenbrock test """ g = np.zeros(int(n)) g[1:-1] = -200 * (x[:-2] ** 2. - x[1:-1]) + \ From 285b5e72ad367c0440b381d8be8ca9ae96ed2608 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Wed, 22 Jun 2022 15:58:12 -0800 Subject: [PATCH 022/195] optimization rosenbrock test now working on test workflow --- seisflows/config.py | 4 +- seisflows/optimize/LBFGS.py | 8 +- seisflows/optimize/NLCG.py | 8 +- seisflows/optimize/base.py | 1 + seisflows/optimize/gradient.py | 103 ++++++++++++--------- seisflows/plugins/line_search/backtrack.py | 3 +- seisflows/plugins/line_search/bracket.py | 7 +- seisflows/solver/base.py | 2 +- seisflows/workflow/inversion.py | 11 +-- seisflows/workflow/test.py | 75 ++++++++++++--- 10 files changed, 143 insertions(+), 79 deletions(-) create mode 120000 seisflows/optimize/base.py diff --git a/seisflows/config.py b/seisflows/config.py index 9441284b..7ba3126b 100644 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -246,8 +246,8 @@ class SeisFlowsPathsParameters: .. note:: if a path or parameter is optional it requires a default value. """ - default_par = "!!! REQUIRED PARAMETER !!!" - default_path = "!!! REQUIRED PATH !!!" + default_par = "REQUIRED PARAMETER" + default_path = "REQUIRED PATH" def __init__(self, base=None): """ diff --git a/seisflows/optimize/LBFGS.py b/seisflows/optimize/LBFGS.py index 82defde6..10a9407c 100644 --- a/seisflows/optimize/LBFGS.py +++ b/seisflows/optimize/LBFGS.py @@ -156,7 +156,7 @@ def compute_direction(self): # Load the current gradient direction, which is the L-BFGS search # direction if this is the first iteration - g = np.load(self.vectors("g_new")) + g = self.load("g_new") if self.LBFGS_iter == 1: self.logger.info("first L-BFGS iteration, setting search direction " "as inverse gradient") @@ -193,7 +193,7 @@ def compute_direction(self): restarted = 1 # Save values to disk and memory - np.save(self.p_new, p_new) + self.save("p_new", p_new) self.restarted = restarted def restart(self): @@ -236,8 +236,8 @@ def update(self): unix.cd(PATH.OPTIMIZE) # Determine the iterates for model m and gradient g - s_k = np.load(self.vectors("m_new")) - np.load(self.vectors("m_old")) - y_k = np.load(self.vectors("g_new")) - np.load(self.vectors("g_old")) + s_k = self.load("m_new") - self.load("m_old") + y_k = self.load("g_new") - self.load("g_old") # Determine the shape of the memory map (length of model, length of mem) m = len(s_k) diff --git a/seisflows/optimize/NLCG.py b/seisflows/optimize/NLCG.py index 35a72460..f05152da 100644 --- a/seisflows/optimize/NLCG.py +++ b/seisflows/optimize/NLCG.py @@ -105,7 +105,7 @@ def compute_direction(self): unix.cd(PATH.OPTIMIZE) # Load the current gradient direction - g_new = np.load(self.vectors("g_new")) + g_new = self.load("g_new") # CASE 1: If first iteration, search direction is the current gradient if self.NLCG_iter == 1: @@ -125,8 +125,8 @@ def compute_direction(self): # Normal NLCG direction compuitation else: # Compute search direction - g_old = np.load(self.vectors("g_old")) - p_old = np.load(self.vectors("p_old")) + g_old = self.load("g_old") + p_old = self.load("p_old") # Apply preconditioner and calc. scale factor for search dir. (beta) if self.precond: @@ -152,7 +152,7 @@ def compute_direction(self): restarted = 0 # Save values to disk and memory - np.save(self.p_new, p_new) + self.save("p_new", p_new) self.restarted = restarted def restart(self): diff --git a/seisflows/optimize/base.py b/seisflows/optimize/base.py new file mode 120000 index 00000000..03ddb0e5 --- /dev/null +++ b/seisflows/optimize/base.py @@ -0,0 +1 @@ +gradient.py \ No newline at end of file diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index 2ae776d9..7ad4c4c4 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -74,6 +74,10 @@ def __init__(self): self.line_search = None self.precond = None self.restarted = False + self.acceptable_vectors = ["m_new", "m_old", "m_try", + "g_new", "g_old", "g_try", + "p_new", "p_old", "alpha", + "f_new", "f_old", "f_try"] @property def required(self): @@ -157,7 +161,7 @@ def setup(self, m_new=None): ) m_new = solver.merge(solver.load(PATH.MODEL_INIT)) - np.save(self.vectors("m_new"), m_new) + self.save("m_new", m_new) self.check_model(m_new) @property @@ -173,12 +177,12 @@ def eval_str(self): step = self.line_search.step_count return f"i{iter_:0>2}s{step:0>2}" - def vectors(self, name): + def load(self, name): """ Convenience function to access the full paths of model and gradient vectors that are saved to disk - .. note:: the available options that can be created + .. note:: the available options that can be loaded m_new: current model m_old: previous model m_try: line search model @@ -193,15 +197,26 @@ def vectors(self, name): :type name: str :param name: name of the vector, acceptable: m, g, p, f, alpha + """ + assert(name in self.acceptable_vectors) + vector = np.load(os.path.join(PATH.OPTIMIZE, f"{name}.npy")) + # Allow single length vectors, which alpha and misfit (f) are + if vector.size == 1: + vector = float(vector) + return vector + + def save(self, name, vector): + """ + Convenience function to save/overwrite vectors on disk + :type name: str + :param name: name of the vector to overwrite + :type vector: np.array + :param vector: vector to save to name """ - # Set model and gradient filenames as attributes - acceptable_names = ["m_new", "m_old", "m_try", - "g_new", "g_old", "g_try", - "p_new", "p_old", "alpha", - "f_new", "f_old", "f_try"] - assert(name in acceptable_names) - return os.path.join(PATH.OPTIMIZE, f"{name}.npy") + assert(name in self.acceptable_vectors) + vector_path = os.path.join(PATH.OPTIMIZE, f"{name}.npy") + np.save(vector_path, vector) def compute_direction(self): """ @@ -213,22 +228,22 @@ def compute_direction(self): """ self.logger.info(f"computing search direction with {PAR.OPTIMIZE}") - g_new = np.load(self.vectors("g_new")) + g_new = self.load("g_new") if self.precond is not None: p_new = -1 * self.precond(g_new) else: p_new = -1 * g_new - np.save(self.vectors("p_new"), p_new) + self.save("p_new", p_new) def initialize_search(self): """ Initialize the plugin line search machinery. Should only be run at the beginning of line search, by the main workflow module. """ - m = np.load(self.vectors("m_new")) - g = np.load(self.vectors("g_new")) - p = np.load(self.vectors("p_new")) - f = np.load(self.vectors("f_new")) + m = self.load("m_new") + g = self.load("g_new") + p = self.load("p_new") + f = self.load("f_new") norm_m = max(abs(m)) norm_p = max(abs(p)) @@ -258,8 +273,8 @@ def initialize_search(self): # gradient threshold to remove any outlier values m_try = m + alpha * p - np.save(self.vectors("m_try"), m_try) - np.save(self.vectors("alpha"), alpha) + self.save("m_try", m_try) + self.save("alpha", alpha) self.check_model(m_try) def update_search(self): @@ -272,18 +287,18 @@ def update_search(self): status == 0 : not finished status == -1 : failed """ - self.line_search.update(step_len=np.load(self.vectors("alpha")), - func_val=np.load(self.vectors("f_try"))) + self.line_search.update(step_len=self.load("alpha"), + func_val=self.load("f_try")) alpha, status = self.line_search.calculate_step() # New search direction needs to be searchable on disk if status in [0, 1]: - m = np.load(self.vectors("m_new")) - p = np.load(self.vectors("p_new")) - np.save(self.vectors("alpha"), alpha) + m = self.load("m_new") + p = self.load("p_new") + self.save("alpha", alpha) m_try = m + alpha * p - np.save(self.vectors("m_try"), m_try) + self.save("m_try", m_try) self.check_model(m_try) return status @@ -301,24 +316,23 @@ def finalize_search(self): # Remove the old model parameters if self.iter > 1: self.logger.info("removing previously accepted model files (old)") - for fid in [self.vectors("m_old"), self.vectors("f_old"), - self.vectors("g_old"), self.vectors("p_old")]: + for fid in ["m_old", "f_old", "g_old", "p_old"]: unix.rm(fid) # Needs to be run before shifting model in next step self.write_stats() self.logger.info("shifting current model (new) to previous model (old)") - unix.mv(self.vectors("m_new"), self.vectors("m_old")) - unix.mv(self.vectors("f_new"), self.vectors("f_old")) - unix.mv(self.vectors("g_new"), self.vectors("g_old")) - unix.mv(self.vectors("p_new"), self.vectors("p_old")) + unix.mv("m_new.npy", "m_old.npy") + unix.mv("f_new.npy", "f_old.npy") + unix.mv("g_new.npy", "g_old.npy") + unix.mv("p_new.npy", "p_old.npy") self.logger.info("setting accepted line search model as current model") - unix.mv(self.vectors("m_try"), self.vectors("m_new")) + unix.mv("m_try.npy", "m_new.npy") f = self.line_search.search_history()[1] - np.save(self.vectors("f_new"), f.min()) + self.save("f_new", f.min()) self.logger.info(f"current misfit is {f.min():.3E}") self.logger.info("resetting line search step count to 0") @@ -330,8 +344,8 @@ def retry_status(self): by checking, in effect, if the search direction was the same as gradient direction """ - g = np.load(self.vectors("g_new")) - p = np.load(self.vectors("p_new")) + g = self.load("g_new") + p = self.load("p_new") theta = angle(p, -g) self.logger.debug(f"theta: {theta:6.3f}") @@ -353,8 +367,8 @@ def restart(self): """ # Steepest descent (base) does not need to be restarted if PAR.OPTIMIZE.capitalize() != "Gradient": - g = np.load(self.vectors("g_new")) - np.save(self.vectors("p_new"), -g) + g = self.load("g_new") + self.save("p_new", -g) self.line_search.clear_history() self.restarted = 1 @@ -385,8 +399,8 @@ def write_stats(self): f.write(f"{header.upper()},") f.write("\n") - g = np.load(self.vectors("g_new")) - p = np.load(self.vectors("p_new")) + g = self.load("g_new") + p = self.load("p_new") x = self.line_search.search_history()[0] f = self.line_search.search_history()[1] @@ -435,16 +449,15 @@ def check_model(self, m, min_pr=-1., max_pr=0.5): # Check Poisson's ratio, which will error our SPECFEM if outside limits if (pars["vp"] is not None) and (pars["vs"] is not None): - self.logger.debug(f"checking poissons ratio") pars["pr"] = poissons_ratio(vp=pars["vp"], vs=pars["vs"]) if pars["pr"].min() < 0: self.logger.warning("minimum poisson's ratio is negative") - if pars["pr"].min() < min_pr: - self.logger.warning(f"minimum poisson's ratio out of bounds: " - f"{pars['pr'].max()} > {max_pr}") - if pars["pr"].max() > max_pr: - self.logger.warning(f"maximum poisson's ratio out of bounds: " - f"{pars['pr'].min()} < {min_pr}") + if pars["pr"].max() < min_pr: + self.logger.warning(f"maximum poisson's ratio out of bounds: " + f"{pars['pr'].max():.2f} > {max_pr}") + if pars["pr"].min() > max_pr: + self.logger.warning(f"minimum poisson's ratio out of bounds: " + f"{pars['pr'].min():.2f} < {min_pr}") # Tell the User min and max values of the updated model self.logger.info(f"model parameters") diff --git a/seisflows/plugins/line_search/backtrack.py b/seisflows/plugins/line_search/backtrack.py index a844232f..d31d96fc 100644 --- a/seisflows/plugins/line_search/backtrack.py +++ b/seisflows/plugins/line_search/backtrack.py @@ -52,7 +52,8 @@ def calculate_step(self): # Assumed well scaled search direction, attempt backtracking line search # with unit step length else: - self.logger.info(msg.sub("EVALUATE BACKTRACKING LINE SEARCH")) + self.logger.debug(msg.sub(f"BACKTRACKING LINE SEARCH STEP" + f"{self.step_count:0>2}")) x_str = ", ".join([f"{_:.2E}" for _ in x]) f_str = ", ".join([f"{_:.2E}" for _ in f]) self.logger.debug(f"step length(s) = {x_str}") diff --git a/seisflows/plugins/line_search/bracket.py b/seisflows/plugins/line_search/bracket.py index 8ea85fd5..4aed9a8c 100644 --- a/seisflows/plugins/line_search/bracket.py +++ b/seisflows/plugins/line_search/bracket.py @@ -175,7 +175,8 @@ def calculate_step(self): x, f, gtg, gtp, step_count, update_count = self.search_history() # Print out the current line search parameters for convenience - self.logger.debug(msg.sub("EVALUATE BRACKETING LINE SEARCH")) + self.logger.debug(msg.sub(f"BRACKETING LINE SEARCH STEP " + f"{self.step_count:0>2}")) x_str = ", ".join([f"{_:.2E}" for _ in x]) f_str = ", ".join([f"{_:.2E}" for _ in f]) self.logger.debug(f"step length(s) = {x_str}") @@ -233,8 +234,8 @@ def calculate_step(self): status = 0 # Stop because safeguard prevents us from going further elif alpha > self.step_len_max: - self.logger.info(f"step_len_max={self.step_len_max} exceeded, " - f"manual set alpha") + self.logger.info(f"step_len_max={self.step_len_max:.2f} " + f"exceeded, manual set alpha") alpha = self.step_len_max status = 1 diff --git a/seisflows/solver/base.py b/seisflows/solver/base.py index da1bf9ac..6de725de 100644 --- a/seisflows/solver/base.py +++ b/seisflows/solver/base.py @@ -608,7 +608,7 @@ def smooth(self, input_path, output_path, parameters=None, span_h=0., if parameters is None: parameters = self.parameters - if not exists(output_path): + if not os.path.exists(output_path): unix.mkdir(output_path) # mpiexec ./bin/xsmooth_sem SMOOTH_H SMOOTH_V name input output use_gpu diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index 23e6c717..e88fe0a1 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -247,8 +247,8 @@ def evaluate_function(self, path, suffix): """ self.logger.info(msg.sub("EVALUATE OBJECTIVE FUNCTION")) - model_tag = optimize.vectors(f"m_{suffix}") - misfit_tag = optimize.vectors(f"f_{suffix}") + model_tag = f"m_{suffix}" + misfit_tag = f"f_{suffix}" self.write_model(path=path, tag=model_tag) @@ -330,12 +330,11 @@ def write_gradient(self): """ self.logger.info(msg.mnr("POSTPROCESSING KERNELS")) src = os.path.join(PATH.GRAD, "gradient") - dst = optimize.vectors("g_new") postprocess.write_gradient(PATH.GRAD) parts = solver.load(src, suffix="_kernel") - np.save(dst, solver.merge(parts)) + optimize.save("g_new", solver.merge(parts)) def write_misfit(self, path, tag): """ @@ -372,7 +371,7 @@ def save_gradient(self): src = os.path.join(PATH.GRAD, "gradient") unix.mv(src, dst) if PAR.SAVEAS in ["vector", "both"]: - src = optimize.vectors("g_old") + src = "g_old" unix.cp(src, dst + ".npy") self.logger.debug(f"saving gradient to path:\n{dst}") @@ -385,7 +384,7 @@ def save_model(self): Saving as a vector saves on file count, but requires numpy and seisflows functions to read """ - src = optimize.vectors("m_new") + src = optimize.load("m_new") dst = os.path.join(PATH.OUTPUT, f"model_{optimize.iter:04d}") self.logger.debug(f"saving model '{src}' to path:\n{dst}") diff --git a/seisflows/workflow/test.py b/seisflows/workflow/test.py index 4341fd4c..ec761720 100644 --- a/seisflows/workflow/test.py +++ b/seisflows/workflow/test.py @@ -157,9 +157,9 @@ def test_solver(self): def test_optimize(self): """ - Test optimization module with a simple rosenbrock function + Test optimization module with a simple Rosenbrock function """ - m_new, objective_function, gradient = rosenbrock() + m_new, m_true, objective_function, gradient = rosenbrock() optimize.setup(m_new=m_new) def evaluate_function(): @@ -167,27 +167,30 @@ def evaluate_function(): Evalaute the misfit function of a given model """ self.logger.info("evaluating objective function") - m_new = np.load(optimize.vectors("m_new")) - f_try = objective_function(m_new) - np.save(optimize.vectors("f_try"), f_try) + m_try = optimize.load("m_try") + f_try = objective_function(m_try) + optimize.save("f_try", f_try) def evaluate_gradient(): """ Evaluate the gradient of a given model """ self.logger.info("evaluating gradient") - m_new = np.load(optimize.vectors("m_new")) + m_new = optimize.load("m_new") f_new = objective_function(m_new) g_new = gradient(m_new) - np.save(optimize.vectors("f_new"), f_new) - np.save(optimize.vectors("g_new"), g_new) + optimize.save("f_new", f_new) + optimize.save("g_new", g_new) def line_search(): """ Run a line search until a suitable model has been found + Note this is almost the same as workflow.inversion.line_search """ + optimize.initialize_search() while True: evaluate_function() + optimize.line_search.step_count += 1 status = optimize.update_search() if status == 1: self.logger.info("finalizing line search") @@ -205,24 +208,69 @@ def line_search(): else: sys.exit(-1) + def finalize(thresh=5e-3): + """ + Finish off one iteration, check the distance between old and new + vectors to see if model stops changing + """ + m_new = optimize.load("m_new") + m_diff = np.linalg.norm(m_new - m_true) / np.linalg.norm(m_new) + if m_diff < thresh: + self.logger.info(f"successful inversion after {optimize.iter} " + f"iterations") + sys.exit(0) + else: + self.logger.info(f"model difference: {m_diff:.2E}") + return self.logger.info("testing optimization library with Rosenbrock problem") for iteration in range(1, 200): - self.logger.info("iteration {iteration}") + self.logger.info(f"iteration {iteration}") evaluate_gradient() optimize.compute_direction() - optimize.initialize_search() line_search() optimize.iter += 1 + finalize() -def rosenbrock(n=1E5): +def rosenbrock(): """ Rosenbrock test problem for optimization library testing https://en.wikipedia.org/wiki/Rosenbrock_function """ - model_init = 0.1 * np.ones(int(n)) + model_init = np.array([-1.2, 1]) # This is the guess for the global min + model_true = np.array([1, 1]) # This is the actual minimum + + def objective_function(x): + """ + Rosenbrock objective function which is defined mathematically as: + + f(x,y) = (a-x)^2 + b(y-x^2)^2 + + where the global minimum is at (x,y) == (a, a^2) + and typical constant values are: a==1, b==100 + """ + return np.array([((1 - x[0]) ** 2 + 100 * (-x[0] ** 2 + x[1]) ** 2)]) + + def gradient(x): + """ + Gradient of the objective function for Rosenbrock test + """ + return np.array([-2*(1-x[0]) - 400*x[0]*(-x[0]**2+x[1]), + 200*(- x[0]**2+x[1])]) + + return model_init, model_true, objective_function, gradient + + +def rosenbrock_n(n=1E5): + """ + N dimensional Rosenbrock test problem for optimization library testing + + https://en.wikipedia.org/wiki/Rosenbrock_function + """ + model_init = 0.1 * np.ones(int(n)) # This is a guess for the global min + model_true = np.ones(int(n)) def objective_function(x): """ @@ -246,4 +294,5 @@ def gradient(x): return g - return model_init, objective_function, gradient + return model_init, model_true, objective_function, gradient + From 78f0c85ea874356be7327ce7c6a13ceb1fcbbb3b Mon Sep 17 00:00:00 2001 From: bch0w Date: Fri, 24 Jun 2022 12:28:03 -0800 Subject: [PATCH 023/195] created a new core.py file which contains all the core SeisFlows class definitions, which avoids some funky late type conversions in config defined a seisflows Base object which dictates the structure that all seisflows modules must have. This is intended to replace the individual Base objects defined in each of the sub-modules old Base modules (preprocess, postprocess) will now be named Default to better signify their role in the workflow (Base felt too abstract) removed Solver plugins. they weren't being used Removed error tools as they seemed overly abstract and were only used in one location. Replaced ParameterError with ValueError in the one instance of it Postprocess.default is now redefined with the new Base class definition, which no longer calls sys modules at the top of the script, removes the required property and defines required in __init__, inherits some properties from Base and explicitely defines super() where functions are being inherited. --- seisflows/config.py | 169 +----------- seisflows/core.py | 243 ++++++++++++++++++ seisflows/plugins/solver/__init__.py | 0 seisflows/plugins/solver/specfem2d.py | 133 ---------- seisflows/plugins/solver/specfem3d.py | 63 ----- seisflows/plugins/solver/specfem3d_globe.py | 55 ---- seisflows/postprocess/{base.py => default.py} | 107 ++++---- seisflows/preprocess/base.py | 2 +- seisflows/solver/specfem3d_globe.py | 5 +- seisflows/templates/parameters.yaml | 4 +- seisflows/tests/test_config.py | 3 +- seisflows/tools/err.py | 37 --- seisflows/workflow/test.py | 2 + 13 files changed, 303 insertions(+), 520 deletions(-) create mode 100644 seisflows/core.py delete mode 100644 seisflows/plugins/solver/__init__.py delete mode 100644 seisflows/plugins/solver/specfem2d.py delete mode 100644 seisflows/plugins/solver/specfem3d.py delete mode 100644 seisflows/plugins/solver/specfem3d_globe.py rename seisflows/postprocess/{base.py => default.py} (68%) delete mode 100644 seisflows/tools/err.py diff --git a/seisflows/config.py b/seisflows/config.py index 7ba3126b..deeddbaf 100644 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -23,9 +23,9 @@ from importlib import import_module from seisflows import logger +from seisflows.core import Dict, Null from seisflows.tools import msg, unix from seisflows.tools.wrappers import module_exists -from seisflows.tools.err import ParameterError """ @@ -52,7 +52,7 @@ # Define a package-wide default directory and file naming schema. This will # be returned as a Dict() object, defined below. All of these files and # directories will be created relative to the user-defined working directory -CFGPATHS = dict( +CFGPATHS = Dict( PAR_FILE="parameters.yaml", # Default SeisFlows parameter file SCRATCHDIR="scratch", # SeisFlows internal working directory OUTPUTDIR="output", # Permanent disk storage for state and outputs @@ -181,167 +181,6 @@ def config_logger(level="DEBUG", filename=None, filemode="a", verbose=True): logger.addHandler(file_handler) -class Dict(dict): - """ - A dictionary replacement which allows for easier parameter access through - getting and setting attributes. Also has some functionality to make string - printing prettier - """ - def __str__(self): - """Pretty print dictionaries and first level nested dictionaries""" - str_ = "" - try: - longest_key = max([len(_) for _ in self.keys()]) - for key, val in self.items(): - str_ += f"{key:<{longest_key}}: {val}\n" - except ValueError: - pass - return str_ - - def __repr__(self): - """Pretty print when calling an instance of this object""" - return(self.__str__()) - - def __getattr__(self, key): - """Attribute-like access of the internal dictionary attributes""" - try: - return self[key] - except KeyError: - raise AttributeError(f"{key} not found in Dict") - - def __setattr__(self, key, val): - """Setting attributes can only be performed one time""" - self.__dict__[key] = val - - -class Null: - """ - A null object that always and reliably does nothing - """ - def __init__(self, *args, **kwargs): - pass - - def __call__(self, *args, **kwargs): - return self - - def __nonzero__(self): - return False - - def __getattr__(self, key): - return self - - def __setattr__(self, key, val): - return self - - def __delattr__(self, key): - return self - - -class SeisFlowsPathsParameters: - """ - A class used to simplify defining required or optional paths and parameters - by enforcing a specific structure to their entry into the environment. - Replaces the functionalities of the old check() functions. - - .. note:: - if a path or parameter is optional it requires a default value. - """ - default_par = "REQUIRED PARAMETER" - default_path = "REQUIRED PATH" - - def __init__(self, base=None): - """ - We simply store paths and parameters as nested dictioanries. Due to the - use of inheritance, the class can be passed to itself on initialization - which means paths and parameters can be adopted from base class - - :type base: seisflows.config.DefinePathsParameters - :param base: paths and parameters from abstract Base class that need to - be inherited by the current child class. - """ - self.parameters, self.paths = {}, {} - if base: - self.parameters.update(base.parameters) - self.paths.update(base.paths) - - def par(self, parameter, required, docstr, par_type, default=None): - """ - Add a parameter to the internal list of parameters - - :type parameter: str - :param parameter: name of the parameter - :type required: bool - :param required: whether or not the parameter is required. If it is not - required, then a default value should be given - :type docstr: str - :param docstr: Short explanatory doc string that defines what the - parameter is used for. - :type par_type: class or str - :param par_type: the parameter type, used for doc strings and also - parameter validation - :param default: default value for the parameter, can be any type - """ - if required: - default = self.default_par - if type(par_type) == type: - par_type = par_type.__name__ - self.parameters[parameter] = {"docstr": docstr, "required": required, - "default": default, "type": par_type} - - def path(self, path, required, docstr, default=None): - """ - Add a path to the internal list of paths - - :type path: str - :param path: name of the parameter - :type required: bool - :param required: whether or not the path is required. If it is not - required, then a default value should be given - :type docstr: str - :param docstr: Short explanatory doc string that defines what the - path is used for. - :type default: str - :param default: default value for the path - - """ - if required: - default = self.default_path - self.paths[path] = {"docstr": docstr, "required": required, - "default": default} - - def validate(self, paths=True, parameters=True): - """ - Set internal paths and parameter values into sys.modules. Should be - called by each modules check() function. - - Ensures that required paths and parameters are set by the User in the - parameter file and that default values are stored for any optional - paths and parameters which are not explicitely set. - - :type paths: bool - :param paths: validate the internal path values - :type parameters: bool - :param parameters: validate the internal parameter values - :raises ParameterError: if a required path or parameter is not set by - the user. - """ - if paths: - sys_path = sys.modules[PATH] - for key, attrs in self.paths.items(): - if attrs["required"] and (key not in sys_path): - raise ParameterError(sys_path, key) - elif key not in sys_path: - setattr(sys_path, key, attrs["default"]) - - if parameters: - sys_par = sys.modules[PAR] - for key, attrs in self.parameters.items(): - if attrs["required"] and (key not in sys_par): - raise ParameterError(sys_par, key) - elif key not in sys_par: - setattr(sys_par, key, attrs["default"]) - - def custom_import(name=None, module=None, classname=None): """ Imports SeisFlows module and extracts class that is the camelcase version @@ -468,7 +307,3 @@ def _unpickle_method(func_name, obj, cls): copyreg.pickle(types.MethodType, _pickle_method, _unpickle_method) -# Because we defined Dict inside this file, we need to convert our CFGPATHS -# to a Dict at the end of the file to allow direct variable access -# !!! TODO I don't really like this implementation, can it be changed? -CFGPATHS = Dict(CFGPATHS) diff --git a/seisflows/core.py b/seisflows/core.py new file mode 100644 index 00000000..2f5057a7 --- /dev/null +++ b/seisflows/core.py @@ -0,0 +1,243 @@ +#!/usr/bin/env python3 +""" +Core class definitions for SeisFlows. Defines some unique class objects that +are used heavily during a Seisflows workflow. +""" +import sys +import logging + + +class Base(object): + """ + Defines the core Base object for all SeisFlows modules. All modules MUST + inherit from the Base object to work properly. This Base class essentially + dictates the required structure of a SeisFlows class. + """ + # Class-specific logger accessed using self.logger. We instantiate loggers + # like this because then inheritance information gets imprinted into the + # logger, making it easier to debug functions which may be multiply + # inherited + + def __init__(self): + """ + Sets the required paths and parameters + """ + self.required = SeisFlowsPathsParameters() + + def module(self, name): + """ + Access globally stored SeisFlows modules located in sys.modules + """ + try: + mod = sys.modules[f"seisflows_{name}"] + except KeyError: + self.logger.warning(f"seisflows_{name} has not been instantiated") + mod = None + return mod + + @property + def logger(self): + """ + An instance specific logger which imprints inheritance information into + the log statements, making it easier to debug functions with + multiple inheritance + """ + return logging.getLogger( + self.__class__.__name__).getChild(self.__class__.__qualname__) + + @property + def par(self): + """ + Access SeisFlows parameters from sys.modules + """ + return self.module("parameters") + + @property + def path(self): + """ + Access SeisFlows paths from sys.modules + """ + return self.module("paths") + + def check(self, validate=True): + """ + General check() function for each module to check the validity of the + user-input parameters and paths + """ + if validate: + self.required.validate() + + def setup(self): + """ + A placeholder function for any initialization or setup tasks that + need to be run once at the beginning of any workflow + """ + return + + def finalize(self): + """ + A placeholder function for any finalization or tear-down tasks that + need to be run at the end of any iteration or workflow + """ + return + + +class Dict(dict): + """ + A dictionary replacement which allows for easier parameter access through + getting and setting attributes. Also has some functionality to make string + printing prettier + """ + def __str__(self): + """Pretty print dictionaries and first level nested dictionaries""" + str_ = "" + try: + longest_key = max([len(_) for _ in self.keys()]) + for key, val in self.items(): + str_ += f"{key:<{longest_key}}: {val}\n" + except ValueError: + pass + return str_ + + def __repr__(self): + """Pretty print when calling an instance of this object""" + return(self.__str__()) + + def __getattr__(self, key): + """Attribute-like access of the internal dictionary attributes""" + try: + return self[key] + except KeyError: + raise AttributeError(f"{key} not found in Dict") + + def __setattr__(self, key, val): + """Setting attributes can only be performed one time""" + self.__dict__[key] = val + + +class Null: + """ + A null object that always and reliably does nothing + """ + def __init__(self, *args, **kwargs): + pass + + def __call__(self, *args, **kwargs): + return self + + def __nonzero__(self): + return False + + def __getattr__(self, key): + return self + + def __setattr__(self, key, val): + return self + + def __delattr__(self, key): + return self + + +class SeisFlowsPathsParameters: + """ + A class used to simplify defining required or optional paths and parameters + by enforcing a specific structure to their entry into the environment. + + .. note:: + if a path or parameter is optional it requires a default value. + """ + default_par = "REQUIRED PARAMETER" + default_path = "REQUIRED PATH" + + def __init__(self, base=None): + """ + We simply store paths and parameters as nested dictioanries. Due to the + use of inheritance, the class can be passed to itself on initialization + which means paths and parameters can be adopted from base class + + :type base: seisflows.config.DefinePathsParameters + :param base: paths and parameters from abstract Base class that need to + be inherited by the current child class. + """ + self.parameters, self.paths = {}, {} + if base: + self.parameters.update(base.parameters) + self.paths.update(base.paths) + + def par(self, parameter, required, docstr, par_type, default=None): + """ + Add a parameter to the internal list of parameters + + :type parameter: str + :param parameter: name of the parameter + :type required: bool + :param required: whether or not the parameter is required. If it is not + required, then a default value should be given + :type docstr: str + :param docstr: Short explanatory doc string that defines what the + parameter is used for. + :type par_type: class or str + :param par_type: the parameter type, used for doc strings and also + parameter validation + :param default: default value for the parameter, can be any type + """ + if required: + default = self.default_par + if type(par_type) == type: + par_type = par_type.__name__ + self.parameters[parameter] = {"docstr": docstr, "required": required, + "default": default, "type": par_type} + + def path(self, path, required, docstr, default=None): + """ + Add a path to the internal list of paths + + :type path: str + :param path: name of the parameter + :type required: bool + :param required: whether or not the path is required. If it is not + required, then a default value should be given + :type docstr: str + :param docstr: Short explanatory doc string that defines what the + path is used for. + :type default: str + :param default: default value for the path + + """ + if required: + default = self.default_path + self.paths[path] = {"docstr": docstr, "required": required, + "default": default} + + def validate(self, paths=True, parameters=True): + """ + Set internal paths and parameter values into sys.modules. Should be + called by each modules check() function. + + Ensures that required paths and parameters are set by the User in the + parameter file and that default values are stored for any optional + paths and parameters which are not explicitely set. + + :type paths: bool + :param paths: validate the internal path values + :type parameters: bool + :param parameters: validate the internal parameter values + :raises ParameterError: if a required path or parameter is not set by + the user. + """ + if paths: + sys_path = sys.modules["seisflows_parameters"] + for key, attrs in self.paths.items(): + if attrs["required"] and (key not in sys_path): + raise KeyError(f"{sys_path} has no key {key}") + elif key not in sys_path: + setattr(sys_path, key, attrs["default"]) + + if parameters: + sys_par = sys.modules["seisflows_paths"] + for key, attrs in self.parameters.items(): + if attrs["required"] and (key not in sys_par): + raise ValueError(sys_par, key) + elif key not in sys_par: + setattr(sys_par, key, attrs["default"]) + diff --git a/seisflows/plugins/solver/__init__.py b/seisflows/plugins/solver/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/seisflows/plugins/solver/specfem2d.py b/seisflows/plugins/solver/specfem2d.py deleted file mode 100644 index ffb7b8aa..00000000 --- a/seisflows/plugins/solver/specfem2d.py +++ /dev/null @@ -1,133 +0,0 @@ -#!/usr/bin/env python3 -""" -Plugins used for the numerical solver SPECFEM2D -""" -import sys -from seisflows.tools import array, unix -from seisflows.tools.wrappers import exists, findpath -from seisflows.tools.specfem import getpar, setpar - - -def write_sources(coords, path='.', ws=1., suffix=''): - """ - Writes source information to text file - TODO this has to be adapted for new versions of specfem because the - source file format has changed - """ - sx, sy, sz = coords - - filename = findpath('seisflows.plugins') + '/' + 'solver/specfem2d/SOURCE' - with open(filename, 'r') as f: - lines = f.readlines() - - filename = 'DATA/SOURCE' + suffix - with open(filename, 'w') as f: - f.writelines(lines) - - # adjust source coordinates - setpar('xs', sx, filename) - setpar('zs', sy, filename) - # setpar('ts', ts[0], filename) - - # adjust source amplitude - try: - fs = float(getpar('factor', filename)) - fs *= ws - setpar('factor', str(fs), filename) - except: - pass - - # adjust source wavelet - if 1: - # Ricker wavelet - setpar('time_function_type', 1, filename) - elif 0: - # first derivative of Gaussian - setpar('time_function_type', 2, filename) - elif 0: - # Gaussian - setpar('time_function_type', 3, filename) - elif 0: - # Dirac - setpar('time_function_type', 4, filename) - elif 0: - # Heaviside - setpar('time_function_type', 5, filename) - - # setpar('f0', par['F0'], filename) - - -def write_receivers(coords, path='.'): - """ Writes receiver information to text file - """ - rx, ry, rz = coords - nr = len(coords[0]) - - filename = path + '/' + 'DATA/STATIONS' - - lines = [] - for ir in range(nr): - line = '' - line += 'S%06d' % ir + ' ' - line += 'AA' + ' ' - line += '%11.5e' % rx[ir] + ' ' - line += '%11.5e' % ry[ir] + ' ' - line += '%3.1f' % 0. + ' ' - line += '%3.1f' % 0. + '\n' - lines.extend(line) - - with open(filename, 'w') as f: - f.writelines(lines) - - -def smooth_legacy(input_path='', output_path='', parameters=[], span=0.): - """ - - :param input_path: - :param output_path: - :param parameters: - :param span: - :return: - """ - solver = sys.modules['seisflows_solver'] - PATH = sys.modules['seisflows_paths'] - - if not exists(input_path): - raise Exception - - if not exists(output_path): - unix.mkdir(output_path) - - if solver.mesh_properties.nproc != 1: - raise NotImplementedError - - # intialize arrays - kernels = {} - for key in parameters or solver.parameters: - kernels[key] = [] - - coords = {} - for key in ['x', 'z']: - coords[key] = [] - - # read kernels - for key in parameters or solver.parameters: - kernels[key] += solver.io.read_slice(input_path, key+'_kernel', 0) - - if not span: - return kernels - - # read coordinates - for key in ['x', 'z']: - coords[key] += solver.io.read_slice(PATH.MODEL_INIT, key, 0) - - mesh = array.stack(coords['x'][0], coords['z'][0]) - - # apply smoother - for key in parameters or solver.parameters: - kernels[key] = [array.meshsmooth(kernels[key][0], mesh, span)] - - # write smooth kernels - for key in parameters or solver.parameters: - solver.io.write_slice(kernels[key][0], output_path, key+'_kernel', - 0) diff --git a/seisflows/plugins/solver/specfem3d.py b/seisflows/plugins/solver/specfem3d.py deleted file mode 100644 index 04ba7496..00000000 --- a/seisflows/plugins/solver/specfem3d.py +++ /dev/null @@ -1,63 +0,0 @@ -#!/usr/bin/env python3 -""" -Plugins used for the numerical solver SPECFEM3D_CARTESIAN -""" -import os -from seisflows.tools.wrappers import findpath -from seisflows.tools.specfem import setpar - - -def write_sources(par, h, path="."): - """ - Writes FORCESOLUTION source information to text file - - :type par: dict - :param par: seisflows.PAR - :type h: - :param h: - :type path: str - :param path: path to write sources to - """ - file = os.path.join(findpath("seisflows.plugins"), "specfem3d", - "FORCESOLUTION") - with open(file, "r") as f: - lines = f.readlines() - - file = "DATA/FORCESOURCE" - with open(file, "w") as f: - f.writelines(lines) - - # adjust coordinates - setpar("xs", h.sx[0], file) - setpar("zs", h.sz[0], file) - setpar("ts", h.ts, file) - - # adjust wavelet - setpar("f0", par["F0"], file) - - -def write_receivers(h): - """ - Writes receiver information to text file - - :type h: - :param h: - """ - file = "DATA/STATIONS" - lines = [] - - # loop over receivers - for ir in range(h.nr): - line = "" - line += "S%06d" % ir + " " - line += "AA" + " " - line += "%11.5e" % h.rx[ir] + " " - line += "%11.5e" % h.rz[ir] + " " - line += "%3.1f" % 0. + " " - line += "%3.1f" % 0. + "\n" - lines.extend(line) - - with open(file, "w") as f: - f.writelines(lines) - - diff --git a/seisflows/plugins/solver/specfem3d_globe.py b/seisflows/plugins/solver/specfem3d_globe.py deleted file mode 100644 index 805cc113..00000000 --- a/seisflows/plugins/solver/specfem3d_globe.py +++ /dev/null @@ -1,55 +0,0 @@ -#!/usr/bin/env python3 -""" -Plugins used for the numerical solver SPECFEM3D_GLOBE -""" - -# Local imports -from seisflows.tools.wrappers import findpath -from seisflows.tools.specfem import setpar - - -def write_sources(PAR, h, path='.'): - """ Writes source information to text file - """ - filename = findpath('seisflows.plugins') + '/' + 'specfem3d/SOURCE' - with open(filename, 'r') as f: - lines = f.readlines() - - filename = 'DATA/SOURCE' - with open(filename, 'w') as f: - f.writelines(lines) - - # adjust coordinates - setpar('xs', h.sx[0], filename) - setpar('zs', h.sz[0], filename) - setpar('ts', h.ts, filename) - - # adjust wavelet - setpar('f0', PAR['F0'], filename) - - -def write_receivers(h): - """ Writes receiver information to text file - """ - filename = 'DATA/STATIONS' - lines = [] - - # loop over receivers - for ir in range(h.nr): - line = '' - line += 'S%06d' % ir + ' ' - line += 'AA' + ' ' - line += '%11.5e' % h.rx[ir] + ' ' - line += '%11.5e' % h.rz[ir] + ' ' - line += '%3.1f' % 0. + ' ' - line += '%3.1f' % 0. + '\n' - lines.extend(line) - - with open(filename, 'w') as f: - f.writelines(lines) - - -def write_parameters(par, version): - """ Writes parameters to text file - """ - raise NotImplementedError diff --git a/seisflows/postprocess/base.py b/seisflows/postprocess/default.py similarity index 68% rename from seisflows/postprocess/base.py rename to seisflows/postprocess/default.py index 1bf03469..4b5c1060 100644 --- a/seisflows/postprocess/base.py +++ b/seisflows/postprocess/default.py @@ -6,79 +6,70 @@ """ import os import sys -import logging +from seisflows.core import Base from seisflows.tools import msg -from seisflows.config import SeisFlowsPathsParameters -PAR = sys.modules['seisflows_parameters'] -PATH = sys.modules['seisflows_paths'] -system = sys.modules['seisflows_system'] -solver = sys.modules['seisflows_solver'] - - -class Base: +class Default(Base): """ Postprocessing in a Seisflows workflow includes tasks such as regularization, smoothing, sharpening, masking and related operations on models or gradients """ - # Class-specific logger accessed using self.logger - logger = logging.getLogger(__name__).getChild(__qualname__) - def __init__(self): """ These parameters should not be set by __init__! Attributes are just initialized as NoneTypes for clarity and docstrings """ - pass - - @property - def required(self): - """ - A hard definition of paths and parameters required by this class, - alongside their necessity for the class and their string explanations. - """ - sf = SeisFlowsPathsParameters() - - # Define the Parameters required by this module - sf.par("SMOOTH_H", required=False, default=0., par_type=float, - docstr="Gaussian half-width for horizontal smoothing in units " - "of meters. If 0., no smoothing applied") - - sf.par("SMOOTH_V", required=False, default=0., par_type=float, - docstr="Gaussian half-width for vertical smoothing in units " - "of meters") - - sf.par("TASKTIME_SMOOTH", required=False, default=1, par_type=int, - docstr="Large radii smoothing may take longer than normal " - "tasks. Allocate additional smoothing task time " - "as a multiple of TASKTIME") + super().__init__() + + self.required.par( + "SMOOTH_H", required=False, default=0., par_type=float, + docstr="Gaussian half-width for horizontal smoothing in units of " + "meters. If 0., no smoothing applied" + ) + + self.required.par( + "SMOOTH_V", required=False, default=0., par_type=float, + docstr="Gaussian half-width for vertical smoothing in units of " + "meters" + ) + + self.required.par( + "TASKTIME_SMOOTH", required=False, default=1, par_type=int, + docstr="Large radii smoothing may take longer than normal tasks. " + "Allocate additional smoothing task time as a multiple of " + "TASKTIME" + ) # Define the Paths required by this module - sf.path("MASK", required=False, - docstr="Directory to mask files for gradient masking") - - return sf + self.required.path( + "MASK", required=False, docstr="Directory to mask files for " + "gradient masking" + ) def check(self, validate=True): """ Checks parameters and paths """ - if validate: - self.required.validate() + super().check(validate=validate) - if PATH.MASK: - assert os.path.exists(PATH.MASK), \ + if self.path.MASK: + assert os.path.exists(self.path.MASK), \ f"PATH.MASK provided but does not exist" def setup(self): """ - A placeholder function for initialization or setup tasks. Base - postprocessing does not require any setup + Setup tasks """ - pass + super().setup() + + def finalize(self): + """ + Finalization tasks + """ + super().finalize() def write_gradient(self, path): """ @@ -94,6 +85,9 @@ def write_gradient(self, path): :param path: directory from which kernels are read and to which gradient is written """ + system = self.module("system") + solver = self.module("solver") + if not os.path.exists(path): print(msg.cli("Gradient path does in postprocess.write_gradient " "does not exist but should", @@ -109,7 +103,7 @@ def write_gradient(self, path): # Run postprocessing as job on system as it's computationally intensive self.logger.info("processing kernels into gradient on system...") - system.run("postprocess", "process_kernels", single=True, + system.run("postprocess", "_process_kernels", single=True, path=path_kernels, logger=self.logger) # Access the gradient information stored in the kernel summation @@ -122,11 +116,11 @@ def write_gradient(self, path): # log dm --> dm (see Eq.13 Tromp et al 2005) gradient *= solver.merge(solver.load(path_model)) - if PATH.MASK: + if self.path.MASK: self.logger.info(f"masking gradient") # to scale the gradient, users can supply "masks" by exactly # mimicking the file format in which models are stored - mask = solver.merge(solver.load(PATH.MASK)) + mask = solver.merge(solver.load(self.path.MASK)) # While both masking and preconditioning involve scaling the # gradient, they are fundamentally different operations: @@ -141,8 +135,7 @@ def write_gradient(self, path): solver.save(solver.split(gradient), path=path_grad, suffix="_kernel") - @staticmethod - def process_kernels(path, logger): + def _process_kernels(self, path, logger): """ Sums kernels from individual sources, with optional smoothing @@ -157,6 +150,8 @@ def process_kernels(path, logger): :param logger: Class-specific logging module, log statements pushed from this logger will be tagged by its specific module/classname """ + solver = self.module("solver") + if not os.path.exists(path): print(msg.cli("Gradient path in postprocess.process_kernels " "does not exist but should", @@ -167,16 +162,16 @@ def process_kernels(path, logger): path_sum_nosmooth = os.path.join(path, "sum_nosmooth") path_sum = os.path.join(path, "sum") - if (PAR.SMOOTH_H > 0) or (PAR.SMOOTH_V > 0): - logger.debug(f"saving unsmoothed and summed kernels to:\n" + if (self.par.SMOOTH_H > 0) or (self.par.SMOOTH_V > 0): + logger.debug(f"saving un-smoothed and summed kernels to:\n" f"{path_sum_nosmooth}") solver.combine(input_path=path, output_path=path_sum_nosmooth) - logger.info(f"smoothing gradient: H={PAR.SMOOTH_H}m, " - f"V={PAR.SMOOTH_V}m") + logger.info(f"smoothing gradient: H={self.par.SMOOTH_H}m, " + f"V={self.par.SMOOTH_V}m") logger.debug(f"saving smoothed kernels to:\n{path_sum}") solver.smooth(input_path=path_sum_nosmooth, output_path=path_sum, - span_h=PAR.SMOOTH_H, span_v=PAR.SMOOTH_V) + span_h=self.par.SMOOTH_H, span_v=self.par.SMOOTH_V) # Combine all the input kernels, generating the unscaled gradient else: diff --git a/seisflows/preprocess/base.py b/seisflows/preprocess/base.py index 2e6b603c..41a2dacb 100644 --- a/seisflows/preprocess/base.py +++ b/seisflows/preprocess/base.py @@ -20,7 +20,7 @@ PATH = sys.modules["seisflows_paths"] -class Base: +class Default: """ Default SeisFlows preprocessing class diff --git a/seisflows/solver/specfem3d_globe.py b/seisflows/solver/specfem3d_globe.py index ca35a90a..66f7dd5c 100644 --- a/seisflows/solver/specfem3d_globe.py +++ b/seisflows/solver/specfem3d_globe.py @@ -10,9 +10,7 @@ import logging from glob import glob -import seisflows.plugins.solver.specfem3d_globe as solvertools -from seisflows.tools.specfem import Minmax # Model, Minmax, -# from seisflows.plugins.io import loadbypar, copybin, loadbin, savebin +from seisflows.tools.specfem import Minmax from seisflows.tools import unix, msg from seisflows.tools.wrappers import Struct, exists from seisflows.config import custom_import, SeisFlowsPathsParameters @@ -29,7 +27,6 @@ class Specfem3DGlobe(custom_import("solver", "specfem3d")): !!! See base class for method descriptions !!! """ - # Class-specific logger accessed using self.logger logger = logging.getLogger(__name__).getChild(__qualname__) def __init__(self): diff --git a/seisflows/templates/parameters.yaml b/seisflows/templates/parameters.yaml index ed38b460..f32a3cb2 100644 --- a/seisflows/templates/parameters.yaml +++ b/seisflows/templates/parameters.yaml @@ -28,5 +28,5 @@ WORKFLOW: inversion SOLVER: specfem2d SYSTEM: workstation OPTIMIZE: gradient -PREPROCESS: base -POSTPROCESS: base +PREPROCESS: default +POSTPROCESS: default diff --git a/seisflows/tests/test_config.py b/seisflows/tests/test_config.py index b3eb7d60..9fe53aae 100644 --- a/seisflows/tests/test_config.py +++ b/seisflows/tests/test_config.py @@ -9,7 +9,6 @@ from unittest.mock import patch from seisflows import config from seisflows.seisflows import SeisFlows -from seisflows.tools.err import ParameterError TEST_DIR = os.path.join(config.ROOT_DIR, "tests") @@ -115,7 +114,7 @@ def test_seisflows_paths_parameters(sfinit): # These parameters are not defined and are expected to throw parameter error sfpp.path("UNDEFINED", required=True, docstr="This path is not in the test parameter file") - with pytest.raises(ParameterError): + with pytest.raises(ValueError): sfpp.validate() diff --git a/seisflows/tools/err.py b/seisflows/tools/err.py deleted file mode 100644 index 2e984051..00000000 --- a/seisflows/tools/err.py +++ /dev/null @@ -1,37 +0,0 @@ -#!/usr/bin/env python3 -""" -Custom errors for Seisflows -""" - - -class ParameterError(ValueError): - """ - A new ValueError class which explains the Parameter's that threw the error - """ - def __init__(self, *args): - if len(args) == 0: - msg = "Bad parameter." - super(ParameterError, self).__init__(msg) - elif len(args) == 1: - msg = f"Bad parameter: {args[0]}" - super(ParameterError, self).__init__(msg) - elif args[1] not in args[0]: - msg = f"{args[1]} is not defined." - super(ParameterError, self).__init__(msg) - elif key in obj: - msg = f"{args[0]} has bad value: {args[1].__getattr__(args[0])}" - super(ParameterError, self).__init__(msg) - -class CheckError(ValueError): - """ - An error called by the Check functions within each module, that returns the - name of the class that raised the error, as well as the parameter in - question. - """ - def __init__(self, cls, par): - """ - CheckError simply returns a print message - """ - msg = f"{cls.__class__.__name__} requires parameter {par}" - super(CheckError, self).__init__(msg) - diff --git a/seisflows/workflow/test.py b/seisflows/workflow/test.py index ec761720..b838406c 100644 --- a/seisflows/workflow/test.py +++ b/seisflows/workflow/test.py @@ -159,6 +159,8 @@ def test_optimize(self): """ Test optimization module with a simple Rosenbrock function """ + PAR.log_level = "CRITICAL" + m_new, m_true, objective_function, gradient = rosenbrock() optimize.setup(m_new=m_new) From 156f820b3ae5dda503bbdcbc49ccf213c312d275 Mon Sep 17 00:00:00 2001 From: bch0w Date: Fri, 24 Jun 2022 12:34:46 -0800 Subject: [PATCH 024/195] added some additional comments to the Base class to explain how things work --- seisflows/core.py | 35 +++++++++++++++++++++++++---------- 1 file changed, 25 insertions(+), 10 deletions(-) diff --git a/seisflows/core.py b/seisflows/core.py index 2f5057a7..53cdb92c 100644 --- a/seisflows/core.py +++ b/seisflows/core.py @@ -13,20 +13,22 @@ class Base(object): inherit from the Base object to work properly. This Base class essentially dictates the required structure of a SeisFlows class. """ - # Class-specific logger accessed using self.logger. We instantiate loggers - # like this because then inheritance information gets imprinted into the - # logger, making it easier to debug functions which may be multiply - # inherited - def __init__(self): """ - Sets the required paths and parameters + SeisFlows instantiates its required parameters through the + SeisFlowsPathsParameters class, which scaffolds a rigid framework of + how parameters and paths should be defined by the program. This is + then used to build the parameter file dynamically. """ self.required = SeisFlowsPathsParameters() def module(self, name): """ Access globally stored SeisFlows modules located in sys.modules + + :rtype: Class or Dict or None + :return: Returns a SeisFlows module or Dictionary containing paths or + parameters. Else None if the chosen module has not been instantiated """ try: mod = sys.modules[f"seisflows_{name}"] @@ -48,14 +50,24 @@ def logger(self): @property def par(self): """ - Access SeisFlows parameters from sys.modules + Quick access SeisFlows parameters from sys.modules. Throws a warning + if parameters have not been instantiated + + :rtype: Dict or None + :return: Returns a Dictionary with instantiated parameters, or None if + parameters have not been instantiated """ return self.module("parameters") @property def path(self): """ - Access SeisFlows paths from sys.modules + Quick access SeisFlows paths from sys.modules. Throws a warning + if paths have not been instantiated + + :rtype: Dict or None + :return: Returns a Dictionary with instantiated paths, or None if + parameters have not been instantiated """ return self.module("paths") @@ -67,17 +79,20 @@ def check(self, validate=True): if validate: self.required.validate() + # Example of a check statement + # assert(self.par.PARAMETER == example_value), f"Parameter != example" + def setup(self): """ A placeholder function for any initialization or setup tasks that - need to be run once at the beginning of any workflow + need to be run once at the beginning of any workflow. """ return def finalize(self): """ A placeholder function for any finalization or tear-down tasks that - need to be run at the end of any iteration or workflow + need to be run at the end of any iteration or workflow. """ return From de08f5315dfd5a3d33577fe7834051903fd789a4 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Mon, 27 Jun 2022 12:41:15 -0800 Subject: [PATCH 025/195] getting rid of PAR and PATH indirection in config which were just variables housing strings for seisflows_parameters and seisflows_paths --- seisflows/config.py | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/seisflows/config.py b/seisflows/config.py index deeddbaf..9f9c5255 100644 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -42,10 +42,6 @@ NAMES = ["system", "preprocess", "solver", "postprocess", "optimize", "workflow"] -# These define the sys.modules names where parameter values and paths are stored -PAR = "seisflows_parameters" -PATH = "seisflows_paths" - # The location of this config file, which is the main repository ROOT_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__))) @@ -78,7 +74,7 @@ def save(path): unix.mkdir(path) # Save the paths and parameters into a JSON file - for name in [PAR, PATH]: + for name in ["seisflows_parameters", "seisflows_paths"]: fullfile = os.path.join(path, f"{name}.json") with open(fullfile, "w") as f: json.dump(sys.modules[name], f, sort_keys=True, indent=4) @@ -99,7 +95,7 @@ def load(path): :param path: path to the previously saved session """ # Load parameters and paths from a JSON file - for name in [PAR, PATH]: + for name in ["seisflows_parameters", "seisflows_paths"]: fullfile = os.path.join(os.path.abspath(path), f"{name}.json") with open(fullfile, "r") as f: sys.modules[name] = Dict(json.load(f)) @@ -223,7 +219,7 @@ class 'Inversion'. # Attempt to retrieve currently assigned classname from parameters if module is None: try: - module = sys.modules[PAR][name.upper()] + module = sys.modules["seisflows_parameters"][name.upper()] except KeyError: return Null # If this still returns nothing, then no module has been assigned From 0294257a76675ccb33617874e0613ae3180f6c02 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Mon, 27 Jun 2022 13:31:20 -0800 Subject: [PATCH 026/195] updatiedclass structure for optimization module, key points are that: 1) not explicitely importing opther modules with sys modules during import, but are doing this at run time with the self.par attribute required paths and parameters are defined in __init__ and the required() attribute is now removed. We explicitely call inherited functions, even if they are just calling the underlying functions. This will make it easier for new users to track down where functions are coming from. Child classes which define new methods will make these methods private (leading underscore) so as to not confuse them with inherited or overwritten methods of the base class also going back to the old method of explicitely stating things (e.g., naming 'scratch' instead of hiding it behind PATH.SCRATCH). This leads to repeated strings, but makes it easier to follow structure --- seisflows/optimize/LBFGS.py | 119 +++++------ seisflows/optimize/NLCG.py | 76 +++---- seisflows/optimize/gradient.py | 196 +++++++++---------- seisflows/preprocess/{base.py => default.py} | 4 +- seisflows/seisflows.py | 6 +- seisflows/solver/base.py | 4 +- seisflows/solver/specfem2d.py | 3 +- seisflows/system/base.py | 3 +- seisflows/system/workstation.py | 5 +- seisflows/templates/base_class.py | 92 --------- seisflows/templates/sub_class.py | 77 -------- seisflows/workflow/base.py | 3 +- seisflows/workflow/inversion.py | 9 +- seisflows/workflow/test.py | 5 +- 14 files changed, 215 insertions(+), 387 deletions(-) rename seisflows/preprocess/{base.py => default.py} (99%) delete mode 100644 seisflows/templates/base_class.py delete mode 100644 seisflows/templates/sub_class.py diff --git a/seisflows/optimize/LBFGS.py b/seisflows/optimize/LBFGS.py index 10a9407c..b6176bb8 100644 --- a/seisflows/optimize/LBFGS.py +++ b/seisflows/optimize/LBFGS.py @@ -4,20 +4,16 @@ It supercedes the `seisflows.optimize.base` class """ import os -import sys import logging import numpy as np +from seisflows.optimize.gradient import Gradient from seisflows.tools import unix from seisflows.tools.msg import DEG from seisflows.tools.math import angle -from seisflows.config import custom_import, SeisFlowsPathsParameters -PAR = sys.modules["seisflows_parameters"] -PATH = sys.modules["seisflows_paths"] - -class LBFGS(custom_import("optimize", "gradient")): +class LBFGS(Gradient): """ The Limited memory BFGS algorithm Calls upon seisflows.plugin.optimize.LBFGS to accomplish LBFGS algorithm @@ -50,9 +46,6 @@ class LBFGS(custom_import("optimize", "gradient")): status == 0 : not finished status < 0 : failed """ - # Class-specific logger accessed using self.logger - logger = logging.getLogger(__name__).getChild(__qualname__) - def __init__(self): """ These parameters should not be set by the user. @@ -81,46 +74,41 @@ def __init__(self): """ super().__init__() + # Define the Parameters required by this module + self.required.par( + "LINESEARCH", required=False, default="Backtrack", par_type=str, + docstr="Algorithm to use for line search, see " + "seisflows.plugins.line_search for available choices" + ) + + self.required.par( + "LBFGSMEM", required=False, default=3, par_type=int, + docstr="Max number of previous gradients to retain in local memory" + ) + + self.required.par( + "LBFGSMAX", required=False, par_type=int, default="inf", + docstr="LBFGS periodic restart interval, between 1 and 'inf'" + ) + + self.required.par( + "LBFGSTHRESH", required=False, default=0., par_type=float, + docstr="LBFGS angle restart threshold" + ) + self.LBFGS_iter = 0 self.memory_used = 0 self.LBFGS_dir = "LBFGS" self.y_file = os.path.join(self.LBFGS_dir, "Y") self.s_file = os.path.join(self.LBFGS_dir, "S") - @property - def required(self): - """ - A hard definition of paths and parameters required by this class, - alongside their necessity for the class and their string explanations. - """ - sf = SeisFlowsPathsParameters(super().required) - - # Define the Parameters required by this module - sf.par("LINESEARCH", required=False, default="Backtrack", par_type=str, - docstr="Algorithm to use for line search, see " - "seisflows.plugins.line_search for available choices") - - sf.par("LBFGSMEM", required=False, default=3, par_type=int, - docstr="Max number of previous gradients to retain " - "in local memory") - - sf.par("LBFGSMAX", required=False, par_type=int, default="inf", - docstr="LBFGS periodic restart interval, between 1 and 'inf'") - - sf.par("LBFGSTHRESH", required=False, default=0., par_type=float, - docstr="LBFGS angle restart threshold") - - return sf - def check(self, validate=True): """ Checks parameters, paths, and dependencies """ - super().check(validate=False) - if validate: - self.required.validate() + super().check(validate=validate) - assert(PAR.LINESEARCH.upper() == "BACKTRACK"), \ + assert(self.par.LINESEARCH.upper() == "BACKTRACK"), \ "LBFGS requires a Backtracking line search" def setup(self): @@ -130,9 +118,21 @@ def setup(self): super().setup() # Create a separate directory for LBFGS matters - unix.cd(PATH.OPTIMIZE) + unix.cd(self.path.OPTIMIZE) unix.mkdir(self.LBFGS_dir) + def finalize(self): + """Inherit from optimize.gradient.Gradient""" + self.finalize() + + def load(self, name): + """Inherit from optimize.gradient.Gradient""" + return self.load(name=name) + + def save(self, name, vector): + """Inherit from optimize.gradient.Gradient""" + self.save(name=name, vector=vector) + def compute_direction(self): """ Call on the L-BFGS optimization machinery to compute a search @@ -152,7 +152,7 @@ def compute_direction(self): self.logger.info(f"computing search direction with L-BFGS") self.LBFGS_iter += 1 - unix.cd(PATH.OPTIMIZE) + unix.cd(self.path.OPTIMIZE) # Load the current gradient direction, which is the L-BFGS search # direction if this is the first iteration @@ -164,7 +164,7 @@ def compute_direction(self): restarted = 0 # Restart condition or first iteration lead to setting search direction # as the inverse gradient (i.e., default to steepest descent) - elif self.LBFGS_iter > PAR.LBFGSMAX: + elif self.LBFGS_iter > self.par.LBFGSMAX: self.logger.info("restarting L-BFGS due to periodic restart " "condition. setting search direction as" "inverse gradient") @@ -176,12 +176,12 @@ def compute_direction(self): # Update the search direction, apply the inverse Hessian such that # 'q' becomes the new search direction 'g' self.logger.info("applying inverse Hessian to gradient") - s, y = self.update() - q = self.apply(g, s, y) + s, y = self._update() + q = self._apply(g, s, y) # Determine if the new search direction is appropriate by checking # its angle to the previous search direction - if self.check_status(g, q): + if self._check_status(g, q): self.logger.info("new L-BFGS search direction found") p_new = -q restarted = 0 @@ -198,8 +198,7 @@ def compute_direction(self): def restart(self): """ - On top of base restart class, include a restart of the LBFGS internal - memory and memmaps + Overwrite the optimization restart to restart the L-BFGS schema """ super().restart() @@ -208,13 +207,21 @@ def restart(self): self.LBFGS_iter = 1 self.memory_used = 0 - unix.cd(PATH.OPTIMIZE) + unix.cd(self.path.OPTIMIZE) s = np.memmap(filename=self.s_file, mode="r+") y = np.memmap(filename=self.y_file, mode="r+") s[:] = 0. y[:] = 0. - def update(self): + def write_stats(self): + """Inherit from optimize.gradient.Gradient""" + self.write_stats() + + def check_model(self, m, min_pr=-1, max_pr=0.5): + """Inherit from optimize.gradient.Gradient""" + self.check_model(m=m, min_pr=min_pr, max_pr=max_pr) + + def _update(self): """ Updates L-BFGS algorithm history @@ -233,7 +240,7 @@ def update(self): :rtype y: np.memmap :return y: memory of the gradient differences `g_new - g_old` """ - unix.cd(PATH.OPTIMIZE) + unix.cd(self.path.OPTIMIZE) # Determine the iterates for model m and gradient g s_k = self.load("m_new") - self.load("m_old") @@ -241,7 +248,7 @@ def update(self): # Determine the shape of the memory map (length of model, length of mem) m = len(s_k) - n = PAR.LBFGSMEM + n = self.par.LBFGSMEM # Initial iteration, need to create the memory map if self.memory_used == 0: @@ -267,12 +274,12 @@ def update(self): y[:, 0] = y_k # Keep track of the memory used - if self.memory_used < PAR.LBFGSMEM: + if self.memory_used < self.par.LBFGSMEM: self.memory_used += 1 return s, y - def apply(self, q, s=None, y=None): + def _apply(self, q, s=None, y=None): """ Applies L-BFGS inverse Hessian to given vector @@ -286,12 +293,12 @@ def apply(self, q, s=None, y=None): :rtype r: np.array :return r: new search direction from application of L-BFGS """ - unix.cd(PATH.OPTIMIZE) + unix.cd(self.path.OPTIMIZE) # If no memmaps are given as arguments, instantiate them if s is None or y is None: m = len(q) - n = PAR.LBFGSMEM + n = self.par.LBFGSMEM s = np.memmap(filename=self.s_file, mode="w+", dtype="float32", shape=(m, n)) y = np.memmap(filename=self.y_file, mode="w+", dtype="float32", @@ -326,7 +333,7 @@ def apply(self, q, s=None, y=None): return r - def check_status(self, g, r): + def _check_status(self, g, r): """ Check the status of the apply() function, determine if restart necessary Return of False means restart, return of True means good to go. @@ -344,7 +351,7 @@ def check_status(self, g, r): if not 0. < theta < 90.: self.logger.info("restarting L-BFGS, theta not a descent direction") okay = False - elif theta > 90. - PAR.LBFGSTHRESH: + elif theta > 90. - self.par.LBFGSTHRESH: self.logger.info("restarting L-BFGS due to practical safeguard") okay = False else: diff --git a/seisflows/optimize/NLCG.py b/seisflows/optimize/NLCG.py index f05152da..dec2cda7 100644 --- a/seisflows/optimize/NLCG.py +++ b/seisflows/optimize/NLCG.py @@ -1,21 +1,14 @@ #!/usr/bin/env python3 """ This is the custom class for an NLCG optimization schema. -It supercedes the `seisflows.optimize.base` class +It inherits from the `seisflows.optimize.gradient.Gradient` class """ -import sys -import logging -import numpy as np - -from seisflows.config import custom_import, SeisFlowsPathsParameters +from seisflows.optimize.gradient import Gradient from seisflows.tools import unix from seisflows.tools.math import dot -PAR = sys.modules['seisflows_parameters'] -PATH = sys.modules['seisflows_paths'] - -class NLCG(custom_import("optimize", "gradient")): +class NLCG(Gradient): """ Nonlinear conjugate gradient method @@ -38,9 +31,6 @@ class NLCG(custom_import("optimize", "gradient")): status == 0 : not finished status < 0 : failed """ - # Class-specific logger accessed using self.logger - logger = logging.getLogger(__name__).getChild(__qualname__) - def __init__(self): """ These parameters should not be set by the user. @@ -51,36 +41,42 @@ def __init__(self): optimization iter. Keeps track of internal NLCG memory. """ super().__init__() + + self.required.par( + "NLCGMAX", required=False, default="null", par_type=float, + docstr="NLCG periodic restart interval, between 1 and inf" + ) + self.required.par( + "NLCGTHRESH", required=False, default="null", par_type=float, + docstr="NLCG conjugacy restart threshold, between 1 and inf" + ) self.NLCG_iter = 0 self.calc_beta = pollak_ribere # !!! Allow the user to choose this fx? - @property - def required(self): + def check(self, validate=True): """ - A hard definition of paths and parameters required by this class, - alongside their necessity for the class and their string explanations. + Checks parameters, paths, and dependencies """ - sf = SeisFlowsPathsParameters(super().required) + super().check(validate=validate) - # Define the Parameters required by this module - sf.par("NLCGMAX", required=False, default="null", par_type=float, - docstr="NLCG periodic restart interval, between 1 and inf") + assert(self.par.LINESEARCH.upper() == "BRACKET"), \ + f"NLCG requires a bracketing line search algorithm" - sf.par("NLCGTHRESH", required=False, default="null", par_type=float, - docstr="NLCG conjugacy restart threshold, between 1 and inf") + def setup(self): + """Inherit from optimize.gradient.Gradient""" + self.setup() - return sf + def finalize(self): + """Inherit from optimize.gradient.Gradient""" + self.finalize() - def check(self, validate=True): - """ - Checks parameters, paths, and dependencies - """ - if validate: - self.required.validate() - super().check(validate=False) + def load(self, name): + """Inherit from optimize.gradient.Gradient""" + return self.load(name=name) - assert(PAR.LINESEARCH.upper() == "BRACKET"), \ - f"NLCG requires a bracketing line search algorithm" + def save(self, name, vector): + """Inherit from optimize.gradient.Gradient""" + self.save(name=name, vector=vector) def compute_direction(self): """ @@ -102,7 +98,7 @@ def compute_direction(self): self.logger.debug(f"computing search direction with NLCG") self.NLCG_iter += 1 - unix.cd(PATH.OPTIMIZE) + unix.cd(self.path.OPTIMIZE) # Load the current gradient direction g_new = self.load("g_new") @@ -115,7 +111,7 @@ def compute_direction(self): restarted = 0 # CASE 2: Force restart if the iterations have surpassed the maximum # number of allowable iter - elif self.NLCG_iter > PAR.NLCGMAX: + elif self.NLCG_iter > self.par.NLCGMAX: self.logger.info("restarting NLCG due to periodic restart " "condition. setting search direction as inverse " "gradient") @@ -137,7 +133,7 @@ def compute_direction(self): p_new = -g_new + beta * p_old # Check restart conditions, return search direction and status - if check_conjugacy(g_new, g_old) > PAR.NLCGTHRESH: + if check_conjugacy(g_new, g_old) > self.par.NLCGTHRESH: self.logger.info("restarting NLCG due to loss of conjugacy") self.restart() p_new = -g_new @@ -162,6 +158,14 @@ def restart(self): super().restart() self.NLCG_iter = 1 + def write_stats(self): + """Inherit from optimize.gradient.Gradient""" + self.write_stats() + + def check_model(self, m, min_pr=-1, max_pr=0.5): + """Inherit from optimize.gradient.Gradient""" + self.check_model(m=m, min_pr=min_pr, max_pr=max_pr) + def fletcher_reeves(g_new, g_old, precond=lambda x: x): """ diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index 7ad4c4c4..ce860415 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -11,22 +11,16 @@ By default the base class implements a steepest descent optimization """ import os -import sys -import logging import numpy as np +from seisflows.core import Base from seisflows.tools import msg, unix from seisflows.tools.math import angle, dot from seisflows.plugins import line_search, preconds from seisflows.tools.math import poissons_ratio -from seisflows.config import SeisFlowsPathsParameters -PAR = sys.modules["seisflows_parameters"] -PATH = sys.modules["seisflows_paths"] -solver = sys.modules["seisflows_solver"] - -class Gradient: +class Gradient(Base): """ Nonlinear optimization abstract base class poviding a gradient/steepest descent optimization algorithm. @@ -51,9 +45,6 @@ class Gradient: algorithm. """ - # Class-specific logger accessed using self.logger - logger = logging.getLogger(__name__).getChild(__qualname__) - def __init__(self): """ Initialize internally used variables for optimization workflow @@ -70,6 +61,41 @@ def __init__(self): :param restarted: a flag signalling if the optimization algorithm has been restarted recently """ + super().__init__() + + # Define the Parameters required by this module + self.required.par( + "LINESEARCH", required=False, default="Bracket", par_type=str, + docstr="Algorithm to use for line search, see " + "seisflows.plugins.line_search for available choices" + ) + self.required.par( + "PRECOND", required=False, par_type=str, + docstr="Algorithm to use for preconditioning gradients, see " + "seisflows.plugins.preconds for available choices" + ) + self.required.par( + "STEPCOUNTMAX", required=False, default=10, par_type=int, + docstr="Max number of trial steps in line search before a " + "change in line search behavior" + ) + self.required.par( + "STEPLENINIT", required=False, default=0.05, par_type=float, + docstr="Initial line search step length, as a fraction of current " + "model parameters" + ) + self.required.par( + "STEPLENMAX", required=False, default=0.5, par_type=float, + docstr="Max allowable step length, as a fraction of current model " + "parameters" + ) + self.required.path( + "OPTIMIZE", required=False, + default=os.path.join(self.path.WORKDIR, "scratch", "optimize"), + docstr="scratch path for nonlinear optimization data" + ) + + # Internally used parameters self.iter = 1 self.line_search = None self.precond = None @@ -79,103 +105,59 @@ def __init__(self): "p_new", "p_old", "alpha", "f_new", "f_old", "f_try"] - @property - def required(self): - """ - A hard definition of paths and parameters required by this class, - alongside their necessity for the class and their string explanations. - """ - sf = SeisFlowsPathsParameters() - - # Define the Parameters required by this module - sf.par("LINESEARCH", required=False, default="Bracket", par_type=str, - docstr="Algorithm to use for line search, see " - "seisflows.plugins.line_search for available choices") - - sf.par("PRECOND", required=False, par_type=str, - docstr="Algorithm to use for preconditioning gradients, see " - "seisflows.plugins.preconds for available choices") - - sf.par("STEPCOUNTMAX", required=False, default=10, par_type=int, - docstr="Max number of trial steps in line search before a " - "change in line search behavior") - - sf.par("STEPLENINIT", required=False, default=0.05, par_type=float, - docstr="Initial line search step length, as a fraction " - "of current model parameters") - - sf.par("STEPLENMAX", required=False, default=0.5, par_type=float, - docstr="Max allowable step length, as a fraction of " - "current model parameters") - - # Define the Paths required by this module - sf.path("OPTIMIZE", required=False, - default=os.path.join(PATH.SCRATCH, "optimize"), - docstr="scratch path for nonlinear optimization data") - - return sf - def check(self, validate=True): """ Checks parameters, paths, and dependencies """ - if validate: - self.required.validate() + super().check(validate=validate) - if PAR.LINESEARCH: - assert PAR.LINESEARCH in dir(line_search), \ + if self.par.LINESEARCH: + assert self.par.LINESEARCH in dir(line_search), \ f"LINESEARCH parameter must be in {dir(line_search)}" - if PAR.PRECOND: - assert PAR.PRECOND in dir(preconds), \ + if self.par.PRECOND: + assert self.par.PRECOND in dir(preconds), \ f"PRECOND must be in {dir(preconds)}" - assert 0. < PAR.STEPLENINIT, f"STEPLENINIT must be >= 0." - assert 0. < PAR.STEPLENMAX, f"STEPLENMAX must be >= 0." - assert PAR.STEPLENINIT < PAR.STEPLENMAX, \ + assert 0. < self.par.STEPLENINIT, f"STEPLENINIT must be >= 0." + assert 0. < self.par.STEPLENMAX, f"STEPLENMAX must be >= 0." + assert self.par.STEPLENINIT < self.par.STEPLENMAX, \ f"STEPLENINIT must be < STEPLENMAX" - def setup(self, m_new=None): + def setup(self): """ Sets up nonlinear optimization machinery - - :type m_new: np.array - :param m_new: Initial model vector which can be user-provided. Otherwise - the initial model will be read from PATH.MODEL_INIT """ - unix.mkdir(PATH.OPTIMIZE) + super().setup() + solver = self.module("solver") + unix.mkdir(self.path.OPTIMIZE) # Line search machinery is defined externally as a plugin class - if PAR.LINESEARCH: - self.line_search = getattr(line_search, PAR.LINESEARCH)( - step_count_max=PAR.STEPCOUNTMAX, - step_len_max=PAR.STEPLENMAX, + if self.par.LINESEARCH: + self.line_search = getattr(line_search, self.par.LINESEARCH)( + step_count_max=self.par.STEPCOUNTMAX, + step_len_max=self.par.STEPLENMAX, ) - if PAR.PRECOND: - self.precond = getattr(preconds, PAR.PRECOND)() + if self.par.PRECOND: + self.precond = getattr(preconds, self.par.PRECOND)() # Read in initial model as a vector and ensure it is a valid model - if m_new is None: - assert(os.path.exists(PATH.MODEL_INIT)), ( - "optimization library requires that PATH.MODEL_INIT exists" + if os.path.exists(self.path.MODEL_INIT): + m_new = solver.merge(solver.load(self.path.MODEL_INIT)) + self.save("m_new", m_new) + self.check_model(m_new) + else: + self.logger.warning( + "PATH.MODEL_INIT not found, cannot save 'm_new'. Either ensure " + "that 'm_new' is present in PATH.OPTIMIZE or restart with a " + "valid PATH.MODEL_INIT" ) - m_new = solver.merge(solver.load(PATH.MODEL_INIT)) - - self.save("m_new", m_new) - self.check_model(m_new) - @property - def eval_str(self): + def finalize(self): """ - Print out the evaluation string, which states what iteration and line - search step count we are at. Useful for log statements - - For example, an inversion at iteration 1 and step count 2 will return - 'i01s02' + Finalization tasks """ - iter_ = self.iter - step = self.line_search.step_count - return f"i{iter_:0>2}s{step:0>2}" + super().finalize() def load(self, name): """ @@ -199,7 +181,7 @@ def load(self, name): :param name: name of the vector, acceptable: m, g, p, f, alpha """ assert(name in self.acceptable_vectors) - vector = np.load(os.path.join(PATH.OPTIMIZE, f"{name}.npy")) + vector = np.load(os.path.join(self.path.OPTIMIZE, f"{name}.npy")) # Allow single length vectors, which alpha and misfit (f) are if vector.size == 1: vector = float(vector) @@ -215,7 +197,7 @@ def save(self, name, vector): :param vector: vector to save to name """ assert(name in self.acceptable_vectors) - vector_path = os.path.join(PATH.OPTIMIZE, f"{name}.npy") + vector_path = os.path.join(self.path.OPTIMIZE, f"{name}.npy") np.save(vector_path, vector) def compute_direction(self): @@ -226,7 +208,7 @@ def compute_direction(self): .. note:: Other optimization algorithms must overload this method """ - self.logger.info(f"computing search direction with {PAR.OPTIMIZE}") + self.logger.info(f"computing search direction with {self.par.OPTIMIZE}") g_new = self.load("g_new") if self.precond is not None: @@ -256,8 +238,9 @@ def initialize_search(self): self.line_search.clear_history() # Optional safeguard to prevent step length from getting too large - if PAR.STEPLENMAX: - self.line_search.step_len_max = PAR.STEPLENMAX * norm_m / norm_p + if self.par.STEPLENMAX: + self.line_search.step_len_max = \ + self.par.STEPLENMAX * norm_m / norm_p self.logger.debug(f"max step length safeguard is: " f"{self.line_search.step_len_max:.2E}") @@ -265,8 +248,8 @@ def initialize_search(self): alpha, _ = self.line_search.calculate_step() # Alpha defines the trial step length. Optional step length override - if PAR.STEPLENINIT and len(self.line_search.step_lens) <= 1: - alpha = PAR.STEPLENINIT * norm_m / norm_p + if self.par.STEPLENINIT and len(self.line_search.step_lens) <= 1: + alpha = self.par.STEPLENINIT * norm_m / norm_p self.logger.debug(f"overwrite initial step length: {alpha:.2E}") # The new model is the old model, scaled by the step direction and @@ -310,7 +293,7 @@ def finalize_search(self): Removes old model/search parameters, moves current parameters to old, sets up new current parameters and writes statistic outputs """ - unix.cd(PATH.OPTIMIZE) + unix.cd(self.path.OPTIMIZE) self.logger.info(msg.sub("FINALIZING LINE SEARCH")) # Remove the old model parameters @@ -366,12 +349,14 @@ def restart(self): numerical stagnation. """ # Steepest descent (base) does not need to be restarted - if PAR.OPTIMIZE.capitalize() != "Gradient": - g = self.load("g_new") - self.save("p_new", -g) + if self.par.OPTIMIZE.capitalize() == "Gradient": + return - self.line_search.clear_history() - self.restarted = 1 + g = self.load("g_new") + self.save("p_new", -g) + + self.line_search.clear_history() + self.restarted = 1 def write_stats(self): """ @@ -379,23 +364,16 @@ def write_stats(self): Used because stats line search information can be overwritten by subsequent iterations so we need to append values to text files if they should be retained. - - :type log: str - :param log: name of the file to write to. Will append .txt to it - :type value: float - :param value: value to write to file - :type format: str - :param format: string formatter for value """ self.logger.info(f"writing optimization stats") - fid = os.path.join(PATH.OUTPUT, f"optim_stats.txt") + fid = os.path.join(self.path.OUTPUT, f"optim_stats.txt") # First time, write header information if not os.path.exists(fid): with open(fid, "w") as f: for header in ["ITER", "FACTOR", "GRAD_NORM_L1", "GRAD_NORM_L2", - "MISFIT", "RESTART", "SLOPE", "STEP", "LENGTH", - "THETA"]: + "MISFIT", "RESTART", "SLOPE", "STEP", "LENGTH", + "THETA"]: f.write(f"{header.upper()},") f.write("\n") @@ -442,6 +420,8 @@ def check_model(self, m, min_pr=-1., max_pr=0.5): :param max_pr: maximum allowable Poisson's ratio value dictated by SPECFEM """ + solver = self.module("solver") + # Dynamic way to split up the model based on number of params pars = {} for i, par in enumerate(solver.parameters): diff --git a/seisflows/preprocess/base.py b/seisflows/preprocess/default.py similarity index 99% rename from seisflows/preprocess/base.py rename to seisflows/preprocess/default.py index 41a2dacb..d2b158e9 100644 --- a/seisflows/preprocess/base.py +++ b/seisflows/preprocess/default.py @@ -14,7 +14,7 @@ from seisflows.tools import msg from seisflows.tools import signal, unix from seisflows.plugins.preprocess import adjoint, misfit, readers, writers -from seisflows.config import SeisFlowsPathsParameters +from seisflows.core import SeisFlowsPathsParameters PAR = sys.modules["seisflows_parameters"] PATH = sys.modules["seisflows_paths"] @@ -105,7 +105,7 @@ def required(self): # Define the Paths required by this module sf.path("PREPROCESS", required=False, - default=os.path.join(PATH.SCRATCH, "preprocess"), + default=os.path.join(PATH.WORKDIR, "scratch", "preprocess"), docstr="scratch path to store any preprocessing outputs") return sf diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index d2478711..de747646 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -26,13 +26,13 @@ from copy import copy from IPython import embed +from seisflows.core import Dict, SeisFlowsPathsParameters +from seisflows.config import (config_logger, custom_import, save, + NAMES, ROOT_DIR, CFGPATHS) from seisflows.tools import unix, msg from seisflows.tools.specfem import (getpar, setpar, getpar_vel_model, setpar_vel_model) from seisflows.tools.wrappers import loadyaml -from seisflows.config import (config_logger, Dict, custom_import, save, - SeisFlowsPathsParameters, NAMES, ROOT_DIR, - CFGPATHS) def sfparser(): diff --git a/seisflows/solver/base.py b/seisflows/solver/base.py index 6de725de..f91e9d80 100644 --- a/seisflows/solver/base.py +++ b/seisflows/solver/base.py @@ -16,7 +16,7 @@ from seisflows.tools import msg, unix from seisflows.tools.specfem import Container, getpar from seisflows.tools.wrappers import diff -from seisflows.config import SeisFlowsPathsParameters, Dict +from seisflows.core import SeisFlowsPathsParameters, Dict PAR = sys.modules['seisflows_parameters'] @@ -149,7 +149,7 @@ def required(self): "['fortran_binary', 'adios']") sf.path("SOLVER", required=False, - default=os.path.join(PATH.SCRATCH, "solver"), + default=os.path.join(PATH.WORKDIR, "scratch", "solver"), docstr="scratch path to hold solver working directories") sf.path("DATA", required=False, diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index 5bbc1b54..6284e73c 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -12,7 +12,8 @@ from seisflows.tools import unix, msg from seisflows.tools.wrappers import exists -from seisflows.config import custom_import, SeisFlowsPathsParameters +from seisflows.core import SeisFlowsPathsParameters +from seisflows.config import custom_import from seisflows.tools.specfem import getpar, setpar diff --git a/seisflows/system/base.py b/seisflows/system/base.py index 04b740c9..632398e4 100644 --- a/seisflows/system/base.py +++ b/seisflows/system/base.py @@ -11,7 +11,8 @@ from seisflows.tools import unix, msg from seisflows.tools.wrappers import number_fid -from seisflows.config import save, SeisFlowsPathsParameters, CFGPATHS +from seisflows.core import SeisFlowsPathsParameters +from seisflows.config import save, CFGPATHS PAR = sys.modules["seisflows_parameters"] diff --git a/seisflows/system/workstation.py b/seisflows/system/workstation.py index 73f89896..1119ece6 100644 --- a/seisflows/system/workstation.py +++ b/seisflows/system/workstation.py @@ -8,8 +8,9 @@ import logging from contextlib import redirect_stdout -from seisflows.tools import unix, msg -from seisflows.config import custom_import, SeisFlowsPathsParameters, CFGPATHS +from seisflows.tools import msg +from seisflows.core import SeisFlowsPathsParameters +from seisflows.config import custom_import, CFGPATHS PAR = sys.modules["seisflows_parameters"] PATH = sys.modules["seisflows_paths"] diff --git a/seisflows/templates/base_class.py b/seisflows/templates/base_class.py deleted file mode 100644 index 4a4012e8..00000000 --- a/seisflows/templates/base_class.py +++ /dev/null @@ -1,92 +0,0 @@ -#!/usr/bin/env python3 -""" -This is a SeisFlows Base class -""" -import os -import sys -import logging -from seisflows.tools import msg -from seisflows.config import SeisFlowsPathsParameters - -# Required SeisFlows configuration -PAR = sys.modules['seisflows_parameters'] -PATH = sys.modules['seisflows_paths'] - -# The number of loaded modules depends on the module this Base class belongs to -system = sys.modules["seisflows_system"] -solver = sys.modules["seisflows_solver"] -optimize = sys.modules["seisflows_optimize"] -preprocess = sys.modules["seisflows_preprocess"] -postprocess = sys.modules["seisflows_postprocess"] - - -class Base: - """ - This is a template Base class - """ - # Class-specific logger accessed using self.logger - # When this logger is called, e.g., self.logger.info("text"), the logging - # package will know exactly which module, class and function the log - # statement has been sent from, extraordinarily helpful for debugging. - logger = logging.getLogger(__name__).getChild(__qualname__) - - @property - def required(self): - """ - A hard definition of paths and parameters required by this class, - alongside their necessity for the class and their string explanations. - - :rtype: seisflows.config.SeisFlowsPathsParameters - :return: Paths and parameters that define the given class - - """ - sf = SeisFlowsPathsParameters() - - # Define the Parameters required by this module - sf.par("EXAMPLE_REQUIRED_PARAMETER", required=True, par_type=str, - docstr="Required parameters do not need default values and will " - "need to be set by the user in the parameter file" - ) - - sf.par("EXAMPLE_OPTIONAL_PARAMETER", required=False, default=0, - par_type=int, docstr="Optional parameters require a default " - "value, if no default value is given, the " - "parameter is set to None" - ) - - # Define the Paths required by this module - sf.path("EXAMPLE_REQUIRED_PATH", required=True, - docstr="Required paths to be set by user in parameter file") - - sf.path("EXAMPLE_OPTIONAL_PATH", required=False, - default=os.path.join(PATH.SCRATCH, "example"), - docstr="Optional paths require default values") - - return sf - - def check(self, validate=True): - """ - Checks parameters and paths. The validate function ensures that all - required paths and parameters are accounted for, and that all - optional paths and parameters are set to user-defined or default values. - - :type validate: bool - :param validate: set required paths and parameters into sys.modules - """ - # The validate statement is used internally to set required paths - # and parameters into sys.modules. Default values are stored for - # optional terms - if validate: - self.required.validate() - - def test(self, *args, **kwargs): - """ - This is an example test function which can take any number of args - or kwargs. The base class is responsible for setting all of the - necessary functions - """ - super.test() - # Multiple logging levels determine how verbose the module will be - self.logger.info("important log statement goes here") - self.logger.debug("debugging log statement goes here") - self.logger.warning("warnings can be passed here") diff --git a/seisflows/templates/sub_class.py b/seisflows/templates/sub_class.py deleted file mode 100644 index 7bf936e4..00000000 --- a/seisflows/templates/sub_class.py +++ /dev/null @@ -1,77 +0,0 @@ -#!/usr/bin/env python3 -""" -This is a SeisFlows subclass which inherits attributes from a parent class -""" -import sys -import logging -from seisflows.tools import msg -from seisflows.config import SeisFlowsPathsParameters, custom_import - -# Required SeisFlows configuration -PAR = sys.modules['seisflows_parameters'] -PATH = sys.modules['seisflows_paths'] - -# The number of loaded modules depends on the module this Base class belongs to -system = sys.modules["seisflows_system"] -solver = sys.modules["seisflows_solver"] -optimize = sys.modules["seisflows_optimize"] -preprocess = sys.modules["seisflows_preprocess"] -postprocess = sys.modules["seisflows_postprocess"] - - -class Subclass(custom_import("MODULE NAME HERE", "PARENT CLASS NAME HERE")): - """ - This is a template subclass - """ - # Class-specific logger accessed using self.logger - # When this logger is called, e.g., self.logger.info("text"), the logging - # package will know exactly which module, class and function the log - # statement has been sent from, extraordinarily helpful for debugging. - logger = logging.getLogger(__name__).getChild(__qualname__) - - @property - def required(self): - """ - A hard definition of paths and parameters required by this class, - alongside their necessity for the class and their string explanations. - - :rtype: seisflows.config.SeisFlowsPathsParameters - :return: Paths and parameters that define the given class - - """ - # The super().required argument ensures that the sublcass inherits the - # paths and parameters defined by its parent class - sf = SeisFlowsPathsParameters(super().required) - - # > Additional or overloading paths and parameters can be set here - - return sf - - def check(self, validate=True): - """ - Checks parameters and paths. The validate function ensures that all - required paths and parameters are accounted for, and that all - optional paths and parameters are set to user-defined or default values. - """ - if validate: - self.required.validate() - - # Validation only required by the lowest subclass, which will validate - # all the paths and parameters from each of its parent classes - super.check(validate=False) - - def test(self, *args, **kwargs): - """ - This is an example OVERWRITE of the base_class.test() function. - If a super() statement is used, all the code within the base class - will be run. - """ - # The super statements calls the code chunk in base_class.test() - # Here it will be executed before the remainder of sub_class.test() is - # executed - super.test() - - # Multiple logging levels determine how verbose the module will be - self.logger.info("important log statement goes here") - self.logger.debug("debugging log statement goes here") - self.logger.warning("warnings can be passed here") \ No newline at end of file diff --git a/seisflows/workflow/base.py b/seisflows/workflow/base.py index d16bb406..97fc5b87 100644 --- a/seisflows/workflow/base.py +++ b/seisflows/workflow/base.py @@ -9,7 +9,8 @@ from seisflows.tools import msg from seisflows.tools.wrappers import exists -from seisflows.config import save, SeisFlowsPathsParameters +from seisflows.core import SeisFlowsPathsParameters +from seisflows.config import save PAR = sys.modules["seisflows_parameters"] diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index e88fe0a1..91c46e34 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -203,14 +203,17 @@ def line_search(self): """ # Calculate the initial step length based on optimization algorithm if optimize.line_search.step_count == 0: - self.logger.info(msg.mjr(f"CONDUCTING LINE SEARCH " - f"({optimize.eval_str})") + self.logger.info(msg.mjr(f"CONDUCTING LINE SEARCH: " + f"i{optimize.iter:0>2}" + f"s{optimize.line_search.step_count:0>2}") ) optimize.initialize_search() # Attempt a new trial step with the given step length optimize.line_search.step_count += 1 - self.logger.info(msg.mnr(f"TRIAL STEP COUNT: {optimize.eval_str}")) + self.logger.info(msg.mnr(f"TRIAL STEP COUNT: " + f"i{optimize.iter:0>2}" + f"s{optimize.line_search.step_count:0>2}")) self.evaluate_function(path=PATH.FUNC, suffix="try") # Check the function evaluation against line search history diff --git a/seisflows/workflow/test.py b/seisflows/workflow/test.py index b838406c..5efeefac 100644 --- a/seisflows/workflow/test.py +++ b/seisflows/workflow/test.py @@ -12,9 +12,8 @@ import numpy as np from glob import glob -from seisflows.tools import msg -from seisflows.config import (SeisFlowsPathsParameters, custom_import, ROOT_DIR, - CFGPATHS) +from seisflows.core import SeisFlowsPathsParameters +from seisflows.config import (custom_import, ROOT_DIR, CFGPATHS) PAR = sys.modules["seisflows_parameters"] PATH = sys.modules["seisflows_paths"] From c6e08c2fea30f6db1781b94571833ca313ca033a Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Mon, 27 Jun 2022 16:04:05 -0800 Subject: [PATCH 027/195] finished updating optimize classes to new architecture --- seisflows/optimize/LBFGS.py | 6 +- seisflows/optimize/NLCG.py | 89 +++++++++++----------- seisflows/optimize/gradient.py | 56 ++++++++------ seisflows/plugins/line_search/backtrack.py | 34 ++++++--- seisflows/plugins/line_search/bracket.py | 9 ++- seisflows/plugins/preconds/__init__.py | 5 -- seisflows/plugins/preconds/diagonal.py | 44 ----------- 7 files changed, 109 insertions(+), 134 deletions(-) delete mode 100644 seisflows/plugins/preconds/__init__.py delete mode 100644 seisflows/plugins/preconds/diagonal.py diff --git a/seisflows/optimize/LBFGS.py b/seisflows/optimize/LBFGS.py index b6176bb8..67bc3a71 100644 --- a/seisflows/optimize/LBFGS.py +++ b/seisflows/optimize/LBFGS.py @@ -4,7 +4,6 @@ It supercedes the `seisflows.optimize.base` class """ import os -import logging import numpy as np from seisflows.optimize.gradient import Gradient @@ -80,17 +79,14 @@ def __init__(self): docstr="Algorithm to use for line search, see " "seisflows.plugins.line_search for available choices" ) - self.required.par( "LBFGSMEM", required=False, default=3, par_type=int, docstr="Max number of previous gradients to retain in local memory" ) - self.required.par( "LBFGSMAX", required=False, par_type=int, default="inf", docstr="LBFGS periodic restart interval, between 1 and 'inf'" ) - self.required.par( "LBFGSTHRESH", required=False, default=0., par_type=float, docstr="LBFGS angle restart threshold" @@ -139,6 +135,8 @@ def compute_direction(self): direction using internally stored memory of previous gradients. The potential outcomes when computing direction with L-BFGS + TODO do we need to precondition L-BFGS? + 1. First iteration of L-BFGS optimization, search direction is defined as the inverse gradient 2. L-BFGS internal iteration ticks over the maximum allowable number of diff --git a/seisflows/optimize/NLCG.py b/seisflows/optimize/NLCG.py index dec2cda7..05957b1f 100644 --- a/seisflows/optimize/NLCG.py +++ b/seisflows/optimize/NLCG.py @@ -36,6 +36,8 @@ def __init__(self): These parameters should not be set by the user. Attributes are initialized as NoneTypes for clarity and docstrings. + TODO allow user to choose the calc_beta function + :type NLCG_iter: Class :param NLCG_iter: an internally used iteration that differs from optimization iter. Keeps track of internal NLCG memory. @@ -51,7 +53,7 @@ def __init__(self): docstr="NLCG conjugacy restart threshold, between 1 and inf" ) self.NLCG_iter = 0 - self.calc_beta = pollak_ribere # !!! Allow the user to choose this fx? + self.calc_beta = self._pollak_ribere def check(self, validate=True): """ @@ -78,6 +80,10 @@ def save(self, name, vector): """Inherit from optimize.gradient.Gradient""" self.save(name=name, vector=vector) + def _precondition(self, q): + """Inherit from optimize.gradient.Gradient""" + return self._precondition(q=q) + def compute_direction(self): """ Compute search direction using the Nonlinear Conjugate Gradient method @@ -125,9 +131,9 @@ def compute_direction(self): p_old = self.load("p_old") # Apply preconditioner and calc. scale factor for search dir. (beta) - if self.precond: - beta = self.calc_beta(g_new, g_old, self.precond) - p_new = -self.precond(g_new) + beta * p_old + if self.precond is not None: + beta = self.calc_beta(g_new, g_old) + p_new = -1 * self._precondition(g_new) + beta * p_old else: beta = self.calc_beta(g_new, g_old) p_new = -g_new + beta * p_old @@ -166,48 +172,41 @@ def check_model(self, m, min_pr=-1, max_pr=0.5): """Inherit from optimize.gradient.Gradient""" self.check_model(m=m, min_pr=min_pr, max_pr=max_pr) + def _fletcher_reeves(self, g_new, g_old): + """ + One method for calculating beta in the NLCG Algorithm from + Fletcher & Reeves, 1964 + + :type g_new: np.array + :param g_new: new search direction + :type g_old: np.array + :param g_old: old search direction + :rtype: float + :return: beta, the scale factor to apply to the old search direction to + determine the new search direction + """ + num = dot(self._precondition(g_new), g_new) + den = dot(g_old, g_old) + beta = num / den + return beta -def fletcher_reeves(g_new, g_old, precond=lambda x: x): - """ - One method for calculating beta in the NLCG Algorithm from - Fletcher & Reeves, 1964 - - :type g_new: np.array - :param g_new: new search direction - :type g_old: np.array - :param g_old: old search direction - :type precond: function - :param precond: preconditioner, defaults to simple return - :rtype: float - :return: beta, the scale factor to apply to the old search direction to - determine the new search direction - """ - num = dot(precond(g_new), g_new) - den = dot(g_old, g_old) - beta = num / den - - return beta - - -def pollak_ribere(g_new, g_old, precond=lambda x: x): - """ - One method for calculating beta in the NLCG Algorithm from - Polak & Ribiere, 1969 - - :type g_new: np.array - :param g_new: new search direction - :type g_old: np.array - :param g_old: old search direction - :type precond: function - :param precond: preconditioner, defaults to simple return - :rtype: float - :return: beta, the scale factor to apply to the old search direction to - determine the new search direction - """ - num = dot(precond(g_new), g_new - g_old) - den = dot(g_old, g_old) - beta = num / den - return beta + def _pollak_ribere(self, g_new, g_old): + """ + One method for calculating beta in the NLCG Algorithm from + Polak & Ribiere, 1969 + + :type g_new: np.array + :param g_new: new search direction + :type g_old: np.array + :param g_old: old search direction + :rtype: float + :return: beta, the scale factor to apply to the old search direction to + determine the new search direction + """ + num = dot(self._precondition(g_new), g_new - g_old) + den = dot(g_old, g_old) + beta = num / den + return beta def check_conjugacy(g_new, g_old): diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index ce860415..7df3507a 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -16,7 +16,7 @@ from seisflows.core import Base from seisflows.tools import msg, unix from seisflows.tools.math import angle, dot -from seisflows.plugins import line_search, preconds +from seisflows.plugins import line_search from seisflows.tools.math import poissons_ratio @@ -54,9 +54,6 @@ def __init__(self): :type line_search: Class :param line_search: a class controlling the line search functionality for determining step length - :type precond: Class - :param precond: a class controlling the preconditioner functionality - for preconditiong gradient information :type restarted: bool :param restarted: a flag signalling if the optimization algorithm has been restarted recently @@ -71,8 +68,8 @@ def __init__(self): ) self.required.par( "PRECOND", required=False, par_type=str, - docstr="Algorithm to use for preconditioning gradients, see " - "seisflows.plugins.preconds for available choices" + docstr="Algorithm to use for preconditioning gradients, see " + "seisflows.plugins.preconds for available choices" ) self.required.par( "STEPCOUNTMAX", required=False, default=10, par_type=int, @@ -116,8 +113,14 @@ def check(self, validate=True): f"LINESEARCH parameter must be in {dir(line_search)}" if self.par.PRECOND: - assert self.par.PRECOND in dir(preconds), \ - f"PRECOND must be in {dir(preconds)}" + # This list should match the logic in self.precondition() + acceptable_preconditioners = ["diagonal"] + assert self.par.PRECOND in acceptable_preconditioners, \ + f"PRECOND must be in {acceptable_preconditioners}" + assert(os.path.exists(self.path.PRECOND)), ( + f"preconditioner requires PATH.PRECOND pointing to a array-like" + f"weight file" + ) assert 0. < self.par.STEPLENINIT, f"STEPLENINIT must be >= 0." assert 0. < self.par.STEPLENMAX, f"STEPLENMAX must be >= 0." @@ -138,9 +141,6 @@ def setup(self): step_count_max=self.par.STEPCOUNTMAX, step_len_max=self.par.STEPLENMAX, ) - if self.par.PRECOND: - self.precond = getattr(preconds, self.par.PRECOND)() - # Read in initial model as a vector and ensure it is a valid model if os.path.exists(self.path.MODEL_INIT): m_new = solver.merge(solver.load(self.path.MODEL_INIT)) @@ -200,6 +200,25 @@ def save(self, name, vector): vector_path = os.path.join(self.path.OPTIMIZE, f"{name}.npy") np.save(vector_path, vector) + def _precondition(self, q): + """ + Apply available preconditioner to a given gradient + + :type q: np.array + :param q: Vector to precondition, typically gradient contained in: g_new + :rtype: np.array + :return: preconditioned vector + """ + solver = self.module("solver") + + if self.par.PRECOND is not None: + p = solver.merge(solver.load(self.path.PRECOND)) + if self.par.PRECOND == "DIAGONAL": + self.logger.info("applying diagonal preconditioner") + return p * q + else: + return q + def compute_direction(self): """ Computes a steepest descent search direction (inverse gradient) @@ -211,10 +230,7 @@ def compute_direction(self): self.logger.info(f"computing search direction with {self.par.OPTIMIZE}") g_new = self.load("g_new") - if self.precond is not None: - p_new = -1 * self.precond(g_new) - else: - p_new = -1 * g_new + p_new = -1 * self._precondition(g_new) self.save("p_new", p_new) def initialize_search(self): @@ -303,7 +319,7 @@ def finalize_search(self): unix.rm(fid) # Needs to be run before shifting model in next step - self.write_stats() + self._write_stats() self.logger.info("shifting current model (new) to previous model (old)") unix.mv("m_new.npy", "m_old.npy") @@ -358,7 +374,7 @@ def restart(self): self.line_search.clear_history() self.restarted = 1 - def write_stats(self): + def _write_stats(self): """ Simplified write function to append values to text files. Used because stats line search information can be overwritten @@ -446,9 +462,3 @@ def check_model(self, m, min_pr=-1., max_pr=0.5): self.logger.info(parts.format(minval=vals.min(), key=key, maxval=vals.max()) ) - - - - - - diff --git a/seisflows/plugins/line_search/backtrack.py b/seisflows/plugins/line_search/backtrack.py index d31d96fc..d5448010 100644 --- a/seisflows/plugins/line_search/backtrack.py +++ b/seisflows/plugins/line_search/backtrack.py @@ -2,17 +2,13 @@ """ This is the subclass class for seisflows.plugins.line_search.backtrack """ -import logging - -from seisflows.tools import msg from seisflows.plugins.line_search.bracket import Bracket +from seisflows.tools import msg from seisflows.tools.math import parabolic_backtrack class Backtrack(Bracket): """ - Overwrites seisflows.plugins.line_search.Bracket - Implements backtracking linesearch. A backtracking line search is used for L-BFGS optimization, where a unit step length is attempted, if this does not satisfy the misfit reduction criteria, the step length is @@ -32,14 +28,34 @@ class Backtrack(Bracket): status == 0 : not finished status < 0 : failed """ - # Class-specific logger accessed using self.logger - logger = logging.getLogger(__name__).getChild(__qualname__) + def __init__(self): + """Inherits from seisflows.plugins.line_search.bracket.Bracket""" + super().__init__() + + def initialize(self, step_len, func_val, gtg, gtp): + """Inherits from seisflows.plugins.line_search.bracket.Bracket""" + self.initialize(step_len, func_val, gtg, gtp) + + def update(self, step_len, func_val): + """Inherits from seisflows.plugins.line_search.bracket.Bracket""" + self.update(step_len, func_val) + + def clear_history(self): + """Inherits from seisflows.plugins.line_search.bracket.Bracket""" + self.clear_history() + + def reset(self): + """Inherits from seisflows.plugins.line_search.bracket.Bracket""" + self.reset() + def search_history(self, sort=True): + """Inherits from seisflows.plugins.line_search.bracket.Bracket""" + return self.search_history(sort=sort) def calculate_step(self): """ - Determines step length and search status. Overloads the bracketing - line search + Determines step length and search status. Overwrites the Bracketing + line search step calculation """ # Determine the line search history x, f, gtg, gtp, step_count, update_count = self.search_history() diff --git a/seisflows/plugins/line_search/bracket.py b/seisflows/plugins/line_search/bracket.py index 4aed9a8c..978ddaf3 100644 --- a/seisflows/plugins/line_search/bracket.py +++ b/seisflows/plugins/line_search/bracket.py @@ -10,12 +10,13 @@ import logging import numpy as np +from seisflows.core import Base from seisflows.tools import msg from seisflows.tools.array import count_zeros from seisflows.tools.math import parabolic_backtrack, polynomial_fit -class Bracket: +class Bracket(Base): """ Abstract base class for line search @@ -32,9 +33,6 @@ class Bracket: status == 0 : not finished status < 0 : failed """ - # Class-specific logger accessed using self.logger - logger = logging.getLogger(__name__).getChild(__qualname__) - def __init__(self, step_count_max, step_len_max): """ Initiate the line search machinery @@ -46,6 +44,8 @@ def __init__(self, step_count_max, step_len_max): :param step_len_max: maximum length of the step, defaults to infinity, that is unbounded step length. set by PAR.STEP_LEN_MAX """ + super().__init__() + self.step_count_max = step_count_max self.step_len_max = step_len_max self.func_vals = [] @@ -264,6 +264,7 @@ def _check_bracket(step_lens, func_vals): okay = False return okay + def _good_enough(step_lens, func_vals, thresh=np.log10(1.2)): """ Checks if step length is reasonably close to quadratic estimate diff --git a/seisflows/plugins/preconds/__init__.py b/seisflows/plugins/preconds/__init__.py deleted file mode 100644 index 07ec545c..00000000 --- a/seisflows/plugins/preconds/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -""" -used by the OPTIMIZE class and specified by the PRECOND parameter -""" -from .diagonal import Diagonal - diff --git a/seisflows/plugins/preconds/diagonal.py b/seisflows/plugins/preconds/diagonal.py deleted file mode 100644 index 03ea4bea..00000000 --- a/seisflows/plugins/preconds/diagonal.py +++ /dev/null @@ -1,44 +0,0 @@ -#!/usr/bin/env python3 -""" -This is the main class for seisflows.line_search.preconds.diagonal -This class provides the utilities for a diagonal preconditioner -""" -import os -import sys - - -class Diagonal(object): - """ - User supplied diagonal preconditioner - Rescales model parameters based on user supplied weights - """ - def __init__(self): - """ - Loads any required dependencies - """ - PATH = sys.modules["seisflows_paths"] - solver = sys.modules["seisflows_solver"] - - if "PRECOND" not in PATH: - raise Exception - - if not os.path.exists(PATH.PRECOND): - raise Exception - - self.path = PATH.PRECOND - self.load = solver.load - self.merge = solver.merge - - def __call__(self, q): - """ - Applies preconditioner to given vector - - :type q: np.array - :param q: search direction - :rtype: np.array - :return: preconditioned search direction - """ - p = self.merge(self.load(self.path)) - return p * q - - From f7133a2d119036b78a2923c4436d1ce298eaa072 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Mon, 27 Jun 2022 16:21:19 -0800 Subject: [PATCH 028/195] updated preprocessing classes to new code style --- seisflows/postprocess/default.py | 3 - seisflows/preprocess/default.py | 332 +++++++++++++-------------- seisflows/preprocess/pyatoa.py | 376 ++++++++++++++++--------------- 3 files changed, 358 insertions(+), 353 deletions(-) diff --git a/seisflows/postprocess/default.py b/seisflows/postprocess/default.py index 4b5c1060..9debb2a5 100644 --- a/seisflows/postprocess/default.py +++ b/seisflows/postprocess/default.py @@ -29,20 +29,17 @@ def __init__(self): docstr="Gaussian half-width for horizontal smoothing in units of " "meters. If 0., no smoothing applied" ) - self.required.par( "SMOOTH_V", required=False, default=0., par_type=float, docstr="Gaussian half-width for vertical smoothing in units of " "meters" ) - self.required.par( "TASKTIME_SMOOTH", required=False, default=1, par_type=int, docstr="Large radii smoothing may take longer than normal tasks. " "Allocate additional smoothing task time as a multiple of " "TASKTIME" ) - # Define the Paths required by this module self.required.path( "MASK", required=False, docstr="Directory to mask files for " diff --git a/seisflows/preprocess/default.py b/seisflows/preprocess/default.py index d2b158e9..fe8cde68 100644 --- a/seisflows/preprocess/default.py +++ b/seisflows/preprocess/default.py @@ -6,189 +6,189 @@ and write adjoint sources that are expected by the solver. """ import os -import sys import obspy -import logging import numpy as np -from seisflows.tools import msg +from seisflows.core import Base from seisflows.tools import signal, unix from seisflows.plugins.preprocess import adjoint, misfit, readers, writers -from seisflows.core import SeisFlowsPathsParameters -PAR = sys.modules["seisflows_parameters"] -PATH = sys.modules["seisflows_paths"] - -class Default: +class Default(Base): """ Default SeisFlows preprocessing class Provides data processing functions for seismic traces, with options for data misfit, filtering, normalization and muting """ - # Class-specific logger accessed using self.logger - logger = logging.getLogger(__name__).getChild(__qualname__) - def __init__(self): """ These parameters should not be set by __init__! Attributes are just initialized as NoneTypes for clarity and docstrings """ - self.misfit = None - self.adjoint = None - self.reader = None - self.writer = None - - @property - def required(self): - """ - A hard definition of paths and parameters required by this class, - alongside their necessity for the class and their string explanations. - """ - sf = SeisFlowsPathsParameters() + super().__init__() # Define the Parameters required by this module - sf.par("MISFIT", required=False, default="waveform", par_type=str, - docstr="Misfit function for waveform comparisons, for available " - "see seisflows.plugins.misfit") - - sf.par("BACKPROJECT", required=False, default="null", par_type=str, - docstr="Backprojection function for migration, for available " - "see seisflows.plugins.adjoint") - - sf.par("NORMALIZE", required=False, default="null", par_type=str, - docstr="Data normalization option") - - sf.par("FILTER", required=False, default="null", par_type=str, - docstr="Data filtering type, available options are:" - "BANDPASS (req. MIN/MAX PERIOD/FREQ);" - "LOWPASS (req. MAX_FREQ or MIN_PERIOD); " - "HIGHPASS (req. MIN_FREQ or MAX_PERIOD) ") - - sf.par("MIN_PERIOD", required=False, par_type=float, - docstr="Minimum filter period applied to time series." - "See also MIN_FREQ, MAX_FREQ, if User defines FREQ " - "parameters, they will overwrite PERIOD parameters.") - - sf.par("MAX_PERIOD", required=False, par_type=float, - docstr="Maximum filter period applied to time series." - "See also MIN_FREQ, MAX_FREQ, if User defines FREQ " - "parameters, they will overwrite PERIOD parameters.") - - sf.par("MIN_FREQ", required=False, par_type=float, - docstr="Maximum filter frequency applied to time series." - "See also MIN_PERIOD, MAX_PERIOD, if User defines FREQ " - "parameters, they will overwrite PERIOD parameters.") - - sf.par("MAX_FREQ", required=False, par_type=float, - docstr="Maximum filter frequency applied to time series," - "See also MIN_PERIOD, MAX_PERIOD, if User defines FREQ " - "parameters, they will overwrite PERIOD parameters.") - - sf.par("MUTE", required=False, par_type=list, default=[], - docstr="Data mute parameters used to zero out early / late " - "arrivals or offsets. Choose any number of: " - "EARLY: mute early arrivals; " - "LATE: mute late arrivals; " - "SHORT: mute short source-receiver distances; " - "LONG: mute long source-receiver distances") - sf.par("NORMALIZE", required=False, par_type=list, default=[], - docstr="Data normalization parameters used to normalize the " - "amplitudes of waveforms. Choose from two sets: " - "ENORML1: normalize per event by L1 of traces; OR " - "ENORML2: normalize per event by L2 of traces; AND " - "TNORML1: normalize per trace by L1 of itself; OR " - "TNORML2: normalize per trace by L2 of itself") - + self.required.par( + "MISFIT", required=False, default="waveform", par_type=str, + docstr="Misfit function for waveform comparisons, for available " + "see seisflows.plugins.misfit" + ) + self.required.par( + "BACKPROJECT", required=False, default="null", par_type=str, + docstr="Backprojection function for migration, for available see " + "seisflows.plugins.adjoint" + ) + self.required.par( + "NORMALIZE", required=False, default="null", par_type=str, + docstr="Data normalization option" + ) + self.required.par( + "FILTER", required=False, default="null", par_type=str, + docstr="Data filtering type, available options are:" + "BANDPASS (req. MIN/MAX PERIOD/FREQ);" + "LOWPASS (req. MAX_FREQ or MIN_PERIOD); " + "HIGHPASS (req. MIN_FREQ or MAX_PERIOD) " + ) + self.required.par( + "MIN_PERIOD", required=False, par_type=float, + docstr="Minimum filter period applied to time series." + "See also MIN_FREQ, MAX_FREQ, if User defines FREQ " + "parameters, they will overwrite PERIOD parameters." + ) + self.required.par( + "MAX_PERIOD", required=False, par_type=float, + docstr="Maximum filter period applied to time series." + "See also MIN_FREQ, MAX_FREQ, if User defines FREQ " + "parameters, they will overwrite PERIOD parameters." + ) + self.required.par( + "MIN_FREQ", required=False, par_type=float, + docstr="Maximum filter frequency applied to time series." + "See also MIN_PERIOD, MAX_PERIOD, if User defines FREQ " + "parameters, they will overwrite PERIOD parameters." + ) + self.required.par( + "MAX_FREQ", required=False, par_type=float, + docstr="Maximum filter frequency applied to time series," + "See also MIN_PERIOD, MAX_PERIOD, if User defines FREQ " + "parameters, they will overwrite PERIOD parameters." + ) + self.required.par( + "MUTE", required=False, par_type=list, default=[], + docstr="Data mute parameters used to zero out early / late " + "arrivals or offsets. Choose any number of: " + "EARLY: mute early arrivals; " + "LATE: mute late arrivals; " + "SHORT: mute short source-receiver distances; " + "LONG: mute long source-receiver distances" + ) + self.required.par( + "NORMALIZE", required=False, par_type=list, default=[], + docstr="Data normalization parameters used to normalize the " + "amplitudes of waveforms. Choose from two sets: " + "ENORML1: normalize per event by L1 of traces; OR " + "ENORML2: normalize per event by L2 of traces; AND " + "TNORML1: normalize per trace by L1 of itself; OR " + "TNORML2: normalize per trace by L2 of itself" + ) # TODO: Add the mute parameters here, const, slope and dist # Define the Paths required by this module - sf.path("PREPROCESS", required=False, - default=os.path.join(PATH.WORKDIR, "scratch", "preprocess"), - docstr="scratch path to store any preprocessing outputs") + self.required.path( + "PREPROCESS", required=False, + default=os.path.join(self.path.WORKDIR, "scratch", "preprocess"), + docstr="scratch path to store any preprocessing outputs" + ) - return sf + self.misfit = None + self.adjoint = None + self.reader = None + self.writer = None def check(self, validate=True): """ Checks parameters and paths """ - if validate: - self.required.validate() + super().check(validate=validate) # Data normalization option - if PAR.NORMALIZE: + if self.par.NORMALIZE: acceptable_norms = {"TNORML1", "TNORML2", "ENORML1", "ENORML2"} - chosen_norms = [_.upper() for _ in PAR.NORMALIZE] + chosen_norms = [_.upper() for _ in self.par.NORMALIZE] assert(set(chosen_norms).issubset(acceptable_norms)) # Data muting options - if PAR.MUTE: + if self.par.MUTE: acceptable_mutes = {"EARLY", "LATE", "LONG", "SHORT"} - chosen_mutes = [_.upper() for _ in PAR.MUTE] + chosen_mutes = [_.upper() for _ in self.par.MUTE] assert(set(chosen_mutes).issubset(acceptable_mutes)) if "EARLY" in chosen_mutes: - assert(PAR.EARLY_SLOPE is not None) - assert(PAR.EARLY_SLOPE >= 0.) - assert(PAR.EARLY_CONST is not None) + assert(self.par.EARLY_SLOPE is not None) + assert(self.par.EARLY_SLOPE >= 0.) + assert(self.par.EARLY_CONST is not None) if "LATE" in chosen_mutes: - assert(PAR.LATE_SLOPE is not None) - assert(PAR.LATE_SLOPE >= 0.) - assert(PAR.LATE_CONST is not None) + assert(self.par.LATE_SLOPE is not None) + assert(self.par.LATE_SLOPE >= 0.) + assert(self.par.LATE_CONST is not None) if "SHORT" in chosen_mutes: - assert(PAR.SHORT_DIST is not None) - assert (PAR.SHORT_DIST > 0) + assert(self.par.SHORT_DIST is not None) + assert (self.par.SHORT_DIST > 0) if "LONG" in chosen_mutes: - assert(PAR.LONG_DIST is not None) - assert (PAR.LONG_DIST > 0) + assert(self.par.LONG_DIST is not None) + assert (self.par.LONG_DIST > 0) # Data filtering options that will be passed to ObsPy filters - if PAR.FILTER: + if self.par.FILTER: acceptable_filters = ["BANDPASS", "LOWPASS", "HIGHPASS"] - assert PAR.FILTER.upper() in acceptable_filters, \ - f"PAR.FILTER must be in {acceptable_filters}" + assert self.par.FILTER.upper() in acceptable_filters, \ + f"self.par.FILTER must be in {acceptable_filters}" # Set the min/max frequencies and periods, frequency takes priority - if PAR.MIN_FREQ is not None: - PAR.MAX_PERIOD = 1 / PAR.MIN_FREQ - elif PAR.MAX_PERIOD is not None: - PAR.MIN_FREQ = 1 / PAR.MAX_PERIOD + if self.par.MIN_FREQ is not None: + self.par.MAX_PERIOD = 1 / self.par.MIN_FREQ + elif self.par.MAX_PERIOD is not None: + self.par.MIN_FREQ = 1 / self.par.MAX_PERIOD - if PAR.MAX_FREQ is not None: - PAR.MIN_PERIOD = 1 / PAR.MAX_FREQ - elif PAR.MIN_PERIOD is not None: - PAR.MAX_FREQ = 1 / PAR.MIN_PERIOD + if self.par.MAX_FREQ is not None: + self.par.MIN_PERIOD = 1 / self.par.MAX_FREQ + elif self.par.MIN_PERIOD is not None: + self.par.MAX_FREQ = 1 / self.par.MIN_PERIOD # Check that the correct filter bounds have been set - if PAR.FILTER.upper() == "BANDPASS": - assert(PAR.MIN_FREQ is not None and PAR.MAX_FREQ is not None), \ + if self.par.FILTER.upper() == "BANDPASS": + assert(self.par.MIN_FREQ is not None and + self.par.MAX_FREQ is not None), \ ("BANDPASS filter PAR.MIN_PERIOD and PAR.MAX_PERIOD or " "PAR.MIN_FREQ and PAR.MAX_FREQ") - elif PAR.FILTER.upper() == "LOWPASS": - assert(PAR.MAX_FREQ is not None or PAR.MIN_PERIOD is not None),\ + elif self.par.FILTER.upper() == "LOWPASS": + assert(self.par.MAX_FREQ is not None or + self.par.MIN_PERIOD is not None),\ "LOWPASS requires PAR.MAX_FREQ or PAR.MIN_PERIOD" - elif PAR.FILTER.upper() == "HIGHPASS": - assert(PAR.MIN_FREQ is not None or PAR.MAX_PERIOD is not None),\ + elif self.par.FILTER.upper() == "HIGHPASS": + assert(self.par.MIN_FREQ is not None or + self.par.MAX_PERIOD is not None),\ "HIGHPASS requires PAR.MIN_FREQ or PAR.MAX_PERIOD" # Check that filter bounds make sense, by this point, MIN and MAX # FREQ and PERIOD should be set, so we just check the FREQ - assert(0 < PAR.MIN_FREQ < np.inf), "0 < PAR.MIN_FREQ < inf" - assert(0 < PAR.MAX_FREQ < np.inf), "0 < PAR.MAX_FREQ < inf" - assert(PAR.MIN_FREQ < PAR.MAX_FREQ), "PAR.MIN_FREQ < PAR.MAX_FREQ" + assert(0 < self.par.MIN_FREQ < np.inf), "0 < PAR.MIN_FREQ < inf" + assert(0 < self.par.MAX_FREQ < np.inf), "0 < PAR.MAX_FREQ < inf" + assert(self.par.MIN_FREQ < self.par.MAX_FREQ), ( + "PAR.MIN_FREQ < PAR.MAX_FREQ" + ) # Assert that readers and writers available # TODO | This is a bit vague as dir contains imported modules and hidden # TODO | variables (e.g., np, __name__) - assert(PAR.FORMAT.lower() in dir(readers)), ( - f"Reader {PAR.FORMAT} not found") - assert(PAR.FORMAT.lower() in dir(writers)), ( - f"Writer {PAR.FORMAT} not found") + assert(self.par.FORMAT.lower() in dir(readers)), ( + f"Reader {self.par.FORMAT} not found") + assert(self.par.FORMAT.lower() in dir(writers)), ( + f"Writer {self.par.FORMAT} not found") - # Assert that either misfit or backproject exists - if PAR.WORKFLOW.upper() == "INVERSION": - assert(PAR.MISFIT is not None) + # Assert that either misfit or backproject exists + if self.par.WORKFLOW.upper() == "INVERSION": + assert(self.par.MISFIT is not None) def setup(self): """ @@ -196,20 +196,29 @@ def setup(self): misfit, adjoint source type, and specifying the expected file type for input and output seismic data. """ - unix.mkdir(PATH.PREPROCESS) + unix.mkdir(self.path.PREPROCESS) # Define misfit function and adjoint trace generator - if PAR.MISFIT: - self.logger.debug(f"misfit function is: '{PAR.MISFIT}'") - self.misfit = getattr(misfit, PAR.MISFIT.lower()) - self.adjoint = getattr(adjoint, PAR.MISFIT.lower()) - elif PAR.BACKPROJECT: - self.logger.debug(f"backproject function is: '{PAR.BACKPROJECT}'") - self.adjoint = getattr(adjoint, PAR.BACKPROJECT.lower()) + if self.par.MISFIT: + self.logger.debug(f"misfit function is: '{self.par.MISFIT}'") + self.misfit = getattr(misfit, self.par.MISFIT.lower()) + self.adjoint = getattr(adjoint, self.par.MISFIT.lower()) + elif self.par.BACKPROJECT: + self.logger.debug( + f"backproject function is: '{self.par.BACKPROJECT}'" + ) + self.adjoint = getattr(adjoint, self.par.BACKPROJECT.lower()) # Define seismic data reader and writer - self.reader = getattr(readers, PAR.FORMAT.lower()) - self.writer = getattr(writers, PAR.FORMAT.lower()) + self.reader = getattr(readers, self.par.FORMAT.lower()) + self.writer = getattr(writers, self.par.FORMAT.lower()) + + def finalize(self): + """ + Any finalization processes that need to take place at the end of an + iteration + """ + super().finalize() def prepare_eval_grad(self, cwd, taskid, filenames, **kwargs): """ @@ -241,31 +250,33 @@ def prepare_eval_grad(self, cwd, taskid, filenames, **kwargs): filename=filename) # Process observations and synthetics identically - if PAR.FILTER: + if self.par.FILTER: if taskid == 0: - self.logger.debug(f"applying {PAR.FILTER} filter to data") + self.logger.debug(f"applying {self.par.FILTER} filter to data") obs = self._apply_filter(obs) syn = self._apply_filter(syn) - if PAR.MUTE: + if self.par.MUTE: if taskid == 0: - self.logger.debug(f"applying {PAR.MUTE} mutes to data") + self.logger.debug(f"applying {self.par.MUTE} mutes to data") obs = self._apply_mute(obs) syn = self._apply_mute(syn) - if PAR.NORMALIZE: + if self.par.NORMALIZE: if taskid == 0: - self.logger.debug(f"normalizing data with: {PAR.NORMALIZE}") + self.logger.debug( + f"normalizing data with: {self.par.NORMALIZE}" + ) obs = self._apply_normalize(obs) syn = self._apply_normalize(syn) - if PAR.MISFIT is not None: + if self.par.MISFIT is not None: self._write_residuals(cwd, syn, obs) # Write the adjoint traces. Rename file extension for Specfem - if PAR.FORMAT.upper() == "ASCII": + if self.par.FORMAT.upper() == "ASCII": # Change the extension to '.adj' from whatever it is ext = os.path.splitext(filename)[-1] filename_out = filename.replace(ext, ".adj") - elif PAR.FORMAT.upper() == "SU": + elif self.par.FORMAT.upper() == "SU": # TODO implement this raise NotImplementedError @@ -293,17 +304,10 @@ def sum_residuals(self, files): return total_misfit - def finalize(self): - """ - Any finalization processes that need to take place at the end of an - iteration - """ - pass - def _write_residuals(self, path, syn, obs): """ Computes residuals between observed and synthetic seismogram based on - the misfit function PAR.MISFIT. Saves the residuals for each + the misfit function self.par.MISFIT. Saves the residuals for each data-synthetic pair into a text file located at: ./scratch/solver/*/residuals @@ -369,13 +373,13 @@ def _apply_filter(self, st): st.detrend("linear") st.taper(0.05, type="hann") - if PAR.FILTER.upper() == "BANDPASS": - st.filter("bandpass", zerophase=True, freqmin=PAR.MIN_FREQ, - freqmax=PAR.FREQMAX) - elif PAR.FILTER.upper() == "LOWPASS": - st.filter("lowpass", zerophase=True, freq=PAR.MAX_FREQ) - elif PAR.FILTER.upper() == "HIGHPASS": - st.filter("highpass", zerophase=True, freq=PAR.MIN_FREQ) + if self.par.FILTER.upper() == "BANDPASS": + st.filter("bandpass", zerophase=True, freqmin=self.par.MIN_FREQ, + freqmax=self.par.FREQMAX) + elif self.par.FILTER.upper() == "LOWPASS": + st.filter("lowpass", zerophase=True, freq=self.par.MAX_FREQ) + elif self.par.FILTER.upper() == "HIGHPASS": + st.filter("highpass", zerophase=True, freq=self.par.MIN_FREQ) return st @@ -394,20 +398,20 @@ def _apply_mute(self, st): :rtype: obspy.core.stream.Stream :return: muted stream object """ - mute_choices = [_.upper() for _ in PAR.MUTE] + mute_choices = [_.upper() for _ in self.par.MUTE] if "EARLY" in mute_choices: - st = signal.mute_arrivals(st, slope=PAR.EARLY_SLOPE, - const=PAR.EARLY_CONST, + st = signal.mute_arrivals(st, slope=self.par.EARLY_SLOPE, + const=self.par.EARLY_CONST, choice="EARLY") if "LATE" in mute_choices: - st = signal.mute_arrivals(st, slope=PAR.LATE_SLOPE, - const=PAR.LATE_CONST, + st = signal.mute_arrivals(st, slope=self.par.LATE_SLOPE, + const=self.par.LATE_CONST, choice="LATE") if "SHORT" in mute_choices: - st = signal.mute_offsets(st, dist=PAR.SHORT_DIST, + st = signal.mute_offsets(st, dist=self.par.SHORT_DIST, choice="SHORT") if "LONG" in mute_choices: - st = signal.mute_arrivals(st, dist=PAR.LONG_DIST, + st = signal.mute_arrivals(st, dist=self.par.LONG_DIST, choice="LONG") return st @@ -427,7 +431,7 @@ def _apply_normalize(self, st): :return: stream with normalized traces """ st_out = st.copy() - norm_choices = [_.upper() for _ in PAR.NORMALIZE] + norm_choices = [_.upper() for _ in self.par.NORMALIZE] # Normalize an event by the L1 norm of all traces if 'ENORML1' in norm_choices: diff --git a/seisflows/preprocess/pyatoa.py b/seisflows/preprocess/pyatoa.py index 565d980e..8b4537b0 100644 --- a/seisflows/preprocess/pyatoa.py +++ b/seisflows/preprocess/pyatoa.py @@ -1,7 +1,7 @@ #!/usr/bin/env python3 """ The Pyatoa preprocessing module abstracts all preprocessing functionality -onto Pyatoa (https://github.com/bch0w/pyatoa/). The module defined below is +onto Pyatoa (https://github.com/adjtomo/pyatoa/). The module defined below is meant to set up and execute Pyatoa within a running SeisFlows workflow. Pyatoa itself aggregates all of its connection with SeisFlows in the Pyaflowa @@ -10,28 +10,22 @@ """ import os import sys -import logging import numpy as np from glob import glob - from pyatoa import Pyaflowa, Inspector +from seisflows.core import Base from seisflows.tools import unix, msg -from seisflows.config import custom_import, SeisFlowsPathsParameters, CFGPATHS - -PAR = sys.modules["seisflows_parameters"] -PATH = sys.modules["seisflows_paths"] +from seisflows.config import CFGPATHS -class Pyatoa(custom_import("preprocess", "base")): +class Pyatoa(Base): """ Data preprocessing class using the Pyaflowa class within the Pyatoa package. In charge of data discovery, preprocessing, filtering, misfiti quantification and data storage. The User does not need to implement Pyatoa, but rather interacts with it via the parameters and paths of SeisFlows. """ - logger = logging.getLogger(__name__).getChild(__qualname__) - def __init__(self): """ These parameters should not be set by __init__! @@ -40,142 +34,149 @@ def __init__(self): :param logger: Class-specific logging module, log statements pushed from this logger will be tagged by its specific module/classname """ - pass - - @property - def required(self): - """ - A hard definition of paths and parameters required by this class, - alongside their necessity for the class and their string explanations. - """ - sf = SeisFlowsPathsParameters() + super().__init__() # Define the Parameters required by this module - sf.par("UNIT_OUTPUT", required=True, par_type=str, - docstr="Data units. Must match the synthetic output of external " - "solver. Available: ['DISP': displacement, " - "'VEL': velocity, 'ACC': acceleration]") - + self.required.par( + "UNIT_OUTPUT", required=True, par_type=str, + docstr="Data units. Must match the synthetic output of external " + "solver. Available: ['DISP': displacement, " + "'VEL': velocity, 'ACC': acceleration]" + ) # TODO Check this against T0 in check() - sf.par("START_PAD", required=False, default=0, par_type=float, - docstr="For data gathering; time before origin time to gather. " - "START_PAD >= T_0 in SPECFEM constants.h.in. " - "Positive values only") - + self.required.par( + "START_PAD", required=False, default=0, par_type=float, + docstr="For data gathering; time before origin time to gather. " + "START_PAD >= T_0 in SPECFEM constants.h.in. " + "Positive values only" + ) # TODO set this automatically by setting equal NT * DT - sf.par("END_PAD", required=True, par_type=float, - docstr="For data gathering; time after origin time to gather. " - "END_PAD >= NT * DT (of Par_file). Positive values only") - - sf.par("MIN_PERIOD", required=False, default="", par_type=float, - docstr="Minimum filter corner in unit seconds. Bandpass filter " - "if set with `MAX_PERIOD`, highpass filter if set " - "without `MAX_PERIOD`, no filtering if not set and " - "`MAX_PERIOD also not set") - - sf.par("MAX_PERIOD", required=False, default="", par_type=float, - docstr="Maximum filter corner in unit seconds. Bandpass filter " - "if set with `MIN_PERIOD`, lowpass filter if set " - "without `MIN_PERIOD`, no filtering if not set and " - "`MIN_PERIOD also not set") - - sf.par("CORNERS", required=False, default=4, par_type=int, - docstr="Number of filter corners applied to filtering") - - sf.par("CLIENT", required=False, par_type=str, - docstr="Client name for ObsPy FDSN data gathering. Pyatoa will " - "attempt to collect waveform and metadata based on " - "network and station codes provided in the SPECFEM " - "STATIONS file. If set None, no FDSN gathering will be " - "attempted") - - sf.par("ROTATE", required=False, default=False, par_type=bool, - docstr="Attempt to rotate waveform components from NEZ -> RTZ") - - sf.par("PYFLEX_PRESET", required=False, default="default", - par_type=str, - docstr="Parameter map for misfit window configuration defined " - "by Pyflex. IF None, misfit and adjoint sources will be " - "calculated on whole traces. For available choices, " - "see Pyatoa docs page (pyatoa.rtfd.io)") - - sf.par("FIX_WINDOWS", required=False, default=False, - par_type="bool or str", - docstr="How to address misfit window evaluation at each " - "evaluation. Options to re-use misfit windows collected " - "during an inversion, available options: " - "[True, False, 'ITER', 'ONCE'] " - "True: Re-use windows after first evaluation (i01s00); " - "False: Calculate new windows each evaluation; " - "'ITER': Calculate new windows at first evaluation of " - "each iteration (e.g., i01s00... i02s00..." - "'ONCE': Calculate new windows at first evaluation of " - "the workflow, i.e., at PAR.BEGIN") - - sf.par("ADJ_SRC_TYPE", required=False, default="cc", par_type=str, - docstr="Adjoint source type to evaluate misfit, defined by " - "Pyadjoint. Currently available options: " - "['cc': cross-correlation, 'mt': multitaper, " - "wav: waveform']") - - sf.par("PLOT", required=False, default=True, par_type=bool, - docstr="Attempt to plot waveforms and maps as PDF files at each " - "function evaluation") - - sf.par("PYATOA_LOG_LEVEL", required=False, default="DEBUG", - par_type=str, - docstr="Log level to set Pyatoa, Pyflex, Pyadjoint. Available: " - "['null': no logging, 'warning': warnings only, " - "'info': task tracking, " - "'debug': log all small details (recommended)]") - + self.required.par( + "END_PAD", required=True, par_type=float, + docstr="For data gathering; time after origin time to gather. " + "END_PAD >= NT * DT (of Par_file). Positive values only" + ) + self.required.par( + "MIN_PERIOD", required=False, default="", par_type=float, + docstr="Minimum filter corner in unit seconds. Bandpass filter " + "if set with `MAX_PERIOD`, highpass filter if set " + "without `MAX_PERIOD`, no filtering if not set and " + "`MAX_PERIOD also not set" + ) + self.required.par( + "MAX_PERIOD", required=False, default="", par_type=float, + docstr="Maximum filter corner in unit seconds. Bandpass filter " + "if set with `MIN_PERIOD`, lowpass filter if set " + "without `MIN_PERIOD`, no filtering if not set and " + "`MIN_PERIOD also not set" + ) + self.required.par( + "CORNERS", required=False, default=4, par_type=int, + docstr="Number of filter corners applied to filtering" + ) + self.required.par( + "CLIENT", required=False, par_type=str, + docstr="Client name for ObsPy FDSN data gathering. Pyatoa will " + "attempt to collect waveform and metadata based on " + "network and station codes provided in the SPECFEM " + "STATIONS file. If set None, no FDSN gathering will be " + "attempted" + ) + self.required.par( + "ROTATE", required=False, default=False, par_type=bool, + docstr="Attempt to rotate waveform components from NEZ -> RTZ" + ) + self.required.par( + "PYFLEX_PRESET", required=False, default="default", par_type=str, + docstr="Parameter map for misfit window configuration defined " + "by Pyflex. IF None, misfit and adjoint sources will be " + "calculated on whole traces. For available choices, " + "see Pyatoa docs page (pyatoa.rtfd.io)" + ) + self.required.par( + "FIX_WINDOWS", required=False, default=False, \ + par_type="bool or str", + docstr="How to address misfit window evaluation at each " + "evaluation. Options to re-use misfit windows collected " + "during an inversion, available options: " + "[True, False, 'ITER', 'ONCE'] " + "True: Re-use windows after first evaluation (i01s00); " + "False: Calculate new windows each evaluation; " + "'ITER': Calculate new windows at first evaluation of " + "each iteration (e.g., i01s00... i02s00..." + "'ONCE': Calculate new windows at first evaluation of " + "the workflow, i.e., at self.par.BEGIN" + ) + self.required.par( + "ADJ_SRC_TYPE", required=False, default="cc", par_type=str, + docstr="Adjoint source type to evaluate misfit, defined by " + "Pyadjoint. Currently available options: " + "['cc': cross-correlation, 'mt': multitaper, " + "wav: waveform']" + ) + self.required.par( + "PLOT", required=False, default=True, par_type=bool, + docstr="Attempt to plot waveforms and maps as PDF files at each " + "function evaluation" + ) + + self.required.par( + "PYATOA_LOG_LEVEL", required=False, default="DEBUG", par_type=str, + docstr="Log level to set Pyatoa, Pyflex, Pyadjoint. Available: " + "['null': no logging, 'warning': warnings only, " + "'info': task tracking, " + "'debug': log all small details (recommended)]" + ) # Parameters to control saving scratch/preprocess files to work dir. - sf.par("SAVE_DATASETS", required=False, default=True, par_type=bool, - docstr="Save PyASDF HDF5 datasets to disk. These datasets store " - "waveform data, metadata, misfit windows, adjoint " - "sources and configuration parameters") - - sf.par("SAVE_FIGURES", required=False, default=True, par_type=bool, - docstr="Save output waveform figures to disk as PDFs") - - sf.par("SAVE_LOGS", required=False, default=True, par_type=bool, - docstr="Save event-specific Pyatoa logs to disk as .txt files") - + self.required.par( + "SAVE_DATASETS", required=False, default=True, par_type=bool, + docstr="Save PyASDF HDF5 datasets to disk. These datasets store " + "waveform data, metadata, misfit windows, adjoint " + "sources and configuration parameters" + ) + self.required.par( + "SAVE_FIGURES", required=False, default=True, par_type=bool, + docstr="Save output waveform figures to disk as PDFs" + ) + self.required.par( + "SAVE_LOGS", required=False, default=True, par_type=bool, + docstr="Save event-specific Pyatoa logs to disk as .txt files" + ) # Define the Paths required by this module - sf.path("PREPROCESS", required=False, - default=os.path.join(PATH.SCRATCH, "preprocess"), - docstr="scratch/ path to store waveform data and figures. " - "Pyatoa will generate an internal directory structure " - "here") - - sf.path("DATA", required=False, - docstr="Directory to locally stored data. Pyatoa looks for " - "waveform and metadata in the 'PATH.DATA/mseed' and " - "'PATH.DATA/seed', directories respectively.") - - return sf + self.required.path( + "PREPROCESS", required=False, + default=os.path.join(self.path.WORKDIR, "scratch", "preprocess"), + docstr="scratch/ path to store waveform data and figures. " + "Pyatoa will generate an internal directory structure " + "here" + ) + self.required.path( + "DATA", required=False, + docstr="Directory to locally stored data. Pyatoa looks for " + "waveform and metadata in the 'self.path.DATA/mseed' and " + "'self.path.DATA/seed', directories respectively." + ) def check(self, validate=True): """ Checks Parameter and Path files, will be run at the start of a Seisflows workflow to ensure that things are set appropriately. """ - if validate: - self.required.validate() + super().check(validate=validate) # Check that other modules have set parameters that will be used here for required_parameter in ["COMPONENTS", "FORMAT"]: - assert(required_parameter in PAR), \ + assert(required_parameter in self.par), \ f"Pyatoa requires {required_parameter}" - assert(PAR.FORMAT.upper() == "ASCII"), \ - "Pyatoa preprocess requires PAR.FORMAT=='ASCII'" + assert(self.par.FORMAT.upper() == "ASCII"), \ + "Pyatoa preprocess requires self.par.FORMAT=='ASCII'" - # assert((PAR.DT * PAR.NT) <= (PAR.START_PAD + PAR.END_PAD)), \ - # ("Pyatoa preprocess must have (PAR.START_PAD + PAR.END_PAD) >= " - # "(PAR.DT * PAR.NT), current values will not provide sufficiently " - # f"long data traces (DT*NT={PAR.DT * PAR.NT}; " - # f"START+END={PAR.START_PAD + PAR.END_PAD}") + # assert((self.par.DT * self.par.NT) <= (self.par.START_PAD + self.par.END_PAD)), \ + # ("Pyatoa preprocess must have (self.par.START_PAD + self.par.END_PAD) >= " + # "(self.par.DT * self.par.NT), current values will not provide sufficiently " + # f"long data traces (DT*NT={self.par.DT * self.par.NT}; " + # f"START+END={self.par.START_PAD + self.par.END_PAD}") def setup(self): """ @@ -185,7 +186,54 @@ def setup(self): Akin to an __init__ class, but to be called externally by the workflow. """ - unix.mkdir(PATH.PREPROCESS) + super().setup() + + unix.mkdir(self.path.PREPROCESS) + + def finalize(self): + """ + Run some serial finalization tasks specific to Pyatoa, which will help + aggregate the collection of output information. + + .. note:: + This finalize function performs the following tasks: + * Generate .csv files using the Inspector + * Aggregate event-specific PDFs into a single evaluation PDF + * Save scratch/ data into output/ if requested + """ + super().finalize() + + # Initiate Pyaflowa to get access to path structure + pyaflowa = Pyaflowa(sfpar=self.par, sfpath=self.path) + unix.cd(pyaflowa.paths.datasets) + + # Generate the Inspector from existing datasets and save to disk + # Allow this is fail, which might happen if we don't have enough data + # or the Dataset is not formatted as expected + insp = Inspector(self.par.TITLE, verbose=False) + try: + insp.discover() + insp.save() + except Exception as e: + self.logger.warning(f"Uncontrolled exception in Inspector creation " + f"will not create inspector:\n{e}") + + # Make the final PDF for easier User ingestion of waveform/map figures + pyaflowa.make_evaluation_composite_pdf() + + # Move scratch/ directory results into more permanent storage + if self.par.SAVE_DATASETS: + datasets = glob(os.path.join(pyaflowa.paths.datasets, "*.h5")) + self._save_quantity(datasets, tag="datasets") + + if self.par.SAVE_FIGURES: + figures = glob(os.path.join(pyaflowa.paths.figures, "*.pdf")) + self._save_quantity(figures, tag="figures") + + if self.par.SAVE_LOGS: + logs = glob(os.path.join(pyaflowa.paths.logs, "*.txt")) + path_out = os.path.join(self.path.WORKDIR, CFGPATHS.LOGDIR) + self._save_quantity(logs, path_out=path_out) def prepare_eval_grad(self, cwd, taskid, source_name, **kwargs): """ @@ -220,7 +268,7 @@ def prepare_eval_grad(self, cwd, taskid, source_name, **kwargs): # Process all the stations for a given event using Pyaflowa pyaflowa = self._setup_event_pyaflowa(source_name) - scaled_misfit = pyaflowa.process(nproc=PAR.NPROC) + scaled_misfit = pyaflowa.process(nproc=self.par.NPROC) if scaled_misfit is None: print(msg.cli(f"Event {source_name} returned no misfit, you may " @@ -232,7 +280,7 @@ def prepare_eval_grad(self, cwd, taskid, source_name, **kwargs): sys.exit(-1) # Event misfit defined by Tape et al. (2010) written to solver dir. - self.write_residuals(path=cwd, scaled_misfit=scaled_misfit) + self._write_residuals(path=cwd, scaled_misfit=scaled_misfit) def _setup_event_pyaflowa(self, source_name=None): """ @@ -248,10 +296,8 @@ def _setup_event_pyaflowa(self, source_name=None): :param source_name: solver source name to evaluate setup for. Must match from list defined by: solver.source_names """ - # Late import because preprocess is loaded before optimize, - # Optimize required to know which iteration/step_count we are at - solver = sys.modules["seisflows_solver"] - optimize = sys.modules["seisflows_optimize"] + solver = self.module("solver") + optimize = self.module("optimize") iteration = optimize.iter if source_name is None: @@ -264,54 +310,11 @@ def _setup_event_pyaflowa(self, source_name=None): step_count = "" # Outsource data processing to an event-specfic Pyaflowa instance - pyaflowa = Pyaflowa(sfpar=PAR, sfpath=PATH) + pyaflowa = Pyaflowa(sfpar=self.par, sfpath=self.path) pyaflowa.setup(source_name=source_name, iteration=iteration, step_count=step_count, loc="*", cha="*") return pyaflowa - - def finalize(self): - """ - Run some serial finalization tasks specific to Pyatoa, which will help - aggregate the collection of output information. - - .. note:: - This finalize function performs the following tasks: - * Generate .csv files using the Inspector - * Aggregate event-specific PDFs into a single evaluation PDF - * Save scratch/ data into output/ if requested - """ - # Initiate Pyaflowa to get access to path structure - pyaflowa = Pyaflowa(sfpar=PAR, sfpath=PATH) - unix.cd(pyaflowa.paths.datasets) - - # Generate the Inspector from existing datasets and save to disk - # Allow this is fail, which might happen if we don't have enough data - # or the Dataset is not formatted as expected - insp = Inspector(PAR.TITLE, verbose=False) - try: - insp.discover() - insp.save() - except Exception as e: - self.logger.warning(f"Uncontrolled exception in Inspector creation " - f"will not create inspector:\n{e}") - - # Make the final PDF for easier User ingestion of waveform/map figures - pyaflowa.make_evaluation_composite_pdf() - - # Move scratch/ directory results into more permanent storage - if PAR.SAVE_DATASETS: - datasets = glob(os.path.join(pyaflowa.paths.datasets, "*.h5")) - self._save_quantity(datasets, tag="datasets") - - if PAR.SAVE_FIGURES: - figures = glob(os.path.join(pyaflowa.paths.figures, "*.pdf")) - self._save_quantity(figures, tag="figures") - - if PAR.SAVE_LOGS: - logs = glob(os.path.join(pyaflowa.paths.logs, "*.txt")) - path_out = os.path.join(PATH.WORKDIR, CFGPATHS.LOGDIR) - self._save_quantity(logs, path_out=path_out) def _save_quantity(self, filepaths, tag="", path_out=""): """ @@ -321,13 +324,13 @@ def _save_quantity(self, filepaths, tag="", path_out=""): :type filepaths: list :param filepaths: full path to files that should be saved to output/ :type tag: str - :param tag: tag for saving the files in PATH.OUTPUT. If not given, will + :param tag: tag for saving the files in self.path.OUTPUT. If not given, will save directly into the output/ directory :type path_out: str :param path_out: overwrite the default output path file naming """ if not path_out: - path_out = os.path.join(PATH.OUTPUT, tag) + path_out = os.path.join(self.path.OUTPUT, tag) if not os.path.exists(path_out): unix.mkdir(path_out) @@ -336,12 +339,13 @@ def _save_quantity(self, filepaths, tag="", path_out=""): dst = os.path.join(path_out, os.path.basename(src)) unix.cp(src, dst) - def write_residuals(self, path, scaled_misfit): + @staticmethod + def _write_residuals(path, scaled_misfit): """ Computes residuals and saves them to a text file in the appropriate path :type path: str - :param path: scratch directory path, e.g. PATH.GRAD or PATH.FUNC + :param path: scratch directory path, e.g. self.path.GRAD or self.path.FUNC :type scaled_misfit: float :param scaled_misfit: the summation of misfit from each source-receiver pair calculated by prepare_eval_grad() @@ -363,10 +367,10 @@ def sum_residuals(self, files): :rtype: float :return: average misfit """ - if len(files) != PAR.NTASK: + if len(files) != self.par.NTASK: print(msg.cli(f"Pyatoa preprocessing module did not recover the " f"correct number of residual files " - f"({len(files)}/{PAR.NTASK}). Please check that " + f"({len(files)}/{self.par.NTASK}). Please check that " f"the preprocessing logs", header="error") ) sys.exit(-1) @@ -375,7 +379,7 @@ def sum_residuals(self, files): for filename in files: total_misfit += np.sum(np.loadtxt(filename)) - total_misfit /= PAR.NTASK + total_misfit /= self.par.NTASK return total_misfit From 4a2781ea6aadaf53703253fc8b4fd33da91846b6 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Mon, 27 Jun 2022 16:32:41 -0800 Subject: [PATCH 029/195] renamed solver.base to solver.specfem to signify that this is really only a specfem dependent base class, and to remove confusion from core base class --- seisflows/solver/{base.py => specfem.py} | 242 +++++++++++------------ 1 file changed, 119 insertions(+), 123 deletions(-) rename seisflows/solver/{base.py => specfem.py} (85%) diff --git a/seisflows/solver/base.py b/seisflows/solver/specfem.py similarity index 85% rename from seisflows/solver/base.py rename to seisflows/solver/specfem.py index f91e9d80..d2541390 100644 --- a/seisflows/solver/base.py +++ b/seisflows/solver/specfem.py @@ -1,34 +1,29 @@ #!/usr/bin/env python3 """ This Solver module is in charge of interacting with external numerical solvers -such as SPECFEM (2D/3D/3D_GLOBE). The Base class provides general functions -that work with SPECFEM, while subclasses provide details to differentiate the -various types of SPECFEM. +such as SPECFEM (2D/3D/3D_GLOBE). This SPECFEM base class provides general +functions that work with all versions of SPECFEM. Subclasses will provide +additional capabilities unique to each version of SPECFEM. """ import os import sys -import logging import subprocess import numpy as np from glob import glob +from seisflows.core import Base from seisflows.plugins import solver_io from seisflows.tools import msg, unix from seisflows.tools.specfem import Container, getpar from seisflows.tools.wrappers import diff -from seisflows.core import SeisFlowsPathsParameters, Dict +from seisflows.core import Dict -PAR = sys.modules['seisflows_parameters'] -PATH = sys.modules['seisflows_paths'] -system = sys.modules['seisflows_system'] -preprocess = sys.modules['seisflows_preprocess'] - - -class Base: +class Specfem(Base): """ This base class provides an interface through which solver simulations can - be set up and run and a parent class for the following subclasses: + be set up and run. It should not be used by itself, but rather it is meant + to provide the foundation for the following child classes: SPECFEM2D SPECFEM3D @@ -85,9 +80,6 @@ class Base: !!! Required functions which must be implemented by subclass !!! """ - # Class-specific logger accessed using self.logger - logger = logging.getLogger(__name__).getChild(__qualname__) - def __init__(self): """ These parameters should not be set by the User_! @@ -106,91 +98,92 @@ def __init__(self): :param logger: Class-specific logging module, log statements pushed from this logger will be tagged by its specific module/classname """ + super().__init__() + + self.required.par( + "MATERIALS", required=False, par_type=str, default="ELASTIC", + docstr="Material parameters used to define model. Available: " + "['ELASTIC': Vp, Vs, 'ACOUSTIC': Vp, 'ISOTROPIC', " + "'ANISOTROPIC']" + ) + self.required.par( + "DENSITY", required=False, par_type=str, default="CONSTANT", + docstr="How to treat density during inversion. Available: " + "['CONSTANT': Do not update density, " + "'VARIABLE': Update density]" + ) + self.required.par( + "ATTENUATION", required=False, par_type=bool, default=False, + docstr="If True, turn on attenuation during forward " + "simulations, otherwise set attenuation off. Attenuation " + "is always off for adjoint simulations." + ) + self.required.par( + "FORMAT", required=False, par_type=float, default="ASCII", + docstr="Format of synthetic waveforms used during workflow, " + "available options: ['ASCII', 'SU']" + ) + self.required.par( + "COMPONENTS", required=False, default="ZNE", par_type=str, + docstr="Components used to generate data, formatted as a single " + "string, e.g. ZNE or NZ or E" + ) + self.required.par( + "SOLVERIO", required=False, default="fortran_binary", par_type=str, + docstr="The format external solver files. Available: " + "['fortran_binary']" + ) + self.required.path( + "SOLVER", required=False, + default=os.path.join(self.path.WORKDIR, "scratch", "solver"), + docstr="scratch path to hold solver working directories" + ) + self.required.path( + "DATA", required=False, + docstr="path to a directory containing any external data " + "required by the workflow. Catch all directory that " + "can be accessed by all modules" + ) + self.required.path( + "SPECFEM_BIN", required=False, + default=os.path.join(self.path.WORKDIR, "specfem", "bin"), + docstr="path to the SPECFEM binary executables" + ) + self.required.path( + "SPECFEM_DATA", required=False, + default=os.path.join(self.path.WORKDIR, "specfem", "DATA"), + docstr="path to the SPECFEM DATA/ directory containing the " + "'Par_file', 'STATIONS' file and 'CMTSOLUTION' files" + ) + self.parameters = [] self.nt = None self.dt = None self._mesh_properties = None self._source_names = None - @property - def required(self): - """ - A hard definition of paths and parameters required by this class, - alongside their necessity for the class and their string explanations. - """ - sf = SeisFlowsPathsParameters() - - sf.par("MATERIALS", required=False, par_type=str, default="ELASTIC", - docstr="Material parameters used to define model. Available: " - "['ELASTIC': Vp, Vs, 'ACOUSTIC': Vp, 'ISOTROPIC', " - "'ANISOTROPIC']") - - sf.par("DENSITY", required=False, par_type=str, default="CONSTANT", - docstr="How to treat density during inversion. Available: " - "['CONSTANT': Do not update density, " - "'VARIABLE': Update density]") - - sf.par("ATTENUATION", required=False, par_type=bool, default=False, - docstr="If True, turn on attenuation during forward " - "simulations, otherwise set attenuation off. Attenuation " - "is always off for adjoint simulations.") - - sf.par("FORMAT", required=False, par_type=float, default="ASCII", - docstr="Format of synthetic waveforms used during workflow, " - "available options: ['ascii', 'su']") - - sf.par("COMPONENTS", required=False, default="ZNE", par_type=str, - docstr="Components used to generate data, formatted as a single " - "string, e.g. ZNE or NZ or E") - - sf.par("SOLVERIO", required=False, default="fortran_binary", - par_type=int, - docstr="The format external solver files. Available: " - "['fortran_binary', 'adios']") - - sf.path("SOLVER", required=False, - default=os.path.join(PATH.WORKDIR, "scratch", "solver"), - docstr="scratch path to hold solver working directories") - - sf.path("DATA", required=False, - docstr="path to a directory containing any external data " - "required by the workflow. Catch all directory that " - "can be accessed by all modules") - - sf.path("SPECFEM_BIN", required=False, - default=os.path.join(PATH.WORKDIR, "specfem", "bin"), - docstr="path to the SPECFEM binary executables") - - sf.path("SPECFEM_DATA", required=False, - default=os.path.join(PATH.WORKDIR, "specfem", "DATA"), - docstr="path to the SPECFEM DATA/ directory containing the " - "'Par_file', 'STATIONS' file and 'CMTSOLUTION' files") - - return sf - def check(self, validate=True): """ Checks parameters and paths """ - if validate: - self.required.validate() + super().check(validate=validate) # Check that other modules have set parameters that will be used here for required_parameter in ["NPROC"]: - assert (required_parameter in PAR), \ + assert (required_parameter in self.par), \ f"Solver requires {required_parameter}" available_materials = ["ELASTIC", "ACOUSTIC", # specfem2d, specfem3d "ISOTROPIC", "ANISOTROPIC"] # specfem3d_globe - assert(PAR.MATERIALS.upper() in available_materials), \ + assert(self.par.MATERIALS.upper() in available_materials), \ f"MATERIALS must be in {available_materials}" acceptable_densities = ["CONSTANT", "VARIABLE"] - assert(PAR.DENSITY.upper() in acceptable_densities), \ + assert(self.par.DENSITY.upper() in acceptable_densities), \ f"DENSITY must be in {acceptable_densities}" acceptable_formats = ["SU", "ASCII"] - if PAR.FORMAT.upper() not in acceptable_formats: + if self.par.FORMAT.upper() not in acceptable_formats: raise Exception(f"'FORMAT' must be {acceptable_formats}") # Internal parameter list based on user-input material choices @@ -198,30 +191,30 @@ def check(self, validate=True): # statements fill it. If not, each time workflow is resumed, parameters # list will append redundant parameters and things stop working self.parameters = [] - if PAR.MATERIALS.upper() == "ELASTIC": - assert(PAR.SOLVER.lower() in ["specfem2d", "specfem3d"]) + if self.par.MATERIALS.upper() == "ELASTIC": + assert(self.par.SOLVER.lower() in ["specfem2d", "specfem3d"]) self.parameters += ["vp", "vs"] - elif PAR.MATERIALS.upper() == "ACOUSTIC": - assert(PAR.SOLVER.lower() in ["specfem2d", "specfem3d"]) + elif self.par.MATERIALS.upper() == "ACOUSTIC": + assert(self.par.SOLVER.lower() in ["specfem2d", "specfem3d"]) self.parameters += ["vp"] - elif PAR.MATERIALS.upper() == "ISOTROPIC": - assert(PAR.SOLVER.lower() in ["specfem3d_globe"]) + elif self.par.MATERIALS.upper() == "ISOTROPIC": + assert(self.par.SOLVER.lower() in ["specfem3d_globe"]) self.parameters += ["vp", "vs"] - elif PAR.MATERIALS.upper() == "ANISOTROPIC": - assert(PAR.SOLVER.lower() in ["specfem3d_globe"]) + elif self.par.MATERIALS.upper() == "ANISOTROPIC": + assert(self.par.SOLVER.lower() in ["specfem3d_globe"]) self.parameters += ["vpv", "vph", "vsv", "vsh", "eta"] - if PAR.DENSITY.upper() == "VARIABLE": + if self.par.DENSITY.upper() == "VARIABLE": self.parameters.append("rho") - assert hasattr(solver_io, PAR.SOLVERIO) + assert hasattr(solver_io, self.par.SOLVERIO) assert hasattr(self.io, "read_slice"), \ "IO method has no attribute 'read_slice'" assert hasattr(self.io, "write_slice"), \ "IO method has no attribute 'write_slice'" def setup(self): - """ + """ Prepares solver for inversion or migration. Sets up directory structure expected by SPECFEM and copies or generates seismic data to be inverted or migrated @@ -268,6 +261,8 @@ def eval_func(self, path, write_residuals=True): :type write_residuals: bool :param write_residuals: calculate and export residuals """ + preprocess = self.module("preprocess") + if self.taskid == 0: self.logger.info("running forward simulations") @@ -367,8 +362,6 @@ def call_solver(self, executable, output="solver.log"): non-zero exit code (failure), this function will return a negative boolean. - :type mpiexec: str - :param mpiexec: call to mpi. If None (e.g., serial run, defaults to ./) :type executable: str :param executable: executable function to call :type output: str @@ -382,11 +375,11 @@ def call_solver(self, executable, output="solver.log"): sys.exit(-1) # mpiexec is None when running in serial mode, so e.g., ./xmeshfem2D - if PAR.SYSTEM in ["workstation"]: + if self.par.SYSTEM in ["workstation"]: exc_cmd = f"./{executable}" # Otherwise mpiexec is system dependent (e.g., srun, mpirun) else: - exc_cmd = f"{PAR.MPIEXEC} {executable}" + exc_cmd = f"{self.par.MPIEXEC} {executable}" # Run solver. Write solver stdout (log files) to text file try: @@ -405,17 +398,8 @@ def call_solver(self, executable, output="solver.log"): ) sys.exit(-1) - @property - def io(self): - """ - Solver IO module set by User. - - Located in seisflows.plugins.solver_io - """ - return getattr(solver_io, PAR.SOLVERIO) - def load(self, path, prefix="", suffix="", parameters=None,): - """ + """ Solver I/O: Loads SPECFEM2D/3D models or kernels :type path: str @@ -444,7 +428,7 @@ def load(self, path, prefix="", suffix="", parameters=None,): return load_dict def save(self, save_dict, path, parameters=None, prefix="", suffix=""): - """ + """ Solver I/O: Saves SPECFEM2D/3D models or kernels :type save_dict: dict or Container @@ -468,7 +452,7 @@ def save(self, save_dict, path, parameters=None, prefix="", suffix=""): for iproc in range(self.mesh_properties.nproc): for key in missing_keys: save_dict[key] += self.io.read_slice( - path=PATH.MODEL_INIT, parameters=f"{prefix}{key}{suffix}", + path=self.path.MODEL_INIT, parameters=f"{prefix}{key}{suffix}", iproc=iproc ) @@ -531,11 +515,11 @@ def split(self, m, parameters=None): def combine(self, input_path, output_path, parameters=None): """ - Postprocessing wrapper: xcombine_sem + Postprocessing wrapper: xcombine_sem Sums kernels from individual source contributions to create gradient. .. note:: - The binary xcombine_sem simply sums matching databases (.bin) + The binary xcombine_sem simply sums matching databases (.bin) .. note:: It is ASSUMED that this function is being called by @@ -731,7 +715,8 @@ def export_traces(self, path, prefix="traces/obs"): dst = os.path.join(path, self.source_name) unix.cp(src, dst) - def rename_kernels(self): + @staticmethod + def rename_kernels(): """ Works around conflicting kernel filename conventions by renaming `alpha` to `vp` and `beta` to `vs` @@ -767,7 +752,7 @@ def initialize_solver_directories(self): extra files. """ if self.taskid == 0: - self.logger.info(f"initializing {PAR.NTASK} solver directories") + self.logger.info(f"initializing {self.par.NTASK} solver directories") unix.rm(self.cwd) unix.mkdir(self.cwd) @@ -780,25 +765,25 @@ def initialize_solver_directories(self): unix.mkdir(cwd_dir) # Copy exectuables into the bin/ directory - src = glob(os.path.join(PATH.SPECFEM_BIN, "*")) + src = glob(os.path.join(self.path.SPECFEM_BIN, "*")) dst = os.path.join("bin", "") unix.cp(src, dst) # Copy all input files except source files - src = glob(os.path.join(PATH.SPECFEM_DATA, "*")) + src = glob(os.path.join(self.path.SPECFEM_DATA, "*")) src = [_ for _ in src if self.source_prefix not in _] dst = os.path.join("DATA", "") unix.cp(src, dst) # Symlink event source specifically, strip the source name as SPECFEM # just expects `source_name` - src = os.path.join(PATH.SPECFEM_DATA, + src = os.path.join(self.path.SPECFEM_DATA, f"{self.source_prefix}_{self.source_name}") dst = os.path.join("DATA", self.source_prefix) unix.ln(src, dst) - if self.taskid == 0: - mainsolver = os.path.join(PATH.SOLVER, "mainsolver") + if self.taskid == 0: + mainsolver = os.path.join(self.path.SOLVER, "mainsolver") # Symlink taskid_0 as mainsolver in solver directory for convenience if not os.path.exists(mainsolver): unix.ln(self.cwd, mainsolver) @@ -807,7 +792,7 @@ def initialize_solver_directories(self): else: # Copy the initial model from mainsolver into current directory # Avoids the need to run multiple instances of xgenerate_databases - src = os.path.join(PATH.SOLVER, "mainsolver", "OUTPUT_FILES", + src = os.path.join(self.path.SOLVER, "mainsolver", "OUTPUT_FILES", "DATABASES_MPI") dst = os.path.join(self.cwd, "OUTPUT_FILES", "DATABASES_MPI") unix.cp(src, dst) @@ -822,7 +807,9 @@ def initialize_adjoint_traces(self): Channels actually in use during an inversion or migration will be overwritten with nonzero values later on. """ - if PAR.PREPROCESS.lower() == "default": + preprocess = self.module("preprocess") + + if self.par.PREPROCESS.upper() == "DEFAULT": if self.taskid == 0: self.logger.debug(f"intializing {len(self.data_filenames)} " f"empty adjoint traces per event") @@ -850,7 +837,7 @@ def check_mesh_properties(self, path=None): """ # Check the given model path or the initial model if path is None: - path = PATH.MODEL_INIT + path = self.path.MODEL_INIT if os.path.exists(path): # Count the number of .bin files and the number of grid points @@ -881,11 +868,11 @@ def check_source_names(self): # Apply wildcard rule and check for available sources, exit if no # sources found because then we can't proceed wildcard = f"{self.source_prefix}_*" - fids = sorted(glob(os.path.join(PATH.SPECFEM_DATA, wildcard))) + fids = sorted(glob(os.path.join(self.path.SPECFEM_DATA, wildcard))) if not fids: print(msg.cli("No matching source files when searching PATH for" "the given WILDCARD", - items=[f"PATH: {PATH.SPECFEM_DATA}", + items=[f"PATH: {self.path.SPECFEM_DATA}", f"WILDCARD: {wildcard}"], header="error" ) ) @@ -893,7 +880,7 @@ def check_source_names(self): # Create internal definition of sources names by stripping prefixes names = [os.path.basename(fid).split("_")[-1] for fid in fids] - self._source_names = names[:PAR.NTASK] + self._source_names = names[:self.par.NTASK] def check_solver_parameter_files(self): """ @@ -903,6 +890,15 @@ def check_solver_parameter_files(self): """ pass + @property + def io(self): + """ + Solver IO module set by User. + + Located in seisflows.plugins.solver_io + """ + return getattr(solver_io, self.par.SOLVERIO) + @property def taskid(self): """ @@ -912,7 +908,7 @@ def taskid(self): :rtype: int :return: task id for given solver """ - return system.taskid() + return self.module("system").taskid() @property def source_name(self): @@ -932,7 +928,7 @@ def cwd(self): :rtype: str :return: current solver working directory """ - return os.path.join(PATH.SOLVER, self.source_name) + return os.path.join(self.path.SOLVER, self.source_name) @property def source_names(self): @@ -995,6 +991,6 @@ def source_prefix(self): :rtype: str :return: source prefix """ - return PAR.SOURCE_PREFIX.upper() + return self.par.SOURCE_PREFIX.upper() From f9ffb1f7ae480a36cf886825d9e3b30f80b36213 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Mon, 27 Jun 2022 17:30:51 -0800 Subject: [PATCH 030/195] specefem2d and specfem3d now have the new code styling. Trying to condense functions into the base Specfem classas there was redundant code between 2D and 3D versions. Also optimizing code by not running mesher for models that should have already been generated --- seisflows/solver/specfem.py | 331 ++++++++++++----------- seisflows/solver/specfem2d.py | 482 +++++++++++++++++++--------------- seisflows/solver/specfem3d.py | 250 ++++++++---------- 3 files changed, 551 insertions(+), 512 deletions(-) diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index d2541390..60261b3d 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -162,6 +162,124 @@ def __init__(self): self._mesh_properties = None self._source_names = None + @property + def io(self): + """ + Solver IO module set by User. Located in seisflows.plugins.solver_io + + :rtype: module + :return: module containing solver input/output options + """ + return getattr(solver_io, self.par.SOLVERIO) + + @property + def taskid(self): + """ + Returns the currently running process for embarassingly parallelized + tasks. + + :rtype: int + :return: task id for given solver + """ + return self.module("system").taskid() + + @property + def source_names(self): + """ + Returns list of source names + + :rtype: list + :return: list of source names + """ + if self._source_names is None: + self.check_source_names() + + return self._source_names + + @property + def source_name(self): + """ + Returns name of source currently under consideration + + :rtype: str + :return: given source name for given task id + """ + return self.source_names[self.taskid] + + @property + def source_prefix(self): + """ + Preferred source prefix + + TODO remove this and replace all instances with the self.par parameter + + :rtype: str + :return: source prefix + """ + return self.par.SOURCE_PREFIX.upper() + + @property + def cwd(self): + """ + Returns working directory currently in use + + :rtype: str + :return: current solver working directory + """ + return os.path.join(self.path.SOLVER, self.source_name) + + @property + def mesh_properties(self): + """ + Returns mesh properties + + :rtype: Struct + :return: Structure of mesh properties + """ + if self._mesh_properties is None: + self.check_mesh_properties() + + return self._mesh_properties + + @property + def data_wildcard(self, comp="?"): + """ + Provide a wildcard string that will match the name of the output + synthetic seismograms + + :type comp: str + :param comp: single letter defining the component that can be inserted + into the wildcard. Defaults to '?' + """ + return NotImplementedError + + @property + def data_filenames(self): + """ + Template filenames for accessing data + + !!! Must be implemented by subclass !!! + """ + return NotImplementedError + + @property + def model_databases(self): + """ + Template filenames for accessing models + + !!! Must be implemented by subclass !!! + """ + return NotImplementedError + + @property + def kernel_databases(self): + """ + Template filenames for accessing kernels + + !!! Must be implemented by subclass !!! + """ + return NotImplementedError + def check(self, validate=True): """ Checks parameters and paths @@ -219,6 +337,8 @@ def setup(self): Sets up directory structure expected by SPECFEM and copies or generates seismic data to be inverted or migrated + TODO !!! what are the arguments of generate data? + .. note:; As input for an inversion or migration, users can choose between providing data, or providing a target model from which data are @@ -227,8 +347,10 @@ def setup(self): in the latter case, a value for PATH.MODEL_TRUE must be provided. """ self.initialize_solver_directories() + self.set_model(model_name="true", model_type="gll") self.generate_data() - self.generate_mesh(model_name="init", model_type="gll") + + self.set_model(model_name="init", model_type="gll") self.initialize_adjoint_traces() # Assuming that NT and DT are set correctly in the Par_file @@ -236,14 +358,45 @@ def setup(self): self.nt = getpar(key="NSTEP", file="DATA/Par_file")[1] self.dt = getpar(key="DT", file="DATA/Par_file")[1] - def generate_mesh(self, model_path, model_name, model_type): + def set_model(self, model_name, model_type=None): """ - Performs meshing and database generation. + Mesh and database files should have been created during the manual set + up phase. This function simply checks the mesh properties of that mesh + and ensures that it is locatable by future SeisFlows processes. - This function is Solver specific and is responsible for generating - the mesh using the external numerical solver. + :type model_name: str + :param model_name: name of the model to be used as identification + :type model_type: str + :param model_type: available model types to be passed to the Specfem3D + Par_file. See Specfem3D Par_file for available options. """ - raise NotImplementedError("must be implemented by solver subclass") + unix.cd(self.cwd) + + # Check the type of model. So far SeisFlows only accepts GLL models + available_model_types = ["gll"] + model_type = model_type or getpar(key="MODEL", file="DATA/Par_file") + assert(model_type in available_model_types), \ + f"{model_type} not in available types {available_model_types}" + + # Determine which model will be set as the starting model + if model_name.upper() == "INIT": + model_path = self.path.MODEL_INIT + elif model_name.upper() == "TRUE": + model_path = self.path.MODEL_TRUE + else: + raise ValueError(f"model name must be 'INIT' or 'TRUE'") + assert(os.path.exists(model_path)), f"model {model_path} does not exist" + + if model_type == "gll": + self.check_mesh_properties(model_path) + # Copy the model files (ex: proc000023_vp.bin ...) into database dir + src = glob(os.path.join(model_path, "*")) + dst = self.model_databases + unix.cp(src, dst) + + # Export the model into output folder, ready to be used by SeisFlows + if self.taskid == 0: + self.export_model(os.path.join(self.path.OUTPUT, model_name)) def generate_data(self): """ @@ -314,26 +467,26 @@ def eval_grad(self, path, export_traces=False): self.export_traces(path=os.path.join(path, "traces", "adj"), prefix="traces/adj") - def apply_hess(self, path): - """ - High level solver interface that computes action of Hessian on a given - model vector. A gradient evaluation must have already been carried out. - - TODO preprocess has no function prepare_apply_hess() - - :type path: str - :param path: directory to which output files are exported - """ - raise NotImplementedError("must be implemented by solver subclass") - - unix.cd(self.cwd) - self.import_model(path) - unix.mkdir("traces/lcg") - - self.forward("traces/lcg") - preprocess.prepare_apply_hess(self.cwd) - self.adjoint() - self.export_kernels(path) + # def apply_hess(self, path): + # """ + # High level solver interface that computes action of Hessian on a given + # model vector. A gradient evaluation must have already been carried out. + # + # TODO preprocess has no function prepare_apply_hess() + # + # :type path: str + # :param path: directory to which output files are exported + # """ + # raise NotImplementedError("must be implemented by solver subclass") + # + # unix.cd(self.cwd) + # self.import_model(path) + # unix.mkdir("traces/lcg") + # + # self.forward("traces/lcg") + # preprocess.prepare_apply_hess(self.cwd) + # self.adjoint() + # self.export_kernels(path) def forward(self, path): """ @@ -398,7 +551,7 @@ def call_solver(self, executable, output="solver.log"): ) sys.exit(-1) - def load(self, path, prefix="", suffix="", parameters=None,): + def load(self, path, prefix="", suffix="", parameters=None): """ Solver I/O: Loads SPECFEM2D/3D models or kernels @@ -560,7 +713,7 @@ def combine(self, input_path, output_path, parameters=None): ) def smooth(self, input_path, output_path, parameters=None, span_h=0., - span_v=0., output="solver.log"): + span_v=0., output="smooth.log"): """ Postprocessing wrapper: xsmooth_sem Smooths kernels by convolving them with a Gaussian. @@ -715,8 +868,7 @@ def export_traces(self, path, prefix="traces/obs"): dst = os.path.join(path, self.source_name) unix.cp(src, dst) - @staticmethod - def rename_kernels(): + def rename_kernels(self): """ Works around conflicting kernel filename conventions by renaming `alpha` to `vp` and `beta` to `vs` @@ -731,15 +883,6 @@ def rename_kernels(): names = glob(f"*proc??????_{tag}_kernel.bin") unix.rename(old="beta", new="vs", names=names) - def rename_data(self, path): - """ - Optional method to rename data to work around conflicting naming schemes - for data outputted by the solver - - !!! Can be implemented by subclass !!! - """ - raise NotImplementedError("must be implemented by solver subclass") - def initialize_solver_directories(self): """ Creates directory structure expected by SPECFEM3D (bin/, DATA/) copies @@ -882,115 +1025,5 @@ def check_source_names(self): names = [os.path.basename(fid).split("_")[-1] for fid in fids] self._source_names = names[:self.par.NTASK] - def check_solver_parameter_files(self): - """ - Optional method - - !!! Can be implemented by subclass !!! - """ - pass - - @property - def io(self): - """ - Solver IO module set by User. - - Located in seisflows.plugins.solver_io - """ - return getattr(solver_io, self.par.SOLVERIO) - - @property - def taskid(self): - """ - Returns the currently running process for embarassingly parallelized - tasks. - - :rtype: int - :return: task id for given solver - """ - return self.module("system").taskid() - - @property - def source_name(self): - """ - Returns name of source currently under consideration - - :rtype: str - :return: given source name for given task id - """ - return self.source_names[self.taskid] - - @property - def cwd(self): - """ - Returns working directory currently in use - - :rtype: str - :return: current solver working directory - """ - return os.path.join(self.path.SOLVER, self.source_name) - - @property - def source_names(self): - """ - Returns list of source names - - :rtype: list - :return: list of source names - """ - if self._source_names is None: - self.check_source_names() - - return self._source_names - - @property - def mesh_properties(self): - """ - Returns mesh properties - - :rtype: Struct - :return: Structure of mesh properties - """ - if self._mesh_properties is None: - self.check_mesh_properties() - - return self._mesh_properties - - @property - def data_filenames(self): - """ - Template filenames for accessing data - - !!! Must be implemented by subclass !!! - """ - return NotImplementedError - - @property - def model_databases(self): - """ - Template filenames for accessing models - - !!! Must be implemented by subclass !!! - """ - return NotImplementedError - - @property - def kernel_databases(self): - """ - Template filenames for accessing kernels - - !!! Must be implemented by subclass !!! - """ - return NotImplementedError - - @property - def source_prefix(self): - """ - Preferred source prefix - - :rtype: str - :return: source prefix - """ - return self.par.SOURCE_PREFIX.upper() diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index 6284e73c..aba62d18 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -7,33 +7,17 @@ """ import os import sys -import logging from glob import glob +from seisflows.solver.specfem import Specfem from seisflows.tools import unix, msg -from seisflows.tools.wrappers import exists -from seisflows.core import SeisFlowsPathsParameters -from seisflows.config import custom_import from seisflows.tools.specfem import getpar, setpar -PAR = sys.modules['seisflows_parameters'] -PATH = sys.modules['seisflows_paths'] - -system = sys.modules['seisflows_system'] -preprocess = sys.modules['seisflows_preprocess'] - - -class Specfem2D(custom_import("solver", "base")): +class Specfem2D(Specfem): """ - Python interface to Specfem2D. This subclass inherits functions from - seisflows.solver.Base - - !!! See base class for method descriptions !!! + Python interface to Specfem2D. """ - # Class-specific logger accessed using self.logger - logger = logging.getLogger(__name__).getChild(__qualname__) - def __init__(self): """ These parameters should not be set by the user. @@ -41,92 +25,147 @@ def __init__(self): """ super().__init__() + self.required.par( + "SOURCE_PREFIX", required=False, default="SOURCE", par_type=str, + docstr="Prefix of SOURCE files in path SPECFEM_DATA. By " + "default, 'SOURCE' for SPECFEM2D" + ) + self.f0 = None @property - def required(self): + def io(self): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.io + + @property + def taskid(self): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.taskid + + @property + def source_names(self): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.source_names + + @property + def source_name(self): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.source_name + + @property + def source_prefix(self): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.source_prefix + + @property + def cwd(self): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.cwd + + @property + def mesh_properties(self): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.mesh_properties + + @property + def data_wildcard(self, comp="?"): + """ + Returns a wildcard identifier for synthetic data based on SPECFEM2D + file naming schema. Allows formatting dcomponent e.g., + when called by solver.data_filenames + + :type comp: str + :param comp: component formatter, defaults to wildcard '?' + :rtype: str + :return: wildcard identifier for channels """ - A hard definition of paths and parameters required by this class, - alongside their necessity for the class and their string explanations. + if self.par.FORMAT.upper() == "SU": + # return f"*.su" # too vague but maybe for a reason? -bryant + return f"U{comp}_file_single.su" + elif self.par.FORMAT.upper() == "ASCII": + return f"*.?X{comp}.sem?" + + @property + def data_filenames(self): """ - sf = SeisFlowsPathsParameters(super().required) + Returns the filenames of all data, either by the requested components + or by all available files in the directory. - sf.par("SOURCE_PREFIX", required=False, default="SOURCE", - par_type=str, - docstr="Prefix of SOURCE files in path SPECFEM_DATA. By " - "default, 'SOURCE' for SPECFEM2D") + .. note:: + If the glob returns an empty list, this function exits the + workflow because filenames should not be empty is they're being + queried - return sf + :rtype: list + :return: list of data filenames + """ + unix.cd(self.cwd) + unix.cd(os.path.join("traces", "obs")) + + if self.par.COMPONENTS: + filenames = [] + if self.par.FORMAT.upper() == "SU": + for comp in self.par.COMPONENTS: + filenames += [self.data_wildcard.format(comp=comp.lower())] + # filenames += [f"U{comp.lower()}_file_single.su"] + elif self.par.FORMAT.upper() == "ASCII": + for comp in self.par.COMPONENTS: + filenames += glob( + self.data_wildcard.format(comp=comp.upper()) + ) + # filenames += glob(f"*.?X{comp.upper()}.sem?") + else: + filenames = glob(self.data_wildcard) + + if not filenames: + print(msg.cli("The property solver.data_filenames, used to search " + "for traces in 'scratch/solver/*/traces' is empty " + "and should not be. Please check solver parameters: ", + items=[f"data_wildcard: {self.data_wildcard}"], + header="data filenames error", border="=") + ) + sys.exit(-1) + + return filenames + + @property + def model_databases(self): + """ + The location of model inputs and outputs as defined by SPECFEM2D + """ + return os.path.join(self.cwd, "DATA") + + @property + def kernel_databases(self): + """ + The location of kernel inputs and outputs as defined by SPECFEM2D + """ + return os.path.join(self.cwd, "OUTPUT_FILES") def check(self, validate=True): """ Checks parameters and paths """ - if validate: - self.required.validate() - - super().check(validate=False) + super().check(validate=validate) def setup(self): """ Additional SPECFEM2D setup steps """ super().setup() - self.f0 = getpar(key="f0", file=os.path.join(self.cwd, - "DATA/SOURCE"))[1] + self.f0 = getpar(key="f0", + file=os.path.join(self.cwd, "DATA/SOURCE"))[1] - if "MULTIPLES" in PAR: - if PAR.MULTIPLES: + if "MULTIPLES" in self.par: + if self.par.MULTIPLES: setpar(key="absorbtop", val=".false.", file="DATA/Par_file") else: setpar(key="absorbtop", val=".true.", file="DATA/Par_file") - def initialize_adjoint_traces(self): - """ - Setup utility: Creates the "adjoint traces" expected by SPECFEM. - This is only done for the 'base' the Preprocess class. - - Note: - Adjoint traces are initialized by writing zeros for all channels. - Channels actually in use during an inversion or migration will be - overwritten with nonzero values later on. - """ - super().initialize_adjoint_traces() - - unix.cd(self.cwd) - unix.cd(os.path.join("traces", "adj")) - - # work around SPECFEM2D's use of different name conventions for - # regular traces and 'adjoint' traces - if PAR.FORMAT.upper() == "SU": - files = glob("*SU") - unix.rename(old="_SU", new="_SU.adj", names=files) - elif PAR.FORMAT.upper() == "ASCII": - files = glob("*sem?") - - # Get the available extensions, which are named based on unit - extensions = set([os.path.splitext(_)[-1] for _ in files]) - for extension in extensions: - unix.rename(old=extension, new=".adj", names=files) - - # SPECFEM2D requires that all components exist even if ununsed - components = ["x", "y", "z", "p"] - - if PAR.FORMAT.upper() == "SU": - for comp in components: - src = f"U{PAR.COMPONENTS[0]}_file_single.su.adj" - dst = f"U{comp.lower()}s_file_single.su.adj" - if not exists(dst): - unix.cp(src, dst) - elif PAR.FORMAT.upper() == "ASCII": - for fid in glob("*.adj"): - net, sta, cha, ext = fid.split(".") - for comp in components: - # Replace the last value in the channel with new component - cha_check = cha[:-1] + comp.upper() - fid_check = ".".join([net, sta, cha_check, ext]) - if not exists(fid_check): - unix.cp(fid, fid_check) + def set_model(self, model_name, model_type="gll"): + """Inherits from seisflows.solver.specfem.Specfem""" + self.set_model(model_name=model_name, model_type=model_type) def generate_data(self): """ @@ -137,64 +176,32 @@ def generate_data(self): 'cwd/traces/obs' """ # If synthetic inversion, generate 'data' with solver - if PAR.CASE.upper() == "SYNTHETIC": - if PATH.MODEL_TRUE is not None: + if self.par.CASE.upper() == "SYNTHETIC": + if self.path.MODEL_TRUE is not None: if self.taskid == 0: self.logger.info("generating 'data' with MODEL_TRUE") # Generate synthetic data on the fly using the true model - self.generate_mesh(model_name="true", model_type="gll") + self.set_model(model_name="true", model_type="gll") self.forward(path=os.path.join("traces", "obs")) # If Data provided by user, copy directly into the solver directory - elif PATH.DATA is not None and os.path.exists(PATH.DATA): - unix.cp(src=glob(os.path.join(PATH.DATA, self.source_name, "*")), - dst=os.path.join("traces", "obs") - ) - if PAR.SAVETRACES: - self.export_traces(path=os.path.join(PATH.OUTPUT, "traces", "obs")) - - def generate_mesh(self, model_name, model_type="gll"): - """ - Performs meshing with internal mesher Meshfem2D and database generation - - :type model_path: str - :param model_path: path to the model to be used for mesh generation - :type model_name: str - :param model_name: name of the model to be used as identification - :type model_type: str - :param model_type: available model types to be passed to the Specfem3D - Par_file. See Specfem3D Par_file for available options. - """ - unix.cd(self.cwd) - - if model_name.upper() == "INIT": - model_path = PATH.MODEL_INIT - elif model_name.upper() == "TRUE": - model_path = PATH.MODEL_TRUE - else: - raise ValueError(f"model name must be 'INIT' or 'TRUE'") - assert(exists(model_path)), f"model {model_path} does not exist" - - if self.taskid == 0: - self.logger.info(f"running mesh generation for " - f"MODEL_{model_name.upper()}") - - available_model_types = ["gll"] - assert(model_type in available_model_types), \ - f"{model_type} not in available types {available_model_types}" - - # Run mesh generation - if model_type == "gll": - self.check_mesh_properties(model_path) - - # Copy the model files (ex: proc000023_vp.bin ...) into DATA - src = glob(os.path.join(model_path, "*")) - dst = self.model_databases - unix.cp(src, dst) - - # Export the model into output folder - if self.taskid == 0: - self.export_model(os.path.join(PATH.OUTPUT, model_name)) + elif self.path.DATA is not None and os.path.exists(self.path.DATA): + unix.cp( + src=glob(os.path.join(self.path.DATA, self.source_name, "*")), + dst=os.path.join("traces", "obs") + ) + if self.par.SAVETRACES: + self.export_traces( + path=os.path.join(self.path.OUTPUT, "traces", "obs") + ) + + def eval_func(self, path, write_residuals=True): + """Inherits from seisflows.solver.specfem.Specfem""" + self.eval_func(path=path, write_residuals=write_residuals) + + def eval_grad(self, path, export_traces=True): + """Inherits from seisflows.solver.specfem.Specfem""" + self.eval_func(path=path, export_traces=export_traces) def forward(self, path="traces/syn"): """ @@ -212,7 +219,7 @@ def forward(self, path="traces/syn"): self.call_solver(executable="bin/xmeshfem2D", output="fwd_mesher.log") self.call_solver(executable="bin/xspecfem2D", output="fwd_solver.log") - if PAR.FORMAT.upper() == "SU": + if self.par.FORMAT.upper() == "SU": # Work around SPECFEM2D's version dependent file names for tag in ["d", "v", "a", "p"]: unix.rename(old=f"single_{tag}.su", new="single.su", @@ -236,29 +243,72 @@ def adjoint(self): # Deal with different SPECFEM2D name conventions for regular traces and # "adjoint" traces - if PAR.FORMAT.upper == "SU": + if self.par.FORMAT.upper == "SU": unix.rename(old=".su", new=".su.adj", names=glob(os.path.join("traces", "adj", "*.su"))) self.call_solver(executable="bin/xspecfem2D", output="adj_solver.log") - def smooth(self, input_path, **kwargs): + def call_solver(self, executable, output="solver.log"): + """Inherits from seisflows.solver.specfem.Specfem""" + self.call_solver(executable=executable, output=output) + + def load(self, path, prefix="", suffix="", parameters=None): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.load(path=path, prefix=prefix, suffix=suffix, + parameters=parameters) + + def save(self, save_dict, path, parameters=None, prefix="", suffix=""): + """Inherits from seisflows.solver.specfem.Specfem""" + self.save(save_dict=save_dict, path=path, parameters=parameters, + prefix=prefix, suffix=suffix) + + def merge(self, model, parameters=None): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.merge(model=model, parameters=parameters) + + def split(self, m, parameters=None): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.split(m=m, parameters=parameters) + + def combine(self, input_path, output_path, parameters=None): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.combine(input_path=input_path, output_path=output_path, + parmameters=parameters) + + def export_traces(self, path, prefix="traces/obs"): + """Inherits from seisflows.solver.specfem.Specfem""" + self.export_traces(path=path, prefix=prefix) + + def smooth(self, input_path, output_path, parameters=None, span_h=0., + span_v=0., output="smooth.log"): """ Specfem2D requires additional model parameters in directory to perform - the xsmooth_sem task. This function will copy these files into the - directory before performing the base smooth operations. + the xsmooth_sem task. This function will copy these files into the + directory before performing the base smooth operations. Kwargs should match arguments of solver.base.smooth() - + .. note:: This operation is usually run with run(single=True) so only one task will be performing these operations. :type input_path: str :param input_path: path to data + :type output_path: str + :param output_path: path to export the outputs of xcombine_sem + :type parameters: list + :param parameters: optional list of parameters, + defaults to `self.parameters` + :type span_h: float + :param span_h: horizontal smoothing length in meters + :type span_v: float + :param span_v: vertical smoothing length in meters + :type output: str + :param output: file to output stdout to """ # Redundant to 'base' class but necessary - if not exists(input_path): + if not os.path.exists(input_path): unix.mkdir(input_path) unix.cd(self.cwd) @@ -269,9 +319,11 @@ def smooth(self, input_path, **kwargs): for tag in ["jacobian", "NSPEC_ibool", "x", "y", "z"]: files += glob(f"*_{tag}.bin") for src in files: - unix.cp(src=src, dst=input_path) + unix.cp(src=src, dst=input_path) - super().smooth(input_path=input_path, **kwargs) + super().smooth(input_path=input_path, output_path=output_path, + parameters=parameters, span_h=span_h, span_v=span_v, + output=output) def import_model(self, path): """ @@ -286,92 +338,88 @@ def import_model(self, path): dst=os.path.join(self.cwd, "DATA") ) - def export_model(self, path): - """ - File transfer utility to move a SPEFEM2D model from the DATA directory - to an external path location + def import_traces(self, path): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.import_traces(path=path) - :type path: str - :param path: path to export the SPECFEM2D model - :return: - """ - unix.mkdir(path) - unix.cp(src=glob(os.path.join(self.cwd, "DATA", "*.bin")), - dst=path) + def export_model(self, path, parameters=None): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.export_model(path=path, parameters=parameters) - @property - def data_filenames(self): - """ - Returns the filenames of all data, either by the requested components - or by all available files in the directory. + def export_kernels(self, path): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.export_kernels(path=path) - .. note:: - If the glob returns an empty list, this function exits the - workflow because filenames should not be empty is they're being - queried + def export_residuals(self, path): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.export_residuals(path=path) - :rtype: list - :return: list of data filenames + def export_traces(self, path, prefix="traces/obs"): + """Inherits from seisflows.solver.specfem.Specfem""" + self.export_traces(path=path, prefix=prefix) + + def rename_kernels(self): + """Inherits from seisflows.solver.specfem.Specfem""" + self.rename_kernels() + + def initialize_solver_directories(self): + """Inherits from seisflows.solver.specfem.Specfem""" + self.initialize_solver_directories() + + def initialize_adjoint_traces(self): + """ + Setup utility: Creates the "adjoint traces" expected by SPECFEM. + This is only done for the 'base' the Preprocess class. + + .. note:: + Adjoint traces are initialized by writing zeros for all channels. + Channels actually in use during an inversion or migration will be + overwritten with nonzero values later on. """ + super().initialize_adjoint_traces() + unix.cd(self.cwd) - unix.cd(os.path.join("traces", "obs")) + unix.cd(os.path.join("traces", "adj")) - if PAR.COMPONENTS: - filenames = [] - if PAR.FORMAT.upper() == "SU": - for comp in PAR.COMPONENTS: - filenames += [self.data_wildcard.format(comp=comp.lower())] - # filenames += [f"U{comp.lower()}_file_single.su"] - elif PAR.FORMAT.upper() == "ASCII": - for comp in PAR.COMPONENTS: - filenames += glob( - self.data_wildcard.format(comp=comp.upper()) - ) - # filenames += glob(f"*.?X{comp.upper()}.sem?") - else: - filenames = glob(self.data_wildcard) + # work around SPECFEM2D's use of different name conventions for + # regular traces and 'adjoint' traces + if self.par.FORMAT.upper() == "SU": + files = glob("*SU") + unix.rename(old="_SU", new="_SU.adj", names=files) + elif self.par.FORMAT.upper() == "ASCII": + files = glob("*sem?") - if not filenames: - print(msg.cli("The property solver.data_filenames, used to search " - "for traces in 'scratch/solver/*/traces' is empty " - "and should not be. Please check solver parameters: ", - items=[f"data_wildcard: {self.data_wildcard}"], - header="data filenames error", border="=") - ) - sys.exit(-1) + # Get the available extensions, which are named based on unit + extensions = set([os.path.splitext(_)[-1] for _ in files]) + for extension in extensions: + unix.rename(old=extension, new=".adj", names=files) - return filenames + # SPECFEM2D requires that all components exist even if ununsed + components = ["x", "y", "z", "p"] - @property - def model_databases(self): - """ - The location of model inputs and outputs as defined by SPECFEM2D - """ - return os.path.join(self.cwd, "DATA") + if self.par.FORMAT.upper() == "SU": + for comp in components: + src = f"U{self.par.COMPONENTS[0]}_file_single.su.adj" + dst = f"U{comp.lower()}s_file_single.su.adj" + if not os.path.exists(dst): + unix.cp(src, dst) + elif self.par.FORMAT.upper() == "ASCII": + for fid in glob("*.adj"): + net, sta, cha, ext = fid.split(".") + for comp in components: + # Replace the last value in the channel with new component + cha_check = cha[:-1] + comp.upper() + fid_check = ".".join([net, sta, cha_check, ext]) + if not os.path.exists(fid_check): + unix.cp(fid, fid_check) - @property - def kernel_databases(self): - """ - The location of kernel inputs and outputs as defined by SPECFEM2D - """ - return os.path.join(self.cwd, "OUTPUT_FILES") + def check_mesh_properties(self, path=None): + """Inherits from seisflows.solver.specfem.Specfem""" + self.check_mesh_properties(path=path) - @property - def data_wildcard(self, comp="?"): - """ - Returns a wildcard identifier for synthetic data based on SPECFEM2D - file naming schema. Allows formatting dcomponent e.g., - when called by solver.data_filenames + def check_source_names(self): + """Inherits from seisflows.solver.specfem.Specfem""" + self.check_source_names() - :type comp: str - :param comp: component formatter, defaults to wildcard '?' - :rtype: str - :return: wildcard identifier for channels - """ - if PAR.FORMAT.upper() == "SU": - # return f"*.su" # too vague but maybe for a reason? -bryant - return f"U{comp}_file_single.su" - elif PAR.FORMAT.upper() == "ASCII": - return f"*.?X{comp}.sem?" diff --git a/seisflows/solver/specfem3d.py b/seisflows/solver/specfem3d.py index 8ef68ad5..72536bca 100644 --- a/seisflows/solver/specfem3d.py +++ b/seisflows/solver/specfem3d.py @@ -6,82 +6,139 @@ and overwrites these functions to provide specified interaction with Specfem3D """ import os -import sys -import logging from glob import glob -from seisflows.tools import unix, msg +from seisflows.solver.specfem import Specfem +from seisflows.tools import unix from seisflows.tools.wrappers import exists -from seisflows.config import custom_import, SeisFlowsPathsParameters from seisflows.tools.specfem import getpar, setpar -# Seisflows configuration -PAR = sys.modules["seisflows_parameters"] -PATH = sys.modules["seisflows_paths"] +class Specfem3D(Specfem): + """ + Python interface to Specfem3D Cartesian. + """ + def __init__(self): + """ + Initiate parameters required for Specfem3D Cartesian + """ + super().__init__() + + self.required.par( + "SOURCE_PREFIX", required=False, default="CMTSOLUTION", + par_type=str, + docstr="Prefix of SOURCE files in path SPECFEM_DATA. Available " + "['CMTSOLUTION', FORCESOLUTION']") -system = sys.modules["seisflows_system"] -preprocess = sys.modules["seisflows_preprocess"] + @property + def io(self): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.io + @property + def taskid(self): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.taskid -class Specfem3D(custom_import("solver", "base")): - """ - Python interface to Specfem3D Cartesian. This subclass inherits functions - from seisflows.solver.Base + @property + def source_names(self): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.source_names - !!! See base class for method descriptions !!! - """ - logger = logging.getLogger(__name__).getChild(__qualname__) + @property + def source_name(self): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.source_name - def __init__(self): + @property + def source_prefix(self): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.source_prefix + + @property + def cwd(self): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.cwd + + @property + def mesh_properties(self): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.mesh_properties + + @property + def data_wildcard(self, comp="?"): """ - These parameters should not be set by the user. - Attributes are initialized as NoneTypes for clarity and docstrings. + Returns a wildcard identifier for synthetic data - :type logger: Logger - :param logger: Class-specific logging module, log statements pushed - from this logger will be tagged by its specific module/classname + :rtype: str + :return: wildcard identifier for channels """ - super().__init__() + if self.par.FORMAT.upper() == "SU": + return f"*_d?_SU" + elif self.par.FORMAT.upper() == "ASCII": + return f"*.?X{comp}.sem?" @property - def required(self): + def data_filenames(self): """ - A hard definition of paths and parameters required by this class, - alongside their necessity for the class and their string explanations. + Returns the filenames of all data, either by the requested components + or by all available files in the directory. + + :rtype: list + :return: list of data filenames """ - sf = SeisFlowsPathsParameters(super().required) + unix.cd(os.path.join(self.cwd, "traces", "obs")) - sf.par("SOURCE_PREFIX", required=False, default="CMTSOLUTION", - par_type=str, - docstr="Prefix of SOURCE files in path SPECFEM_DATA. Available " - "['CMTSOLUTION', FORCESOLUTION']") + if self.par.COMPONENTS: + components = self.par.COMPONENTS - return sf + if self.par.FORMAT.upper() == "SU": + return sorted(glob(f"*_d[{components.lower()}]_SU")) + elif self.par.FORMAT.upper() == "ASCII": + return sorted(glob(f"*.?X[{components.upper()}].sem?")) + else: + if self.par.FORMAT.upper() == "SU": + return sorted(glob("*_d?_SU")) + elif self.par.FORMAT.upper() == "ASCII": + return sorted(glob("*.???.sem?")) + + @property + def model_databases(self): + """ + The location of databases for model outputs + """ + return os.path.join(self.cwd, "OUTPUT_FILES", "DATABASES_MPI") + + @property + def kernel_databases(self): + """ + The location of databases for kernel outputs + """ + return self.model_databases def check(self, validate=True): """ Checks parameters and paths """ - if validate: - self.required.validate() + super().check(validate=validate) - super().check(validate=False) + def set_model(self, model_name, model_type="gll"): + """Inherits from seisflows.solver.specfem.Specfem""" + self.set_model(model_name=model_name, model_type=model_type) - def generate_data(self, **model_kwargs): + def generate_data(self, model_name, model_type="gll"): """ Generates data using the True model, exports traces to `traces/obs` :param model_kwargs: keyword arguments to pass to `generate_mesh` """ # Create the mesh - self.generate_mesh(**model_kwargs) # Run the Forward simulation unix.cd(self.cwd) setpar(key="SIMULATION_TYPE", val="1", file="DATA/Par_file") setpar(key="SAVE_FORWARD", val=".true.", file="DATA/Par_file") - if PAR.ATTENUATION: + if self.par.ATTENUATION: setpar(key="ATTENUATION", val=".true.", file="DATA/Par_file") else: setpar(key="ATTENUATION", val=".false.", file="DATA/Par_file") @@ -92,46 +149,8 @@ def generate_data(self, **model_kwargs): dst=os.path.join("traces", "obs")) # Export traces to disk for permanent storage - if PAR.SAVETRACES: - self.export_traces(os.path.join(PATH.OUTPUT, "traces", "obs")) - - def generate_mesh(self, model_path, model_name, model_type=None): - """ - Performs meshing with internal mesher Meshfem3D and database generation - - :type model_path: str - :param model_path: path to the model to be used for mesh generation - :type model_name: str - :param model_name: name of the model to be used as identification - :type model_type: str - :param model_type: available model types to be passed to the Specfem3D - Par_file. See Specfem3D Par_file for available options. - """ - available_model_types = ["gll"] - assert(exists(model_path)), f"model {model_path} does not exist" - - model_type = model_type or getpar(key="MODEL", file="DATA/Par_file") - assert(model_type in available_model_types), \ - f"{model_type} not in available types {available_model_types}" - - unix.cd(self.cwd) - - # Run mesh generation - if model_type == "gll": - self.check_mesh_properties(model_path) - - src = glob(os.path.join(model_path, "*")) - dst = self.model_databases - unix.cp(src, dst) - - self.call_solver(executable="bin/xmeshfem3D", - output="true_mesher.log") - self.call_solver(executable="bin/xgenerate_databases", - output="true_solver.log") - - # Export the model for future use in the workflow - if self.taskid == 0: - self.export_model(os.path.join(PATH.OUTPUT, model_name)) + if self.par.SAVETRACES: + self.export_traces(os.path.join(self.path.OUTPUT, "traces", "obs")) def eval_func(self, *args, **kwargs): """ @@ -140,7 +159,7 @@ def eval_func(self, *args, **kwargs): super().eval_func(*args, **kwargs) # Work around SPECFEM3D conflicting name conventions of SU data - self.rename_data() + self._rename_data() def forward(self, path="traces/syn"): """ @@ -152,7 +171,7 @@ def forward(self, path="traces/syn"): # Set parameters and run forward simulation setpar(key="SIMULATION_TYPE", val="1", file="DATA/Par_file") setpar(key="SAVE_FORWARD", val=".true.", file="DATA/Par_file") - if PAR.ATTENUATION: + if self.par.ATTENUATION: setpar(key="ATTENUATION", val=".true.", file="DATA/Par_file") else: setpar(key="ATTENUATION", val=".false`.", file="DATA/Par_file") @@ -197,86 +216,25 @@ def initialize_adjoint_traces(self): # Workaround for Specfem3D's requirement that all components exist, # even ones not in use as adjoint traces - if PAR.FORMAT.upper() == "SU": + if self.par.FORMAT.upper() == "SU": unix.cd(os.path.join(self.cwd, "traces", "adj")) - for iproc in range(PAR.NPROC): + for iproc in range(self.par.NPROC): for channel in ["x", "y", "z"]: dst = f"{iproc:d}_d{channel}_SU.adj" if not exists(dst): - src = f"{iproc:d}_d{PAR.COMPONENTS[0]}_SU.adj" + src = f"{iproc:d}_d{self.par.COMPONENTS[0]}_SU.adj" unix.cp(src, dst) - def rename_data(self): + def _rename_data(self): """ Works around conflicting data filename conventions Specfem3D's uses different name conventions for regular traces and 'adjoint' traces """ - if PAR.FORMAT.upper() == "SU": + if self.par.FORMAT.upper() == "SU": files = glob(os.path.join(self.cwd, "traces", "adj", "*SU")) unix.rename(old='_SU', new='_SU.adj', names=files) - @property - def data_wildcard(self): - """ - Returns a wildcard identifier for synthetic data - - :rtype: str - :return: wildcard identifier for channels - """ - if PAR.FORMAT.upper() == "SU": - return f"*_d?_SU" - elif PAR.FORMAT.upper() == "ASCII": - return f"*.?X?.sem?" - - @property - def data_filenames(self): - """ - Returns the filenames of all data, either by the requested components - or by all available files in the directory. - - :rtype: list - :return: list of data filenames - """ - unix.cd(os.path.join(self.cwd, "traces", "obs")) - - if PAR.COMPONENTS: - components = PAR.COMPONENTS - - if PAR.FORMAT.upper() == "SU": - return sorted(glob(f"*_d[{components.lower()}]_SU")) - elif PAR.FORMAT.upper() == "ASCII": - return sorted(glob(f"*.?X[{components.upper()}].sem?")) - else: - if PAR.FORMAT.upper() == "SU": - return sorted(glob("*_d?_SU")) - elif PAR.FORMAT.upper() == "ASCII": - return sorted(glob("*.???.sem?")) - - @property - def kernel_databases(self): - """ - The location of databases for kernel outputs, relative to the current - working directory. - """ - return os.path.join(self.cwd, "OUTPUT_FILES", "DATABASES_MPI") - - @property - def model_databases(self): - """ - The location of databases for model outputs - """ - return os.path.join(self.cwd, "OUTPUT_FILES", "DATABASES_MPI") - - @property - def source_prefix(self): - """ - Specfem3D's preferred source prefix - - :rtype: str - :return: source prefix - """ - return PAR.SOURCE_PREFIX.upper() From 3f7c34f5e94cd7ff58040e215030823b2f4dd6b8 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Tue, 28 Jun 2022 13:45:08 -0800 Subject: [PATCH 031/195] finished condensing and rewiriting SOLVER and SYSTEM modules. Removed old system.base class which did not serve much. Workstation system class now serves as the base class for all System --- seisflows/solver/specfem.py | 241 +++++++++++---------- seisflows/solver/specfem2d.py | 111 ++++------ seisflows/solver/specfem3d.py | 187 +++++++++++------ seisflows/solver/specfem3d_globe.py | 74 ++----- seisflows/system/base.py | 213 ------------------- seisflows/system/chinook.py | 54 ++--- seisflows/system/cluster.py | 103 ++++----- seisflows/system/lsf.py | 312 +++++++++++++--------------- seisflows/system/maui.py | 237 +++++++++++---------- seisflows/system/slurm.py | 232 +++++++++++---------- seisflows/system/workstation.py | 199 ++++++++++++++---- 11 files changed, 950 insertions(+), 1013 deletions(-) delete mode 100644 seisflows/system/base.py diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 60261b3d..aa295fd9 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -157,13 +157,11 @@ def __init__(self): ) self.parameters = [] - self.nt = None - self.dt = None self._mesh_properties = None self._source_names = None @property - def io(self): + def _io(self): """ Solver IO module set by User. Located in seisflows.plugins.solver_io @@ -186,14 +184,15 @@ def taskid(self): @property def source_names(self): """ - Returns list of source names + Returns list of source names which should be stored in PAR.SPECFEM_DATA + Source names are expected to match the following wildcard, + 'PREFIX_*' where PREFIX is something like 'CMTSOLUTION' or 'FORCE' :rtype: list :return: list of source names """ if self._source_names is None: - self.check_source_names() - + self._check_source_names() return self._source_names @property @@ -221,7 +220,7 @@ def source_prefix(self): @property def cwd(self): """ - Returns working directory currently in use + Returns working directory currently in use by a running solver instance :rtype: str :return: current solver working directory @@ -231,17 +230,22 @@ def cwd(self): @property def mesh_properties(self): """ - Returns mesh properties + Returns mesh properties including number of GLL points and number of + processors (NPROC). + + .. note:: + We assume that there are only two available models MODEL_INIT and + MODEL_TRUE, and that both models have the SAME underlying mesh + structure. That way we can only check MODEL_INIT and be sure that + MODEL_TRUE has the same structure. - :rtype: Struct - :return: Structure of mesh properties + :rtype: Dict + :return: Dictionary containing information on mesh properties """ if self._mesh_properties is None: - self.check_mesh_properties() - + self._check_mesh_properties(model_path=self.path.MODEL_INIT) return self._mesh_properties - @property def data_wildcard(self, comp="?"): """ Provide a wildcard string that will match the name of the output @@ -250,33 +254,35 @@ def data_wildcard(self, comp="?"): :type comp: str :param comp: single letter defining the component that can be inserted into the wildcard. Defaults to '?' + :rtype: str + :return: a wildcard string that can be used to search for data """ return NotImplementedError @property def data_filenames(self): """ - Template filenames for accessing data + A list of waveform files matching the `data_wildcard` which is used + to keep track of data - !!! Must be implemented by subclass !!! + :rtype: list + :return: list of data filenames """ return NotImplementedError @property def model_databases(self): """ - Template filenames for accessing models - - !!! Must be implemented by subclass !!! + SPECFEM directory where model database files are saved. This directory + is SPECFEM version dep. """ return NotImplementedError @property def kernel_databases(self): """ - Template filenames for accessing kernels - - !!! Must be implemented by subclass !!! + SPECFEM directory where kernel database files are saved. This directory + is SPECFEM version dep. """ return NotImplementedError @@ -337,8 +343,6 @@ def setup(self): Sets up directory structure expected by SPECFEM and copies or generates seismic data to be inverted or migrated - TODO !!! what are the arguments of generate data? - .. note:; As input for an inversion or migration, users can choose between providing data, or providing a target model from which data are @@ -346,19 +350,12 @@ def setup(self): In the former case, a value for PATH.DATA must be supplied; in the latter case, a value for PATH.MODEL_TRUE must be provided. """ - self.initialize_solver_directories() - self.set_model(model_name="true", model_type="gll") + self._initialize_solver_directories() self.generate_data() + self._set_model(model_name="init", model_type="gll") + self._initialize_adjoint_traces() - self.set_model(model_name="init", model_type="gll") - self.initialize_adjoint_traces() - - # Assuming that NT and DT are set correctly in the Par_file - unix.cd(self.cwd) - self.nt = getpar(key="NSTEP", file="DATA/Par_file")[1] - self.dt = getpar(key="DT", file="DATA/Par_file")[1] - - def set_model(self, model_name, model_type=None): + def _set_model(self, model_name, model_type=None): """ Mesh and database files should have been created during the manual set up phase. This function simply checks the mesh properties of that mesh @@ -371,10 +368,10 @@ def set_model(self, model_name, model_type=None): Par_file. See Specfem3D Par_file for available options. """ unix.cd(self.cwd) + available_model_types = ["gll"] # Check the type of model. So far SeisFlows only accepts GLL models - available_model_types = ["gll"] - model_type = model_type or getpar(key="MODEL", file="DATA/Par_file") + model_type = model_type or getpar(key="MODEL", file="DATA/Par_file")[1] assert(model_type in available_model_types), \ f"{model_type} not in available types {available_model_types}" @@ -388,25 +385,59 @@ def set_model(self, model_name, model_type=None): assert(os.path.exists(model_path)), f"model {model_path} does not exist" if model_type == "gll": - self.check_mesh_properties(model_path) + self._check_mesh_properties(model_path=model_path) # Copy the model files (ex: proc000023_vp.bin ...) into database dir src = glob(os.path.join(model_path, "*")) dst = self.model_databases unix.cp(src, dst) - # Export the model into output folder, ready to be used by SeisFlows + # Export the model into output folder, ready to be used by other tasks if self.taskid == 0: - self.export_model(os.path.join(self.path.OUTPUT, model_name)) + self._export_model(os.path.join(self.path.OUTPUT, model_name)) def generate_data(self): """ - Performs meshing and data generation for "true" data. - """ - raise NotImplementedError("must be implemented by solver subclass") + Generates observation data to be compared to synthetics. This must + only be run once. If `PAR.CASE`=='data', then real data will be copied + over. + + If `PAR.CASE`=='synthetic' then the external solver will use the + True model to generate 'observed' synthetics. Finally exports traces to + 'cwd/traces/obs' + + Elif `PAR.CASE`=='DATA', will look in PATH.DATA for directories matching + the given source name and copy ANY files that exist there. e.g., if + source name is '001', you must store waveform data in PATH.DATA/001/* + + Also exports observed data to OUTPUT if desired + """ + # If synthetic inversion, generate 'data' with solver + if self.par.CASE.upper() == "SYNTHETIC": + if self.path.MODEL_TRUE is not None: + if self.taskid == 0: + self.logger.info("generating 'data' with MODEL_TRUE") + # Generate synthetic data on the fly using the true model + self._set_model(model_name="true", model_type="gll") + self._forward(output_path=os.path.join("traces", "obs")) + # If Data provided by user, copy directly into the solver directory + elif self.path.DATA is not None and os.path.exists(self.path.DATA): + unix.cp( + src=glob(os.path.join(self.path.DATA, self.source_name, "*")), + dst=os.path.join("traces", "obs") + ) + # Save observation data to disk + if self.par.SAVETRACES: + self._export_traces( + path=os.path.join(self.path.OUTPUT, "traces", "obs") + ) def eval_func(self, path, write_residuals=True): """ - Performs forward simulations and evaluates the misfit function + Performs forward simulations and evaluates the misfit function using + the preprocess module. + + .. note:: + This task should be run in parallel by system.run() :type path: str :param path: directory from which model is imported and where residuals @@ -420,8 +451,8 @@ def eval_func(self, path, write_residuals=True): self.logger.info("running forward simulations") unix.cd(self.cwd) - self.import_model(path) - self.forward() + self._import_model(path) + self._forward(output_path=os.path.join("traces", "syn")) if write_residuals: if self.taskid == 0: @@ -430,13 +461,15 @@ def eval_func(self, path, write_residuals=True): source_name=self.source_name, filenames=self.data_filenames ) - self.export_residuals(path) + self._export_residuals(path) def eval_grad(self, path, export_traces=False): """ - High level solver interface that evaluates gradient by carrying out - adjoint simulations. A function evaluation must already have been - carried out. + Evaluates gradient by carrying out adjoint simulations. + + .. note:: + It is expected that eval_func() has already been run as this + function looks for adjoint sources in 'cwd/traces/adj' :type path: str :param path: directory from which model is imported @@ -458,13 +491,13 @@ def eval_grad(self, path, export_traces=False): ) sys.exit(-1) - self.adjoint() - self.export_kernels(path) + self._adjoint() + self._export_kernels(path) if export_traces: - self.export_traces(path=os.path.join(path, "traces", "syn"), + self._export_traces(path=os.path.join(path, "traces", "syn"), prefix="traces/syn") - self.export_traces(path=os.path.join(path, "traces", "adj"), + self._export_traces(path=os.path.join(path, "traces", "adj"), prefix="traces/adj") # def apply_hess(self, path): @@ -488,27 +521,28 @@ def eval_grad(self, path, export_traces=False): # self.adjoint() # self.export_kernels(path) - def forward(self, path): + def _forward(self, output_path): """ - Low level solver interface - - Calls forward solver + Calls forward solver with the appropriate parameters in the Par_file set + Also exports data to the correct output_path - !!! Must be implemented by subclass !!! + :type output_path: str + :param output_path: path to export traces to after completion of + simulation expected values are either 'traces/obs' for 'observation' + data (i.e., synthetics generated by the TRUE model), or + 'traces/syn', for synthetics generated during function evaluations """ raise NotImplementedError("must be implemented by solver subclass") - def adjoint(self): + def _adjoint(self): """ - Low level solver interface - - Calls adjoint solver - - !!! Must be implemented by subclass !!! + Calls adjoint solver with the appropriate parameters in the Par_file set + Also takes care of setting up the SEM/ directory where SPECFEM expects + adjoint sources to be """ raise NotImplementedError("must be implemented by solver subclass") - def call_solver(self, executable, output="solver.log"): + def _call_solver(self, executable, output="solver.log"): """ Calls MPI solver executable to run solver binaries, used by individual processes to run the solver on system. If the external solver returns a @@ -705,12 +739,12 @@ def combine(self, input_path, output_path, parameters=None): # Call on xcombine_sem to combine kernels into a single file for name in parameters: # e.g.: mpiexec ./bin/xcombine_sem alpha_kernel kernel_paths output - self.call_solver(executable=" ".join([f"bin/xcombine_sem", - f"{name}_kernel", - "kernel_paths", - output_path] - ) - ) + self._call_solver(executable=" ".join([f"bin/xcombine_sem", + f"{name}_kernel", + "kernel_paths", + output_path] + ) + ) def smooth(self, input_path, output_path, parameters=None, span_h=0., span_v=0., output="smooth.log"): @@ -750,20 +784,20 @@ def smooth(self, input_path, output_path, parameters=None, span_h=0., # mpiexec ./bin/xsmooth_sem SMOOTH_H SMOOTH_V name input output use_gpu for name in parameters: - self.call_solver(executable=" ".join(["bin/xsmooth_sem", - str(span_h), str(span_v), - f"{name}_kernel", - os.path.join(input_path, ""), - os.path.join(output_path, ""), - ".false"]), - output=output - ) + self._call_solver(executable=" ".join(["bin/xsmooth_sem", + str(span_h), str(span_v), + f"{name}_kernel", + os.path.join(input_path, ""), + os.path.join(output_path, ""), + ".false"]), + output=output + ) # Rename output files files = glob(os.path.join(output_path, "*")) unix.rename(old="_smooth", new="", names=files) - def import_model(self, path): + def _import_model(self, path): """ File transfer utility. Import the model into the workflow. @@ -771,9 +805,9 @@ def import_model(self, path): :param path: path to model """ model = self.load(path=os.path.join(path, "model")) - self.save(model, self.model_databases) + self.save(model, self._model_databases) - def import_traces(self, path): + def _import_traces(self, path): """ File transfer utility. Import traces into the workflow. @@ -784,7 +818,7 @@ def import_traces(self, path): dst = os.path.join(self.cwd, 'traces', 'obs') unix.cp(src, dst) - def export_model(self, path, parameters=None): + def _export_model(self, path, parameters=None): """ File transfer utility. Export the model to disk. @@ -801,10 +835,10 @@ def export_model(self, path, parameters=None): if self.taskid == 0: unix.mkdir(path) for key in parameters: - files = glob(os.path.join(self.model_databases, f"*{key}.bin")) + files = glob(os.path.join(self._model_databases, f"*{key}.bin")) unix.cp(files, path) - def export_kernels(self, path): + def _export_kernels(self, path): """ File transfer utility. Export kernels to disk @@ -814,17 +848,17 @@ def export_kernels(self, path): if self.taskid == 0: self.logger.debug(f"exporting kernels to:\n{path}") - unix.cd(self.kernel_databases) + unix.cd(self._kernel_databases) # Work around conflicting name conventions - self.rename_kernels() + self._rename_kernels() src = glob("*_kernel.bin") dst = os.path.join(path, "kernels", self.source_name) unix.mkdir(dst) unix.mv(src, dst) - def export_residuals(self, path): + def _export_residuals(self, path): """ File transfer utility. Export residuals to disk. @@ -850,7 +884,7 @@ def export_residuals(self, path): dst = os.path.join(path, "residuals", self.source_name) unix.mv(src, dst) - def export_traces(self, path, prefix="traces/obs"): + def _export_traces(self, path, prefix="traces/obs"): """ File transfer utility. Export traces to disk. @@ -868,7 +902,7 @@ def export_traces(self, path, prefix="traces/obs"): dst = os.path.join(path, self.source_name) unix.cp(src, dst) - def rename_kernels(self): + def _rename_kernels(self): """ Works around conflicting kernel filename conventions by renaming `alpha` to `vp` and `beta` to `vs` @@ -883,7 +917,7 @@ def rename_kernels(self): names = glob(f"*proc??????_{tag}_kernel.bin") unix.rename(old="beta", new="vs", names=names) - def initialize_solver_directories(self): + def _initialize_solver_directories(self): """ Creates directory structure expected by SPECFEM3D (bin/, DATA/) copies executables, and prepares input files. Executables must be supplied @@ -940,7 +974,7 @@ def initialize_solver_directories(self): dst = os.path.join(self.cwd, "OUTPUT_FILES", "DATABASES_MPI") unix.cp(src, dst) - def initialize_adjoint_traces(self): + def _initialize_adjoint_traces(self): """ Setup utility: Creates the "adjoint traces" expected by SPECFEM. This is only done for the 'base' the Preprocess class. @@ -971,36 +1005,33 @@ def initialize_adjoint_traces(self): path=os.path.join(self.cwd, "traces", "adj") ) - def check_mesh_properties(self, path=None): + def _check_mesh_properties(self, model_path): """ Determine if Mesh properties are okay for workflow. - :type path: str - :param path: path to the mesh file + :type model_path: str + :param model_path: path to the mesh file """ - # Check the given model path or the initial model - if path is None: - path = self.path.MODEL_INIT - - if os.path.exists(path): + if os.path.exists(model_path): # Count the number of .bin files and the number of grid points key = self.parameters[0] - bin_files = glob(os.path.join(path, f"proc*_{key}.bin")) + bin_files = glob(os.path.join(model_path, f"proc*_{key}.bin")) nproc = len(bin_files) ngll = [] for i in range(0, len(bin_files)): ngll.append( - len(self.io.read_slice(path=path, - parameters=key, iproc=i)[0]) + len(self._io.read_slice(path=model_path, + parameters=key, iproc=i)[0]) ) # Define internal mesh properties - self._mesh_properties = Dict(nproc=nproc, ngll=ngll, path=path) + self._mesh_properties = Dict(nproc=nproc, ngll=ngll, + path=model_path) else: self.logger.warning("solver cannot find mesh and will not have " "access to mesh properties") - def check_source_names(self): + def _check_source_names(self): """ Determines names of sources by applying wildcard rule to user-supplied input files diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index aba62d18..a36afc02 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -34,9 +34,9 @@ def __init__(self): self.f0 = None @property - def io(self): + def _io(self): """Inherits from seisflows.solver.specfem.Specfem""" - return self.io + return self._io @property def taskid(self): @@ -68,7 +68,6 @@ def mesh_properties(self): """Inherits from seisflows.solver.specfem.Specfem""" return self.mesh_properties - @property def data_wildcard(self, comp="?"): """ Returns a wildcard identifier for synthetic data based on SPECFEM2D @@ -163,37 +162,13 @@ def setup(self): else: setpar(key="absorbtop", val=".true.", file="DATA/Par_file") - def set_model(self, model_name, model_type="gll"): + def _set_model(self, model_name, model_type="gll"): """Inherits from seisflows.solver.specfem.Specfem""" - self.set_model(model_name=model_name, model_type=model_type) + self._set_model(model_name=model_name, model_type=model_type) def generate_data(self): - """ - Generates observation data to be compared to synthetics. This must - only be run once. If `PAR.CASE`=='data', then real data will be copied - over. If `PAR.CASE`=='synthetic' then the external solver will use the - True model to generate 'observed' synthetics. Finally exports traces to - 'cwd/traces/obs' - """ - # If synthetic inversion, generate 'data' with solver - if self.par.CASE.upper() == "SYNTHETIC": - if self.path.MODEL_TRUE is not None: - if self.taskid == 0: - self.logger.info("generating 'data' with MODEL_TRUE") - # Generate synthetic data on the fly using the true model - self.set_model(model_name="true", model_type="gll") - self.forward(path=os.path.join("traces", "obs")) - - # If Data provided by user, copy directly into the solver directory - elif self.path.DATA is not None and os.path.exists(self.path.DATA): - unix.cp( - src=glob(os.path.join(self.path.DATA, self.source_name, "*")), - dst=os.path.join("traces", "obs") - ) - if self.par.SAVETRACES: - self.export_traces( - path=os.path.join(self.path.OUTPUT, "traces", "obs") - ) + """Inherits from seisflows.solver.specfem.Specfem""" + self.generate_data() def eval_func(self, path, write_residuals=True): """Inherits from seisflows.solver.specfem.Specfem""" @@ -201,23 +176,25 @@ def eval_func(self, path, write_residuals=True): def eval_grad(self, path, export_traces=True): """Inherits from seisflows.solver.specfem.Specfem""" - self.eval_func(path=path, export_traces=export_traces) + self.eval_grad(path=path, export_traces=export_traces) - def forward(self, path="traces/syn"): + def _forward(self, output_path): """ Calls SPECFEM2D forward solver, exports solver outputs to traces dir - :type path: str - :param path: path to export traces to after completion of simulation - relatiev to the solver cwd + :type output_path: str + :param output_path: path to export traces to after completion of + simulation expected values are either 'traces/obs' for 'observation' + data (i.e., synthetics generated by the TRUE model), or + 'traces/syn', for synthetics generated during function evaluations """ unix.cd(self.cwd) setpar(key="SIMULATION_TYPE", val="1", file="DATA/Par_file") setpar(key="SAVE_FORWARD", val=".true.", file="DATA/Par_file") - self.call_solver(executable="bin/xmeshfem2D", output="fwd_mesher.log") - self.call_solver(executable="bin/xspecfem2D", output="fwd_solver.log") + self._call_solver(executable="bin/xmeshfem2D", output="fwd_mesher.log") + self._call_solver(executable="bin/xspecfem2D", output="fwd_solver.log") if self.par.FORMAT.upper() == "SU": # Work around SPECFEM2D's version dependent file names @@ -226,9 +203,9 @@ def forward(self, path="traces/syn"): names=glob(os.path.join("OUTPUT_FILES", "*.su"))) unix.mv(src=glob(os.path.join("OUTPUT_FILES", self.data_wildcard)), - dst=path) + dst=output_path) - def adjoint(self): + def _adjoint(self): """ Calls SPECFEM2D adjoint solver, creates the `SEM` folder with adjoint traces which is required by the adjoint solver @@ -247,11 +224,11 @@ def adjoint(self): unix.rename(old=".su", new=".su.adj", names=glob(os.path.join("traces", "adj", "*.su"))) - self.call_solver(executable="bin/xspecfem2D", output="adj_solver.log") + self._call_solver(executable="bin/xspecfem2D", output="adj_solver.log") - def call_solver(self, executable, output="solver.log"): + def _call_solver(self, executable, output="solver.log"): """Inherits from seisflows.solver.specfem.Specfem""" - self.call_solver(executable=executable, output=output) + self._call_solver(executable=executable, output=output) def load(self, path, prefix="", suffix="", parameters=None): """Inherits from seisflows.solver.specfem.Specfem""" @@ -274,11 +251,7 @@ def split(self, m, parameters=None): def combine(self, input_path, output_path, parameters=None): """Inherits from seisflows.solver.specfem.Specfem""" return self.combine(input_path=input_path, output_path=output_path, - parmameters=parameters) - - def export_traces(self, path, prefix="traces/obs"): - """Inherits from seisflows.solver.specfem.Specfem""" - self.export_traces(path=path, prefix=prefix) + parameters=parameters) def smooth(self, input_path, output_path, parameters=None, span_h=0., span_v=0., output="smooth.log"): @@ -325,7 +298,7 @@ def smooth(self, input_path, output_path, parameters=None, span_h=0., parameters=parameters, span_h=span_h, span_v=span_v, output=output) - def import_model(self, path): + def _import_model(self, path): """ File transfer utility to move a SPEFEM2D model into the correct location for a workflow. @@ -338,35 +311,35 @@ def import_model(self, path): dst=os.path.join(self.cwd, "DATA") ) - def import_traces(self, path): + def _import_traces(self, path): """Inherits from seisflows.solver.specfem.Specfem""" - return self.import_traces(path=path) + return self._import_traces(path=path) - def export_model(self, path, parameters=None): + def _export_model(self, path, parameters=None): """Inherits from seisflows.solver.specfem.Specfem""" - return self.export_model(path=path, parameters=parameters) + return self._export_model(path=path, parameters=parameters) - def export_kernels(self, path): + def _export_kernels(self, path): """Inherits from seisflows.solver.specfem.Specfem""" - return self.export_kernels(path=path) + return self._export_kernels(path=path) - def export_residuals(self, path): + def _export_residuals(self, path): """Inherits from seisflows.solver.specfem.Specfem""" - return self.export_residuals(path=path) + return self._export_residuals(path=path) - def export_traces(self, path, prefix="traces/obs"): + def _export_traces(self, path, prefix="traces/obs"): """Inherits from seisflows.solver.specfem.Specfem""" - self.export_traces(path=path, prefix=prefix) + self._export_traces(path=path, prefix=prefix) - def rename_kernels(self): + def _rename_kernels(self): """Inherits from seisflows.solver.specfem.Specfem""" - self.rename_kernels() + self._rename_kernels() - def initialize_solver_directories(self): + def _initialize_solver_directories(self): """Inherits from seisflows.solver.specfem.Specfem""" - self.initialize_solver_directories() + self._initialize_solver_directories() - def initialize_adjoint_traces(self): + def _initialize_adjoint_traces(self): """ Setup utility: Creates the "adjoint traces" expected by SPECFEM. This is only done for the 'base' the Preprocess class. @@ -376,7 +349,7 @@ def initialize_adjoint_traces(self): Channels actually in use during an inversion or migration will be overwritten with nonzero values later on. """ - super().initialize_adjoint_traces() + super()._initialize_adjoint_traces() unix.cd(self.cwd) unix.cd(os.path.join("traces", "adj")) @@ -413,13 +386,13 @@ def initialize_adjoint_traces(self): if not os.path.exists(fid_check): unix.cp(fid, fid_check) - def check_mesh_properties(self, path=None): + def _check_mesh_properties(self, path=None): """Inherits from seisflows.solver.specfem.Specfem""" - self.check_mesh_properties(path=path) + self._check_mesh_properties(path=path) - def check_source_names(self): + def _check_source_names(self): """Inherits from seisflows.solver.specfem.Specfem""" - self.check_source_names() + self._check_source_names() diff --git a/seisflows/solver/specfem3d.py b/seisflows/solver/specfem3d.py index 72536bca..d286a301 100644 --- a/seisflows/solver/specfem3d.py +++ b/seisflows/solver/specfem3d.py @@ -11,7 +11,7 @@ from seisflows.solver.specfem import Specfem from seisflows.tools import unix from seisflows.tools.wrappers import exists -from seisflows.tools.specfem import getpar, setpar +from seisflows.tools.specfem import setpar, getpar class Specfem3D(Specfem): @@ -31,9 +31,9 @@ def __init__(self): "['CMTSOLUTION', FORCESOLUTION']") @property - def io(self): + def _io(self): """Inherits from seisflows.solver.specfem.Specfem""" - return self.io + return self._io @property def taskid(self): @@ -65,7 +65,6 @@ def mesh_properties(self): """Inherits from seisflows.solver.specfem.Specfem""" return self.mesh_properties - @property def data_wildcard(self, comp="?"): """ Returns a wildcard identifier for synthetic data @@ -90,29 +89,26 @@ def data_filenames(self): unix.cd(os.path.join(self.cwd, "traces", "obs")) if self.par.COMPONENTS: - components = self.par.COMPONENTS - - if self.par.FORMAT.upper() == "SU": - return sorted(glob(f"*_d[{components.lower()}]_SU")) - elif self.par.FORMAT.upper() == "ASCII": - return sorted(glob(f"*.?X[{components.upper()}].sem?")) + files = glob(self.data_wildcard(comp=self.par.COMPONENTS.lower())) else: - if self.par.FORMAT.upper() == "SU": - return sorted(glob("*_d?_SU")) - elif self.par.FORMAT.upper() == "ASCII": - return sorted(glob("*.???.sem?")) + files = glob(self.data_wildcard(comp="?")) + return sorted(files) @property def model_databases(self): """ - The location of databases for model outputs + The location of databases for model outputs, usually + OUTPUT_FILES/DATABASES_MPI. Value is grabbed from the Par_file """ - return os.path.join(self.cwd, "OUTPUT_FILES", "DATABASES_MPI") + local_path = getpar(key="LOCAL_PATH", + file=os.path.join(self.cwd, "DATA", "Par_file"))[1] + return os.path.join(self.cwd, local_path) @property def kernel_databases(self): """ - The location of databases for kernel outputs + The location of databases for kernel outputs, usually the same as + 'model_databases' """ return self.model_databases @@ -122,51 +118,49 @@ def check(self, validate=True): """ super().check(validate=validate) - def set_model(self, model_name, model_type="gll"): + def setup(self): """Inherits from seisflows.solver.specfem.Specfem""" - self.set_model(model_name=model_name, model_type=model_type) - - def generate_data(self, model_name, model_type="gll"): - """ - Generates data using the True model, exports traces to `traces/obs` + self.setup() - :param model_kwargs: keyword arguments to pass to `generate_mesh` - """ - # Create the mesh - - # Run the Forward simulation - unix.cd(self.cwd) - setpar(key="SIMULATION_TYPE", val="1", file="DATA/Par_file") - setpar(key="SAVE_FORWARD", val=".true.", file="DATA/Par_file") - if self.par.ATTENUATION: - setpar(key="ATTENUATION", val=".true.", file="DATA/Par_file") - else: - setpar(key="ATTENUATION", val=".false.", file="DATA/Par_file") + def _set_model(self, model_name, model_type="gll"): + """Inherits from seisflows.solver.specfem.Specfem""" + self._set_model(model_name=model_name, model_type=model_type) - self.call_solver(executable="bin/xspecfem3D", output="true_solver.log") + def generate_data(self): + """Inherits from seisflows.solver.specfem.Specfem""" + self.generate_data() - unix.mv(src=glob(os.path.join("OUTPUT_FILES", self.data_wildcard)), - dst=os.path.join("traces", "obs")) + def eval_func(self, path, write_residuals=True): + """ + Performs forward simulations and evaluates the misfit function using + the preprocess module. Overrides to add a data renaming call - # Export traces to disk for permanent storage - if self.par.SAVETRACES: - self.export_traces(os.path.join(self.path.OUTPUT, "traces", "obs")) + .. note:: + This task should be run in parallel by system.run() - def eval_func(self, *args, **kwargs): - """ - Call eval_func from Base class - """ - super().eval_func(*args, **kwargs) + :type path: str + :param path: directory from which model is imported and where residuals + will be exported + :type write_residuals: bool + :param write_residuals: calculate and export residuals """ + super().eval_func(path=path, write_residuals=write_residuals) # Work around SPECFEM3D conflicting name conventions of SU data self._rename_data() - def forward(self, path="traces/syn"): + def eval_grad(self, path, export_traces=False): + """Inherits from seisflows.solver.specfem.Specfem""" + self.eval_grad(path=path, export_traces=export_traces) + + def _forward(self, output_path): """ Calls SPECFEM3D forward solver, exports solver outputs to traces dir - :type path: str - :param path: path to export traces to after completion of simulation + :type output_path: str + :param output_path: path to export traces to after completion of + simulation expected values are either 'traces/obs' for 'observation' + data (i.e., synthetics generated by the TRUE model), or + 'traces/syn', for synthetics generated during function evaluations """ # Set parameters and run forward simulation setpar(key="SIMULATION_TYPE", val="1", file="DATA/Par_file") @@ -176,29 +170,98 @@ def forward(self, path="traces/syn"): else: setpar(key="ATTENUATION", val=".false`.", file="DATA/Par_file") - self.call_solver(executable="bin/xgenerate_databases", - output="fwd_mesher.log") - self.call_solver(executable="bin/xmeshfem3D", output="fwd_solver.log") + self._call_solver(executable="bin/xgenerate_databases", + output="fwd_mesher.log") + self._call_solver(executable="bin/xmeshfem3D", output="fwd_solver.log") # Find and move output traces, by default to synthetic traces dir unix.mv(src=glob(os.path.join("OUTPUT_FILES", self.data_wildcard)), - dst=path) + dst=output_path) - def adjoint(self): + def _adjoint(self): """ Calls SPECFEM3D adjoint solver, creates the `SEM` folder with adjoint traces which is required by the adjoint solver """ setpar(key="SIMULATION_TYPE", val="3", file="DATA/Par_file") setpar(key="SAVE_FORWARD", val=".false.", file="DATA/Par_file") + + # Attenuation should always be OFF during adjoint simulations, else + # you will get a floating point error setpar(key="ATTENUATION", val=".false.", file="DATA/Par_file") unix.rm("SEM") unix.ln("traces/adj", "SEM") - self.call_solver(executable="bin/xmeshfem3D", output="adj_solver.log") + self._call_solver(executable="bin/xspecfem3D", output="adj_solver.log") + + def _call_solver(self, executable, output="solver.log"): + """Inherits from seisflows.solver.specfem.Specfem""" + self._call_solver(executable=executable, output=output) + + def load(self, path, prefix="", suffix="", parameters=None): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.load(path=path, prefix=prefix, suffix=suffix, + parameters=parameters) + + def save(self, save_dict, path, parameters=None, prefix="", suffix=""): + """Inherits from seisflows.solver.specfem.Specfem""" + self.save(save_dict=save_dict, path=path, parameters=parameters, + prefix=prefix, suffix=suffix) + + def merge(self, model, parameters=None): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.merge(model=model, parameters=parameters) + + def split(self, m, parameters=None): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.split(m=m, parameters=parameters) + + def combine(self, input_path, output_path, parameters=None): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.combine(input_path=input_path, output_path=output_path, + parameters=parameters) + + def smooth(self, input_path, output_path, parameters=None, span_h=0., + span_v=0., output="smooth.log"): + """Inherits from seisflows.solver.specfem.Specfem""" + return self.smooth(input_path=input_path, output_path=output_path, + parameters=parameters, span_h=span_h, + span_v=span_v, output=output) - def initialize_adjoint_traces(self): + def _import_model(self, path): + """Inherits from seisflows.solver.specfem.Specfem""" + return self._import_model(path=path) + + def _import_traces(self, path): + """Inherits from seisflows.solver.specfem.Specfem""" + return self._import_traces(path=path) + + def _export_model(self, path, parameters=None): + """Inherits from seisflows.solver.specfem.Specfem""" + return self._export_model(path=path, parameters=parameters) + + def _export_kernels(self, path): + """Inherits from seisflows.solver.specfem.Specfem""" + return self._export_kernels(path=path) + + def _export_residuals(self, path): + """Inherits from seisflows.solver.specfem.Specfem""" + return self._export_residuals(path=path) + + def _export_traces(self, path, prefix="traces/obs"): + """Inherits from seisflows.solver.specfem.Specfem""" + self._export_traces(path=path, prefix=prefix) + + def _rename_kernels(self): + """Inherits from seisflows.solver.specfem.Specfem""" + self._rename_kernels() + + def _initialize_solver_directories(self): + """Inherits from seisflows.solver.specfem.Specfem""" + self._initialize_solver_directories() + + def _initialize_adjoint_traces(self): """ Setup utility: Creates the "adjoint traces" expected by SPECFEM @@ -209,10 +272,10 @@ def initialize_adjoint_traces(self): """ # Initialize adjoint traces as zeroes for all data_filenames # write to `traces/adj` - super().initialize_adjoint_traces() + super()._initialize_adjoint_traces() # Rename data to work around Specfem naming convetions - self.rename_data() + self._rename_data() # Workaround for Specfem3D's requirement that all components exist, # even ones not in use as adjoint traces @@ -226,6 +289,14 @@ def initialize_adjoint_traces(self): src = f"{iproc:d}_d{self.par.COMPONENTS[0]}_SU.adj" unix.cp(src, dst) + def _check_mesh_properties(self, path=None): + """Inherits from seisflows.solver.specfem.Specfem""" + self._check_mesh_properties(path=path) + + def _check_source_names(self): + """Inherits from seisflows.solver.specfem.Specfem""" + self._check_source_names() + def _rename_data(self): """ Works around conflicting data filename conventions diff --git a/seisflows/solver/specfem3d_globe.py b/seisflows/solver/specfem3d_globe.py index 66f7dd5c..1e6fb021 100644 --- a/seisflows/solver/specfem3d_globe.py +++ b/seisflows/solver/specfem3d_globe.py @@ -6,49 +6,39 @@ and overwrites these functions to provide specified interaction with Specfem3D. """ import os -import sys -import logging from glob import glob +from seisflows.solver.specfem3d import Specfem3D from seisflows.tools.specfem import Minmax -from seisflows.tools import unix, msg +from seisflows.tools import unix from seisflows.tools.wrappers import Struct, exists -from seisflows.config import custom_import, SeisFlowsPathsParameters -PAR = sys.modules["seisflows_parameters"] -PATH = sys.modules["seisflows_paths"] -system = sys.modules["seisflows_system"] - -class Specfem3DGlobe(custom_import("solver", "specfem3d")): +class Specfem3DGlobe(Specfem3D): """ - Python interface to Specfem3D Globe. This subclass inherits functions - from seisflows.solver.specfem3d.Specfem3D + Python interface to Specfem3D Globe. A very simple overload of Specfem3D - !!! See base class for method descriptions !!! + See class `seisflows.solver.specfem3d.Specfem3D` for a more detailed + explanation of methods and attributes of this class """ - logger = logging.getLogger(__name__).getChild(__qualname__) - def __init__(self): """ These parameters should not be set by the user. Attributes are initialized as NoneTypes for clarity and docstrings. - - :type logger: Logger - :param logger: Class-specific logging module, log statements pushed - from this logger will be tagged by its specific module/classname """ super().__init__() - @property - def required(self): + def data_wildcard(self, comp="?"): """ - A hard definition of paths and parameters required by this class, - alongside their necessity for the class and their string explanations. - """ - sf = SeisFlowsPathsParameters(super().required) + Returns a wildcard identifier for synthetic data - return sf + :rtype: str + :return: wildcard identifier for channels + """ + if self.par.FORMAT.upper() == "SU": + raise NotImplementedError("SU file access is still a WIP") + elif self.par.FORMAT.upper() == "ASCII": + return f"*.?X{comp}.sem.ascii" def load(self, path, prefix="reg1_", suffix="", parameters=None): """ @@ -108,7 +98,7 @@ def save(self, path, model, prefix="reg1_", suffix=""): elif 'kernel' in suffix: pass else: - src = PATH.OUTPUT + '/' + 'model_init' + src = self.path.OUTPUT + '/' + 'model_init' dst = path copybin(src, dst, iproc, prefix+key+suffix) @@ -117,7 +107,7 @@ def save(self, path, model, prefix="reg1_", suffix=""): elif 'kernel' in suffix: pass else: - src = PATH.OUTPUT + '/' + 'model_init' + src = self.path.OUTPUT + '/' + 'model_init' dst = path copybin(src, dst, iproc, prefix+'rho'+suffix) @@ -130,7 +120,7 @@ def check_mesh_properties(self, path=None, parameters=None): """ if not hasattr(self, '_mesh_properties'): if path is None: - path = PATH.MODEL_INIT + path = self.path.MODEL_INIT if parameters is None: parameters = self.parameters @@ -166,7 +156,7 @@ def initialize_adjoint_traces(self): !!! This probably doesnt work - Note: + .. note:: Adjoint traces are initialized by writing zeros for all channels. Channels actually in use during an inversion or migration will be overwritten with nonzero values later on. @@ -175,34 +165,10 @@ def initialize_adjoint_traces(self): # workaround for SPECFEM's use of different name conventions for # regular traces and 'adjoint' traces - if PAR.FORMAT.upper() in ['ASCII', 'ascii']: + if self.par.FORMAT.upper() in ['ASCII', 'ascii']: files = glob(os.path.join(self.cwd, "traces", "adj", "*sem.ascii")) unix.rename("sem.ascii", "adj", files) - @property - def data_wildcard(self): - """ - Returns a wildcard identifier for synthetic data - - :rtype: str - :return: wildcard identifier for channels - """ - if PAR.FORMAT.upper() == "ASCII": - return f"*.?X?.sem.ascii" - - @property - def data_filenames(self): - """ - Returns the filenames of all data, either by the requested components - or by all available files in the directory. - - :rtype: list - :return: list of data filenames - """ - unix.cd(os.path.join(self.cwd, "traces", "obs")) - - if PAR.FORMAT.upper() == "ASCII": - return sorted(glob("*.???.sem.ascii")) diff --git a/seisflows/system/base.py b/seisflows/system/base.py deleted file mode 100644 index 632398e4..00000000 --- a/seisflows/system/base.py +++ /dev/null @@ -1,213 +0,0 @@ -#!/usr/bin/env python3 -""" -The System module provides the basic core utilities for interaction with compute -systems. The Base class must be overloaded by subclasses related to specific -compute system types (cluster vs. workstation) and even specific HPCs. -""" -import os -import sys -import pickle -import logging - -from seisflows.tools import unix, msg -from seisflows.tools.wrappers import number_fid -from seisflows.core import SeisFlowsPathsParameters -from seisflows.config import save, CFGPATHS - - -PAR = sys.modules["seisflows_parameters"] -PATH = sys.modules["seisflows_paths"] - - -class Base: - """ - Abstract base class for the Systems module which controls interaction with - compute systems such as HPC clusters. - """ - # Class-specific logger accessed using self.logger - logger = logging.getLogger(__name__).getChild(__qualname__) - - def __init__(self): - """ - These parameters should not be set by the user. - Attributes are initialized as NoneTypes for clarity and docstrings. - """ - self.output_log = os.path.join(PATH.WORKDIR, CFGPATHS.LOGFILE) - self.error_log = os.path.join(PATH.WORKDIR, CFGPATHS.ERRLOGFILE) - - @property - def required(self): - """ - A hard definition of paths and parameters required by this class, - alongside their necessity for the class and their string explanations. - """ - sf = SeisFlowsPathsParameters() - - sf.par("TITLE", required=False, - default=os.path.basename(os.path.abspath(".")), par_type=str, - docstr="The name used to submit jobs to the system, defaults " - "to the name of the working directory") - - sf.par("PRECHECK", required=False, par_type=list, default=["TITLE"], - docstr="A list of parameters that will be displayed to stdout " - "before 'submit' or 'resume' is run. Useful for " - "manually reviewing important parameters prior to " - "system submission") - - sf.par("LOG_LEVEL", required=False, par_type=str, default="DEBUG", - docstr="Verbosity output of SF logger. Available from least to " - "most verbosity: 'CRITICAL', 'WARNING', 'INFO', 'DEBUG'; " - "defaults to 'DEBUG'") - - sf.par("VERBOSE", required=False, default=False, par_type=bool, - docstr="Level of verbosity provided to the output log. If True, " - "log statements will declare what module/class/function " - "they are being called from. Useful for debugging but " - "also very noisy.") - - # Define the Paths required by this module - # note: PATH.WORKDIR has been set by the entry point seisflows.setup() - sf.path("SCRATCH", required=False, - default=os.path.join(PATH.WORKDIR, CFGPATHS.SCRATCHDIR), - docstr="scratch path to hold temporary data during workflow") - - sf.path("OUTPUT", required=False, - default=os.path.join(PATH.WORKDIR, CFGPATHS.OUTPUTDIR), - docstr="directory to save workflow outputs to disk") - - sf.path("SYSTEM", required=False, - default=os.path.join(PATH.WORKDIR, CFGPATHS.SCRATCHDIR, - "system"), - docstr="scratch path to hold any system related data") - - sf.path("LOCAL", required=False, - docstr="path to local data to be used during workflow") - - sf.path("LOGFILE", required=False, default=self.output_log, - docstr="the main output log file where all processes will " - "track their status") - - return sf - - def check(self, validate=True): - """ - Checks parameters and paths - """ - if validate: - self.required.validate() - - # !!! This system could be better, currently defining log file twice - if self.output_log != PATH.LOGFILE: - self.output_log = PATH.LOGFILE - - def setup(self): - """ - Create the SeisFlows directory structure in preparation for a - SeisFlows workflow. Ensure that if any config information is left over - from a previous workflow, that these files are not overwritten by - the new workflow. Should be called by submit() - - .. note:: - This function is expected to create dirs: SCRATCH, SYSTEM, OUTPUT - and the following log files: output, error - - .. note:: - Logger is configured here as all workflows, independent of system, - will be calling setup() - - :rtype: tuple of str - :return: (path to output log, path to error log) - """ - # Create scratch directories - unix.mkdir(PATH.SCRATCH) - unix.mkdir(PATH.SYSTEM) - - # Create output directories - unix.mkdir(PATH.OUTPUT) - log_files = os.path.join(PATH.WORKDIR, CFGPATHS.LOGDIR) - unix.mkdir(log_files) - - # If resuming, move old log files to keep them out of the way. Number - # in ascending order, so we don't end up overwriting things - for src in [self.output_log, self.error_log, PATH.PAR_FILE]: - i = 1 - if os.path.exists(src): - dst = os.path.join(log_files, number_fid(src, i)) - while os.path.exists(dst): - i += 1 - dst = os.path.join(log_files, number_fid(src, i)) - self.logger.debug(f"copying par/log file to: {dst}") - unix.cp(src=src, dst=dst) - - def submit(self): - """ - Main insertion point of SeisFlows onto the compute system. - - .. rubric:: - $ seisflows submit - - .. note:: - The expected behavior of the submit() function is to: - 1) run system setup, creating directory structure, - 2) execute workflow by submitting workflow.main() - """ - raise NotImplementedError("Must be implemented by subclass") - - def run(self, classname, method, single=False, **kwargs): - """ - Runs a task multiple times in am embarassingly parallel fashion - - .. note:: - The expected behavior of the run() function is to: submit N jobs to - the system in parallel. For example, in a simulation step, run() - submits N jobs to the compute system where N is the number of - events requiring an adjoint simulation. - - :type classname: str - :param classname: the class to run - :type method: str - :param method: the method from the given `classname` to run - :type single: bool - :param single: run a single-process, non-parallel task, such as - smoothing the gradient, which only needs to be run by once. - This will change how the job array and the number of tasks is - defined, such that the job is submitted as a single-core job to - the system. - :rtype: None - :return: This function is not expected to return anything - """ - raise NotImplementedError("Must be implemented by subclass") - - def taskid(self): - """ - Provides a unique identifier for each running task. This is - compute system specific. - - :rtype: int - :return: this function is expected to return a unique numerical - identifier. - """ - raise NotImplementedError("Must be implemented by subclass") - - def checkpoint(self, path, classname, method, kwargs): - """ - Writes the SeisFlows working environment to disk so that new tasks can - be executed in a separate/new/restarted working environment. - - :type path: str - :param path: path to save the checkpointed pickle files to - :type classname: str - :param classname: name of the class to save - :type method: str - :param method: the specific function to be checkpointed - :type kwargs: dict - :param kwargs: dictionary to pass to object saving - """ - argspath = os.path.join(path, "kwargs") - argsfile = os.path.join(argspath, f"{classname}_{method}.p") - - unix.mkdir(argspath) - with open(argsfile, "wb") as f: - pickle.dump(kwargs, f) - save(path=PATH.OUTPUT) - diff --git a/seisflows/system/chinook.py b/seisflows/system/chinook.py index f3c2792a..f988f0e5 100644 --- a/seisflows/system/chinook.py +++ b/seisflows/system/chinook.py @@ -8,24 +8,14 @@ Information on Chinook can be found here: https://uaf-rcs.gitbook.io/uaf-rcs-hpc-docs/hpc """ -import os -import sys -import logging +from seisflows.system.slurm import Slurm -from seisflows.config import custom_import, SeisFlowsPathsParameters -PAR = sys.modules["seisflows_parameters"] -PATH = sys.modules["seisflows_paths"] - - -class Chinook(custom_import("system", "slurm")): +class Chinook(Slurm): """ System interface for the University of Alaska HPC Chinook, which operates on a SLURM system. """ - # Class-specific logger accessed using self.logger - logger = logging.getLogger(__name__).getChild(__qualname__) - def __init__(self): """ These parameters should not be set by the user. @@ -36,39 +26,31 @@ def __init__(self): own number of cores per compute node, defined here """ super().__init__() - self.partitions = {"debug": 24, "t1small": 28, "t2small": 28, - "t1standard": 40, "t2standard": 40, "analysis": 28 - } - @property - def required(self): - """ - A hard definition of paths and parameters required by this class, - alongside their necessity for the class and their string explanations. - """ - sf = SeisFlowsPathsParameters(super().required) - - sf.par("PARTITION", required=False, default="t1small", par_type=int, - docstr="Name of partition on main cluster, available: " - "analysis, t1small, t2small, t1standard, t2standard, gpu") + self.required.par( + "PARTITION", required=False, default="t1small", par_type=int, + docstr="Name of partition on main cluster, available: " + "analysis, t1small, t2small, t1standard, t2standard, gpu") - sf.par("MPIEXEC", required=False, default="srun", par_type=str, - docstr="Function used to invoke parallel executables") + self.required.par( + "MPIEXEC", required=False, default="srun", par_type=str, + docstr="Function used to invoke parallel executables") - return sf + self.partitions = {"debug": 24, "t1small": 28, "t2small": 28, + "t1standard": 40, "t2standard": 40, "analysis": 28 + } def check(self, validate=True): """ Checks parameters and paths """ - if validate: - self.required.validate() - super().check(validate=False) + super().check(validate=validate) - assert(PAR.PARTITION in self.partitions.keys()), \ + assert(self.par.PARTITION in self.partitions.keys()), \ f"Chinook partition must be in {self.partitions.keys()}" - assert(PAR.NODESIZE == self.partitions[PAR.PARTITION]), \ - (f"PARTITION {PAR.PARTITION} is expected to have NODESIZE=" - f"{self.partitions[PAR.PARTITION]}, not current {PAR.NODESIZE}") + assert(self.par.NODESIZE == self.partitions[self.par.PARTITION]), \ + (f"PARTITION {self.par.PARTITION} is expected to have NODESIZE=" + f"{self.partitions[self.par.PARTITION]}, not current " + f"{self.par.NODESIZE}") diff --git a/seisflows/system/cluster.py b/seisflows/system/cluster.py index c3e51eff..f5e6f049 100644 --- a/seisflows/system/cluster.py +++ b/seisflows/system/cluster.py @@ -5,64 +5,58 @@ specific clusters. """ import sys -import logging import subprocess +from seisflows.system.workstation import Workstation -from seisflows.tools import msg -from seisflows.config import custom_import, save, SeisFlowsPathsParameters - -PAR = sys.modules["seisflows_parameters"] -PATH = sys.modules["seisflows_paths"] - - -class Cluster(custom_import("system", "base")): +class Cluster(Workstation): """ Abstract base class for the Systems module which controls interaction with compute systems such as HPC clusters. """ - # Class-specific logger accessed using self.logger - logger = logging.getLogger(__name__).getChild(__qualname__) - - @property - def required(self): + def __init__(self): """ - A hard definition of paths and parameters required by this class, - alongside their necessity for the class and their string explanations. + Instantiate the Cluster System class """ - sf = SeisFlowsPathsParameters(super().required) - - # Define the Parameters required by this module - sf.par("WALLTIME", required=True, par_type=float, - docstr="Maximum job time in minutes for main SeisFlows job") - - sf.par("TASKTIME", required=True, par_type=float, - docstr="Maximum job time in minutes for each SeisFlows task") - - sf.par("NTASK", required=True, par_type=int, - docstr="Number of separate, individual tasks. Also equal to " - "the number of desired sources in workflow") - - sf.par("NPROC", required=True, par_type=int, - docstr="Number of processor to use for each simulation") - - sf.par("ENVIRONS", required=False, default="", par_type=str, - docstr="Optional environment variables to be provided in the" - "following format VAR1=var1,VAR2=var2... Will be set" - "using os.environs") - - return sf + super().__init__() + + self.required.par( + "WALLTIME", required=True, par_type=float, + docstr="Maximum job time in minutes for main SeisFlows job" + ) + self.required.par( + "TASKTIME", required=True, par_type=float, + docstr="Maximum job time in minutes for each SeisFlows task" + ) + # note: OVERLOADS the Workstation `NTASK` parameter + self.required.par( + "NTASK", required=True, par_type=int, + docstr="Number of separate, individual tasks. Also equal to " + "the number of desired sources in workflow" + ) + # note: OVERLOADS the Workstation `NPROC` parameter + self.required.par( + "NPROC", required=True, par_type=int, + docstr="Number of processor to use for each simulation" + ) + self.required.par( + "ENVIRONS", required=False, default="", par_type=str, + docstr="Optional environment variables to be provided in the" + "following format VAR1=var1,VAR2=var2... Will be set" + "using os.environs" + ) def check(self, validate=True): """ Checks parameters and paths """ - if validate: - self.required.validate() + super().check(validate=validate) - super().check(validate=False) + def setup(self): + """Inherits from workflow.system.workstation.Workstation""" + self.setup() - def submit(self, submit_call): + def submit(self, submit_call=None): """ Main insertion point of SeisFlows onto the compute system. @@ -74,21 +68,18 @@ def submit(self, submit_call): 1) run system setup, creating directory structure, 2) execute workflow by submitting workflow.main() - :type workflow: seisflows.workflow - :param workflow: an active seisflows workflow instance :type submit_call: str :param submit_call: the command line workload manager call to be run by subprocess. These need to be passed in by specific workload manager subclasses. """ self.setup() - workflow = sys.modules["seisflows_workflow"] + workflow = self.module("workflow") workflow.checkpoint() - # check==True: subprocess will wait for workflow.main() to finish subprocess.run(submit_call, shell=True, check=True) - def run(self, classname, method, **kwargs): + def run(self, classname, method, single=False, **kwargs): """ Runs a task multiple times in parallel @@ -98,8 +89,16 @@ def run(self, classname, method, **kwargs): submits N jobs to the compute system where N is the number of events requiring an adjoint simulation. - :rtype: None - :return: This function is not expected to return anything + :type classname: str + :param classname: the class to run + :type method: str + :param method: the method from the given `classname` to run + :type single: bool + :param single: run a single-process, non-parallel task, such as + smoothing the gradient, which only needs to be run by once. + This will change how the job array and the number of tasks is + defined, such that the job is submitted as a single-core job to + the system. """ raise NotImplementedError('Must be implemented by subclass.') @@ -113,3 +112,9 @@ def taskid(self): identifier. """ raise NotImplementedError('Must be implemented by subclass.') + + def checkpoint(self, path, classname, method, kwargs): + """Inherits from workflow.system.workstation.Workstation""" + self.checkpoint(path=path, classname=classname, method=method, + kwargs=kwargs) + diff --git a/seisflows/system/lsf.py b/seisflows/system/lsf.py index f7f9d29c..01a55244 100644 --- a/seisflows/system/lsf.py +++ b/seisflows/system/lsf.py @@ -1,26 +1,20 @@ #!/usr/bin/env python3 """ -This is the subclass seisflows.system.lsf_lg +This is the subclass seisflows.system.lsf.Lsf This class provides the core utilities interaction with HPC systems which run using the Platform Load Sharing Facility (LSF) workload management platform. """ import os -import sys import time -import logging import subprocess -from seisflows.tools import msg, unix -from seisflows.tools.wrappers import findpath -from seisflows.config import custom_import, SeisFlowsPathsParameters +from seisflows.system.cluster import Cluster +from seisflows.tools import unix +from seisflows.config import ROOT_DIR -PAR = sys.modules['seisflows_parameters'] -PATH = sys.modules['seisflows_paths'] - - -class Lsf(custom_import("system", "cluster")): +class Lsf(Cluster): """ An interface through which to submit workflows, run tasks in serial or parallel, and perform other system functions. @@ -29,17 +23,16 @@ class Lsf(custom_import("system", "cluster")): classes provide a consistent command set across different computing environments. - Intermediate files are written to a global scratch path PATH.SCRATCH, + Intermediate files are written to a global scratch path self.path.SCRATCH, which must be accessible to all compute nodes. - Optionally, users can provide a local scratch path PATH.LOCAL if each + Optionally, users can provide a local scratch path self.path.LOCAL if each compute node has its own local filesystem. For important additional information, please see - http://seisflows.readthedocs.org/en/latest/manual/manual.html#system-configuration + http://seisflows.readthedocs.org/en/latest/manual/ + manual.html#system-configuration """ - logger = logging.getLogger(__name__).getChild(__qualname__) - def __init__(self): """ These parameters should not be set by the user. @@ -47,51 +40,57 @@ def __init__(self): """ super().__init__() - @property - def required(self): - """ - Checks parameters and paths - """ - sf = SeisFlowsPathsParameters(super().required) - - sf.par("MPIEXEC", required=False, default="mpiexec", par_type=str, - docstr="Function used to invoke executables on the system. " - "For example 'srun' on SLURM systems, or './' on a " - "workstation. If left blank, will guess based on the " - "system.") - + self.logger.warning("system.LSF is underdeveloped and " + "will likely not work without significant testing " + "and source code edits") + + self.required.par( + "MPIEXEC", required=False, default="mpiexec", par_type=str, + docstr="Function used to invoke executables on the system. " + "For example 'srun' on SLURM systems, or './' on a " + "workstation. If left blank, will guess based on the " + "system." + ) # Define the Parameters required by this module - sf.par("NTASKMAX", required=False, default=100, par_type=int, - docstr="Limit on the number of concurrent tasks in array") - - sf.par("NODESIZE", required=True, par_type=int, - docstr="The number of cores per node defined by the system") - - sf.par("LSFARGS", required=False, default="", par_type=str, - docstr="Any optional, additional LSG arguments that will be " - "passed to the LSF submit scripts") - - def submit(self, workflow): - """ - Submits workflow - """ - # Prepare 'bsub' arguments - submit_call = " ".join([ - f"bsub", - f"{PAR.LSFARGS}", - f"-J {PAR.TITLE}", - f"-o {self.output_log}.log", - f"-e {self.error_log}.log", - f"-n {PAR.NODESIZE}", - f'-R "span[ptile={PAR.NODESIZE}"', - f"-W {PAR.WALLTIME:d}:00", - os.path.join(findpath("seisflows.system"), "wrappers", "submit"), - PATH.OUTPUT - ]) - - super().submit(workflow, submit_call) - - def run(self, classname, method, *args, **kwargs): + self.required.par( + "NTASKMAX", required=False, default=100, par_type=int, + docstr="Limit on the number of concurrent tasks in array" + ) + self.required.par( + "NODESIZE", required=True, par_type=int, + docstr="The number of cores per node defined by the system" + ) + self.required.par( + "LSFARGS", required=False, default="", par_type=str, + docstr="Any optional, additional LSG arguments that will be " + "passed to the LSF submit scripts" + ) + self.required.path( + "LOCAL", required=False, + docstr="path to local data to be used during workflow" + ) + + def submit(self, submit_call=None): + """ + Submits workflow using 'bsub' arguments + """ + if submit_call is None: + submit_call = " ".join([ + f"bsub", + f"{self.par.LSFARGS}", + f"-J {self.par.TITLE}", + f"-o {self.output_log}.log", + f"-e {self.error_log}.log", + f"-n {self.par.NODESIZE}", + f'-R "span[ptile={self.par.NODESIZE}"', + f"-W {self.par.WALLTIME:d}:00", + f"{os.path.join(ROOT_DIR, 'scripts', 'submit')}", + f"--output {self.path.OUTPUT}" + ]) + + super().submit(submit_call=submit_call) + + def run(self, classname, method, single=False, run_call=None, **kwargs): """ Runs task multiple times in embarrassingly parallel fasion on the maui cluster @@ -105,117 +104,103 @@ def run(self, classname, method, *args, **kwargs): :param method: the method from the given `classname` to run """ # Checkpoint this individual method before proceeding - self.checkpoint(PATH.OUTPUT, classname, method, args, kwargs) + self.checkpoint(self.path.OUTPUT, classname, method, kwargs) # Submit job array run_call = " ".join([ f"bsub", - f"{PAR.LSFARGS}", - f"-J {PAR.TITLE}", - f"-n {PAR.NPROC}", - f'-R "span[ptile={PAR.NODESIZE}"', - f"-W {PAR.TASKTIME:d}:00", - f"-o {os.path.join(PATH.WORKDIR, 'output.logs', '%J_%I')}", - f"[1-{PAR.NTASK}] % {PAR.NTASKMAX}", - f"{os.path.join(findpath('seisflows.system'), 'wrappers', 'run')}", - f"{PATH.OUTPUT}", - f"{classname}", - f"{method}", - f"{PAR.ENVIRONS}" + f"{self.par.LSFARGS}", + f"-J {self.par.TITLE}", + f"-n {self.par.NPROC}", + f'-R "span[ptile={self.par.NODESIZE}"', + f"-W {self.par.TASKTIME:d}:00", + f"-o {os.path.join(self.path.WORKDIR, 'output.logs', '%J_%I')}", + f"[1-{self.par.NTASK}] % {self.par.NTASKMAX}", + f"{os.path.join(ROOT_DIR, 'scripts', 'run')}", + f"--output {self.path.OUTPUT}" + f"--classname {classname}", + f"--funcname {method}", + f"--environment {self.par.ENVIRONS or ''}" ]) + self.logger.debug(run_call) - stdout = subprocess.check_output(run_call, shell=True) - - # keep track of job ids - jobs = self.job_id_list(stdout, PAR.NTASK) - - while True: - # Wait seconds before checking status again - time.sleep(30) - self.timestamp() - isdone, jobs = self.job_status(classname, method, jobs) - if isdone: - return - - def run_single(self, classname, method, *args, **kwargs): - """ Runs task multiple times in embarrassingly parallel fasion - - Executes classname.method(*args, **kwargs) NTASK times, each time on - NPROC cpu cores - """ - # Checkpoint this individual method before proceeding - self.checkpoint(PATH.OUTPUT, classname, method, args, kwargs) - - # Submit job array - run_call = " ".join([ - f"bsub", - f"{PAR.LSFARGS}", - f"-J {PAR.TITLE}", - f"-n {PAR.NPROC}", - f'-R "span[ptile={PAR.NODESIZE}"', - f"-W {PAR.TASKTIME:d}:00", - f"-o {os.path.join(PATH.WORKDIR, 'output.logs', '%J')}", - f"[1-1]", - f"{os.path.join(findpath('seisflows.system'), 'wrappers', 'run')}", - f"{PATH.OUTPUT}", - f"{classname}", - f"{method}", - f"{PAR.ENVIRONS}" - ]) + # Single-process jobs simply need to replace a few sbatch arguments. + # Do it AFTER `run_call` has been defined so that subclasses submitting + # custom run calls can still benefit from this + if single: + self.logger.info("replacing parts of sbatch run call for single " + "process job") + run_call = _modify_run_call_single_proc(run_call) - stdout = check_output(run_call, shell=True) + # The standard response from SLURM when submitting jobs + # is something like 'Submitted batch job 441636', we want job number + stdout = subprocess.run(run_call, stdout=subprocess.PIPE, + text=True, shell=True).stdout # keep track of job ids - jobs = self.job_id_list(stdout, ntask=1) + jobs = self._job_id_list(stdout, single) while True: # Wait seconds before checking status again - time.sleep(30) - self.timestamp() - isdone, jobs = self.job_status(classname, method, jobs) + time.sleep(5) + isdone, jobs = self._job_status(classname, method, jobs) if isdone: return - def job_id_list(self, stdout, ntask): + def taskid(self): """ - Parses job id list from sbatch standard output - - :type stdout: str - :param stdout: the output of subprocess.check_output() - :type ntask: int - :param ntask: number of tasks currently running + Provides a unique identifier for each running task """ - job = stdout.split()[1].strip()[1:-1] - if ntask == 1: - return [job] - else: - number_jobs = range(1, PAR.NSRC + 1) - return ["{job}[{}]".format(_) for _ in number_jobs] + return int(os.getenv('LSB_JOBINDEX')) - 1 + + def checkpoint(self, path, classname, method, kwargs): + """Inherits from workflow.system.workstation.Workstation""" + self.checkpoint(path=path, classname=classname, method=method, + kwargs=kwargs) - def job_status(self, classname, method, jobs): + def _check_job_status(self, job_ids): """ Queries completion status of a single job + TODO this function is mangled, needs to be rewritten + :type job: str :param job: job id to query """ job_finished = [] - for job in jobs: - state = self._query(job) + for job_id in job_ids: + state = self._query(job_id) if state == "DONE": job_finished.append(True) else: job_finished.append(False) if state == "EXIT": - print(msg.cli(f"LSF job {job} failed to execute " - f"{classname}.{method}.", header="error", - border="=")) - sys.exit(-1) + return job_id, "FAILED" isdone = all(job_finished) - return isdone, jobs + return None, "OKAY" + + def _job_id_list(self, stdout, single): + """ + Parses job id list from LSF standard output + + :type stdout: str + :param stdout: the output of subprocess.check_output() + :type single: bool + :param single: if running a single process job, returns a list of length + 1 with a single job id, else returns a list of length self.par.NTASK + for all arrayed jobs + :rtype: list + :return: a list of array jobs that should be currently running + """ + job = stdout.split()[1].strip()[1:-1] + if single: + return [job] + else: + number_jobs = range(1, self.par.NSRC + 1) + return ["{job}[{}]".format(_) for _ in number_jobs] def _query(self, jobid): """ @@ -225,41 +210,34 @@ def _query(self, jobid): :param jobid: job id to query LSF system about """ # Write the job status output to a temporary file - with open(os.path.join(PATH.SYSTEM, "job_status", "w")) as f: - call('bjobs -a -d "{jobid}"', stdout=f) + with open(os.path.join(self.path.SYSTEM, "job_status", "w")) as f: + subprocess.call('bjobs -a -d "{jobid}"', stdout=f) # Read the job status back from the text file - with open(os.path.join(PATH.SYSTEM, "job_status", "r")) as f: + with open(os.path.join(self.path.SYSTEM, "job_status", "r")) as f: lines = f.readlines() state = lines[1].split()[2].strip() return state - def taskid(self): - """ - Provides a unique identifier for each running task - """ - return int(os.getenv('LSB_JOBINDEX')) - 1 - - def timestamp(self): - """ - Timestamp the current running job - """ - with open(os.path.join(PATH.SYSTEM, "timestamps", "a")) as f: - f.write(time.strftime("%H:%M:%S")) - f.write("\n") - - def save_kwargs(self, classname, method, kwargs): - """ - Save key word arguments as a pickle object. - - :type classname: str - :param classname: the class to run - :type method: str - :param method: the method from the given `classname` to run - """ - kwargspath = os.path.join(PATH.OUTPUT, "kwargs") - kwargsfile = os.path.join(kwargspath, f"{classname}_{method}.p") + # def save_kwargs(self, classname, method, kwargs): + # """ + # Save key word arguments as a pickle object. + # + # :type classname: str + # :param classname: the class to run + # :type method: str + # :param method: the method from the given `classname` to run + # """ + # kwargspath = os.path.join(self.path.OUTPUT, "kwargs") + # kwargsfile = os.path.join(kwargspath, f"{classname}_{method}.p") + # + # unix.mkdir(kwargspath) + # saveobj(kwargsfile, kwargs) + + +def _modify_run_call_single_proc(run_call): + """ - unix.mkdir(kwargspath) - saveobj(kwargsfile, kwargs) + """ + raise NotImplementedError("This function needs to be written") diff --git a/seisflows/system/maui.py b/seisflows/system/maui.py index 57bd43e4..1fbe6481 100644 --- a/seisflows/system/maui.py +++ b/seisflows/system/maui.py @@ -21,19 +21,18 @@ import sys import math import logging + +from seisflows.system.slurm import Slurm from seisflows.config import custom_import, SeisFlowsPathsParameters, ROOT_DIR PAR = sys.modules["seisflows_parameters"] PATH = sys.modules["seisflows_paths"] -class Maui(custom_import("system", "slurm")): +class Maui(Slurm): """ System interface for Maui, which operates on a SLURM system """ - # Class-specific logger accessed using self.logger - logger = logging.getLogger(__name__).getChild(__qualname__) - def __init__(self): """ These parameters should not be set by the user. @@ -44,97 +43,109 @@ def __init__(self): own number of cores per compute node, defined here """ super().__init__() - self.partitions = {"nesi_research": 40} - - @property - def required(self): - """ - A hard definition of paths and parameters required by this class, - alongside their necessity for the class and their string explanations. - """ - sf = SeisFlowsPathsParameters(super().required) - - sf.par("ACCOUNT", required=True, par_type=str, - docstr="The account name to submit jobs under") - - sf.par("CPUS_PER_TASK", required=False, default=1, par_type=int, - docstr="Multiple CPUS per task allows for multithreading jobs") - - sf.par("CLUSTER", required=False, default="maui", par_type=str, - docstr="Name of main cluster for parallel job submission") - - sf.par("PARTITION", required=False, default="nesi_research", - par_type=str, docstr="Name of partition on main cluster") - - sf.par("ANCIL_CLUSTER", required=False, default="maui_ancil", - par_type=str, - docstr="Name of ancillary cluster for prepost tasks") - - sf.par("ANCIL_PARTITION", required=False, default="nesi_prepost", - par_type=str, - docstr="Name of ancillary partition for prepost tasks") - sf.par("ANCIL_TASKTIME", required=False, default="null", par_type=float, - docstr="Tasktime for prepost jobs on ancillary nodes") + self.required.par( + "ACCOUNT", required=True, par_type=str, + docstr="Maui account name to submit jobs under" + ) + self.required.par( + "NODESIZE", required=False, default=40, par_type=int, + docstr="The number of cores per node defined by the Maui cluster. " + "Assumed to be 40 cores per node." + ) + self.required.par( + "MPIEXEC", required=False, default="srun", par_type=str, + docstr="MPI call function used to invoke parallel executables, " + "defaults to 'srun'" + ) + self.required.par( + "CPUS_PER_TASK", required=False, default=1, par_type=int, + docstr="Multiple CPUS per task allows for multithreading jobs" + ) + self.required.par( + "CLUSTER", required=False, default="maui", par_type=str, + docstr="Name of main cluster for job submission. Available options: " + "'maui', 'maui_ancil', 'mahuika'. Note Mahuika untested" + ) + self.required.par( + "PARTITION", required=False, default="nesi_research", + par_type=str, docstr="Name of cluster partition to submit job to" + ) + self.required.par( + "ANCIL_CLUSTER", required=False, default="maui_ancil", par_type=str, + docstr="Ancillary cluster for pre- and post-processing tasks." + "Defaults to 'maui_ancil'") + + self.required.par( + "ANCIL_PARTITION", required=False, default="nesi_prepost", + par_type=str, + docstr="Name of ancillary partition for prepost tasks. Defaults to" + "'nesi_prepost'" + ) + self.required.par( + "ANCIL_TASKTIME", required=False, default="null", par_type=float, + docstr="Tasktime for prepost jobs submitted to ancillary nodes " + "matching 'ANCIL_CLUSTER' and 'ANCIL_PARTITION'" + ) - sf.par("NODESIZE", required=False, default=40, par_type=int, - docstr="The number of cores per node defined by the system") - - sf.par("MPIEXEC", required=False, default="srun", par_type=str, - docstr="Function used to invoke parallel executables") - - return sf + self.partitions = {"nesi_research": 40} def check(self, validate=True): """ Checks parameters and paths """ - if validate: - self.required.validate() - super().check(validate=False) + super().check(validate=validate) - assert(PAR.NODESIZE == self.partitions[PAR.PARTITION]), \ - (f"PARTITION {PAR.PARTITION} is expected to have NODESIZE=" - f"{self.partitions[PAR.PARTITION]}, not current {PAR.NODESIZE}") + assert(self.par.NODESIZE == self.partitions[self.par.PARTITION]), \ + (f"PARTITION {self.par.PARTITION} is expected to have NODESIZE=" + f"{self.partitions[self.par.PARTITION]}, not current " + f"{self.par.NODESIZE}") - assert("SLURM_MEM_PER_CPU" in (PAR.ENVIRONS or "")), \ + assert("SLURM_MEM_PER_CPU" in (self.par.ENVIRONS or "")), \ ("Maui runs Slurm>=21 which enforces mutually exclusivity of Slurm " "memory environment variables SLURM_MEM_PER_CPU and " "SLURM_MEM_PER_NODE. Due to the cross-cluster nature of " "running SeisFlows3 on Maui, we must remove one env. variable. " - "Please add 'SLURM_MEM_PER_CPU' to PAR.ENVIRONS.") + "Please add 'SLURM_MEM_PER_CPU' to self.par.ENVIRONS.") - def submit(self): + def setup(self): + """Inherits from workflow.system.workstation.Workstation""" + self.setup() + + def submit(self, submit_call=None): """ Submits master job workflow to maui_ancil cluster as a single-core process .. note:: The master job must be run on maui_ancil because Maui does - not have the ability to run the command "sacct" + not have the ability to run the command "sacct", nor can it + use the Conda environment that has been set by Ancil .. note:: We do not place SLURMARGS into the sbatch command to avoid the export=None which will not propagate the conda environment """ - maui_submit_call = " ".join([ - f"sbatch", - f"--account={PAR.ACCOUNT}", - f"--cluster={PAR.ANCIL_CLUSTER}", - f"--partition={PAR.ANCIL_PARTITION}", - f"--job-name={PAR.TITLE}", - f"--output={self.output_log}", - f"--error={self.error_log}", - f"--ntasks=1", - f"--cpus-per-task=1", - f"--time={PAR.WALLTIME:d}", - f"{os.path.join(ROOT_DIR, 'scripts', 'submit')}", - f"--output {PATH.OUTPUT}" - ]) - self.logger.debug(maui_submit_call) - super().submit(maui_submit_call) - - def run(self, classname, method, single=False, **kwargs): + if submit_call is None: + submit_call = " ".join([ + f"sbatch", + f"--account={self.par.ACCOUNT}", + f"--cluster={self.par.ANCIL_CLUSTER}", + f"--partition={self.par.ANCIL_PARTITION}", + f"--job-name={self.par.TITLE}", + f"--output={self.output_log}", + f"--error={self.error_log}", + f"--ntasks=1", + f"--cpus-per-task=1", + f"--time={self.par.WALLTIME:d}", + f"{os.path.join(ROOT_DIR, 'scripts', 'submit')}", + f"--output {self.path.OUTPUT}" + ]) + self.logger.debug(submit_call) + + super().submit(submit_call=submit_call) + + def run(self, classname, method, single=False, run_call=None, **kwargs): """ Runs task multiple times in embarrassingly parallel fasion on a SLURM cluster. Executes classname.method(*args, **kwargs) `NTASK` times, @@ -144,33 +155,38 @@ def run(self, classname, method, single=False, **kwargs): :param classname: the class to run :type method: str :param method: the method from the given `classname` to run - :type scale_tasktime: int - :param scale_tasktime: a way to get over the hard-set tasktime, because - some tasks take longer (e.g. smoothing), but you don't want these - to set the tasktimes for all other tasks. This lets you scale the - time of specific tasks by PAR.TASKTIME * scale_tasktime + :type single: bool + :param single: run a single-process, non-parallel task, such as + smoothing the gradient, which only needs to be run by once. + This will change how the job array and the number of tasks is + defined, such that the job is submitted as a single-core job to + the system. """ - maui_run_call = " ".join([ - "sbatch", - f"{PAR.SLURMARGS or ''}", - f"--account={PAR.ACCOUNT}", - f"--job-name={PAR.TITLE}", - f"--clusters={PAR.CLUSTER}", - f"--partition={PAR.PARTITION}", - f"--cpus-per-task={PAR.CPUS_PER_TASK}", - f"--nodes={math.ceil(PAR.NPROC / float(PAR.NODESIZE)):d}", - f"--ntasks={PAR.NPROC:d}", - f"--time={PAR.TASKTIME:d}", - f"--output={os.path.join(PATH.WORKDIR, 'logs', '%A_%a')}", - f"--array=0-{PAR.NTASK-1 % PAR.NTASKMAX}", - f"{os.path.join(ROOT_DIR, 'scripts', 'run')}", - f"--output {PATH.OUTPUT}", - f"--classname {classname}", - f"--funcname {method}", - f"--environment {PAR.ENVIRONS or ''}" - ]) - self.logger.debug(maui_run_call) - super().run(classname, method, single, run_call=maui_run_call, **kwargs) + if run_call is None: + _nodes = math.ceil(self.par.NPROC / float(self.par.NODESIZE)) + + run_call = " ".join([ + "sbatch", + f"{self.par.SLURMARGS or ''}", + f"--account={self.par.ACCOUNT}", + f"--job-name={self.par.TITLE}", + f"--clusters={self.par.CLUSTER}", + f"--partition={self.par.PARTITION}", + f"--cpus-per-task={self.par.CPUS_PER_TASK}", + f"--nodes={_nodes:d}", + f"--ntasks={self.par.NPROC:d}", + f"--time={self.par.TASKTIME:d}", + f"--output={os.path.join(self.path.WORKDIR, 'logs', '%A_%a')}", + f"--array=0-{self.par.NTASK-1 % self.par.NTASKMAX}", + f"{os.path.join(ROOT_DIR, 'scripts', 'run')}", + f"--output {self.path.OUTPUT}", + f"--classname {classname}", + f"--funcname {method}", + f"--environment {self.par.ENVIRONS or ''}" + ]) + self.logger.debug(run_call) + + super().run(classname, method, single, run_call=run_call, **kwargs) def run_ancil(self, classname, method, **kwargs): """ @@ -184,26 +200,33 @@ def run_ancil(self, classname, method, **kwargs): """ ancil_run_call = " ".join([ "sbatch", - f"{PAR.SLURMARGS or ''}", - f"--account={PAR.ACCOUNT}", - f"--job-name={PAR.TITLE}", - f"--clusters={PAR.ANCIL_CLUSTER}", - f"--partition={PAR.ANCIL_PARTITION}", - f"--cpus-per-task={PAR.CPUS_PER_TASK}", - f"--time={PAR.ANCIL_TASKTIME:d}", - f"--output={os.path.join(PATH.WORKDIR, 'logs', '%A_%a')}", - f"--array=0-{PAR.NTASK-1 % PAR.NTASKMAX}", + f"{self.par.SLURMARGS or ''}", + f"--account={self.par.ACCOUNT}", + f"--job-name={self.par.TITLE}", + f"--clusters={self.par.ANCIL_CLUSTER}", + f"--partition={self.par.ANCIL_PARTITION}", + f"--cpus-per-task={self.par.CPUS_PER_TASK}", + f"--time={self.par.ANCIL_TASKTIME:d}", + f"--output={os.path.join(self.path.WORKDIR, 'logs', '%A_%a')}", + f"--array=0-{self.par.NTASK-1 % self.par.NTASKMAX}", f"{os.path.join(ROOT_DIR, 'scripts', 'run')}", - f"--output {PATH.OUTPUT}", + f"--output {self.path.OUTPUT}", f"--classname {classname}", f"--funcname {method}", - f"--environment {PAR.ENVIRONS or ''}" + f"--environment {self.par.ENVIRONS or ''}" ]) self.logger.debug(ancil_run_call) super().run(classname, method, single=False, run_call=ancil_run_call, **kwargs) + def taskid(self): + """Inherits from seisflows.system.slurm.Slurm""" + return self.taskid() + def checkpoint(self, path, classname, method, kwargs): + """Inherits from workflow.system.workstation.Workstation""" + self.checkpoint(path=path, classname=classname, method=method, + kwargs=kwargs) diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index 1c3beb3a..235ee280 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -17,63 +17,61 @@ import sys import math import time -import logging import subprocess +from seisflows.system.cluster import Cluster from seisflows.tools import msg -from seisflows.config import ROOT_DIR, custom_import, SeisFlowsPathsParameters +from seisflows.config import ROOT_DIR -PAR = sys.modules["seisflows_parameters"] -PATH = sys.modules["seisflows_paths"] - -class Slurm(custom_import("system", "cluster")): +class Slurm(Cluster): """ Generalized interface for submitting jobs to and interfacing with a SLURM workload management system. """ - # Class-specific logger accessed using self.logger - logger = logging.getLogger(__name__).getChild(__qualname__) - def __init__(self): """ These parameters should not be set by the user. Attributes are initialized as NoneTypes for clarity and docstrings. """ super().__init__() - - @property - def required(self): + + self.required.par( + "MPIEXEC", required=False, default="srun -u", par_type=str, + docstr="Function used to invoke executables on the system. " + "For example 'srun' on SLURM systems, or './' on a " + "workstation. If left blank, will guess based on the " + "system." + ) + self.required.par( + "NTASKMAX", required=False, default=100, par_type=int, + docstr="Limit on the number of concurrent tasks in array" + ) + self.required.par( + "NODESIZE", required=True, par_type=int, + docstr="The number of cores per node defined by the system" + ) + + self.required.par( + "SLURMARGS", required=False, default="", par_type=str, + docstr="Any optional, additional SLURM arguments that will be " + "passed to the SBATCH scripts" + ) + + def check(self, validate=True): """ - A hard definition of paths and parameters required by this class, - alongside their necessity for the class and their string explanations. + Checks parameters and paths """ - sf = SeisFlowsPathsParameters(super().required) - - sf.par("MPIEXEC", required=False, default="srun -u", par_type=str, - docstr="Function used to invoke executables on the system. " - "For example 'srun' on SLURM systems, or './' on a " - "workstation. If left blank, will guess based on the " - "system.") + super().check(validate=validate) - sf.par("NTASKMAX", required=False, default=100, par_type=int, - docstr="Limit on the number of concurrent tasks in array") - - sf.par("NODESIZE", required=True, par_type=int, - docstr="The number of cores per node defined by the system") - - sf.par("SLURMARGS", required=False, default="", par_type=str, - docstr="Any optional, additional SLURM arguments that will be " - "passed to the SBATCH scripts") - - return sf + def setup(self): + """Inherits from workflow.system.workstation.Workstation""" + self.setup() def submit(self, submit_call=None): """ - Submits workflow as a single process master job + Submits workflow as a single process master job on a SLURM system - :type workflow: module - :param workflow: :type submit_call: str :param submit_call: subclasses (e.g., specific SLURM cluster subclasses) can overload the sbatch command line input by setting @@ -82,19 +80,19 @@ def submit(self, submit_call=None): if submit_call is None: submit_call = " ".join([ f"sbatch", - f"{PAR.SLURMARGS or ''}", - f"--job-name={PAR.TITLE}", + f"{self.par.SLURMARGS or ''}", + f"--job-name={self.par.TITLE}", f"--output={self.output_log}", f"--error={self.error_log}", - f"--ntasks-per-node={PAR.NODESIZE}", + f"--ntasks-per-node={self.par.NODESIZE}", f"--nodes=1", - f"--time={PAR.WALLTIME:d}", + f"--time={self.par.WALLTIME:d}", f"{os.path.join(ROOT_DIR, 'scripts', 'submit')}", - "--output {PATH.OUTPUT}" + f"--output {self.path.OUTPUT}" ]) self.logger.debug(submit_call) - super().submit(submit_call) + super().submit(submit_call=submit_call) def run(self, classname, method, single=False, run_call=None, **kwargs): """ @@ -121,26 +119,26 @@ def run(self, classname, method, single=False, run_call=None, **kwargs): can overload the sbatch command line input by setting run_call. If set to None, default run_call will be set here. """ - self.checkpoint(PATH.OUTPUT, classname, method, kwargs) + self.checkpoint(self.path.OUTPUT, classname, method, kwargs) # Default sbatch command line input, can be overloaded by subclasses # Copy-paste this default run_call and adjust accordingly for subclass if run_call is None: run_call = " ".join([ "sbatch", - f"{PAR.SLURMARGS or ''}", - f"--job-name={PAR.TITLE}", - f"--nodes={math.ceil(PAR.NPROC/float(PAR.NODESIZE)):d}", - f"--ntasks-per-node={PAR.NODESIZE:d}", - f"--ntasks={PAR.NPROC:d}", - f"--time={PAR.TASKTIME:d}", - f"--output={os.path.join(PATH.WORKDIR, 'logs', '%A_%a')}", - f"--array=0-{PAR.NTASK-1 % PAR.NTASKMAX}", + f"{self.par.SLURMARGS or ''}", + f"--job-name={self.par.TITLE}", + f"--nodes={math.ceil(self.par.NPROC/float(self.par.NODESIZE)):d}", + f"--ntasks-per-node={self.par.NODESIZE:d}", + f"--ntasks={self.par.NPROC:d}", + f"--time={self.par.TASKTIME:d}", + f"--output={os.path.join(self.path.WORKDIR, 'logs', '%A_%a')}", + f"--array=0-{self.par.NTASK-1 % self.par.NTASKMAX}", f"{os.path.join(ROOT_DIR, 'scripts', 'run')}", - f"--output {PATH.OUTPUT}", + f"--output {self.path.OUTPUT}", f"--classname {classname}", f"--funcname {method}", - f"--environment {PAR.ENVIRONS or ''}" + f"--environment {self.par.ENVIRONS or ''}" ]) self.logger.debug(run_call) @@ -158,8 +156,8 @@ def run(self, classname, method, single=False, run_call=None, **kwargs): text=True, shell=True).stdout # Continuously check for job completion on ALL running array jobs - job_ids = job_id_list(stdout, single) - job_id, status = self.check_job_status(job_ids) + job_ids = self._job_id_list(stdout, single) + job_id, status = self._check_job_status(job_ids) if status != "OKAY": print(msg.cli((f"Stopping workflow for {status} job. " f"Please check log file for details."), @@ -172,7 +170,33 @@ def run(self, classname, method, single=False, run_call=None, **kwargs): self.logger.info(f"Task {classname}.{method} finished successfully") - def check_job_status(self, job_ids): + def taskid(self): + """ + Provides a unique identifier for each running task + + :rtype: int + :return: identifier for a given task + """ + # If not set, this environment variable will return None + sftaskid = os.getenv("SEISFLOWS_TASKID") + + if sftaskid is None: + sftaskid = os.getenv("SLURM_ARRAY_TASK_ID") + if sftaskid is None: + print(msg.cli("system.taskid() environment variable not found. " + "Assuming DEBUG mode and returning taskid==0. " + "If not DEBUG mode, please check SYSTEM.run()", + header="warning", border="=")) + sftaskid = 0 + + return int(sftaskid) + + def checkpoint(self, path, classname, method, kwargs): + """Inherits from workflow.system.workstation.Workstation""" + self.checkpoint(path=path, classname=classname, method=method, + kwargs=kwargs) + + def _check_job_status(self, job_ids): """ Repeatedly check the status of a currently running job using 'sacct'. If the job goes into a bad state like 'FAILED', return the failing @@ -214,69 +238,47 @@ def check_job_status(self, job_ids): return None, "OKAY" - def taskid(self): - """ - Provides a unique identifier for each running task - - :rtype: int - :return: identifier for a given task + def _job_id_list(self, stdout, single): """ - # If not set, this environment variable will return None - sftaskid = os.getenv("SEISFLOWS_TASKID") + Parses job id list from sbatch standard output. Stdout typically looks + like: 'Submitted batch job 441636', but if submitting jobs cross-cluster + (e.g., like on Maui), stdout might be: + 'Submitted batch job 441636 on cluster Maui' - if sftaskid is None: - sftaskid = os.getenv("SLURM_ARRAY_TASK_ID") - if sftaskid is None: - print(msg.cli("system.taskid() environment variable not found. " - "Assuming DEBUG mode and returning taskid==0. " - "If not DEBUG mode, please check SYSTEM.run()", - header="warning", border="=")) - sftaskid = 0 + .. note:: + In order to find the job number, we just scan each word in stdout + until we find the number, ASSUMING that there is only one number in + the string - return int(sftaskid) + TODO Should failing to return job_id break in reasonable way? + The output job arrays will look something like: + [44163_0, 44163_1, ..., 44163_self.par.NTASK] -def job_id_list(stdout, single): - """ - Parses job id list from sbatch standard output. Stdout typically looks - like: 'Submitted batch job 441636', but if submitting jobs cross-cluster - (e.g., like on Maui), stdout might be: - 'Submitted batch job 441636 on cluster Maui' - - .. note:: - In order to find the job number, we just scan each word in stdout - until we find the number, ASSUMING that there is only one number in - the string - - TODO Should failing to return job_id break in reasonable way? - - The output job arrays will look something like: - [44163_0, 44163_1, ..., 44163_PAR.NTASK] - - :type stdout: str - :param stdout: the text response from running 'sbatch' on SLURM, which - should be returned by subprocess.run(stdout=PIPE) - :type single: bool - :param single: if running a single process job, returns a list of length - 1 with a single job id, else returns a list of length PAR.NTASK - for all arrayed jobs - :rtype: list - :return: a list of array jobs that should be currently running - """ - if single: - ntask = 1 - else: - ntask = PAR.NTASK + :type stdout: str + :param stdout: the text response from running 'sbatch' on SLURM, which + should be returned by subprocess.run(stdout=PIPE) + :type single: bool + :param single: if running a single process job, returns a list of length + 1 with a single job id, else returns a list of length self.par.NTASK + for all arrayed jobs + :rtype: list + :return: a list of array jobs that should be currently running + """ + if single: + ntask = 1 + else: + ntask = self.par.NTASK - # Splitting e.g.,: 'Submitted batch job 441636\n' - for part in stdout.strip().split(): - try: - # The int will keep throwing ValueError until we find the num - job_id = int(part) - break - except ValueError: - continue - return [f"{job_id}_{i}" for i in range(ntask)] + # Splitting e.g.,: 'Submitted batch job 441636\n' + for part in stdout.strip().split(): + try: + # The int will keep throwing ValueError until we find the num + job_id = int(part) + break + except ValueError: + continue + return [f"{job_id}_{i}" for i in range(ntask)] def job_array_status(job_ids): @@ -320,8 +322,8 @@ def check_job_state(job_id): cluster that ran the `sacct` call -X supress the .batch and .extern jobname - :type job: str - :param job: job id to query + :type job_id: str + :param job_id: job id to query """ cmd = f"sacct -nLX -o jobid,state -j {job_id}" stdout = subprocess.run(cmd, stdout=subprocess.PIPE, @@ -361,7 +363,7 @@ def _modify_run_call_single_proc(run_call): run_call.replace(part, "--ntasks=1") # Append taskid to environment variable, deal with the case where - # PAR.ENVIRONS is an empty string + # self.par.ENVIRONS is an empty string task_id_str = "SEISFLOWS_TASKID=0" if not run_call.strip().endswith("--environment"): task_id_str = f",{task_id_str}" # appending to the list of vars diff --git a/seisflows/system/workstation.py b/seisflows/system/workstation.py index 1119ece6..bf2e9e29 100644 --- a/seisflows/system/workstation.py +++ b/seisflows/system/workstation.py @@ -5,60 +5,157 @@ """ import os import sys -import logging +import pickle from contextlib import redirect_stdout -from seisflows.tools import msg -from seisflows.core import SeisFlowsPathsParameters -from seisflows.config import custom_import, CFGPATHS +from seisflows.core import Base +from seisflows.config import CFGPATHS, save +from seisflows.tools import msg, unix +from seisflows.tools.wrappers import number_fid -PAR = sys.modules["seisflows_parameters"] -PATH = sys.modules["seisflows_paths"] - -class Workstation(custom_import("system", "base")): +class Workstation(Base): """ - Run tasks in a serial fashion on a single local machine + Run tasks in a serial fashion on a single local machine. Also serves as the + Base System module, upon which all other System classes should be built. """ - logger = logging.getLogger(__name__).getChild(__qualname__) + def __init__(self): + """ + Instantiate the Workstation base class + """ + super().__init__() + + self.output_log = os.path.join(self.path.WORKDIR, CFGPATHS.LOGFILE) + self.error_log = os.path.join(self.path.WORKDIR, CFGPATHS.ERRLOGFILE) + + self.required.par( + "TITLE", required=False, + default=os.path.basename(os.path.abspath(".")), par_type=str, + docstr="The name used to submit jobs to the system, defaults " + "to the name of the working directory" + ) + self.required.par( + "MPIEXEC", required=False, default=None, par_type=str, + docstr="Function used to invoke executables on the system. " + "For example 'srun' on SLURM systems, or './' on a " + "workstation. If left blank, will guess based on the " + "system." + ) + self.required.par( + "NTASK", required=False, default=1, par_type=int, + docstr="Number of separate, individual tasks. Also equal to " + "the number of desired sources in workflow" + ) + self.required.par( + "NPROC", required=False, default=1, par_type=int, + docstr="Number of processor to use for each simulation" + ) + self.required.par( + "PRECHECK", required=False, par_type=list, default=["TITLE"], + docstr="A list of parameters that will be displayed to stdout " + "before 'submit' or 'resume' is run. Useful for " + "manually reviewing important parameters prior to " + "system submission" + ) + self.required.par( + "LOG_LEVEL", required=False, par_type=str, default="DEBUG", + docstr="Verbosity output of SF logger. Available from least to " + "most verbosity: 'CRITICAL', 'WARNING', 'INFO', 'DEBUG'; " + "defaults to 'DEBUG'" + ) + self.required.par( + "VERBOSE", required=False, default=False, par_type=bool, + docstr="Level of verbosity provided to the output log. If True, " + "log statements will declare what module/class/function " + "they are being called from. Useful for debugging but " + "also very noisy." + ) + # note: self.path.WORKDIR has been set by the entry point seisflows.setup() + self.required.path( + "SCRATCH", required=False, + default=os.path.join(self.path.WORKDIR, "scratch"), + docstr="scratch path to hold temporary data during workflow" + ) + self.required.path( + "OUTPUT", required=False, + default=os.path.join(self.path.WORKDIR, "output"), + docstr="directory to save workflow outputs to disk" + ) + self.required.path( + "SYSTEM", required=False, + default=os.path.join(self.required.SCRATCH, "system"), + docstr="scratch path to hold any system related data" + ) + self.required.path( + "LOGFILE", required=False, default=self.output_log, + docstr="the main output log file where all processes will track " + "their status" + ) - @property - def required(self): + def check(self, validate=True): """ - A hard definition of paths and parameters required by this class, - alongside their necessity for the class and their string explanations. + Checks parameters and paths """ - sf = SeisFlowsPathsParameters(super().required) + super().check(validate=validate) - sf.par("MPIEXEC", required=False, default=None, par_type=str, - docstr="Function used to invoke executables on the system. " - "For example 'srun' on SLURM systems, or './' on a " - "workstation. If left blank, will guess based on the " - "system.") + if self.output_log != self.path.LOGFILE: + self.output_log = self.path.LOGFILE - sf.par("NTASK", required=False, default=1, par_type=int, - docstr="Number of separate, individual tasks. Also equal to " - "the number of desired sources in workflow") + def setup(self): + """ + Create the SeisFlows directory structure in preparation for a + SeisFlows workflow. Ensure that if any config information is left over + from a previous workflow, that these files are not overwritten by + the new workflow. Should be called by submit() - sf.par("NPROC", required=False, default=1, par_type=int, - docstr="Number of processor to use for each simulation") + .. note:: + This function is expected to create dirs: SCRATCH, SYSTEM, OUTPUT + and the following log files: output, error - return sf + .. note:: + Logger is configured here as all workflows, independent of system, + will be calling setup() - def check(self, validate=True): + :rtype: tuple of str + :return: (path to output log, path to error log) """ - Checks parameters and paths + # Create scratch directories + unix.mkdir(self.path.SCRATCH) + unix.mkdir(self.path.SYSTEM) + + # Create output directories + unix.mkdir(self.path.OUTPUT) + log_files = os.path.join(self.path.WORKDIR, CFGPATHS.LOGDIR) + unix.mkdir(log_files) + + # If resuming, move old log files to keep them out of the way. Number + # in ascending order, so we don't end up overwriting things + for src in [self.output_log, self.error_log, self.path.PAR_FILE]: + i = 1 + if os.path.exists(src): + dst = os.path.join(log_files, number_fid(src, i)) + while os.path.exists(dst): + i += 1 + dst = os.path.join(log_files, number_fid(src, i)) + self.logger.debug(f"copying par/log file to: {dst}") + unix.cp(src=src, dst=dst) + + def finalize(self): + """Inherits from seisflows.core.Base""" + super().finalize() + + def submit(self, submit_call=None): """ - super().check(validate=False) - if validate: - self.required.validate() + Submits the main workflow job as a serial job submitted directly to + the compute node that is running the master job - def submit(self): - """ - Submits the main workflow job + :type submit_call: str or None + :param submit_call: the command line workload manager call to be run by + subprocess. This is only needed for overriding classes, it has no + effect on the Workstation class """ self.setup() - workflow = sys.modules["seisflows_workflow"] + workflow = self.module("workflow") workflow.checkpoint() workflow.main() @@ -80,18 +177,18 @@ def run(self, classname, method, single=False, **kwargs): defined, such that the job is submitted as a single-core job to the system. """ - self.checkpoint(PATH.OUTPUT, classname, method, kwargs) + self.checkpoint(self.path.OUTPUT, classname, method, kwargs) # Allows dynamic retrieval of any function from within package, e.g., # Date: Tue, 28 Jun 2022 14:23:18 -0800 Subject: [PATCH 032/195] making Forward workflow class the base workflow class, building everything else on top of that. Migration class will define adjoint simulations, and then inversion will build on top of Migration --- seisflows/system/cluster.py | 1 - seisflows/system/lsf.py | 1 - seisflows/workflow/base.py | 231 -------------------------- seisflows/workflow/forward.py | 285 ++++++++++++++++++++++++++++++++ seisflows/workflow/inversion.py | 42 ++++- seisflows/workflow/migration.py | 125 +++++--------- 6 files changed, 368 insertions(+), 317 deletions(-) delete mode 100644 seisflows/workflow/base.py create mode 100644 seisflows/workflow/forward.py diff --git a/seisflows/system/cluster.py b/seisflows/system/cluster.py index f5e6f049..07f2fadb 100644 --- a/seisflows/system/cluster.py +++ b/seisflows/system/cluster.py @@ -4,7 +4,6 @@ which must be overloaded by subclasses for specific workload managers, or specific clusters. """ -import sys import subprocess from seisflows.system.workstation import Workstation diff --git a/seisflows/system/lsf.py b/seisflows/system/lsf.py index 01a55244..4bcc277f 100644 --- a/seisflows/system/lsf.py +++ b/seisflows/system/lsf.py @@ -10,7 +10,6 @@ import subprocess from seisflows.system.cluster import Cluster -from seisflows.tools import unix from seisflows.config import ROOT_DIR diff --git a/seisflows/workflow/base.py b/seisflows/workflow/base.py deleted file mode 100644 index 97fc5b87..00000000 --- a/seisflows/workflow/base.py +++ /dev/null @@ -1,231 +0,0 @@ -#!/usr/bin/env python3 -""" -This is the Base class for seisflows.workflow. -It contains mandatory functions that must be called by subclasses -""" -import os -import sys -import logging - -from seisflows.tools import msg -from seisflows.tools.wrappers import exists -from seisflows.core import SeisFlowsPathsParameters -from seisflows.config import save - - -PAR = sys.modules["seisflows_parameters"] -PATH = sys.modules["seisflows_paths"] - - -class Base: - """ - Workflow abstract base class - """ - # Class-specific logger accessed using self.logger - logger = logging.getLogger(__name__).getChild(__qualname__) - - def __init__(self): - """ - These parameters should not be set by the user. - Attributes are initialized as NoneTypes for clarity and docstrings. - """ - pass - - @property - def required(self): - """ - A hard definition of paths and parameters required by this class, - alongside their necessity for the class and their string explanations. - """ - sf = SeisFlowsPathsParameters() - - sf.par("CASE", required=False, default="data", par_type=str, - docstr="Type of inversion, available: " - "['data': real data inversion, " - "'synthetic': synthetic-synthetic inversion]") - - sf.par("RESUME_FROM", required=False, par_type=str, - docstr="Name of task to resume inversion from") - - sf.par("STOP_AFTER", required=False, par_type=str, - docstr="Name of task to stop inversion after finishing") - - sf.par("SAVEMODEL", required=False, default=True, par_type=bool, - docstr="Save final model files after each iteration") - - sf.par("SAVEGRADIENT", required=False, default=True, par_type=bool, - docstr="Save gradient files after each iteration") - - sf.par("SAVEKERNELS", required=False, default=False, par_type=bool, - docstr="Save event kernel files after each iteration") - - sf.par("SAVETRACES", required=False, default=False, par_type=bool, - docstr="Save waveform traces after each iteration") - - sf.par("SAVERESIDUALS", required=False, default=False, par_type=bool, - docstr="Save waveform residuals after each iteration") - - sf.par("SAVEAS", required=False, default="binary", par_type=str, - docstr="Format to save models, gradients, kernels. " - "Available: " - "['binary': save files in native SPECFEM .bin format, " - "'vector': save files as NumPy .npy files, " - "'both': save as both binary and vectors]") - - sf.path("DATA", required=False, default=None, - docstr="path to data available to workflow") - - sf.path("MODEL_INIT", required=False, - default=os.path.join(PATH.WORKDIR, "specfem", "MODEL_INIT"), - docstr="location of the initial model to be used for workflow") - - sf.path("MODEL_TRUE", required=False, - default=os.path.join(PATH.WORKDIR, "specfem", "MODEL_TRUE"), - docstr="Target model to be used for PAR.CASE == 'synthetic'") - - return sf - - def check(self, validate=True): - """ - Checks parameters and paths. Must be implemented by sub-class - """ - if validate: - self.required.validate() - - if PAR.CASE.upper() == "SYNTHETIC": - assert exists(PATH.MODEL_TRUE), \ - "CASE == SYNTHETIC requires PATH.MODEL_TRUE" - - if not exists(PATH.DATA): - assert "MODEL_TRUE" in PATH, f"DATA or MODEL_TRUE must exist" - - def main(self, return_flow=False): - """ - Execution of a workflow is equal to stepping through workflow.main() - - An example main() script is provided below which details the requisite - parts. This function will NOT execute as it is written in pseudocode. - - :type return_flow: bool - :param return_flow: for CLI tool, simply returns the flow function - rather than running the workflow. Used for print statements etc. - """ - self.logger.warning("The current definition of workflow.main() will " - "NOT execute, it must be overwritten by a " - "subclass") - - # The FLOW function defines a list of functions to execute IN ORDER - flow = (self.func1, - self.func2, - # ... - self.funcN - ) - - # REQUIRED: CLI command `seisflows print flow` needs this for output - if return_flow: - return flow - - # Allow User to start the workflow mid-FLOW, in the event that a - # previous workflow errored, or if the User had previously stopped - # a workflow to look at results and they want to pick up where - # they left off - start, stop = self.check_stop_resume_cond(flow) - - self.logger.info(msg.mjr("BEGINNING EXAMPLE WORKFLOW")) - - # Iterate through the `FLOW` to step through workflow.main() - for func in flow[start:stop]: - func() - - # If an multi-iteration workflow is run, `FLOW` will be executed - # repeatedly, so reset start and stop for subsequent iterations - start, stop = 0, -1 - - self.logger.info(msg.mjr("FINISHED EXAMPLE WORKFLOW")) - - def check_stop_resume_cond(self, flow): - """ - Chek the stop after and resume from conditions - - Allow the main() function to resume a workflow from a given flow - argument, or stop the workflow after a given argument. In the event - that a previous workflow errored, or if the User had previously - stopped a workflow to look at results and they want to pick up where - they left off. - - Late check: Exits the workflow if RESUME_FROM or STOP_AFTER arguments - do not match any of the given flow arguments. - - :type flow: tuple of functions - :param flow: an ordered list of functions that will be - :rtype: tuple of int - :return: (start, stop) indices of the `flow` input dictating where the - list should be begun and ended. If RESUME_FROM and STOP_AFTER - conditions are NOT given by the user, start and stop will be 0 and - -1 respectively, meaning we should execute the ENTIRE list - """ - fxnames = [func.__name__ for func in flow] - - # Default values which dictate that flow will execute in its entirety - start_idx = None - stop_idx = None - - # Overwrite start_idx if RESUME_FROM given, exit condition if no match - if PAR.RESUME_FROM: - try: - start_idx = fxnames.index(PAR.RESUME_FROM) - fx_name = flow[start_idx].__name__ - self.logger.info( - msg.mnr(f"WORKFLOW WILL RESUME FROM FUNC: '{fx_name}'") - ) - except ValueError: - self.logger.info( - msg.cli(f"{PAR.RESUME_FROM} does not correspond to any FLOW " - f"functions. Please check that PAR.RESUME_FROM " - f"matches one of the functions listed out in " - f"`seisflows print flow`.", header="error", - border="=") - ) - sys.exit(-1) - - # Overwrite stop_idx if STOP_AFTER provided, exit condition if no match - if PAR.STOP_AFTER: - try: - stop_idx = fxnames.index(PAR.STOP_AFTER) - fx_name = flow[stop_idx].__name__ - stop_idx += 1 # increment to stop AFTER, due to python indexing - self.logger.info( - msg.mnr(f"WORKFLOW WILL STOP AFTER FUNC: '{fx_name}'") - ) - except ValueError: - self.logger.info( - msg.cli(f"{PAR.STOP_AFTER} does not correspond to any FLOW " - f"functions. Please check that PAR.STOP_AFTER " - f"matches one of the functions listed out in " - f"`seisflows print flow`.", header="error", - border="=") - ) - sys.exit(-1) - - # Make sure stop after doesn't come before resume_from, otherwise none - # of the flow will execute - if PAR.STOP_AFTER and PAR.RESUME_FROM: - if stop_idx <= start_idx: - self.logger.info( - msg.cli(f"PAR.STOP_AFTER=='{PAR.STOP_AFTER}' is called " - f"before PAR.RESUME_FROM=='{PAR.RESUME_FROM}' in " - f"the FLOW functions. Please adjust accordingly " - f"and rerun.", header="error", border="=") - ) - sys.exit(-1) - - return start_idx, stop_idx - - @staticmethod - def checkpoint(): - """ - Writes information to disk so workflow can be resumed following a break - """ - save(path=PATH.OUTPUT) - - diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py new file mode 100644 index 00000000..2d7c9cdd --- /dev/null +++ b/seisflows/workflow/forward.py @@ -0,0 +1,285 @@ +#!/usr/bin/env python3 +""" +The simplest simulation workflow you can run is a large number of forward +simulations to generate synthetics from a velocity model. Therefore the +Forward class represents the BASE workflow. All other workflows will build off +of the scaffolding defined by the Forward class. +""" +import os +import sys +import numpy as np +from glob import glob + +from seisflows.core import Base +from seisflows.tools import msg +from seisflows.config import save + + +class Forward(Base): + """ + Workflow abstract base class + """ + def __init__(self): + """ + These parameters should not be set by the user. + Attributes are initialized as NoneTypes for clarity and docstrings. + """ + super().__init__() + + self.required.par( + "SAVETRACES", required=False, default=False, par_type=bool, + docstr="Save waveform traces to disk after they have been " + "generated by the external solver" + ) + self.required.par( + "SAVERESIDUALS", required=False, default=False, par_type=bool, + docstr="Save data-synthetic residuals each time they are " + "caluclated" + ) + self.required.path( + "DATA", required=False, default=None, + docstr="path to observed waveform data available to workflow" + ) + self.required.path( + "MODEL_INIT", required=False, + default=os.path.join(self.path.WORKDIR, "specfem", "MODEL_INIT"), + docstr="Path location of the initial model to be used to generate " + "the the first evaluation of synthetic seismograms." + ) + self.required.path( + "GRAD", required=False, + default=os.path.join(self.path.WORKDIR, "scratch", "evalgrad"), + docstr="scratch path to store data related to gradient evaluations" + ) + + # For keeping track of what functions to start and stop a workflow with + self.start = None + self.stop = None + + def check(self, validate=True): + """ + Checks parameters and paths. Must be implemented by sub-class + """ + super().check(validate=validate) + + def setup(self, flow=None, return_flow=False): + """ + Setup workflow by intaking functions to be run and checking start and + stop criteria + """ + # The FLOW function defines a list of functions to execute IN ORDER + if flow is None: + flow = (self.evaluate_initial_misfit) + + # REQUIRED: CLI command `seisflows print flow` needs this for output + if return_flow: + return flow + + # Allow User to start the workflow mid-FLOW, in the event that a + # previous workflow errored, or if the User had previously stopped + # a workflow to look at results and they want to pick up where + # they left off + self.start, self.stop = self._check_stop_resume_cond(flow) + + self.logger.info( + msg.mjr(f"BEGINNING {self.__class__.__name__.upper()} WORKFLOW") + ) + + # Required modules that need to be set up + system = self.module("system") + preprocess = self.module("preprocess") + + system.setup() + preprocess.setup() + system.run("solver", "setup") + + def finalize(self): + """ + Tasks related to tearing down a workflow + """ + super().finalize() + + self.logger.info( + msg.mjr(f"FINISHED {self.__class__.__name__} WORKFLOW") + ) + + def main(self, flow=None, return_flow=False): + """ + Execution of a workflow is equal to stsepping through workflow.main() + + An example main() script is provided below which details the requisite + parts. This function will NOT execute as it is written in pseudocode. + + :type flow: list or tuple + :param flow: list of Class methods that will be run in the order they + are provided. If None, defaults to the evaluate_function() as + defined by Forward class + :type return_flow: bool + :param return_flow: for CLI tool, simply returns the flow function + rather than running the workflow. Used for print statements etc. + """ + self.setup(flow, return_flow) + # Iterate through the `FLOW` to step through workflow.main() + for func in flow[self.start: self.stop]: + func() + self.finalize() + + def evaluate_initial_misfit(self): + """ + Wrapper for evaluate_function that sends residuals to PATH.GRAD and + sets up the 'm_new' model for future evaluations + """ + self._evaluate_function(path=self.path.GRAD, suffix="new") + + def _evaluate_function(self, path, suffix): + """ + Performs forward simulation, and evaluates the objective function + + :type path: str + :param path: path in the scratch directory to use for I/O + :type suffix: str + :param suffix: suffix to use for I/O + """ + system = self.module("system") + + self.logger.info(msg.sub("EVALUATING OBJECTIVE FUNCTION")) + + model_tag = f"m_{suffix}" + misfit_tag = f"f_{suffix}" + + self._write_model(path=path, tag=model_tag) + + self.logger.debug(f"evaluating objective function {self.par.NTASK} times " + f"on system...") + system.run("solver", "eval_func", path=path) + + self._write_misfit(path=path, tag=misfit_tag) + + def _write_model(self, path, tag): + """ + Writes model in format expected by solver + + :type path: str + :param path: path to write the model to + :type tag: str + :param tag: name of the model to be saved, usually tagged as 'm' with + a suffix depending on where in the inversion we are. e.g., 'm_try'. + Expected that these tags are defined in OPTIMIZE module + """ + solver = self.module("solver") + + src = tag + dst = os.path.join(path, "model") + self.logger.debug(f"saving model '{src}' to:\n{dst}") + solver.save(solver.split(np.load(src)), dst) + + def _write_misfit(self, path, tag): + """ + Writes misfit in format expected by nonlinear optimization library. + Collects all misfit values within the given residuals directory and sums + them in a manner chosen by the preprocess class. + + :type path: str + :param path: path to write the misfit to + :type tag: str + :param tag: name of the model to be saved, usually tagged as 'f' with + a suffix depending on where in the inversion we are. e.g., 'f_try'. + Expected that these tags are defined in OPTIMIZE module + """ + preprocess = self.module("preprocess") + + self.logger.info("summing residuals with preprocess module") + src = glob(os.path.join(path, "residuals", "*")) + dst = tag + total_misfit = preprocess.sum_residuals(src) + + self.logger.debug(f"saving misfit {total_misfit:.3E} to tag '{dst}'") + np.save(dst, total_misfit) + + def _check_stop_resume_cond(self, flow): + """ + Chek the stop after and resume from conditions + + Allow the main() function to resume a workflow from a given flow + argument, or stop the workflow after a given argument. In the event + that a previous workflow errored, or if the User had previously + stopped a workflow to look at results and they want to pick up where + they left off. + + Late check: Exits the workflow if RESUME_FROM or STOP_AFTER arguments + do not match any of the given flow arguments. + + :type flow: tuple of functions + :param flow: an ordered list of functions that will be + :rtype: tuple of int + :return: (start, stop) indices of the `flow` input dictating where the + list should be begun and ended. If RESUME_FROM and STOP_AFTER + conditions are NOT given by the user, start and stop will be 0 and + -1 respectively, meaning we should execute the ENTIRE list + """ + fxnames = [func.__name__ for func in flow] + + # Default values which dictate that flow will execute in its entirety + start_idx = None + stop_idx = None + + # Overwrite start_idx if RESUME_FROM given, exit condition if no match + if self.par.RESUME_FROM: + try: + start_idx = fxnames.index(self.par.RESUME_FROM) + fx_name = flow[start_idx].__name__ + self.logger.info( + msg.mnr(f"WORKFLOW WILL RESUME FROM FUNC: '{fx_name}'") + ) + except ValueError: + self.logger.info( + msg.cli(f"{self.par.RESUME_FROM} does not correspond to any FLOW " + f"functions. Please check that self.par.RESUME_FROM " + f"matches one of the functions listed out in " + f"`seisflows print flow`.", header="error", + border="=") + ) + sys.exit(-1) + + # Overwrite stop_idx if STOP_AFTER provided, exit condition if no match + if self.par.STOP_AFTER: + try: + stop_idx = fxnames.index(self.par.STOP_AFTER) + fx_name = flow[stop_idx].__name__ + stop_idx += 1 # increment to stop AFTER, due to python indexing + self.logger.info( + msg.mnr(f"WORKFLOW WILL STOP AFTER FUNC: '{fx_name}'") + ) + except ValueError: + self.logger.info( + msg.cli( + f"{self.par.STOP_AFTER} does not correspond to any " + f"FLOW functions. Please check that PAR.STOP_AFTER " + f"matches one of the functions listed out in " + f"`seisflows print flow`.", header="error", + border="=") + ) + sys.exit(-1) + + # Make sure stop after doesn't come before resume_from, otherwise none + # of the flow will execute + if self.par.STOP_AFTER and self.par.RESUME_FROM: + if stop_idx <= start_idx: + self.logger.info( + msg.cli( + f"PAR.STOP_AFTER=='{self.par.STOP_AFTER}' is called " + f"before PAR.RESUME_FROM=='{self.par.RESUME_FROM}' in " + f"the FLOW functions. Please adjust accordingly " + f"and rerun.", header="error", border="=") + ) + sys.exit(-1) + + return start_idx, stop_idx + + def checkpoint(self): + """ + Writes information to disk so workflow can be resumed following a break + """ + save(path=self.PATH.OUTPUT) + + diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index 91c46e34..740922d1 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -10,7 +10,8 @@ import numpy as np from glob import glob -from seisflows.config import custom_import, CFGPATHS +from seisflows.core import Base +from seisflows.config import custom_import from seisflows.tools import msg, unix from seisflows.config import save, SeisFlowsPathsParameters @@ -24,7 +25,7 @@ postprocess = sys.modules["seisflows_postprocess"] -class Inversion(custom_import("workflow", "base")): +class Inversion(Base): """ Waveform inversion base class @@ -52,6 +53,36 @@ def __init__(self): """ super().__init__() + self.required.par("SAVEMODEL", required=False, default=True, par_type=bool, + docstr="Save final model files after each iteration") + + self.required.par("SAVEGRADIENT", required=False, default=True, par_type=bool, + docstr="Save gradient files after each iteration") + + self.required.par("SAVEKERNELS", required=False, default=False, par_type=bool, + docstr="Save event kernel files after each iteration") + + self.required.par("RESUME_FROM", required=False, par_type=str, + docstr="Name of task to resume inversion from") + + self.required.par("STOP_AFTER", required=False, par_type=str, + docstr="Name of task to stop inversion after finishing") + + self.required.par("CASE", required=False, default="data", par_type=str, + docstr="Type of inversion, available: " + "['data': real data inversion, " + "'synthetic': synthetic-synthetic inversion]") + + self.required.par("SAVEAS", required=False, default="binary", par_type=str, + docstr="Format to save models, gradients, kernels. " + "Available: " + "['binary': save files in native SPECFEM .bin format, " + "'vector': save files as NumPy .npy files, " + "'both': save as both binary and vectors]") + + self.required.path("MODEL_TRUE", required=False, + default=os.path.join(PATH.WORKDIR, "specfem", "MODEL_TRUE"), + docstr="Target model to be used for PAR.CASE == 'synthetic'") @property def required(self): """ @@ -98,6 +129,13 @@ def check(self, validate=True): if validate: self.required.validate() + if PAR.CASE.upper() == "SYNTHETIC": + assert os.path.exists(PATH.MODEL_TRUE), \ + "CASE == SYNTHETIC requires PATH.MODEL_TRUE" + + if not os.path.exists(PATH.DATA): + assert "MODEL_TRUE" in PATH, f"DATA or MODEL_TRUE must exist" + for required_path in ["SCRATCH", "OUTPUT", "LOCAL"]: assert(required_path in PATH), \ f"Inversion requires path {required_path}" diff --git a/seisflows/workflow/migration.py b/seisflows/workflow/migration.py index 49fabc7b..141d4440 100644 --- a/seisflows/workflow/migration.py +++ b/seisflows/workflow/migration.py @@ -5,32 +5,18 @@ This is a main Seisflows class, it controls the main workflow. """ import os -import sys -import logging +from seisflows.workflow.forward import Forward from seisflows.tools import unix, msg -from seisflows.config import custom_import, SeisFlowsPathsParameters -PAR = sys.modules["seisflows_parameters"] -PATH = sys.modules["seisflows_paths"] - -system = sys.modules["seisflows_system"] -solver = sys.modules["seisflows_solver"] -preprocess = sys.modules["seisflows_preprocess"] -postprocess = sys.modules["seisflows_postprocess"] - - -class Migration(custom_import("workflow", "base")): +class Migration(Forward): """ Migration base class. Performs the workflow of an inversion up to the postprocessing. In the terminology of seismic exploration, implements a 'reverse time migration'. """ - # Class-specific logger accessed using self.logger - logger = logging.getLogger(__name__).getChild(__qualname__) - def __init__(self): """ These parameters should not be set by the user. @@ -39,87 +25,62 @@ def __init__(self): """ super().__init__() - @property - def required(self): + def check(self, validate=True): """ - A hard definition of paths and parameters required by this class, - alongside their necessity for the class and their string explanations. + Checks parameters and paths. Must be implemented by sub-class """ - sf = SeisFlowsPathsParameters(super().required) + super().check(validate=validate) - return sf - - def main(self, return_flow=False): - """s - Migrates seismic data to generate sensitivity kernels - - :type return_flow: bool - :param return_flow: for CLI tool, simply returns the flow function - rather than running the workflow. Used for print statements etc. + def setup(self, flow=None, return_flow=False): """ - flow = (self.setup, - self.generate_synthetics, - self.backproject, - self.process_kernels, - self.finalize, - ) - if return_flow: - return flow - - # Allow workflow resume from and stop after given flow functions - start, stop = self.check_stop_resume_cond(flow) + Override the Forward.setup() method to include new flow functions + AND run setup for a the Postprocess module which will be used to deal + with the gradient + """ + if flow is None: + flow = (self.evaluate_initial_misfit, + self.evaluate_gradient) - # Run each argument in flow - self.logger.info(msg.mjr("STARTING MIGRATION WORKFLOW")) - for func in flow[start:stop]: - func() - self.logger.info(msg.mjr("FINISHED MIGRATION WORKFLOW")) + super().setup(flow=flow, return_flow=return_flow) - def setup(self): - """ - Sets up the SeisFlows modules for the Migration - """ - # Set up all the requisite modules from the master job - self.logger.info(msg.mnr("PERFORMING MODULE SETUP")) - preprocess.setup() + postprocess = self.module("postprocess") postprocess.setup() - system.run("solver", "setup") - - def generate_synthetics(self): - """ - Performs forward simulation, and evaluates the objective function - """ - self.logger.info(msg.sub("PREPARING VELOCITY MODEL")) - src = os.path.join(PATH.OUTPUT, "model_init") - dst = os.path.join(PATH.SCRATCH, "model") - assert os.path.exists(src) - unix.cp(src, dst) + def main(self, flow=None, return_flow=False): + """Inherits from seisflows.workflow.forward.Forward""" + self.main(flow=flow, return_flow=return_flow) - self.logger.info(msg.sub("EVALUATE OBJECTIVE FUNCTION")) - system.run("solver", "eval_func", path=PATH.SCRATCH, - write_residuals=True) + def evaluate_initial_misfit(self): + """Inherits from seisflows.workflow.forward.Forward""" + self.evaluate_initial_misfit() - def backproject(self): + def evaluate_gradient(self, path=None): """ - Backproject or create kernels by running adjoint simulations + Performs adjoint simulation to retrieve the gradient of the objective """ - self.logger.info(msg.sub("BACKPROJECT / EVALUATE GRADIENT")) - system.run("solver", "eval_grad", path=PATH.SCRATCH, - export_traces=PAR.SAVETRACES) + system = self.module("system") + + self.logger.info(msg.mnr("EVALUATING GRADIENT")) + + self.logger.debug(f"evaluating gradient {self.par.NTASK} times on system...") + system.run("solver", "eval_grad", path=path or self.path.GRAD, + export_traces=self.par.SAVETRACES) def process_kernels(self): """ Backproject to create kernels from synthetics """ + system = self.module("system") + solver = self.module("solver") + system.run("postprocess", "process_kernels", single=True, - path=os.path.join(PATH.SCRATCH, "kernels"), + path=os.path.join(self.path.SCRATCH, "kernels"), parameters=solver.parameters) try: # TODO Figure out a better method for running this try except system.run("postprocess", "process_kernels", single=True, - path=os.path.join(PATH.SCRATCH, "kernels"), + path=os.path.join(self.path.SCRATCH, "kernels"), parameters=["rhop"]) except: pass @@ -130,9 +91,9 @@ def finalize(self): """ self.logger.info(msg.mnr("FINALIZING MIGRATION WORKFLOW")) - if PAR.SAVETRACES: + if self.par.SAVETRACES: self.save_traces() - if PAR.SAVEKERNELS: + if self.par.SAVEKERNELS: self.save_kernels() else: self.save_kernels_sum() @@ -141,8 +102,8 @@ def save_kernels_sum(self): """ Same summed kernels into the output directory """ - src = os.path.join(PATH.SCRATCH, "kernels", "sum") - dst = os.path.join(PATH.OUTPUT, "kernels") + src = os.path.join(self.path.SCRATCH, "kernels", "sum") + dst = os.path.join(self.path.OUTPUT, "kernels") unix.mkdir(dst) unix.cp(src, dst) @@ -150,8 +111,8 @@ def save_kernels(self): """ Save individual kernels into the output directory """ - src = os.path.join(PATH.SCRATCH, "kernels") - dst = PATH.OUTPUT + src = os.path.join(self.path.SCRATCH, "kernels") + dst = self.path.OUTPUT unix.mkdir(dst) unix.cp(src, dst) @@ -159,7 +120,7 @@ def save_traces(self): """ Save waveform traces into the output directory """ - src = os.path.join(PATH.SCRATCH, "traces") - dst = PATH.OUTPUT + src = os.path.join(self.path.SCRATCH, "traces") + dst = self.path.OUTPUT unix.cp(src, dst) From 88cedb12f42bb143d1d71f336714c1410e1a87a9 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Wed, 29 Jun 2022 14:10:07 -0800 Subject: [PATCH 033/195] added frontera work in progress system with new submit and run calls --- seisflows/system/frontera.py | 143 +++++++++++++++++++++++++++++++++++ seisflows/system/maui.py | 25 +++--- seisflows/system/slurm.py | 5 +- 3 files changed, 159 insertions(+), 14 deletions(-) create mode 100644 seisflows/system/frontera.py diff --git a/seisflows/system/frontera.py b/seisflows/system/frontera.py new file mode 100644 index 00000000..0de37b32 --- /dev/null +++ b/seisflows/system/frontera.py @@ -0,0 +1,143 @@ +#!/usr/bin/env python3 +""" +Frontera is one of the Texas Advanced Computing Center (TACC) HPCs. +https://frontera-portal.tacc.utexas.edu/ + +TODO we may need to include or create a "singularity" class or run script which +runs jobs through singularity +""" +import os +import numpy as np +from seisflows.config import ROOT_DIR +from seisflows.system.slurm import Slurm + + +class Frontera(Slurm): + """ + System interface for TACC Frontera based on SLURM workload manager + """ + def __init__(self): + """ + These parameters should not be set by the user. + Attributes are initialized as NoneTypes for clarity and docstrings. + + :type partitions: dict + :param partitions: Chinook has various partitions which each have their + own number of cores per compute node, defined here + """ + super().__init__() + + self.required.par( + "PARTITION", required=False, default="small", par_type=str, + docstr="Name of partition on main cluster" + ) + self.required.par( + "ALLOCATION", required=False, default="", par_type=str, + docstr="Name of allocation/project on the Frontera system. " + "Required if you have more than one active allocation." + ) + self.required.par( + "MPIEXEC", required=False, default="ibrun", par_type=str, + docstr="Function used to invoke parallel executables. Defaults to" + "'ibrun' based on TACC user manual.") + + # TODO find out the cores-per-node values for these partitions + # self.partitions = {"small":, "normal":, "large":, "development:" + # "flex":} + + def check(self, validate=True): + """ + Checks parameters and paths + """ + super().check(validate=validate) + + assert(self.par.PARTITION in self.partitions.keys()), \ + f"Chinook partition must be in {self.partitions.keys()}" + + assert(self.par.NODESIZE == self.partitions[self.par.PARTITION]), \ + (f"PARTITION {self.par.PARTITION} is expected to have NODESIZE=" + f"{self.partitions[self.par.PARTITION]}, not current " + f"{self.par.NODESIZE}") + + def submit(self, submit_call=None): + """ + Submits workflow as a serial job on the TACC partition 'small'. + + .. note:: + The SBATCH commands can either be short or full length. TACC's + start up guide uses short length keys so that's what we do here, but + their long names can be substituted + + :type submit_call: str + :param submit_call: SBATCH command line call to submit workflow.main() + to the system. If None, will generate one on the fly with + user-defined parameters + """ + if submit_call is None: + submit_call = " ".join([ + "sbatch", + f"{self.par.SLURMARGS or ''}", + f"-J {self.par.TITLE}", # job name + f"-O {self.output_log}", # stdout output file + f"-E {self.error_log}", # stderr error file + f"-P {self.par.PARTITION}", # queue/partition name + f"-A {self.par.ALLOCATION}", # project/allocation name + f"-N 1", # total number of nodes requested + f"-n 1", # number of mpi tasks + f"-t {self.par.WALLTIME}", # job walltime + f"{os.path.join(ROOT_DIR, 'scripts', 'submit')}", + f"--output {self.path.OUTPUT}" + ]) + super().submit(submit_call=submit_call) + + def run(self, classname, method, single=False, run_call=None, **kwargs): + """ + Runs task multiple times in embarrassingly parallel fasion on a SLURM + cluster. + + :type classname: str + :param classname: the class to run + :type method: str + :param method: the method from the given `classname` to run + :type single: bool + :param single: run a single-process, non-parallel task, such as + smoothing the gradient, which only needs to be run by once. + This will change how the job array and the number of tasks is + defined, such that the job is submitted as a single-core job to + the system. + :type run_call: str + :param run_call: SBATCH command line run call to be submitted to the + system. If None, will generate one on the fly with user-defined + parameters + """ + if run_call is None: + _nodes = np.ceil(self.par.NPROC / float(self.par.NODESIZE)) + + run_call = " ".join([ + "sbatch", + f"{self.par.SLURMARGS or ''}", + f"-J {self.par.TITLE}", # job name + f"-O {self.output_log}", # stdout output file + f"-E {self.error_log}", # stderr error file + f"-P {self.par.PARTITION}", # queue/partition name + f"-A {self.par.ALLOCATION}", # project/allocation name + f"-N {_nodes}", # total number of nodes requested + f"-n {self.par.NPROC}", # number of mpi tasks + f"-t {self.par.WALLTIME}", # job walltime + f"{os.path.join(ROOT_DIR, 'scripts', 'run')}", + f"--output {self.path.OUTPUT}" + f"--classname {classname}", + f"--funcname {method}", + f"--environment {self.par.ENVIRONS or ''}" + ]) + + super().run(classname, method, single, run_call=run_call, **kwargs) + + def taskid(self): + """Inherits from seisflows.system.slurm.Slurm""" + return self.taskid() + + def checkpoint(self, path, classname, method, kwargs): + """Inherits from workflow.system.workstation.Workstation""" + self.checkpoint(path=path, classname=classname, method=method, + kwargs=kwargs) diff --git a/seisflows/system/maui.py b/seisflows/system/maui.py index 1fbe6481..1f64f7d1 100644 --- a/seisflows/system/maui.py +++ b/seisflows/system/maui.py @@ -18,15 +18,9 @@ """ import os -import sys -import math -import logging - +import numpy as np from seisflows.system.slurm import Slurm -from seisflows.config import custom_import, SeisFlowsPathsParameters, ROOT_DIR - -PAR = sys.modules["seisflows_parameters"] -PATH = sys.modules["seisflows_paths"] +from seisflows.config import ROOT_DIR class Maui(Slurm): @@ -125,6 +119,11 @@ def submit(self, submit_call=None): .. note:: We do not place SLURMARGS into the sbatch command to avoid the export=None which will not propagate the conda environment + + :type submit_call: str + :param submit_call: SBATCH command line call to submit workflow.main() + to the system. If None, will generate one on the fly with + user-defined parameters """ if submit_call is None: submit_call = " ".join([ @@ -141,7 +140,6 @@ def submit(self, submit_call=None): f"{os.path.join(ROOT_DIR, 'scripts', 'submit')}", f"--output {self.path.OUTPUT}" ]) - self.logger.debug(submit_call) super().submit(submit_call=submit_call) @@ -161,9 +159,13 @@ def run(self, classname, method, single=False, run_call=None, **kwargs): This will change how the job array and the number of tasks is defined, such that the job is submitted as a single-core job to the system. + :type run_call: str + :param run_call: SBATCH command line run call to be submitted to the + system. If None, will generate one on the fly with user-defined + parameters """ if run_call is None: - _nodes = math.ceil(self.par.NPROC / float(self.par.NODESIZE)) + _nodes = np.ceil(self.par.NPROC / float(self.par.NODESIZE)) run_call = " ".join([ "sbatch", @@ -184,7 +186,6 @@ def run(self, classname, method, single=False, run_call=None, **kwargs): f"--funcname {method}", f"--environment {self.par.ENVIRONS or ''}" ]) - self.logger.debug(run_call) super().run(classname, method, single, run_call=run_call, **kwargs) @@ -228,5 +229,5 @@ def checkpoint(self, path, classname, method, kwargs): self.checkpoint(path=path, classname=classname, method=method, kwargs=kwargs) - + diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index 235ee280..be9da7f2 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -90,8 +90,8 @@ def submit(self, submit_call=None): f"{os.path.join(ROOT_DIR, 'scripts', 'submit')}", f"--output {self.path.OUTPUT}" ]) - self.logger.debug(submit_call) + self.logger.debug(submit_call) super().submit(submit_call=submit_call) def run(self, classname, method, single=False, run_call=None, **kwargs): @@ -140,7 +140,8 @@ def run(self, classname, method, single=False, run_call=None, **kwargs): f"--funcname {method}", f"--environment {self.par.ENVIRONS or ''}" ]) - self.logger.debug(run_call) + + self.logger.debug(run_call) # Single-process jobs simply need to replace a few sbatch arguments. # Do it AFTER `run_call` has been defined so that subclasses submitting From ba7118f4b1b7f70eda9a0cb801435649b6b44025 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Wed, 29 Jun 2022 14:10:58 -0800 Subject: [PATCH 034/195] updated workflow classes to new code structure. inversion workflow now builds on top of migration and forward classes for less code redundancy, added more docstring explanations of workflow styles --- seisflows/postprocess/default.py | 91 ++---- seisflows/solver/specfem.py | 3 +- seisflows/workflow/forward.py | 26 +- seisflows/workflow/inversion.py | 517 +++++++++++-------------------- seisflows/workflow/migration.py | 148 +++++---- 5 files changed, 323 insertions(+), 462 deletions(-) diff --git a/seisflows/postprocess/default.py b/seisflows/postprocess/default.py index 9debb2a5..cc20cf14 100644 --- a/seisflows/postprocess/default.py +++ b/seisflows/postprocess/default.py @@ -68,7 +68,7 @@ def finalize(self): """ super().finalize() - def write_gradient(self, path): + def scale_gradient(self, input_path): """ Combines contributions from individual sources and material parameters to get the gradient, and optionally applies user-supplied scaling @@ -78,39 +78,25 @@ def write_gradient(self, path): run through the HPC system interface; processing does not involve embarassingly parallel tasks, we use run(single=True) - :type path: str - :param path: directory from which kernels are read and to which - gradient is written + :type input_path: str + :param input_path: directory from which kernels are read and to which + gradient is written. Should probably point to PATH.GRAD + :rtype: np.array + :return: scaled gradient as a vector """ - system = self.module("system") solver = self.module("solver") - if not os.path.exists(path): - print(msg.cli("Gradient path does in postprocess.write_gradient " - "does not exist but should", - items=[path], header="error")) - sys.exit(-1) - # Postprocess file structure defined here once-and-for-all - path_grad = os.path.join(path, "gradient") - path_grad_nomask = os.path.join(path, "gradient_nomask") - path_kernels = os.path.join(path, "kernels") - path_kernels_sum = os.path.join(path_kernels, "sum") - path_model = os.path.join(path, "model") - - # Run postprocessing as job on system as it's computationally intensive - self.logger.info("processing kernels into gradient on system...") - system.run("postprocess", "_process_kernels", single=True, - path=path_kernels, logger=self.logger) - - # Access the gradient information stored in the kernel summation - gradient = solver.load(path_kernels_sum, suffix="_kernel") + path_grad_nomask = os.path.join(input_path, "gradient_nomask") + path_model = os.path.join(input_path, "model") + path_kernels_sum = os.path.join(input_path, "kernels", "sum") - # Merge the gradients into a single vector - gradient = solver.merge(gradient) + # Access the gradient information stored in as kernel files + gradient = solver.load(path_kernels_sum, suffix="_kernel") - # Convert to absolute perturbations: + # Merge to vector and convert to absolute perturbations: # log dm --> dm (see Eq.13 Tromp et al 2005) + gradient = solver.merge(gradient) gradient *= solver.merge(solver.load(path_model)) if self.path.MASK: @@ -125,14 +111,11 @@ def write_gradient(self, path): # For more info, see Modrak & Tromp 2016 GJI solver.save(solver.split(gradient), path=path_grad_nomask, suffix="_kernel") + gradient *= mask - solver.save(solver.split(gradient * mask), path=path_grad, - suffix="_kernel") - else: - solver.save(solver.split(gradient), path=path_grad, - suffix="_kernel") + return gradient - def _process_kernels(self, path, logger): + def sum_smooth_kernels(self, kernel_path): """ Sums kernels from individual sources, with optional smoothing @@ -140,37 +123,31 @@ def _process_kernels(self, path, logger): This function needs to be run on system, i.e., called by system.run(single=True) - :type path: str - :param path: directory containing sensitivity kernels in the scratch - directory - :type logger: Logger - :param logger: Class-specific logging module, log statements pushed - from this logger will be tagged by its specific module/classname + :type kernel_path: str + :param kernel_path: directory containing sensitivity kernels in the + scratch directory to be summed and smoothed. Output summed and + summed + smoothed kernels will be saved here as well. """ solver = self.module("solver") - if not os.path.exists(path): - print(msg.cli("Gradient path in postprocess.process_kernels " - "does not exist but should", - items=[path], header="error")) - sys.exit(-1) - - # If specified, smooth the kernels in the vertical and horizontal - path_sum_nosmooth = os.path.join(path, "sum_nosmooth") - path_sum = os.path.join(path, "sum") + # If specified, smooth the kernels in the vertical and horizontal and + # save both (summed, summed+smoothed) to separate output directories + path_sum_nosmooth = os.path.join(kernel_path, "sum_nosmooth") + path_sum = os.path.join(kernel_path, "sum") if (self.par.SMOOTH_H > 0) or (self.par.SMOOTH_V > 0): - logger.debug(f"saving un-smoothed and summed kernels to:\n" - f"{path_sum_nosmooth}") - solver.combine(input_path=path, output_path=path_sum_nosmooth) - - logger.info(f"smoothing gradient: H={self.par.SMOOTH_H}m, " - f"V={self.par.SMOOTH_V}m") - logger.debug(f"saving smoothed kernels to:\n{path_sum}") + self.logger.debug(f"saving un-smoothed and summed kernels to:\n" + f"{path_sum_nosmooth}") + solver.combine(input_path=kernel_path, + output_path=path_sum_nosmooth) + + self.logger.info(f"smoothing gradient: H={self.par.SMOOTH_H}m, " + f"V={self.par.SMOOTH_V}m") + self.logger.debug(f"saving smoothed kernels to:\n{path_sum}") solver.smooth(input_path=path_sum_nosmooth, output_path=path_sum, span_h=self.par.SMOOTH_H, span_v=self.par.SMOOTH_V) # Combine all the input kernels, generating the unscaled gradient else: - logger.debug(f"saving summed kernels to:\n{path_sum}") - solver.combine(input_path=path, output_path=path_sum) + self.logger.debug(f"saving summed kernels to:\n{path_sum}") + solver.combine(input_path=kernel_path, output_path=path_sum) diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index aa295fd9..7108565d 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -790,8 +790,7 @@ def smooth(self, input_path, output_path, parameters=None, span_h=0., os.path.join(input_path, ""), os.path.join(output_path, ""), ".false"]), - output=output - ) + output=output) # Rename output files files = glob(os.path.join(output_path, "*")) diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index 2d7c9cdd..c91fa1c2 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -17,7 +17,8 @@ class Forward(Base): """ - Workflow abstract base class + Workflow abstract base class representing an en-masse forward solver and + misfit calculator. """ def __init__(self): """ @@ -67,10 +68,6 @@ def setup(self, flow=None, return_flow=False): Setup workflow by intaking functions to be run and checking start and stop criteria """ - # The FLOW function defines a list of functions to execute IN ORDER - if flow is None: - flow = (self.evaluate_initial_misfit) - # REQUIRED: CLI command `seisflows print flow` needs this for output if return_flow: return flow @@ -86,12 +83,11 @@ def setup(self, flow=None, return_flow=False): ) # Required modules that need to be set up - system = self.module("system") - preprocess = self.module("preprocess") - - system.setup() - preprocess.setup() - system.run("solver", "setup") + self.logger.info(msg.mnr("PERFORMING MODULE SETUP")) + self.module("system").setup() + self.module("preprocess").setup() + self.logger.info("setting up solver on system...") + self.module("system").run("solver", "setup") def finalize(self): """ @@ -118,6 +114,10 @@ def main(self, flow=None, return_flow=False): :param return_flow: for CLI tool, simply returns the flow function rather than running the workflow. Used for print statements etc. """ + # The FLOW function defines a list of functions to execute IN ORDER + if flow is None: + flow = (self.evaluate_initial_misfit) + self.setup(flow, return_flow) # Iterate through the `FLOW` to step through workflow.main() for func in flow[self.start: self.stop]: @@ -126,9 +126,11 @@ def main(self, flow=None, return_flow=False): def evaluate_initial_misfit(self): """ - Wrapper for evaluate_function that sends residuals to PATH.GRAD and + Wrapper for evaluate_function that generates synthetics via forward + simulations, calculates misfits and sends residuals to PATH.GRAD and sets up the 'm_new' model for future evaluations """ + self.logger.info(msg.mjr("EVALUATING INITIAL MISFIT")) self._evaluate_function(path=self.path.GRAD, suffix="new") def _evaluate_function(self, path, suffix): diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index 740922d1..4516988c 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -1,33 +1,35 @@ #!/usr/bin/env python3 """ -This is the base class seisflows.workflow.Inversion - -This is a main Seisflows class, it controls the main workflow. +A seismic inversion (a.k.a full waveform inversion, adjoint tomography, full +waveform tomography) perturbs seismic velocity models by minimizing objective +functions defining differences between observed and synthetic waveforms. + +This seismic inversion workflow performs a linear set of tasks involving: + +1) Generating synthetic seismograms using an external numerical solver +2) Calculating time-dependent misfit (adjoint sources) between data + (or other synthetics) and synthetics +3) Using adjoint sources to generate misfit kernels defining volumetric + perturbations sensitive to data-synthetic misfit +4) Smoothing and summing misfit kernels into a single gradient +5) Perturbing the starting model with the gradient to reduce misfit defined by + the objective function during a line search + +The Inversion workflow runs the above tasks in a loop (iterations) while +exporting updated models, kernels and/or gradients to disk. """ import os import sys -import logging import numpy as np -from glob import glob -from seisflows.core import Base -from seisflows.config import custom_import +from seisflows.workflow.migration import Migration from seisflows.tools import msg, unix -from seisflows.config import save, SeisFlowsPathsParameters - -PAR = sys.modules["seisflows_parameters"] -PATH = sys.modules["seisflows_paths"] -system = sys.modules["seisflows_system"] -solver = sys.modules["seisflows_solver"] -optimize = sys.modules["seisflows_optimize"] -preprocess = sys.modules["seisflows_preprocess"] -postprocess = sys.modules["seisflows_postprocess"] - -class Inversion(Base): +class Inversion(Migration): """ - Waveform inversion base class + Waveform inversion base class, built on top of the Migration child class, + which in-turn is built on top of the Forward child class. Peforms iterative nonlinear inversion and provides a base class on top of which specialized strategies can be implemented. @@ -43,190 +45,175 @@ class Inversion(Base): Commands for running in serial or parallel on a workstation or cluster are abstracted through the "system" interface. """ - # Class-specific logger accessed using self.logger - logger = logging.getLogger(__name__).getChild(__qualname__) - def __init__(self): """ - These parameters should not be set by the user. - Attributes are initialized as NoneTypes for clarity and docstrings. + Init is used to instantiate global parameters defined by the input + parameter file. """ super().__init__() - self.required.par("SAVEMODEL", required=False, default=True, par_type=bool, - docstr="Save final model files after each iteration") - - self.required.par("SAVEGRADIENT", required=False, default=True, par_type=bool, - docstr="Save gradient files after each iteration") - - self.required.par("SAVEKERNELS", required=False, default=False, par_type=bool, - docstr="Save event kernel files after each iteration") - - self.required.par("RESUME_FROM", required=False, par_type=str, - docstr="Name of task to resume inversion from") - - self.required.par("STOP_AFTER", required=False, par_type=str, - docstr="Name of task to stop inversion after finishing") + self.required.par( + "BEGIN", required=False, default=1, par_type=int, + docstr="First iteration of an inversion workflow, 1 <= BEGIN <= inf" + ) + + self.required.par( + "END", required=True, par_type=int, + docstr="Last iteration of the inverison workflow," + "BEGIN <= END <= inf" + ) + self.required.par( + "RESUME_FROM", required=False, par_type=str, + docstr="Name of flow task to resume workflow from. Useful for " + "restarting failed workflows or re-trying sections of " + "workflows with new parameters. To determine available " + "options for your given workflow: > seisflows print flow" + ) + self.required.par( + "STOP_AFTER", required=False, par_type=str, + docstr="Name of flow task to stop workflow after. Useful for " + "stopping mid-workflow to look at results before " + "proceeding (e.g., to look at waveform misfits before " + "evaluating the gradient). To determine available options " + "for your given workflow: > seisflows print flow" + ) + self.required.par( + "SAVEMODEL", required=False, default=True, par_type=bool, + docstr="Save updated model files after each iteration" + ) - self.required.par("CASE", required=False, default="data", par_type=str, - docstr="Type of inversion, available: " - "['data': real data inversion, " - "'synthetic': synthetic-synthetic inversion]") - - self.required.par("SAVEAS", required=False, default="binary", par_type=str, - docstr="Format to save models, gradients, kernels. " - "Available: " - "['binary': save files in native SPECFEM .bin format, " - "'vector': save files as NumPy .npy files, " - "'both': save as both binary and vectors]") + # Define the Paths required by this module + self.required.path( + "FUNC", required=False, + default=os.path.join(self.path.WORKDIR, "scratch", "evalfunc"), + docstr="scratch path to store data related to misfit function " + "evaluations that take place during the line search. Data " + "stored here include residuals from data-synthetic misfit, " + "and a given 'try' model being used to generate synthetics." + ) + # !!! Currently not used + self.required.path( + "HESS", required=False, + default=os.path.join(self.path.WORKDIR, "scratch", "evalhess"), + docstr="scratch path to store data related to Hessian evaluations" + ) + self.required.path( + "OPTIMIZE", required=False, + default=os.path.join(self.path.WORKDIR, "scratch", "optimize"), + docstr="scratch path to store data related to nonlinear " + "optimization library. Data stored here include model, " + "gradient, and search direction vectors (numpy arrays), and" + "additional arrays related to specific optimization " + "algorithms" + ) - self.required.path("MODEL_TRUE", required=False, - default=os.path.join(PATH.WORKDIR, "specfem", "MODEL_TRUE"), - docstr="Target model to be used for PAR.CASE == 'synthetic'") - @property - def required(self): + def check(self, validate=True): """ - A hard definition of paths and parameters required by this class, - alongside their necessity for the class and their string explanations. + Checks parameters and paths """ - sf = SeisFlowsPathsParameters(super().required) - - # Define the Parameters required by this module - sf.par("BEGIN", required=False, default=1, par_type=int, - docstr="First iteration of workflow, 1 <= BEGIN <= inf") - - sf.par("END", required=True, par_type=int, - docstr="Last iteration of workflow, BEGIN <= END <= inf") - - # Define the Paths required by this module - sf.path("FUNC", required=False, - default=os.path.join(PATH.SCRATCH, "evalfunc"), - docstr="scratch path to store data related to function " - "evaluations") - - sf.path("GRAD", required=False, - default=os.path.join(PATH.SCRATCH, "evalgrad"), - docstr="scratch path to store data related to gradient " - "evaluations") + super().check(validate=validate) + assert(1 <= self.par.BEGIN <= self.par.END), \ + f"Incorrect BEGIN or END parameter. Values must be in order: " \ + f"1 <= {self.par.BEGIN} <= {self.par.END}" - sf.path("HESS", required=False, - default=os.path.join(PATH.SCRATCH, "evalhess"), - docstr="scratch path to store data related to Hessian " - "evaluations") + def setup(self, flow=None, return_flow=False): + """ + Lays groundwork for inversion by running setup() functions for the + involved sub-modules, generating True model synthetic data if necessary, + and generating the pre-requisite database files. - sf.path("OPTIMIZE", required=False, - default=os.path.join(PATH.SCRATCH, "optimize"), - docstr="scratch path to store data related to nonlinear " - "optimization") + .. note:: + This function should only be run one time, at the start of iter 1 + """ + super().setup(flow=flow, return_flow=return_flow) - return sf + self.module("optimize").setup() - def check(self, validate=True): + def finalize(self): """ - Checks parameters and paths + Saves results from current model update iteration and increment the + iteration number to set up for the next iteration. Finalization is + expected to the be LAST function in workflow.main()'s flow list. """ - super().check(validate=False) - if validate: - self.required.validate() + self.logger.info(msg.mjr(f"FINALIZING ITERATION {optimize.iter}")) - if PAR.CASE.upper() == "SYNTHETIC": - assert os.path.exists(PATH.MODEL_TRUE), \ - "CASE == SYNTHETIC requires PATH.MODEL_TRUE" + self.checkpoint() + preprocess.finalize() + + # Save files from scratch before discarding + if self.par.SAVEMODEL: + self.save_model() - if not os.path.exists(PATH.DATA): - assert "MODEL_TRUE" in PATH, f"DATA or MODEL_TRUE must exist" + if self.par.SAVEGRADIENT: + self.save_gradient() - for required_path in ["SCRATCH", "OUTPUT", "LOCAL"]: - assert(required_path in PATH), \ - f"Inversion requires path {required_path}" + if self.par.SAVEKERNELS: + self.save_kernels() - assert(1 <= PAR.BEGIN <= PAR.END), \ - f"Incorrect BEGIN or END parameter: 1 <= {PAR.BEGIN} <= {PAR.END}" + if self.par.SAVETRACES: + self.save_traces() - def main(self, return_flow=False): + if self.par.SAVERESIDUALS: + self.save_residuals() + + def main(self, flow=None, return_flow=False): """ - This function controls the main SeisFlows workflow, and is submitted - to system by the call `seisflows submit` or `seisflows resume`. It - proceeds to evaluate a list of functions in order until a User defined - stop criteria is met. + Overwrites the forward() main function to provide the ability to run + multiple iterations in a single workflow. + :type flow: list or tuple + :param flow: list of Class methods that will be run in the order they + are provided. :type return_flow: bool :param return_flow: for CLI tool, simply returns the flow function rather than running the workflow. Used for print statements etc. """ - # The workFLOW is a tuple of functions that can be called dynamic ally - flow = (self.setup, - self.initialize, - self.evaluate_gradient, - self.write_gradient, - self.compute_direction, - self.line_search, - self.finalize, - self.clean - ) - if return_flow: - return flow - - # Allow workflow resume from and stop after given flow functions - start, stop = self.check_stop_resume_cond(flow) + if flow is None: + flow = (self.evaluate_initial_misfit, + self.evaluate_gradient, + self.process_kernels, + self.write_gradient, + self.compute_direction, + self.line_search, + self.export, + self.clean + ) + + self.setup(flow, return_flow) + optimize = self.module("optimize") # Run the workflow until from the current iteration until PAR.END - optimize.iter = PAR.BEGIN + optimize.iter = self.par.BEGIN self.logger.info(msg.mjr("STARTING INVERSION WORKFLOW")) while True: - self.logger.info(msg.mnr(f"ITERATION {optimize.iter} / {PAR.END}")) + self.logger.info( + msg.mnr(f"ITERATION {optimize.iter} / {self.par.END}") + ) # Execute the functions within the flow - for func in flow[start:stop]: + for func in flow[self.start:self.stop]: func() - - # Finish. Assuming completion of all arguments in flow() self.logger.info(msg.mjr(f"FINISHED FLOW EXECUTION")) # Reset flow for subsequent iterations - start, stop = None, None - - if optimize.iter >= PAR.END: + self.start, self.stop = None, None + if optimize.iter >= self.par.END: break - optimize.iter += 1 + self.logger.info(msg.sub(f"INCREMENT ITERATION TO {optimize.iter}")) self.logger.info(msg.mjr("FINISHED INVERSION WORKFLOW")) - def setup(self): - """ - Lays groundwork for inversion by running setup() functions for the - involved sub-modules, generating True model synthetic data if necessary, - and generating the pre-requisite database files. - - .. note:: - This function should only be run one time, at the start of iter 1 - """ - # Iter check is done inside setup() so that we can include fx in FLOW - if optimize.iter == 1: - # Set up all the requisite modules from the master job - self.logger.info(msg.mnr("PERFORMING MODULE SETUP")) - preprocess.setup() - postprocess.setup() - optimize.setup() - - # Run solver.setup() in parallel - self.logger.info("setting up solver on system...") - system.run("solver", "setup") - - def initialize(self): - """ - Generates synthetics via a forward simulation, calculates misfits - for the forward simulation. Writes misfit for use in optimization. - """ - self.logger.info(msg.mjr("INITIALIZING INVERSION")) - self.evaluate_function(path=PATH.GRAD, suffix="new") + def evaluate_initial_misfit(self): + """Inherits from seisflows.workflow.forward.Forward""" + self.evaluate_initial_misfit() def compute_direction(self): """ - Computes search direction + Computes search direction using the optimization library """ + optimize = self.module("optimize") self.logger.info(msg.mnr("COMPUTING SEARCH DIRECTION")) optimize.compute_direction() @@ -239,6 +226,8 @@ def line_search(self): status == 0 : not finished status < 0 : failed """ + optimize = self.module("optimize") + # Calculate the initial step length based on optimization algorithm if optimize.line_search.step_count == 0: self.logger.info(msg.mjr(f"CONDUCTING LINE SEARCH: " @@ -252,7 +241,7 @@ def line_search(self): self.logger.info(msg.mnr(f"TRIAL STEP COUNT: " f"i{optimize.iter:0>2}" f"s{optimize.line_search.step_count:0>2}")) - self.evaluate_function(path=PATH.FUNC, suffix="try") + self._evaluate_function(path=self.path.FUNC, suffix="try") # Check the function evaluation against line search history status = optimize.update_search() @@ -277,65 +266,6 @@ def line_search(self): self.logger.info("line search failed. aborting inversion.") sys.exit(-1) - def evaluate_function(self, path, suffix): - """ - Performs forward simulation, and evaluates the objective function - - :type path: str - :param path: path in the scratch directory to use for I/O - :type suffix: str - :param suffix: suffix to use for I/O - """ - self.logger.info(msg.sub("EVALUATE OBJECTIVE FUNCTION")) - - model_tag = f"m_{suffix}" - misfit_tag = f"f_{suffix}" - - self.write_model(path=path, tag=model_tag) - - self.logger.debug(f"evaluating objective function {PAR.NTASK} times " - f"on system...") - system.run("solver", "eval_func", path=path) - - self.write_misfit(path=path, tag=misfit_tag) - - def evaluate_gradient(self, path=None): - """ - Performs adjoint simulation to retrieve the gradient of the objective - """ - self.logger.info(msg.mnr("EVALUATING GRADIENT")) - - self.logger.debug(f"evaluating gradient {PAR.NTASK} times on system...") - system.run("solver", "eval_grad", path=path or PATH.GRAD, - export_traces=PAR.SAVETRACES) - - def finalize(self): - """ - Saves results from current model update iteration and increment the - iteration number to set up for the next iteration. Finalization is - expected to the be LAST function in workflow.main()'s flow list. - """ - self.logger.info(msg.mjr(f"FINALIZING ITERATION {optimize.iter}")) - - self.checkpoint() - preprocess.finalize() - - # Save files from scratch before discarding - if PAR.SAVEMODEL: - self.save_model() - - if PAR.SAVEGRADIENT: - self.save_gradient() - - if PAR.SAVEKERNELS: - self.save_kernels() - - if PAR.SAVETRACES: - self.save_traces() - - if PAR.SAVERESIDUALS: - self.save_residuals() - def clean(self): """ Cleans directories in which function and gradient evaluations were @@ -343,131 +273,54 @@ def clean(self): """ self.logger.info(msg.mnr("CLEANING WORKDIR FOR NEXT ITERATION")) - unix.rm(PATH.GRAD) - unix.rm(PATH.FUNC) - unix.mkdir(PATH.GRAD) - unix.mkdir(PATH.FUNC) - - def write_model(self, path, tag): - """ - Writes model in format expected by solver - - :type path: str - :param path: path to write the model to - :type src: str - :param src: name of the model to be saved, usually tagged as 'm' with - a suffix depending on where in the inversion we are. e.g., 'm_try'. - Expected that these tags are defined in OPTIMIZE module - """ - src = tag - dst = os.path.join(path, "model") - self.logger.debug(f"saving model '{src}' to:\n{dst}") - solver.save(solver.split(np.load(src)), dst) - - def write_gradient(self): - """ - Writes gradient in format expected by non-linear optimization library. - Calls the postprocess module, which will smooth/precondition gradient. - """ - self.logger.info(msg.mnr("POSTPROCESSING KERNELS")) - src = os.path.join(PATH.GRAD, "gradient") - - postprocess.write_gradient(PATH.GRAD) - parts = solver.load(src, suffix="_kernel") + unix.rm(self.path.GRAD) + unix.rm(self.path.FUNC) + unix.mkdir(self.path.GRAD) + unix.mkdir(self.path.FUNC) - optimize.save("g_new", solver.merge(parts)) - - def write_misfit(self, path, tag): + def export(self): """ - Writes misfit in format expected by nonlinear optimization library. - Collects all misfit values within the given residuals directory and sums - them in a manner chosen by the preprocess class. - - :type path: str - :param path: path to write the misfit to - :type tag: str - :param tag: name of the model to be saved, usually tagged as 'f' with - a suffix depending on where in the inversion we are. e.g., 'f_try'. - Expected that these tags are defined in OPTIMIZE module + Exports various quantities to PATH.OUTPUT (to disk) from the SCRATCH + directory as SCRATCH is liable to be overwritten at any point of the + workflow. This takes place at the end of each iteration, before + the clean() function is called. """ - self.logger.info("summing residuals with preprocess module") - src = glob(os.path.join(path, "residuals", "*")) - dst = tag - total_misfit = preprocess.sum_residuals(src) + optimize = self.module("optimize") - self.logger.debug(f"saving misfit {total_misfit:.3E} to tag '{dst}'") - np.save(dst, total_misfit) + if self.par.SAVEMODEL: + src = optimize.load("m_new") + dst = os.path.join(self.path.OUTPUT, f"model_{optimize.iter:04d}") + self.logger.debug(f"exporting model 'm_new' to disk") + self._write_vector(src, dst) - def save_gradient(self): - """ - Save the gradient vector. Allows saving numpy array or standard - Fortran .bin files + if self.par.SAVEGRADIENT: + src = optimize.load("g_old") + dst = os.path.join(self.path.OUTPUT, f"grad_{optimize.iter:04d}") + self._write_vector(src, dst) - Saving as a vector saves on file count, but requires numpy and seisflows - functions to read - """ - dst = os.path.join(PATH.OUTPUT, f"gradient_{optimize.iter:04d}") - - if PAR.SAVEAS in ["binary", "both"]: - src = os.path.join(PATH.GRAD, "gradient") + if self.par.SAVEKERNELS: + src = os.path.join(self.path.GRAD, "kernels") + dst = os.path.join(self.path.OUTPUT, f"kernels_{optimize.iter:04d}") + self.logger.debug(f"saving kernels to path:\n{dst}") unix.mv(src, dst) - if PAR.SAVEAS in ["vector", "both"]: - src = "g_old" - unix.cp(src, dst + ".npy") - - self.logger.debug(f"saving gradient to path:\n{dst}") - - def save_model(self): - """ - Save the model vector. Allows saving numpy array or standard - Fortran .bin files - - Saving as a vector saves on file count, but requires numpy and seisflows - functions to read - """ - src = optimize.load("m_new") - dst = os.path.join(PATH.OUTPUT, f"model_{optimize.iter:04d}") - self.logger.debug(f"saving model '{src}' to path:\n{dst}") - - if PAR.SAVEAS in ["binary", "both"]: - solver.save(solver.split(np.load(src)), dst) - if PAR.SAVEAS in ["vector", "both"]: - np.save(file=dst, arr=np.load(src)) - - def save_kernels(self): - """ - Save the kernel vector as a Fortran binary file on disk - """ - src = os.path.join(PATH.GRAD, "kernels") - dst = os.path.join(PATH.OUTPUT, f"kernels_{optimize.iter:04d}") - - self.logger.debug(f"saving kernels to path:\n{dst}") - - unix.mv(src, dst) - - def save_traces(self): - """ - Save the waveform traces to disk. - - !!! This doesn't work? Traces are not saved to PATH.GRAD so src does - !!! not exist - """ - src = os.path.join(PATH.GRAD, "traces") - dst = os.path.join(PATH.OUTPUT, f"traces_{optimize.iter:04d}") - - self.logger.debug(f"saving traces to path:\n{dst}") + if self.par.SAVETRACES: + self.save_traces() - unix.mv(src, dst) + if self.par.SAVERESIDUALS: + src = os.path.join(self.path.GRAD, "residuals") + dst = os.path.join(self.path.OUTPUT, + f"residuals_{optimize.iter:04d}") + unix.mv(src, dst) - def save_residuals(self): + def _write_vector(self, vector, path): """ - Save the residuals to disk + Convenience function to write vectors as numpy arrays or as model files + to a given path """ - src = os.path.join(PATH.GRAD, "residuals") - dst = os.path.join(PATH.OUTPUT, f"residuals_{optimize.iter:04d}") - - self.logger.debug(f"saving residuals to path:\n{dst}") - - unix.mv(src, dst) + solver = self.module("solver") + if self.par.SAVEAS in ["binary", "both"]: + solver.save(solver.split(vector), path) + if self.par.SAVEAS in ["vector", "both"]: + np.save(file=path, arr=vector) diff --git a/seisflows/workflow/migration.py b/seisflows/workflow/migration.py index 141d4440..c7445a74 100644 --- a/seisflows/workflow/migration.py +++ b/seisflows/workflow/migration.py @@ -1,13 +1,15 @@ #!/usr/bin/env python3 """ -This is the base class seisflows.workflow.migration - -This is a main Seisflows class, it controls the main workflow. +Seismic migration performs a 'time-reverse migration', or backprojection. +In the terminology of seismic imaging, we are running a forward and adjoint +simulation to derive the gradient of the objective function. This workflow +sets up the machinery to derive a scaled, smoothed gradient from an initial +model """ import os from seisflows.workflow.forward import Forward -from seisflows.tools import unix, msg +from seisflows.tools import msg class Migration(Forward): @@ -19,28 +21,71 @@ class Migration(Forward): """ def __init__(self): """ - These parameters should not be set by the user. - Attributes are initialized as NoneTypes for clarity and docstrings. - + Init is used to instantiate global parameters defined by the input + parameter file. """ super().__init__() + self.required.par( + "CASE", required=False, default="data", par_type=str, + docstr="How to address 'data' in your workflow, available options: " + "1) 'data': Real data inversion. Observed waveforms must be " + "provided by the user in PATH.DATA/{SOURCE_NAME}. OR if " + "PAR.PREPROCESS=='pyatoa' data should be discoverable " + "via IRIS webservices based on event ID and station codes" + "2) 'synthetic': A synthetic-synthetic workflow. 'Data' " + "will be generated as synthetics using PATH.MODEL_TRUE. " + ) + self.required.par( + "SAVEGRADIENT", required=False, default=True, par_type=bool, + docstr="Save gradient files each time the gradient is evaluated" + ) + self.required.par( + "SAVEKERNELS", required=False, default=False, par_type=bool, + docstr="Save event kernel files each time they are evaluated" + ) + self.required.par( + "SAVEAS", required=False, default="binary", par_type=str, + docstr="Format to save models, gradients, kernels. Available: " + "['binary': save files in native SPECFEM .bin format, " + "'vector': save files as NumPy .npy files, " + "'both': save as both binary and vectors]" + ) + self.required.path( + "GRAD", required=False, + default=os.path.join(self.path.WORKDIR, "scratch", "evalgrad"), + docstr="scratch path to store any models or kernels related to " + "gradient evaluations. Sub-directories will be generated " + "inside PATH.GRAD to save various stages of gradient " + "manipulation" + ) + self.required.path( + "MODEL_TRUE", required=False, + default=os.path.join(self.path.WORKDIR, "specfem", "MODEL_TRUE"), + docstr="Target model to be used for PAR.CASE == 'synthetic'. The " + "TRUE model will be used to evaluate forward simulations " + "ONCE at the beginning of the workflow, to generate 'data'." + ) + def check(self, validate=True): """ Checks parameters and paths. Must be implemented by sub-class """ super().check(validate=validate) + if self.par.CASE.upper() == "SYNTHETIC": + assert os.path.exists(self.path.MODEL_TRUE), \ + "CASE == SYNTHETIC requires PATH.MODEL_TRUE" + + if not os.path.exists(self.path.DATA): + assert "MODEL_TRUE" in self.path, f"DATA or MODEL_TRUE must exist" + def setup(self, flow=None, return_flow=False): """ Override the Forward.setup() method to include new flow functions AND run setup for a the Postprocess module which will be used to deal with the gradient """ - if flow is None: - flow = (self.evaluate_initial_misfit, - self.evaluate_gradient) - super().setup(flow=flow, return_flow=return_flow) postprocess = self.module("postprocess") @@ -48,6 +93,12 @@ def setup(self, flow=None, return_flow=False): def main(self, flow=None, return_flow=False): """Inherits from seisflows.workflow.forward.Forward""" + flow = (self.evaluate_initial_misfit, + self.evaluate_gradient, + self.process_kernels, + self.write_gradient + ) + self.main(flow=flow, return_flow=return_flow) def evaluate_initial_misfit(self): @@ -56,71 +107,50 @@ def evaluate_initial_misfit(self): def evaluate_gradient(self, path=None): """ - Performs adjoint simulation to retrieve the gradient of the objective + Performs adjoint simulation to retrieve the gradient of the objective. + + .. note:: + In the terminology of seismic exploration, we are 'backprojecting' """ system = self.module("system") - - self.logger.info(msg.mnr("EVALUATING GRADIENT")) + self.logger.info(msg.mnr("EVALUATING GRADIENT")) self.logger.debug(f"evaluating gradient {self.par.NTASK} times on system...") system.run("solver", "eval_grad", path=path or self.path.GRAD, export_traces=self.par.SAVETRACES) def process_kernels(self): """ - Backproject to create kernels from synthetics + System-run wrapper for postprocess.process_kernels which is meant to + sum and smooth all individual event kernels """ system = self.module("system") - solver = self.module("solver") - - system.run("postprocess", "process_kernels", single=True, - path=os.path.join(self.path.SCRATCH, "kernels"), - parameters=solver.parameters) + self.logger.info(msg.mnr("PROCESSING KERNELS")) - try: - # TODO Figure out a better method for running this try except - system.run("postprocess", "process_kernels", single=True, - path=os.path.join(self.path.SCRATCH, "kernels"), - parameters=["rhop"]) - except: - pass + # Runs kernel processing as a single parallel process + system.run("postprocess", "sum_smooth_kernels", single=True, + input_path=self.path.GRAD) - def finalize(self): - """ - Saves results from current model update iteration + def write_gradient(self): """ - self.logger.info(msg.mnr("FINALIZING MIGRATION WORKFLOW")) + Uses the optimization and postprocess modules to scale the gradient + to the given model, write the gradient in vector form and model form, + and apply an optional mask to the gradient - if self.par.SAVETRACES: - self.save_traces() - if self.par.SAVEKERNELS: - self.save_kernels() - else: - self.save_kernels_sum() + .. note:: - def save_kernels_sum(self): """ - Same summed kernels into the output directory - """ - src = os.path.join(self.path.SCRATCH, "kernels", "sum") - dst = os.path.join(self.path.OUTPUT, "kernels") - unix.mkdir(dst) - unix.cp(src, dst) + postprocess = self.module("postprocess") + optimize = self.module("optimize") + solver = self.module("solver") - def save_kernels(self): - """ - Save individual kernels into the output directory - """ - src = os.path.join(self.path.SCRATCH, "kernels") - dst = self.path.OUTPUT - unix.mkdir(dst) - unix.cp(src, dst) + # Scale the gradient by a mask and by the model + gradient = postprocess.scale_gradient(input_path=self.path.GRAD) + # Save the new gradient as a vector in PATH.OPTIMIZE + optimize.save("g_new", gradient) + # Save the new gradient as a set of model files (i.e., proc*_kernel.bin) + solver.save(solver.split(gradient), + path=os.path.join(self.path.GRAD, "gradient"), + suffix="_kernel") - def save_traces(self): - """ - Save waveform traces into the output directory - """ - src = os.path.join(self.path.SCRATCH, "traces") - dst = self.path.OUTPUT - unix.cp(src, dst) From a2694c6e6c4758cc344fe5a6af4581fa31f252ca Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Wed, 29 Jun 2022 17:01:01 -0800 Subject: [PATCH 035/195] rearranging main directory structure to be more easily navigable. moved examples into the main directory. Moved system run scripts into the system module directory. Also ran PyCharm code refactor on directory --- environment.yml | 8 + seisflows/config.py | 2 +- seisflows/core.py | 16 +- seisflows/{scripts => examples}/__init__.py | 0 .../ex1_specfem2d_workstation_inversion.py | 0 ...pecfem2d_workstation_inversion_w_pyatoa.py | 10 +- .../{templates => examples}/parameters.yaml | 0 .../{scripts => }/examples/sfexample2d.py | 1 - seisflows/optimize/base.py | 1 - seisflows/scripts/dsh | 19 --- seisflows/seisflows.py | 159 +++--------------- seisflows/solver/specfem.py | 7 +- seisflows/solver/specfem2d.py | 8 +- seisflows/system/frontera.py | 4 +- seisflows/system/maui.py | 6 +- .../runscripts}/__init__.py | 0 seisflows/{scripts => system/runscripts}/run | 0 .../runscripts}/run_function.py | 0 .../{scripts => system/runscripts}/submit | 0 .../runscripts}/submit_workflow.py | 0 seisflows/system/slurm.py | 4 +- seisflows/system/workstation.py | 9 +- seisflows/templates/__init__.py | 0 seisflows/tests/test_seisflows.py | 2 +- seisflows/workflow/forward.py | 15 +- seisflows/workflow/inversion.py | 20 +-- seisflows/workflow/migration.py | 6 +- seisflows/workflow/test.py | 102 +++++------ seisflows/workflow/thrifty_inversion.py | 59 ++++--- 29 files changed, 158 insertions(+), 300 deletions(-) create mode 100644 environment.yml rename seisflows/{scripts => examples}/__init__.py (100%) rename seisflows/{scripts => }/examples/ex1_specfem2d_workstation_inversion.py (100%) rename seisflows/{scripts => }/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py (96%) rename seisflows/{templates => examples}/parameters.yaml (100%) rename seisflows/{scripts => }/examples/sfexample2d.py (99%) delete mode 120000 seisflows/optimize/base.py delete mode 100644 seisflows/scripts/dsh rename seisflows/{scripts/examples => system/runscripts}/__init__.py (100%) rename seisflows/{scripts => system/runscripts}/run (100%) rename seisflows/{scripts => system/runscripts}/run_function.py (100%) rename seisflows/{scripts => system/runscripts}/submit (100%) rename seisflows/{scripts => system/runscripts}/submit_workflow.py (100%) delete mode 100644 seisflows/templates/__init__.py diff --git a/environment.yml b/environment.yml new file mode 100644 index 00000000..423f2e00 --- /dev/null +++ b/environment.yml @@ -0,0 +1,8 @@ +name: seisflows +channels: + - conda-forge +dependencies: + - python=3.10 + - obspy>=1.2.2 + - pyyaml>=5.3.1 + - IPython>=7.31.1 diff --git a/seisflows/config.py b/seisflows/config.py index 9f9c5255..9aa0480f 100644 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -48,7 +48,7 @@ # Define a package-wide default directory and file naming schema. This will # be returned as a Dict() object, defined below. All of these files and # directories will be created relative to the user-defined working directory -CFGPATHS = Dict( +DIR = Dict( PAR_FILE="parameters.yaml", # Default SeisFlows parameter file SCRATCHDIR="scratch", # SeisFlows internal working directory OUTPUTDIR="output", # Permanent disk storage for state and outputs diff --git a/seisflows/core.py b/seisflows/core.py index 53cdb92c..2625e77f 100644 --- a/seisflows/core.py +++ b/seisflows/core.py @@ -240,19 +240,23 @@ def validate(self, paths=True, parameters=True): :raises ParameterError: if a required path or parameter is not set by the user. """ - if paths: + if parameters: sys_path = sys.modules["seisflows_parameters"] - for key, attrs in self.paths.items(): + for key, attrs in self.parameters.items(): if attrs["required"] and (key not in sys_path): - raise KeyError(f"{sys_path} has no key {key}") + raise KeyError( + f"Required parameter '{key}' not found in parameter file" + ) elif key not in sys_path: setattr(sys_path, key, attrs["default"]) - if parameters: + if paths: sys_par = sys.modules["seisflows_paths"] - for key, attrs in self.parameters.items(): + for key, attrs in self.paths.items(): if attrs["required"] and (key not in sys_par): - raise ValueError(sys_par, key) + raise KeyError( + f"Required path '{key}' not found in parameter file" + ) elif key not in sys_par: setattr(sys_par, key, attrs["default"]) diff --git a/seisflows/scripts/__init__.py b/seisflows/examples/__init__.py similarity index 100% rename from seisflows/scripts/__init__.py rename to seisflows/examples/__init__.py diff --git a/seisflows/scripts/examples/ex1_specfem2d_workstation_inversion.py b/seisflows/examples/ex1_specfem2d_workstation_inversion.py similarity index 100% rename from seisflows/scripts/examples/ex1_specfem2d_workstation_inversion.py rename to seisflows/examples/ex1_specfem2d_workstation_inversion.py diff --git a/seisflows/scripts/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py b/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py similarity index 96% rename from seisflows/scripts/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py rename to seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py index 812fcfbe..9d0ab43c 100644 --- a/seisflows/scripts/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py +++ b/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py @@ -14,16 +14,10 @@ """ import os import sys -import glob -import shutil -import subprocess -import numpy as np from seisflows.tools import msg -from seisflows.config import Dict -from seisflows.seisflows import SeisFlows -from seisflows.tools.unix import cd, cp, rm, ln, mv, mkdir -from seisflows.scripts.examples.sfexample2d import SFExample2D +from seisflows.tools.unix import cd, rm, ln +from seisflows.examples.sfexample2d import SFExample2D class SFPyatoaEx2D(SFExample2D): diff --git a/seisflows/templates/parameters.yaml b/seisflows/examples/parameters.yaml similarity index 100% rename from seisflows/templates/parameters.yaml rename to seisflows/examples/parameters.yaml diff --git a/seisflows/scripts/examples/sfexample2d.py b/seisflows/examples/sfexample2d.py similarity index 99% rename from seisflows/scripts/examples/sfexample2d.py rename to seisflows/examples/sfexample2d.py index e2057537..72166c0e 100644 --- a/seisflows/scripts/examples/sfexample2d.py +++ b/seisflows/examples/sfexample2d.py @@ -30,7 +30,6 @@ import os import sys import glob -import shutil import subprocess import numpy as np diff --git a/seisflows/optimize/base.py b/seisflows/optimize/base.py deleted file mode 120000 index 03ddb0e5..00000000 --- a/seisflows/optimize/base.py +++ /dev/null @@ -1 +0,0 @@ -gradient.py \ No newline at end of file diff --git a/seisflows/scripts/dsh b/seisflows/scripts/dsh deleted file mode 100644 index e5228915..00000000 --- a/seisflows/scripts/dsh +++ /dev/null @@ -1,19 +0,0 @@ -#!/bin/bash - -hosts=$( echo $1 | tr ',' ' ' ) -exe=$2 -path=$3 -class=$4 -func=$5 -env=$6 - - -k=0; -for host in $hosts; -do - ssh $host "export SEISFLOWS_TASK_ID=$k; $exe $path $class $func $env" & - k=$((k+1)); - sleep 0.5 -done -wait - diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index de747646..07f99103 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -136,8 +136,6 @@ def _format_action(self, action): error checking, and establish the active working environment before executing the workflow.""" ) - submit.add_argument("-f", "--force", action="store_true", - help="Turn off the default parameter precheck") submit.add_argument("-s", "--stop_after", default=None, type=str, help="Optional override of the 'STOP_AFTER' parameter") # ========================================================================= @@ -147,8 +145,6 @@ def _format_action(self, action): an active environment exists in the working directory, and must be submitted to the system again.""" ) - resume.add_argument("-f", "--force", action="store_true", - help="Turn off the default parameter precheck") resume.add_argument("-r", "--resume_from", default=None, type=str, help="Optional override of the 'RESUME_FROM' parameter") resume.add_argument("-s", "--stop_after", default=None, type=str, @@ -161,14 +157,14 @@ def _format_action(self, action): fresh workflow.""" ) restart.add_argument("-f", "--force", action="store_true", - help="Skip the clean and submit precheck statements") + help="Skip the clean warning check statement") # ========================================================================= clean = subparser.add_parser( "clean", help="Remove files relating to an active working environment", description="""Delete all SeisFlows related files in the working directory, except for the parameter file.""" ) - clean.add_argument("-f", "--force", action="store_true", + clean.add_argument("-f", "--force", action="store_true", help="Skip the warning check that precedes the clean " "function") # ========================================================================= @@ -385,7 +381,7 @@ def _public_methods(self): """ return [_ for _ in dir(self) if not _.startswith("_")] - def _register_parameters(self, force=True): + def _register_parameters(self): """ Load the paths and parameters from file into sys.modules, set the default parameters if they are missing from the file, and expand all @@ -395,12 +391,6 @@ def _register_parameters(self, force=True): This is ideally the FIRST thing that happens everytime SeisFlows is initiated. The package cannot do anything without the resulting PATH and PARAMETER variables. - - :type force: bool - :param force: if False, print out a few key parameters and require - user-input before allowing workflow to be submitted. This is - usually run before submit and resume, to prevent job submission - without user evaluation. """ # Check if the filepaths exist if not os.path.exists(self._args.parameter_file): @@ -415,10 +405,7 @@ def _register_parameters(self, force=True): parameters = loadyaml(self._args.parameter_file) except Exception as e: print(msg.cli(f"Please check that your parameter file is properly " - f"formatted in the YAML format. If you have just run " - f"'seisflows configure', you may have some required " - f"parameters that will need to be filled out before " - f"you can proceed. The error message is:", + f"formatted in the YAML format. The read error is:", items=[str(e)], header="parameter file read error", border="=")) sys.exit(-1) @@ -438,11 +425,6 @@ def _register_parameters(self, force=True): if "PAR_FILE" not in paths: paths["PAR_FILE"] = self._args.parameter_file - # For submit() and resume(), provide a dialogue to stdout requiring a - # visual pre-check of parameters before submitting workflow - # if not force and parameters["PRECHECK"]: - # self._precheck_parameters(parameters) - # Expand all paths to be absolute on the filesystem for key, val in paths.items(): try: @@ -456,30 +438,7 @@ def _register_parameters(self, force=True): self._paths = Dict(paths) self._parameters = Dict(parameters) - # def _precheck_parameters(self, parameters): - # """ - # Visually display a list of user-chosen parameters to the User before - # proceeding with the _register_parameters command. Allows the User to quickly - # determine if workflow parameters have been set correctly - # - # :type parameters: dict - # :param parameters: parameters read in from the YAML parameter file - # """ - # items = [] - # for p in parameters["PRECHECK"]: - # try: - # items.append(f"{p.upper()}: {parameters[p.upper()]}") - # except KeyError: - # items.append(f"{p.upper()}: !!! PARAMETER NOT FOUND !!!") - # print(msg.cli("Please ensure that the parameters listed below " - # "are set correctly. You can edit this list with " - # "the PRECHECK parameter.", items=items, - # header="seisflows precheck", border="=")) - # check = input("Continue? (y/[n])\n") - # if check != "y": - # sys.exit(-1) - - def _register_modules(self, check=True): + def _register_modules(self): """ First time setup procedure which loads in the user-chosen modules and registers them into sys.modules so that they are globally accessible @@ -514,9 +473,6 @@ def _register_modules(self, check=True): for name in NAMES: sys.modules[f"seisflows_{name}"] = custom_import(name)() - if check: - self._check_modules() - # Bare minimum Module requirements for SeisFlows req_modules = ["WORKFLOW", "SYSTEM"] for req in req_modules: @@ -527,7 +483,7 @@ def _register_modules(self, check=True): "choices.", header="error", border="=")) sys.exit(-1) - def _check_modules(self): + def _check_parameters(self): """ Runs the .check() function on each of the modules, which validates the given parameters in a parameter file to ensure that a workflow will not @@ -570,7 +526,7 @@ def _load_modules(self): with open(fid, "rb") as f: sys.modules[f"seisflows_{NAME}"] = pickle.load(f) - self._check_modules() + self._check_parameters() def setup(self, force=False, **kwargs): """ @@ -585,7 +541,7 @@ def setup(self, force=False, **kwargs): :type force: bool :param force: flag to force parameter file overwriting """ - PAR_FILE = os.path.join(ROOT_DIR, "templates", "parameters.yaml") + PAR_FILE = os.path.join(ROOT_DIR, "examples", "parameters.yaml") if os.path.exists(self._args.parameter_file): if force: @@ -616,7 +572,7 @@ def configure(self, absolute_paths=False, **kwargs): Defaults to False, uses relative paths. """ print(msg.cli(f"filling {self._args.parameter_file} w/ default values")) - self._register_parameters(force=True) + self._register_parameters() # Check if the User set turn off any modules (if None, dont instantiate) names = copy(NAMES) @@ -657,7 +613,7 @@ def configure(self, absolute_paths=False, **kwargs): # If requested, set the paths relative to the current dir if not absolute_paths: for key, attrs in seisflows_paths.items(): - if attrs["default"] is not None: + if attrs["default"]: seisflows_paths[key]["default"] = os.path.relpath( attrs["default"]) msg.write_par_file_paths_pars(f, seisflows_paths, indent=4) @@ -681,8 +637,9 @@ def init(self, **kwargs): unix.mkdir(self._args.workdir) unix.cd(self._args.workdir) - self._register_parameters(force=True) - self._register_modules(check=True) + self._register_parameters() + self._register_modules() + self._check_parameters() save(path=self._paths.OUTPUT) @@ -694,34 +651,20 @@ def init(self, **kwargs): print(msg.cli(f"instantiating SeisFlows working state in directory: " f"{CFGPATHS.OUTPUTDIR}")) - def submit(self, stop_after=None, force=False, **kwargs): + def submit(self, **kwargs): """ Main SeisFlows execution command. Submit the SeisFlows workflow to the chosen system, and execute seisflows.workflow.main(). Will create the working directory and any required paths and ensure that all required paths exist. - - :type stop_after: str - :param stop_after: allow the function to overwrite the 'STOP_AFTER' - parameter in the parameter file, which dictates how far the workflow - will proceed until stopping. Must match flow function names in - workflow.main() - :type force: bool - :param force: if True, turns off the parameter precheck and - simply submits the workflow """ unix.mkdir(self._args.workdir) unix.cd(self._args.workdir) - # Ensure that the 'RESUME_FROM' parameter is not set, in case of restart - self.par(parameter="resume_from", value="", skip_print=True) - if stop_after is not None: - self.par(parameter="stop_after", value=stop_after, skip_print=True) - # Read in the Parameter file and set parameters into sys.modules. - self._register_parameters(force=force) - # self._check_required_paths() - self._register_modules(check=True) + self._register_parameters() + self._register_modules() + self._check_parameters() # Set logger to print to stdout and write to a file config_logger(level=self._parameters.LOG_LEVEL, @@ -732,29 +675,6 @@ def submit(self, stop_after=None, force=False, **kwargs): system = sys.modules["seisflows_system"] system.submit() - # def _check_required_paths(self): - # """ - # If the User provides certain paths to the program, they MUST exist. - # This function simply checks these required paths and throws a sys exit - # if any of them does not exist - # """ - # # A list of paths that need to exist if provided by user - # REQ_PATHS = ["SPECFEM_BIN", "SPECFEM_DATA", "MODEL_INIT", "MODEL_TRUE", - # "DATA", "LOCAL", "MASK"] - # - # # Check that all required paths exist before submitting workflow - # paths_dont_exist = [] - # for key in REQ_PATHS: - # if key in self._paths: - # # If a required path is given (not None) and doesnt exist, exit - # if self._paths[key] and not os.path.exists(self._paths[key]): - # paths_dont_exist.append(f"{key}: {self._paths[key]}") - # if paths_dont_exist: - # print(msg.cli("The following paths do not exist but need to:", - # items=paths_dont_exist, header="path error", - # border="=")) - # sys.exit(-1) - def clean(self, force=False, **kwargs): """ Clean the SeisFlows working directory except for the parameter file. @@ -796,33 +716,12 @@ def clean(self, force=False, **kwargs): continue print(msg.cli(items=items, header="clean", border="=")) - def resume(self, stop_after=None, resume_from=None, force=False, - **kwargs): + def resume(self, **kwargs): """ Resume a previously started workflow by loading the module pickle files and submitting the workflow from where it left off. - - :type stop_after: str - :param stop_after: allow the function to overwrite the 'STOP_AFTER' - parameter in the parameter file, which dictates how far the workflow - will proceed until stopping. Must match flow function names in - workflow.main() - :type resume_from: str - :param resume_from: allow the function to overwrite the 'RESUME_FROM' - parameter in the parameter file, which dictates which function the - workflow starts from, must match the flow functions given in - workflow.main() - :type force: bool - :param force: if True, turns off the parameter precheck and - simply submits the workflow """ - if stop_after is not None: - self.par(parameter="STOP_AFTER", value=stop_after, skip_print=True) - if resume_from is not None: - self.par(parameter="RESUME_FROM", value=resume_from, - skip_print=True) - - self._register_parameters(force=force) + self._register_parameters() self._load_modules() # Set logger to print to stdout and write to a file @@ -836,13 +735,9 @@ def resume(self, stop_after=None, resume_from=None, force=False, def restart(self, force=False, **kwargs): """ Restart simply means clean the workding dir and submit a new workflow. - - :type force: bool - :param force: ignore the warning check that precedes the clean() - function, useful if you don't want any input messages popping up """ self.clean(force=force) - self.submit(force=force) + self.submit() def debug(self, **kwargs): """ @@ -851,7 +746,7 @@ def debug(self, **kwargs): interactive environment allowing exploration of the package space. Does not allow stepping through of code (not a breakpoint). """ - self._register_parameters(force=True) + self._register_parameters() self._load_modules() # Distribute modules to common names for easy access during debug mode @@ -1076,7 +971,7 @@ def examples(self, run=None, choice=None, **kwargs): :param choice: The choice of example, must match the given tag or file name that is assigned to it """ - examples_dir = os.path.join(ROOT_DIR, "scripts", "examples") + examples_dir = os.path.join(ROOT_DIR, "examples") examples_list = [] example_names = sorted(glob(os.path.join(examples_dir, "ex*.py"))) @@ -1141,7 +1036,7 @@ def check(self, choice=None, **kwargs): self._subparser.print_help() sys.exit(0) - self._register_parameters(force=True) + self._register_parameters() self._load_modules() acceptable_args[choice](*self._args.args, **kwargs) @@ -1175,7 +1070,7 @@ def reset(self, choice=None, **kwargs): self._subparser.print_help() sys.exit(0) - self._register_parameters(force=True) + self._register_parameters() self._load_modules() acceptable_args[choice](*self._args.args, **kwargs) @@ -1200,7 +1095,7 @@ def convert(self, name, path=None, **kwargs): default to saving in the output directory under the name of the model """ - self._load_modules(force=True) + self._load_modules() solver = sys.modules["seisflows_solver"] optimize = sys.modules["seisflows_optimize"] @@ -1346,7 +1241,7 @@ def _print_flow(self, **kwargs): .. rubric:: $ seisflows print flow """ - self._register_parameters(force=True) + self._register_parameters() self._load_modules() workflow = custom_import("workflow")() @@ -1379,7 +1274,7 @@ def _print_inheritance(self, name=None, func=None, **kwargs): seisflows inspect solver eval_func """ - self._register_parameters(force=True) + self._register_parameters() self._load_modules() if func is None: self._inspect_module_hierarchy(name, **kwargs) diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 7108565d..08115fd6 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -11,12 +11,11 @@ import numpy as np from glob import glob -from seisflows.core import Base +from seisflows.core import Base, Dict from seisflows.plugins import solver_io from seisflows.tools import msg, unix from seisflows.tools.specfem import Container, getpar from seisflows.tools.wrappers import diff -from seisflows.core import Dict class Specfem(Base): @@ -332,9 +331,9 @@ def check(self, validate=True): self.parameters.append("rho") assert hasattr(solver_io, self.par.SOLVERIO) - assert hasattr(self.io, "read_slice"), \ + assert hasattr(self._io, "read_slice"), \ "IO method has no attribute 'read_slice'" - assert hasattr(self.io, "write_slice"), \ + assert hasattr(self._io, "write_slice"), \ "IO method has no attribute 'write_slice'" def setup(self): diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index a36afc02..b23d6640 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -33,10 +33,10 @@ def __init__(self): self.f0 = None - @property - def _io(self): - """Inherits from seisflows.solver.specfem.Specfem""" - return self._io + # @property + # def _io(self): + # """Inherits from seisflows.solver.specfem.Specfem""" + # return self._io @property def taskid(self): diff --git a/seisflows/system/frontera.py b/seisflows/system/frontera.py index 0de37b32..f0a43d04 100644 --- a/seisflows/system/frontera.py +++ b/seisflows/system/frontera.py @@ -85,7 +85,7 @@ def submit(self, submit_call=None): f"-N 1", # total number of nodes requested f"-n 1", # number of mpi tasks f"-t {self.par.WALLTIME}", # job walltime - f"{os.path.join(ROOT_DIR, 'scripts', 'submit')}", + f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'submit')}", f"--output {self.path.OUTPUT}" ]) super().submit(submit_call=submit_call) @@ -124,7 +124,7 @@ def run(self, classname, method, single=False, run_call=None, **kwargs): f"-N {_nodes}", # total number of nodes requested f"-n {self.par.NPROC}", # number of mpi tasks f"-t {self.par.WALLTIME}", # job walltime - f"{os.path.join(ROOT_DIR, 'scripts', 'run')}", + f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'run')}", f"--output {self.path.OUTPUT}" f"--classname {classname}", f"--funcname {method}", diff --git a/seisflows/system/maui.py b/seisflows/system/maui.py index 1f64f7d1..6dad56ef 100644 --- a/seisflows/system/maui.py +++ b/seisflows/system/maui.py @@ -137,7 +137,7 @@ def submit(self, submit_call=None): f"--ntasks=1", f"--cpus-per-task=1", f"--time={self.par.WALLTIME:d}", - f"{os.path.join(ROOT_DIR, 'scripts', 'submit')}", + f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'submit')}", f"--output {self.path.OUTPUT}" ]) @@ -180,7 +180,7 @@ def run(self, classname, method, single=False, run_call=None, **kwargs): f"--time={self.par.TASKTIME:d}", f"--output={os.path.join(self.path.WORKDIR, 'logs', '%A_%a')}", f"--array=0-{self.par.NTASK-1 % self.par.NTASKMAX}", - f"{os.path.join(ROOT_DIR, 'scripts', 'run')}", + f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'run')}", f"--output {self.path.OUTPUT}", f"--classname {classname}", f"--funcname {method}", @@ -210,7 +210,7 @@ def run_ancil(self, classname, method, **kwargs): f"--time={self.par.ANCIL_TASKTIME:d}", f"--output={os.path.join(self.path.WORKDIR, 'logs', '%A_%a')}", f"--array=0-{self.par.NTASK-1 % self.par.NTASKMAX}", - f"{os.path.join(ROOT_DIR, 'scripts', 'run')}", + f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'run')}", f"--output {self.path.OUTPUT}", f"--classname {classname}", f"--funcname {method}", diff --git a/seisflows/scripts/examples/__init__.py b/seisflows/system/runscripts/__init__.py similarity index 100% rename from seisflows/scripts/examples/__init__.py rename to seisflows/system/runscripts/__init__.py diff --git a/seisflows/scripts/run b/seisflows/system/runscripts/run similarity index 100% rename from seisflows/scripts/run rename to seisflows/system/runscripts/run diff --git a/seisflows/scripts/run_function.py b/seisflows/system/runscripts/run_function.py similarity index 100% rename from seisflows/scripts/run_function.py rename to seisflows/system/runscripts/run_function.py diff --git a/seisflows/scripts/submit b/seisflows/system/runscripts/submit similarity index 100% rename from seisflows/scripts/submit rename to seisflows/system/runscripts/submit diff --git a/seisflows/scripts/submit_workflow.py b/seisflows/system/runscripts/submit_workflow.py similarity index 100% rename from seisflows/scripts/submit_workflow.py rename to seisflows/system/runscripts/submit_workflow.py diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index be9da7f2..8e9a42af 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -87,7 +87,7 @@ def submit(self, submit_call=None): f"--ntasks-per-node={self.par.NODESIZE}", f"--nodes=1", f"--time={self.par.WALLTIME:d}", - f"{os.path.join(ROOT_DIR, 'scripts', 'submit')}", + f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'submit')}", f"--output {self.path.OUTPUT}" ]) @@ -134,7 +134,7 @@ def run(self, classname, method, single=False, run_call=None, **kwargs): f"--time={self.par.TASKTIME:d}", f"--output={os.path.join(self.path.WORKDIR, 'logs', '%A_%a')}", f"--array=0-{self.par.NTASK-1 % self.par.NTASKMAX}", - f"{os.path.join(ROOT_DIR, 'scripts', 'run')}", + f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'run')}", f"--output {self.path.OUTPUT}", f"--classname {classname}", f"--funcname {method}", diff --git a/seisflows/system/workstation.py b/seisflows/system/workstation.py index bf2e9e29..293570e5 100644 --- a/seisflows/system/workstation.py +++ b/seisflows/system/workstation.py @@ -50,13 +50,6 @@ def __init__(self): "NPROC", required=False, default=1, par_type=int, docstr="Number of processor to use for each simulation" ) - self.required.par( - "PRECHECK", required=False, par_type=list, default=["TITLE"], - docstr="A list of parameters that will be displayed to stdout " - "before 'submit' or 'resume' is run. Useful for " - "manually reviewing important parameters prior to " - "system submission" - ) self.required.par( "LOG_LEVEL", required=False, par_type=str, default="DEBUG", docstr="Verbosity output of SF logger. Available from least to " @@ -83,7 +76,7 @@ def __init__(self): ) self.required.path( "SYSTEM", required=False, - default=os.path.join(self.required.SCRATCH, "system"), + default=os.path.join(self.path.WORKDIR, "scratch", "system"), docstr="scratch path to hold any system related data" ) self.required.path( diff --git a/seisflows/templates/__init__.py b/seisflows/templates/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/seisflows/tests/test_seisflows.py b/seisflows/tests/test_seisflows.py index 5ff8b91f..9ea85647 100644 --- a/seisflows/tests/test_seisflows.py +++ b/seisflows/tests/test_seisflows.py @@ -231,7 +231,7 @@ def test_cmd_clean(tmpdir): sf = SeisFlows() sf.clean(force=True) - for fid in [path, CFGPATHS.PAR_FILE]: + for fid in [path, "parameters.yaml"]: assert(os.path.exists(fid)) diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index c91fa1c2..c383e277 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -99,6 +99,14 @@ def finalize(self): msg.mjr(f"FINISHED {self.__class__.__name__} WORKFLOW") ) + def checkpoint(self): + """ + Saves active SeisFlows working state to disk as Pickle files such that + the workflow can be resumed following a crash, pause or termination of + workflow. + """ + save(path=self.path.OUTPUT) + def main(self, flow=None, return_flow=False): """ Execution of a workflow is equal to stsepping through workflow.main() @@ -278,10 +286,3 @@ def _check_stop_resume_cond(self, flow): return start_idx, stop_idx - def checkpoint(self): - """ - Writes information to disk so workflow can be resumed following a break - """ - save(path=self.PATH.OUTPUT) - - diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index 4516988c..fb4a9c24 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -58,7 +58,7 @@ def __init__(self): ) self.required.par( - "END", required=True, par_type=int, + "END", required=False, default=1, par_type=int, docstr="Last iteration of the inverison workflow," "BEGIN <= END <= inf" ) @@ -81,7 +81,6 @@ def __init__(self): "SAVEMODEL", required=False, default=True, par_type=bool, docstr="Save updated model files after each iteration" ) - # Define the Paths required by this module self.required.path( "FUNC", required=False, @@ -112,6 +111,7 @@ def check(self, validate=True): Checks parameters and paths """ super().check(validate=validate) + import pdb;pdb.set_trace() assert(1 <= self.par.BEGIN <= self.par.END), \ f"Incorrect BEGIN or END parameter. Values must be in order: " \ f"1 <= {self.par.BEGIN} <= {self.par.END}" @@ -140,22 +140,6 @@ def finalize(self): self.checkpoint() preprocess.finalize() - # Save files from scratch before discarding - if self.par.SAVEMODEL: - self.save_model() - - if self.par.SAVEGRADIENT: - self.save_gradient() - - if self.par.SAVEKERNELS: - self.save_kernels() - - if self.par.SAVETRACES: - self.save_traces() - - if self.par.SAVERESIDUALS: - self.save_residuals() - def main(self, flow=None, return_flow=False): """ Overwrites the forward() main function to provide the ability to run diff --git a/seisflows/workflow/migration.py b/seisflows/workflow/migration.py index c7445a74..06d0d12e 100644 --- a/seisflows/workflow/migration.py +++ b/seisflows/workflow/migration.py @@ -77,7 +77,7 @@ def check(self, validate=True): assert os.path.exists(self.path.MODEL_TRUE), \ "CASE == SYNTHETIC requires PATH.MODEL_TRUE" - if not os.path.exists(self.path.DATA): + if not self.path.DATA or not os.path.exists(self.path.DATA): assert "MODEL_TRUE" in self.path, f"DATA or MODEL_TRUE must exist" def setup(self, flow=None, return_flow=False): @@ -115,7 +115,9 @@ def evaluate_gradient(self, path=None): system = self.module("system") self.logger.info(msg.mnr("EVALUATING GRADIENT")) - self.logger.debug(f"evaluating gradient {self.par.NTASK} times on system...") + self.logger.debug( + f"evaluating gradient {self.par.NTASK} times on system..." + ) system.run("solver", "eval_grad", path=path or self.path.GRAD, export_traces=self.par.SAVETRACES) diff --git a/seisflows/workflow/test.py b/seisflows/workflow/test.py index 5efeefac..9e80aba4 100644 --- a/seisflows/workflow/test.py +++ b/seisflows/workflow/test.py @@ -1,59 +1,39 @@ #!/usr/bin/env python3 """ -This is a SeisFlows Test class which is used to test out the underlying +This is the SeisFlows Test class which is used to test out the underlying machinery before running an actual workflow. Contains simple functions used to -make sure that all parts of the package are working as expected. +make sure that all parts of the package are working as expected. Creates +its own directory structure and acts as a standalone workflow tool """ import os import sys import time -import logging import subprocess import numpy as np - from glob import glob -from seisflows.core import SeisFlowsPathsParameters -from seisflows.config import (custom_import, ROOT_DIR, CFGPATHS) - -PAR = sys.modules["seisflows_parameters"] -PATH = sys.modules["seisflows_paths"] -system = sys.modules["seisflows_system"] -solver = sys.modules["seisflows_solver"] -optimize = sys.modules["seisflows_optimize"] -preprocess = sys.modules["seisflows_preprocess"] -postprocess = sys.modules["seisflows_postprocess"] +from seisflows.core import Base +from seisflows.config import ROOT_DIR, CFGPATHS, save -class Test(custom_import("workflow", "base")): +class Test(Base): """ - This is a template Base class + The Test workflow class provides a base parameter and directory structure + as well as test functions which can be run to ensure that the chosen + modules are working as expected """ - # Class-specific logger accessed using self.logger - # When this logger is called, e.g., self.logger.info("text"), the logging - # package will know exactly which module, class and function the log - # statement has been sent from, extraordinarily helpful for debugging. - logger = logging.getLogger(__name__).getChild(__qualname__) - - @property - def required(self): + def __init__(self): """ - A hard definition of paths and parameters required by this class, - alongside their necessity for the class and their string explanations. - - :rtype: seisflows.config.SeisFlowsPathsParameters - :return: Paths and parameters that define the given class - + Initiate the TEST workflow """ - sf = SeisFlowsPathsParameters(super().required) + super().__init__() - sf.path("TEST_DATA", required=False, - default=os.path.join(ROOT_DIR, "tests", "test_data", - "work"), - docstr="Example data for test system" - ) - - return sf + self.required.path( + "TEST_DATA", required=False, + default=os.path.join(ROOT_DIR, "tests", "test_data"), + docstr="Example data for test system which is shipped with the " + "SeisFlows repository" + ) def check(self, validate=True): """ @@ -64,8 +44,15 @@ def check(self, validate=True): :type validate: bool :param validate: set required paths and parameters into sys.modules """ - if validate: - self.required.validate() + self.required.validate() + + def checkpoint(self): + """ + Saves active SeisFlows working state to disk as Pickle files such that + the workflow can be resumed following a crash, pause or termination of + workflow. + """ + save(path=self.path.OUTPUT) def main(self, return_flow=False): """ @@ -82,10 +69,12 @@ def main(self, return_flow=False): for func in FLOW: func() - def test_function(self, check_value): + def _test_function_print(self, check_value): """ A simple function that can be called by system.run() """ + system = self.module("system") + print(f"Hello world, from taskid {system.taskid()}. " f"Check: {check_value}") @@ -97,6 +86,9 @@ def test_system(self): Check that these functions perform as expected by passing in a random value and checking that this value gets logged back """ + system = self.module("system") + system.setup() + # Run a very simple test function using system.run() check_value_1 = 1234.5 system.run(classname="workflow", method="test_function", @@ -118,15 +110,18 @@ def test_system(self): assert(float(line.strip().split(" ")[-1]) == check) # Check that MPI Exec works - assert("MPIEXEC" in PAR), f"MPIEXEC is not defined for this system" - stdout = subprocess.run(PAR.MPIEXEC, shell=True, check=True, + assert("MPIEXEC" in self.par), f"MPIEXEC is not defined for this system" + stdout = subprocess.run(self.par.MPIEXEC, shell=True, check=True, stdout=subprocess.PIPE) def test_preprocess(self): """ Test the exposed 'prepare_eval_grad()' preprocessing function """ - cwd = PATH.TEST_DATA + preprocess = self.module("preprocess") + preprocess.setup() + + cwd = os.path.join(self.path.TEST_DATA, "test_solver") taskid = 0 filenames = ["AA.S0001.BXY.semd"] source_name = "001" @@ -140,14 +135,16 @@ def test_solver(self): Simply test that the solver binaries can be called, which is what the solver module is ultimately responsible for """ - assert(PATH.SPECFEM_BIN is not None and - os.path.exists(PATH.SPECFEM_BIN)), ( - f"SPECFEM_BIN {PATH.SPECFEM_BIN} directory does not exist" + solver = self.module("solver") + + assert(self.path.SPECFEM_BIN is not None and + os.path.exists(self.path.SPECFEM_BIN)), ( + f"SPECFEM_BIN {self.path.SPECFEM_BIN} directory does not exist" ) try: solver.call_solver( - executable=f"{PATH.SPECFEM_BIN}/xcombine_sem", - output=os.path.join(PATH.TEST_DATA, "test_solver.log") + executable=f"{self.path.SPECFEM_BIN}/xcombine_sem", + output=os.path.join(self.path.TEST_DATA, "test_solver.log") ) # We expect this to throw a system exit because we are not running with # MPI @@ -158,10 +155,13 @@ def test_optimize(self): """ Test optimization module with a simple Rosenbrock function """ - PAR.log_level = "CRITICAL" + optimize = self.module("optimize") + self.par.log_level = "CRITICAL" m_new, m_true, objective_function, gradient = rosenbrock() - optimize.setup(m_new=m_new) + + optimize.setup() + optimize.save("m_new", m_new) def evaluate_function(): """ diff --git a/seisflows/workflow/thrifty_inversion.py b/seisflows/workflow/thrifty_inversion.py index 11f9336d..aa5e4f13 100644 --- a/seisflows/workflow/thrifty_inversion.py +++ b/seisflows/workflow/thrifty_inversion.py @@ -4,28 +4,21 @@ A thrifty inversion skips the costly intialization step (i.e., forward simulations and misfit quantification) if the final forward simulations from -the previous iteration's line search can be used in the current one. +the previous iteration's line search can be used in the current one. Otherwise +it performs the same as the Inversion workflow """ import sys import logging +from seisflows.workflow.inversion import Inversion from seisflows.tools import unix, msg -from seisflows.config import custom_import -PAR = sys.modules["seisflows_parameters"] -PATH = sys.modules["seisflows_paths"] -optimize = sys.modules["seisflows_optimize"] - - -class ThriftyInversion(custom_import("workflow", "inversion")): +class ThriftyInversion(Inversion): """ Thrifty inversion which attempts to save resources by re-using previous line search results for the current iteration. """ - # Class-specific logger accessed using self.logger - logger = logging.getLogger(__name__).getChild(__qualname__) - def __init__(self): """ :type thrifty: bool @@ -38,28 +31,30 @@ def __init__(self): previous iteration """ super().__init__() + self.thrifty = False def check(self, validate=True): """ Checks parameters and paths """ - super().check(validate=False) - if validate: - self.required.validate() + super().check(validate=validate) - assert PAR.LINESEARCH == "Backtrack", \ - "Thrifty inversion requires backtracking line search" + assert self.par.LINESEARCH.upper() == "BACKTRACK", ( + "Thrifty inversion requires PAR.LINESEARCH == 'backtrack'" + ) - def initialize(self): + def evaluate_initial_misfit(self): """ If line search can be carried over, skip initialization step Or if manually starting a new run, start with normal inversion init """ - if not self.thrifty or optimize.iter == PAR.BEGIN: - super().initialize() + optimize = self.module("optimize") + + if not self.thrifty or optimize.iter == self.par.BEGIN: + super().evaluate_initial_misfit() else: - self.logger.info(msg.mjr("INITIALIZING THRIFTY INVERSION")) + self.logger.info(msg.mjr("SKIPPING INITIAL MISFIT EVALUATION")) def clean(self): """ @@ -67,31 +62,35 @@ def clean(self): We assume clean() is the final flow() argument so that we can update the thrifty status here. """ - self.update_status() + self._update_status() if self.thrifty: - self.logger.info(msg.mnr("THRIFTY CLEANING WORKDIR FOR NEXT " - "ITERATION")) - unix.rm(PATH.GRAD) - unix.mv(PATH.FUNC, PATH.GRAD) - unix.mkdir(PATH.FUNC) + self.logger.info( + msg.mnr("THRIFTY CLEANING WORKDIR FOR NEXT ITERATION") + ) + unix.rm(self.path.GRAD) + # Last line search evaluation becomes the new gradient evaluation + unix.mv(self.path.FUNC, self.path.GRAD) + unix.mkdir(self.path.FUNC) else: super().clean() - def update_status(self): + def _update_status(self): """ Determine if line search forward simulation can be carried over based - on a number of criteria + on a variety of criteria relating to location in the inversion. """ + optimize = self.module("optimize") + self.logger.info("updating thrifty inversion status") - if optimize.iter == PAR.BEGIN: + if optimize.iter == self.par.BEGIN: self.logger.info("1st iteration, defaulting to inversion workflow") thrifty = False elif optimize.restarted: self.logger.info("optimization has been restarted, defaulting to " "inversion workflow") thrifty = False - elif optimize.iter == PAR.END: + elif optimize.iter == self.par.END: self.logger.info("final iteration, defaulting to inversion workflow") thrifty = False else: From 5bfc552b8755e28e1b703d3a6c7d4d5433dda347 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Wed, 29 Jun 2022 17:33:42 -0800 Subject: [PATCH 036/195] moved logging establishment into the core.base class, rather than as a config object run at startup. bug fixing specfem2d pyatoa example --- seisflows/config.py | 57 +------------------------------ seisflows/core.py | 45 ++++++++++++++++++++++++- seisflows/seisflows.py | 14 +------- seisflows/solver/specfem.py | 6 ++-- seisflows/solver/specfem2d.py | 60 ++++++++++++++++----------------- seisflows/workflow/inversion.py | 2 +- 6 files changed, 80 insertions(+), 104 deletions(-) diff --git a/seisflows/config.py b/seisflows/config.py index 9aa0480f..2adc66e8 100644 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -48,7 +48,7 @@ # Define a package-wide default directory and file naming schema. This will # be returned as a Dict() object, defined below. All of these files and # directories will be created relative to the user-defined working directory -DIR = Dict( +CFGPATHS = Dict( PAR_FILE="parameters.yaml", # Default SeisFlows parameter file SCRATCHDIR="scratch", # SeisFlows internal working directory OUTPUTDIR="output", # Permanent disk storage for state and outputs @@ -122,61 +122,6 @@ def flush(): del sys.modules[mod_name] -def config_logger(level="DEBUG", filename=None, filemode="a", verbose=True): - """ - Explicitely configure the logging module with some parameters defined - by the user in the System module. Instantiates a stream logger to write - to stdout, and a file logger which writes to `filename`. Two levels of - verbosity and three levels of log messages allow the user to determine - how much output they want to see. - - :type level: str - :param level: log level to be passed to logger, available are - 'CRITICAL', 'WARNING', 'INFO', 'DEBUG' - :type filename: str or None - :param filename: name of the log file to write log statements to. If None, - logs will be written to STDOUT ONLY, and `filemode` will not be used. - :type filemode: str - :param filemode: method for opening the log file. defaults to append 'a' - :type verbose: bool - :param verbose: if True, writes a more detailed log message stating the - type of log (warning, info, debug), and the class and method which - called the logger (e.g., seisflows.solver.specfem2d.save()). This - is much more useful for debugging but clutters up the log file. - if False, only write the time and message in the log statement. - """ - # Make sure that we don't already have handlers described, which may happen - # if this function gets run multiple times, and leads to duplicate logs - while logger.hasHandlers() and logger.handlers: - logger.removeHandler(logger.handlers[0]) - - # Two levels of verbosity on log level, triggered with PAR.VERBOSE - if verbose: - # More verbose logging statement with levelname and func name - fmt_str = ( - "%(asctime)s | %(levelname)-5s | %(name)s.%(funcName)s()\n" - "> %(message)s" - ) - else: - # Clean logging statement with only time and message - fmt_str = "%(asctime)s | %(message)s" - - # Instantiate logger during _register() as we now have user-defined pars - logger.setLevel(level) - formatter = logging.Formatter(fmt_str, datefmt="%Y-%m-%d %H:%M:%S") - - # Stream handler to print log statements to stdout - st_handler = logging.StreamHandler(sys.stdout) - st_handler.setFormatter(formatter) - logger.addHandler(st_handler) - - # File handler to print log statements to text file `filename` - if filename is not None: - file_handler = logging.FileHandler(filename, filemode) - file_handler.setFormatter(formatter) - logger.addHandler(file_handler) - - def custom_import(name=None, module=None, classname=None): """ Imports SeisFlows module and extracts class that is the camelcase version diff --git a/seisflows/core.py b/seisflows/core.py index 2625e77f..b94b67f0 100644 --- a/seisflows/core.py +++ b/seisflows/core.py @@ -21,6 +21,7 @@ def __init__(self): then used to build the parameter file dynamically. """ self.required = SeisFlowsPathsParameters() + self._logger = None def module(self, name): """ @@ -44,9 +45,51 @@ def logger(self): the log statements, making it easier to debug functions with multiple inheritance """ - return logging.getLogger( + if self._logger is None: + self._logger = self._get_logger() + return self._logger + + def _get_logger(self): + """ + Define an instance specific logger at run time which will imprint + inheritance information onto log statements, making it easier to debug + functions that might have multiple points of inheritance. + + All loggers will write to the same main log file and also print to + stdout. PAR.VERBOSE and PAR.LOG_LEVEL both control the amount of + information that gets printed to the log file. + """ + logger = logging.getLogger( self.__class__.__name__).getChild(self.__class__.__qualname__) + # Two levels of verbosity on log level, triggered with PAR.VERBOSE + if self.par.VERBOSE: + # More verbose logging statement with levelname and func name + fmt_str = ( + "%(asctime)s | %(levelname)-5s | %(name)s.%(funcName)s()\n" + "> %(message)s" + ) + else: + # Clean logging statement with only time and message + fmt_str = "%(asctime)s | %(message)s" + + # Instantiate logger during _register() as we now have user-defined pars + logger.setLevel(self.par.LOG_LEVEL) + formatter = logging.Formatter(fmt_str, datefmt="%Y-%m-%d %H:%M:%S") + + # Stream handler to print log statements to stdout + st_handler = logging.StreamHandler(sys.stdout) + st_handler.setFormatter(formatter) + logger.addHandler(st_handler) + + # File handler to print log statements to text file `filename` + if self.path.LOGFILE is not None: + file_handler = logging.FileHandler(self.path.LOGFILE, "a") + file_handler.setFormatter(formatter) + logger.addHandler(file_handler) + + return logger + @property def par(self): """ diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 07f99103..df2ed5aa 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -27,8 +27,7 @@ from IPython import embed from seisflows.core import Dict, SeisFlowsPathsParameters -from seisflows.config import (config_logger, custom_import, save, - NAMES, ROOT_DIR, CFGPATHS) +from seisflows.config import custom_import, save, NAMES, ROOT_DIR, CFGPATHS from seisflows.tools import unix, msg from seisflows.tools.specfem import (getpar, setpar, getpar_vel_model, setpar_vel_model) @@ -666,11 +665,6 @@ def submit(self, **kwargs): self._register_modules() self._check_parameters() - # Set logger to print to stdout and write to a file - config_logger(level=self._parameters.LOG_LEVEL, - verbose=self._parameters.VERBOSE, - filename=self._paths.LOGFILE) - # Submit workflow.main() to the system system = sys.modules["seisflows_system"] system.submit() @@ -723,12 +717,6 @@ def resume(self, **kwargs): """ self._register_parameters() self._load_modules() - - # Set logger to print to stdout and write to a file - config_logger(level=self._parameters.LOG_LEVEL, - verbose=self._parameters.VERBOSE, - filename=self._paths.LOGFILE) - system = sys.modules["seisflows_system"] system.submit() diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 08115fd6..4b92f389 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -803,7 +803,7 @@ def _import_model(self, path): :param path: path to model """ model = self.load(path=os.path.join(path, "model")) - self.save(model, self._model_databases) + self.save(model, self.model_databases) def _import_traces(self, path): """ @@ -833,7 +833,7 @@ def _export_model(self, path, parameters=None): if self.taskid == 0: unix.mkdir(path) for key in parameters: - files = glob(os.path.join(self._model_databases, f"*{key}.bin")) + files = glob(os.path.join(self.model_databases, f"*{key}.bin")) unix.cp(files, path) def _export_kernels(self, path): @@ -846,7 +846,7 @@ def _export_kernels(self, path): if self.taskid == 0: self.logger.debug(f"exporting kernels to:\n{path}") - unix.cd(self._kernel_databases) + unix.cd(self.kernel_databases) # Work around conflicting name conventions self._rename_kernels() diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index b23d6640..900b4ad6 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -33,40 +33,40 @@ def __init__(self): self.f0 = None - # @property - # def _io(self): - # """Inherits from seisflows.solver.specfem.Specfem""" - # return self._io + @property + def _io(self): + """Inherits from seisflows.solver.specfem.Specfem""" + return super()._io @property def taskid(self): """Inherits from seisflows.solver.specfem.Specfem""" - return self.taskid + return super().taskid @property def source_names(self): """Inherits from seisflows.solver.specfem.Specfem""" - return self.source_names + return super().source_names @property def source_name(self): """Inherits from seisflows.solver.specfem.Specfem""" - return self.source_name + return super().source_name @property def source_prefix(self): """Inherits from seisflows.solver.specfem.Specfem""" - return self.source_prefix + return super().source_prefix @property def cwd(self): """Inherits from seisflows.solver.specfem.Specfem""" - return self.cwd + return super().cwd @property def mesh_properties(self): """Inherits from seisflows.solver.specfem.Specfem""" - return self.mesh_properties + return super().mesh_properties def data_wildcard(self, comp="?"): """ @@ -164,19 +164,19 @@ def setup(self): def _set_model(self, model_name, model_type="gll"): """Inherits from seisflows.solver.specfem.Specfem""" - self._set_model(model_name=model_name, model_type=model_type) + super()._set_model(model_name=model_name, model_type=model_type) def generate_data(self): """Inherits from seisflows.solver.specfem.Specfem""" - self.generate_data() + super().generate_data() def eval_func(self, path, write_residuals=True): """Inherits from seisflows.solver.specfem.Specfem""" - self.eval_func(path=path, write_residuals=write_residuals) + super().eval_func(path=path, write_residuals=write_residuals) def eval_grad(self, path, export_traces=True): """Inherits from seisflows.solver.specfem.Specfem""" - self.eval_grad(path=path, export_traces=export_traces) + super().eval_grad(path=path, export_traces=export_traces) def _forward(self, output_path): """ @@ -228,29 +228,29 @@ def _adjoint(self): def _call_solver(self, executable, output="solver.log"): """Inherits from seisflows.solver.specfem.Specfem""" - self._call_solver(executable=executable, output=output) + super()._call_solver(executable=executable, output=output) def load(self, path, prefix="", suffix="", parameters=None): """Inherits from seisflows.solver.specfem.Specfem""" - return self.load(path=path, prefix=prefix, suffix=suffix, + super().load(path=path, prefix=prefix, suffix=suffix, parameters=parameters) def save(self, save_dict, path, parameters=None, prefix="", suffix=""): """Inherits from seisflows.solver.specfem.Specfem""" - self.save(save_dict=save_dict, path=path, parameters=parameters, + super().save(save_dict=save_dict, path=path, parameters=parameters, prefix=prefix, suffix=suffix) def merge(self, model, parameters=None): """Inherits from seisflows.solver.specfem.Specfem""" - return self.merge(model=model, parameters=parameters) + super().merge(model=model, parameters=parameters) def split(self, m, parameters=None): """Inherits from seisflows.solver.specfem.Specfem""" - return self.split(m=m, parameters=parameters) + super().split(m=m, parameters=parameters) def combine(self, input_path, output_path, parameters=None): """Inherits from seisflows.solver.specfem.Specfem""" - return self.combine(input_path=input_path, output_path=output_path, + super().combine(input_path=input_path, output_path=output_path, parameters=parameters) def smooth(self, input_path, output_path, parameters=None, span_h=0., @@ -313,31 +313,31 @@ def _import_model(self, path): def _import_traces(self, path): """Inherits from seisflows.solver.specfem.Specfem""" - return self._import_traces(path=path) + super()._import_traces(path=path) def _export_model(self, path, parameters=None): """Inherits from seisflows.solver.specfem.Specfem""" - return self._export_model(path=path, parameters=parameters) + super()._export_model(path=path, parameters=parameters) def _export_kernels(self, path): """Inherits from seisflows.solver.specfem.Specfem""" - return self._export_kernels(path=path) + super()._export_kernels(path=path) def _export_residuals(self, path): """Inherits from seisflows.solver.specfem.Specfem""" - return self._export_residuals(path=path) + super()._export_residuals(path=path) def _export_traces(self, path, prefix="traces/obs"): """Inherits from seisflows.solver.specfem.Specfem""" - self._export_traces(path=path, prefix=prefix) + super()._export_traces(path=path, prefix=prefix) def _rename_kernels(self): """Inherits from seisflows.solver.specfem.Specfem""" - self._rename_kernels() + super()._rename_kernels() def _initialize_solver_directories(self): """Inherits from seisflows.solver.specfem.Specfem""" - self._initialize_solver_directories() + super()._initialize_solver_directories() def _initialize_adjoint_traces(self): """ @@ -386,13 +386,13 @@ def _initialize_adjoint_traces(self): if not os.path.exists(fid_check): unix.cp(fid, fid_check) - def _check_mesh_properties(self, path=None): + def _check_mesh_properties(self, model_path=None): """Inherits from seisflows.solver.specfem.Specfem""" - self._check_mesh_properties(path=path) + super()._check_mesh_properties(model_path=model_path) def _check_source_names(self): """Inherits from seisflows.solver.specfem.Specfem""" - self._check_source_names() + super()._check_source_names() diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index fb4a9c24..1e88a3e6 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -111,7 +111,7 @@ def check(self, validate=True): Checks parameters and paths """ super().check(validate=validate) - import pdb;pdb.set_trace() + assert(1 <= self.par.BEGIN <= self.par.END), \ f"Incorrect BEGIN or END parameter. Values must be in order: " \ f"1 <= {self.par.BEGIN} <= {self.par.END}" From 6a1ac53dea6aa1f8ce2fc8f5bfaadfebff7ce3ae Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Wed, 29 Jun 2022 18:31:49 -0800 Subject: [PATCH 037/195] removed the explicit calls to parent functions that did not change anything. Although this slightly obscures where methods are being called from, it reduces code length and also makes it easier to edit parent functions. further bug fixing for specfem2d pyatoa example. working up to line search --- seisflows/core.py | 5 +- seisflows/optimize/LBFGS.py | 20 ----- seisflows/optimize/NLCG.py | 28 ------- seisflows/postprocess/default.py | 8 +- seisflows/solver/specfem.py | 13 +-- seisflows/solver/specfem2d.py | 138 ++----------------------------- seisflows/solver/specfem3d.py | 133 +---------------------------- seisflows/system/cluster.py | 4 - seisflows/system/frontera.py | 9 -- seisflows/system/maui.py | 16 ---- seisflows/system/slurm.py | 4 - seisflows/system/workstation.py | 13 ++- seisflows/workflow/forward.py | 37 +++++---- seisflows/workflow/inversion.py | 6 +- seisflows/workflow/migration.py | 4 +- 15 files changed, 51 insertions(+), 387 deletions(-) diff --git a/seisflows/core.py b/seisflows/core.py index b94b67f0..71f30e77 100644 --- a/seisflows/core.py +++ b/seisflows/core.py @@ -59,8 +59,9 @@ def _get_logger(self): stdout. PAR.VERBOSE and PAR.LOG_LEVEL both control the amount of information that gets printed to the log file. """ - logger = logging.getLogger( - self.__class__.__name__).getChild(self.__class__.__qualname__) + # logger = logging.getLogger( + # self.__class__.__name__).getChild(self.__class__.__qualname__) + logger = logging.getLogger(self.__class__.__name__) # Two levels of verbosity on log level, triggered with PAR.VERBOSE if self.par.VERBOSE: diff --git a/seisflows/optimize/LBFGS.py b/seisflows/optimize/LBFGS.py index 67bc3a71..5e9ed721 100644 --- a/seisflows/optimize/LBFGS.py +++ b/seisflows/optimize/LBFGS.py @@ -117,18 +117,6 @@ def setup(self): unix.cd(self.path.OPTIMIZE) unix.mkdir(self.LBFGS_dir) - def finalize(self): - """Inherit from optimize.gradient.Gradient""" - self.finalize() - - def load(self, name): - """Inherit from optimize.gradient.Gradient""" - return self.load(name=name) - - def save(self, name, vector): - """Inherit from optimize.gradient.Gradient""" - self.save(name=name, vector=vector) - def compute_direction(self): """ Call on the L-BFGS optimization machinery to compute a search @@ -211,14 +199,6 @@ def restart(self): s[:] = 0. y[:] = 0. - def write_stats(self): - """Inherit from optimize.gradient.Gradient""" - self.write_stats() - - def check_model(self, m, min_pr=-1, max_pr=0.5): - """Inherit from optimize.gradient.Gradient""" - self.check_model(m=m, min_pr=min_pr, max_pr=max_pr) - def _update(self): """ Updates L-BFGS algorithm history diff --git a/seisflows/optimize/NLCG.py b/seisflows/optimize/NLCG.py index 05957b1f..891c12bf 100644 --- a/seisflows/optimize/NLCG.py +++ b/seisflows/optimize/NLCG.py @@ -64,26 +64,6 @@ def check(self, validate=True): assert(self.par.LINESEARCH.upper() == "BRACKET"), \ f"NLCG requires a bracketing line search algorithm" - def setup(self): - """Inherit from optimize.gradient.Gradient""" - self.setup() - - def finalize(self): - """Inherit from optimize.gradient.Gradient""" - self.finalize() - - def load(self, name): - """Inherit from optimize.gradient.Gradient""" - return self.load(name=name) - - def save(self, name, vector): - """Inherit from optimize.gradient.Gradient""" - self.save(name=name, vector=vector) - - def _precondition(self, q): - """Inherit from optimize.gradient.Gradient""" - return self._precondition(q=q) - def compute_direction(self): """ Compute search direction using the Nonlinear Conjugate Gradient method @@ -164,14 +144,6 @@ def restart(self): super().restart() self.NLCG_iter = 1 - def write_stats(self): - """Inherit from optimize.gradient.Gradient""" - self.write_stats() - - def check_model(self, m, min_pr=-1, max_pr=0.5): - """Inherit from optimize.gradient.Gradient""" - self.check_model(m=m, min_pr=min_pr, max_pr=max_pr) - def _fletcher_reeves(self, g_new, g_old): """ One method for calculating beta in the NLCG Algorithm from diff --git a/seisflows/postprocess/default.py b/seisflows/postprocess/default.py index cc20cf14..4cfc2e6a 100644 --- a/seisflows/postprocess/default.py +++ b/seisflows/postprocess/default.py @@ -115,7 +115,7 @@ def scale_gradient(self, input_path): return gradient - def sum_smooth_kernels(self, kernel_path): + def sum_smooth_kernels(self, path_grad): """ Sums kernels from individual sources, with optional smoothing @@ -123,8 +123,8 @@ def sum_smooth_kernels(self, kernel_path): This function needs to be run on system, i.e., called by system.run(single=True) - :type kernel_path: str - :param kernel_path: directory containing sensitivity kernels in the + :type path_grad: str + :param path_grad: directory containing sensitivity kernels in the scratch directory to be summed and smoothed. Output summed and summed + smoothed kernels will be saved here as well. """ @@ -132,6 +132,8 @@ def sum_smooth_kernels(self, kernel_path): # If specified, smooth the kernels in the vertical and horizontal and # save both (summed, summed+smoothed) to separate output directories + kernel_path = os.path.join(path_grad, "kernels") + path_sum_nosmooth = os.path.join(kernel_path, "sum_nosmooth") path_sum = os.path.join(kernel_path, "sum") diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 4b92f389..f0448ad0 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -607,7 +607,7 @@ def load(self, path, prefix="", suffix="", parameters=None): load_dict = Container() for iproc in range(self.mesh_properties.nproc): for key in parameters: - load_dict[key] += self.io.read_slice( + load_dict[key] += self._io.read_slice( path=path, parameters=f"{prefix}{key}{suffix}", iproc=iproc ) @@ -637,17 +637,18 @@ def save(self, save_dict, path, parameters=None, prefix="", suffix=""): missing_keys = diff(parameters, save_dict.keys()) for iproc in range(self.mesh_properties.nproc): for key in missing_keys: - save_dict[key] += self.io.read_slice( - path=self.path.MODEL_INIT, parameters=f"{prefix}{key}{suffix}", + save_dict[key] += self._io.read_slice( + path=self.path.MODEL_INIT, + parameters=f"{prefix}{key}{suffix}", iproc=iproc ) # Write slices to disk for iproc in range(self.mesh_properties.nproc): for key in parameters: - self.io.write_slice(data=save_dict[key][iproc], path=path, - parameters=f"{prefix}{key}{suffix}", - iproc=iproc) + self._io.write_slice(data=save_dict[key][iproc], path=path, + parameters=f"{prefix}{key}{suffix}", + iproc=iproc) def merge(self, model, parameters=None): """ diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index 900b4ad6..7cb7b29d 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -33,41 +33,6 @@ def __init__(self): self.f0 = None - @property - def _io(self): - """Inherits from seisflows.solver.specfem.Specfem""" - return super()._io - - @property - def taskid(self): - """Inherits from seisflows.solver.specfem.Specfem""" - return super().taskid - - @property - def source_names(self): - """Inherits from seisflows.solver.specfem.Specfem""" - return super().source_names - - @property - def source_name(self): - """Inherits from seisflows.solver.specfem.Specfem""" - return super().source_name - - @property - def source_prefix(self): - """Inherits from seisflows.solver.specfem.Specfem""" - return super().source_prefix - - @property - def cwd(self): - """Inherits from seisflows.solver.specfem.Specfem""" - return super().cwd - - @property - def mesh_properties(self): - """Inherits from seisflows.solver.specfem.Specfem""" - return super().mesh_properties - def data_wildcard(self, comp="?"): """ Returns a wildcard identifier for synthetic data based on SPECFEM2D @@ -106,22 +71,18 @@ def data_filenames(self): filenames = [] if self.par.FORMAT.upper() == "SU": for comp in self.par.COMPONENTS: - filenames += [self.data_wildcard.format(comp=comp.lower())] - # filenames += [f"U{comp.lower()}_file_single.su"] + filenames += [self.data_wildcard(comp=comp.lower())] elif self.par.FORMAT.upper() == "ASCII": for comp in self.par.COMPONENTS: - filenames += glob( - self.data_wildcard.format(comp=comp.upper()) - ) - # filenames += glob(f"*.?X{comp.upper()}.sem?") + filenames += glob(self.data_wildcard(comp=comp.upper())) else: - filenames = glob(self.data_wildcard) + filenames = glob(self.data_wildcard()) if not filenames: print(msg.cli("The property solver.data_filenames, used to search " "for traces in 'scratch/solver/*/traces' is empty " "and should not be. Please check solver parameters: ", - items=[f"data_wildcard: {self.data_wildcard}"], + items=[f"data_wildcard: {self.data_wildcard()}"], header="data filenames error", border="=") ) sys.exit(-1) @@ -142,17 +103,12 @@ def kernel_databases(self): """ return os.path.join(self.cwd, "OUTPUT_FILES") - def check(self, validate=True): - """ - Checks parameters and paths - """ - super().check(validate=validate) - def setup(self): """ Additional SPECFEM2D setup steps """ super().setup() + self.f0 = getpar(key="f0", file=os.path.join(self.cwd, "DATA/SOURCE"))[1] @@ -162,22 +118,6 @@ def setup(self): else: setpar(key="absorbtop", val=".true.", file="DATA/Par_file") - def _set_model(self, model_name, model_type="gll"): - """Inherits from seisflows.solver.specfem.Specfem""" - super()._set_model(model_name=model_name, model_type=model_type) - - def generate_data(self): - """Inherits from seisflows.solver.specfem.Specfem""" - super().generate_data() - - def eval_func(self, path, write_residuals=True): - """Inherits from seisflows.solver.specfem.Specfem""" - super().eval_func(path=path, write_residuals=write_residuals) - - def eval_grad(self, path, export_traces=True): - """Inherits from seisflows.solver.specfem.Specfem""" - super().eval_grad(path=path, export_traces=export_traces) - def _forward(self, output_path): """ Calls SPECFEM2D forward solver, exports solver outputs to traces dir @@ -202,7 +142,7 @@ def _forward(self, output_path): unix.rename(old=f"single_{tag}.su", new="single.su", names=glob(os.path.join("OUTPUT_FILES", "*.su"))) - unix.mv(src=glob(os.path.join("OUTPUT_FILES", self.data_wildcard)), + unix.mv(src=glob(os.path.join("OUTPUT_FILES", self.data_wildcard())), dst=output_path) def _adjoint(self): @@ -226,33 +166,6 @@ def _adjoint(self): self._call_solver(executable="bin/xspecfem2D", output="adj_solver.log") - def _call_solver(self, executable, output="solver.log"): - """Inherits from seisflows.solver.specfem.Specfem""" - super()._call_solver(executable=executable, output=output) - - def load(self, path, prefix="", suffix="", parameters=None): - """Inherits from seisflows.solver.specfem.Specfem""" - super().load(path=path, prefix=prefix, suffix=suffix, - parameters=parameters) - - def save(self, save_dict, path, parameters=None, prefix="", suffix=""): - """Inherits from seisflows.solver.specfem.Specfem""" - super().save(save_dict=save_dict, path=path, parameters=parameters, - prefix=prefix, suffix=suffix) - - def merge(self, model, parameters=None): - """Inherits from seisflows.solver.specfem.Specfem""" - super().merge(model=model, parameters=parameters) - - def split(self, m, parameters=None): - """Inherits from seisflows.solver.specfem.Specfem""" - super().split(m=m, parameters=parameters) - - def combine(self, input_path, output_path, parameters=None): - """Inherits from seisflows.solver.specfem.Specfem""" - super().combine(input_path=input_path, output_path=output_path, - parameters=parameters) - def smooth(self, input_path, output_path, parameters=None, span_h=0., span_v=0., output="smooth.log"): """ @@ -311,34 +224,6 @@ def _import_model(self, path): dst=os.path.join(self.cwd, "DATA") ) - def _import_traces(self, path): - """Inherits from seisflows.solver.specfem.Specfem""" - super()._import_traces(path=path) - - def _export_model(self, path, parameters=None): - """Inherits from seisflows.solver.specfem.Specfem""" - super()._export_model(path=path, parameters=parameters) - - def _export_kernels(self, path): - """Inherits from seisflows.solver.specfem.Specfem""" - super()._export_kernels(path=path) - - def _export_residuals(self, path): - """Inherits from seisflows.solver.specfem.Specfem""" - super()._export_residuals(path=path) - - def _export_traces(self, path, prefix="traces/obs"): - """Inherits from seisflows.solver.specfem.Specfem""" - super()._export_traces(path=path, prefix=prefix) - - def _rename_kernels(self): - """Inherits from seisflows.solver.specfem.Specfem""" - super()._rename_kernels() - - def _initialize_solver_directories(self): - """Inherits from seisflows.solver.specfem.Specfem""" - super()._initialize_solver_directories() - def _initialize_adjoint_traces(self): """ Setup utility: Creates the "adjoint traces" expected by SPECFEM. @@ -385,14 +270,3 @@ def _initialize_adjoint_traces(self): fid_check = ".".join([net, sta, cha_check, ext]) if not os.path.exists(fid_check): unix.cp(fid, fid_check) - - def _check_mesh_properties(self, model_path=None): - """Inherits from seisflows.solver.specfem.Specfem""" - super()._check_mesh_properties(model_path=model_path) - - def _check_source_names(self): - """Inherits from seisflows.solver.specfem.Specfem""" - super()._check_source_names() - - - diff --git a/seisflows/solver/specfem3d.py b/seisflows/solver/specfem3d.py index d286a301..0be10ffc 100644 --- a/seisflows/solver/specfem3d.py +++ b/seisflows/solver/specfem3d.py @@ -30,41 +30,6 @@ def __init__(self): docstr="Prefix of SOURCE files in path SPECFEM_DATA. Available " "['CMTSOLUTION', FORCESOLUTION']") - @property - def _io(self): - """Inherits from seisflows.solver.specfem.Specfem""" - return self._io - - @property - def taskid(self): - """Inherits from seisflows.solver.specfem.Specfem""" - return self.taskid - - @property - def source_names(self): - """Inherits from seisflows.solver.specfem.Specfem""" - return self.source_names - - @property - def source_name(self): - """Inherits from seisflows.solver.specfem.Specfem""" - return self.source_name - - @property - def source_prefix(self): - """Inherits from seisflows.solver.specfem.Specfem""" - return self.source_prefix - - @property - def cwd(self): - """Inherits from seisflows.solver.specfem.Specfem""" - return self.cwd - - @property - def mesh_properties(self): - """Inherits from seisflows.solver.specfem.Specfem""" - return self.mesh_properties - def data_wildcard(self, comp="?"): """ Returns a wildcard identifier for synthetic data @@ -112,24 +77,6 @@ def kernel_databases(self): """ return self.model_databases - def check(self, validate=True): - """ - Checks parameters and paths - """ - super().check(validate=validate) - - def setup(self): - """Inherits from seisflows.solver.specfem.Specfem""" - self.setup() - - def _set_model(self, model_name, model_type="gll"): - """Inherits from seisflows.solver.specfem.Specfem""" - self._set_model(model_name=model_name, model_type=model_type) - - def generate_data(self): - """Inherits from seisflows.solver.specfem.Specfem""" - self.generate_data() - def eval_func(self, path, write_residuals=True): """ Performs forward simulations and evaluates the misfit function using @@ -148,10 +95,6 @@ def eval_func(self, path, write_residuals=True): # Work around SPECFEM3D conflicting name conventions of SU data self._rename_data() - def eval_grad(self, path, export_traces=False): - """Inherits from seisflows.solver.specfem.Specfem""" - self.eval_grad(path=path, export_traces=export_traces) - def _forward(self, output_path): """ Calls SPECFEM3D forward solver, exports solver outputs to traces dir @@ -175,7 +118,7 @@ def _forward(self, output_path): self._call_solver(executable="bin/xmeshfem3D", output="fwd_solver.log") # Find and move output traces, by default to synthetic traces dir - unix.mv(src=glob(os.path.join("OUTPUT_FILES", self.data_wildcard)), + unix.mv(src=glob(os.path.join("OUTPUT_FILES", self.data_wildcard())), dst=output_path) def _adjoint(self): @@ -195,72 +138,6 @@ def _adjoint(self): self._call_solver(executable="bin/xspecfem3D", output="adj_solver.log") - def _call_solver(self, executable, output="solver.log"): - """Inherits from seisflows.solver.specfem.Specfem""" - self._call_solver(executable=executable, output=output) - - def load(self, path, prefix="", suffix="", parameters=None): - """Inherits from seisflows.solver.specfem.Specfem""" - return self.load(path=path, prefix=prefix, suffix=suffix, - parameters=parameters) - - def save(self, save_dict, path, parameters=None, prefix="", suffix=""): - """Inherits from seisflows.solver.specfem.Specfem""" - self.save(save_dict=save_dict, path=path, parameters=parameters, - prefix=prefix, suffix=suffix) - - def merge(self, model, parameters=None): - """Inherits from seisflows.solver.specfem.Specfem""" - return self.merge(model=model, parameters=parameters) - - def split(self, m, parameters=None): - """Inherits from seisflows.solver.specfem.Specfem""" - return self.split(m=m, parameters=parameters) - - def combine(self, input_path, output_path, parameters=None): - """Inherits from seisflows.solver.specfem.Specfem""" - return self.combine(input_path=input_path, output_path=output_path, - parameters=parameters) - - def smooth(self, input_path, output_path, parameters=None, span_h=0., - span_v=0., output="smooth.log"): - """Inherits from seisflows.solver.specfem.Specfem""" - return self.smooth(input_path=input_path, output_path=output_path, - parameters=parameters, span_h=span_h, - span_v=span_v, output=output) - - def _import_model(self, path): - """Inherits from seisflows.solver.specfem.Specfem""" - return self._import_model(path=path) - - def _import_traces(self, path): - """Inherits from seisflows.solver.specfem.Specfem""" - return self._import_traces(path=path) - - def _export_model(self, path, parameters=None): - """Inherits from seisflows.solver.specfem.Specfem""" - return self._export_model(path=path, parameters=parameters) - - def _export_kernels(self, path): - """Inherits from seisflows.solver.specfem.Specfem""" - return self._export_kernels(path=path) - - def _export_residuals(self, path): - """Inherits from seisflows.solver.specfem.Specfem""" - return self._export_residuals(path=path) - - def _export_traces(self, path, prefix="traces/obs"): - """Inherits from seisflows.solver.specfem.Specfem""" - self._export_traces(path=path, prefix=prefix) - - def _rename_kernels(self): - """Inherits from seisflows.solver.specfem.Specfem""" - self._rename_kernels() - - def _initialize_solver_directories(self): - """Inherits from seisflows.solver.specfem.Specfem""" - self._initialize_solver_directories() - def _initialize_adjoint_traces(self): """ Setup utility: Creates the "adjoint traces" expected by SPECFEM @@ -289,14 +166,6 @@ def _initialize_adjoint_traces(self): src = f"{iproc:d}_d{self.par.COMPONENTS[0]}_SU.adj" unix.cp(src, dst) - def _check_mesh_properties(self, path=None): - """Inherits from seisflows.solver.specfem.Specfem""" - self._check_mesh_properties(path=path) - - def _check_source_names(self): - """Inherits from seisflows.solver.specfem.Specfem""" - self._check_source_names() - def _rename_data(self): """ Works around conflicting data filename conventions diff --git a/seisflows/system/cluster.py b/seisflows/system/cluster.py index 07f2fadb..ef9d87d0 100644 --- a/seisflows/system/cluster.py +++ b/seisflows/system/cluster.py @@ -51,10 +51,6 @@ def check(self, validate=True): """ super().check(validate=validate) - def setup(self): - """Inherits from workflow.system.workstation.Workstation""" - self.setup() - def submit(self, submit_call=None): """ Main insertion point of SeisFlows onto the compute system. diff --git a/seisflows/system/frontera.py b/seisflows/system/frontera.py index f0a43d04..40e5d903 100644 --- a/seisflows/system/frontera.py +++ b/seisflows/system/frontera.py @@ -132,12 +132,3 @@ def run(self, classname, method, single=False, run_call=None, **kwargs): ]) super().run(classname, method, single, run_call=run_call, **kwargs) - - def taskid(self): - """Inherits from seisflows.system.slurm.Slurm""" - return self.taskid() - - def checkpoint(self, path, classname, method, kwargs): - """Inherits from workflow.system.workstation.Workstation""" - self.checkpoint(path=path, classname=classname, method=method, - kwargs=kwargs) diff --git a/seisflows/system/maui.py b/seisflows/system/maui.py index 6dad56ef..1f7ed3df 100644 --- a/seisflows/system/maui.py +++ b/seisflows/system/maui.py @@ -102,10 +102,6 @@ def check(self, validate=True): "running SeisFlows3 on Maui, we must remove one env. variable. " "Please add 'SLURM_MEM_PER_CPU' to self.par.ENVIRONS.") - def setup(self): - """Inherits from workflow.system.workstation.Workstation""" - self.setup() - def submit(self, submit_call=None): """ Submits master job workflow to maui_ancil cluster as a single-core @@ -219,15 +215,3 @@ def run_ancil(self, classname, method, **kwargs): self.logger.debug(ancil_run_call) super().run(classname, method, single=False, run_call=ancil_run_call, **kwargs) - - def taskid(self): - """Inherits from seisflows.system.slurm.Slurm""" - return self.taskid() - - def checkpoint(self, path, classname, method, kwargs): - """Inherits from workflow.system.workstation.Workstation""" - self.checkpoint(path=path, classname=classname, method=method, - kwargs=kwargs) - - - diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index 8e9a42af..2b3b7946 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -64,10 +64,6 @@ def check(self, validate=True): """ super().check(validate=validate) - def setup(self): - """Inherits from workflow.system.workstation.Workstation""" - self.setup() - def submit(self, submit_call=None): """ Submits workflow as a single process master job on a SLURM system diff --git a/seisflows/system/workstation.py b/seisflows/system/workstation.py index 293570e5..4028e3fe 100644 --- a/seisflows/system/workstation.py +++ b/seisflows/system/workstation.py @@ -4,7 +4,6 @@ Provides utilities for submitting jobs in serial on a single machine """ import os -import sys import pickle from contextlib import redirect_stdout @@ -133,10 +132,6 @@ def setup(self): self.logger.debug(f"copying par/log file to: {dst}") unix.cp(src=src, dst=dst) - def finalize(self): - """Inherits from seisflows.core.Base""" - super().finalize() - def submit(self, submit_call=None): """ Submits the main workflow job as a serial job submitted directly to @@ -200,10 +195,12 @@ def run(self, classname, method, single=False, **kwargs): if taskid == 0: self.logger.info(f"running task {classname}.{method} " f"{self.par.NTASK} times") + function(**kwargs) + # Redirect output to a log file to mimic cluster runs - with open(log_file, "w") as f: - with redirect_stdout(f): - function(**kwargs) + # with open(log_file, "w") as f: + # with redirect_stdout(f): + # function(**kwargs) def taskid(self): """ diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index c383e277..800c4ea4 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -157,33 +157,33 @@ def _evaluate_function(self, path, suffix): model_tag = f"m_{suffix}" misfit_tag = f"f_{suffix}" - self._write_model(path=path, tag=model_tag) + self._write_model(path=path, model_tag=model_tag) - self.logger.debug(f"evaluating objective function {self.par.NTASK} times " - f"on system...") + self.logger.debug(f"evaluating objective function {self.par.NTASK} " + f"times on system...") system.run("solver", "eval_func", path=path) - self._write_misfit(path=path, tag=misfit_tag) + self._write_misfit(path=path, misfit_tag=misfit_tag) - def _write_model(self, path, tag): + def _write_model(self, path, model_tag): """ Writes model in format expected by solver :type path: str :param path: path to write the model to - :type tag: str - :param tag: name of the model to be saved, usually tagged as 'm' with + :type model_tag: str + :param model_tag: name of the model to be saved, usually tagged as 'm' with a suffix depending on where in the inversion we are. e.g., 'm_try'. Expected that these tags are defined in OPTIMIZE module """ solver = self.module("solver") + optimize = self.module("optimize") - src = tag dst = os.path.join(path, "model") - self.logger.debug(f"saving model '{src}' to:\n{dst}") - solver.save(solver.split(np.load(src)), dst) + self.logger.debug(f"saving model '{model_tag}' to:\n{dst}") + solver.save(solver.split(optimize.load(model_tag)), dst) - def _write_misfit(self, path, tag): + def _write_misfit(self, path, misfit_tag): """ Writes misfit in format expected by nonlinear optimization library. Collects all misfit values within the given residuals directory and sums @@ -191,20 +191,21 @@ def _write_misfit(self, path, tag): :type path: str :param path: path to write the misfit to - :type tag: str - :param tag: name of the model to be saved, usually tagged as 'f' with - a suffix depending on where in the inversion we are. e.g., 'f_try'. - Expected that these tags are defined in OPTIMIZE module + :type misfit_tag: str + :param misfit_tag: name of the model to be saved, usually tagged as + 'f' with a suffix depending on where in the inversion we are. + e.g., 'f_try'. Expected that these tags are defined in OPTIMIZE + module """ preprocess = self.module("preprocess") + optimize = self.module("optimize") self.logger.info("summing residuals with preprocess module") src = glob(os.path.join(path, "residuals", "*")) - dst = tag total_misfit = preprocess.sum_residuals(src) - self.logger.debug(f"saving misfit {total_misfit:.3E} to tag '{dst}'") - np.save(dst, total_misfit) + self.logger.debug(f"saving misfit {total_misfit:.3E} to '{misfit_tag}'") + optimize.save(misfit_tag, total_misfit) def _check_stop_resume_cond(self, flow): """ diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index 1e88a3e6..898e5a16 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -191,7 +191,7 @@ def main(self, flow=None, return_flow=False): def evaluate_initial_misfit(self): """Inherits from seisflows.workflow.forward.Forward""" - self.evaluate_initial_misfit() + super().evaluate_initial_misfit() def compute_direction(self): """ @@ -288,8 +288,8 @@ def export(self): self.logger.debug(f"saving kernels to path:\n{dst}") unix.mv(src, dst) - if self.par.SAVETRACES: - self.save_traces() + # if self.par.SAVETRACES: + # do some stuff if self.par.SAVERESIDUALS: src = os.path.join(self.path.GRAD, "residuals") diff --git a/seisflows/workflow/migration.py b/seisflows/workflow/migration.py index 06d0d12e..b24f803c 100644 --- a/seisflows/workflow/migration.py +++ b/seisflows/workflow/migration.py @@ -103,7 +103,7 @@ def main(self, flow=None, return_flow=False): def evaluate_initial_misfit(self): """Inherits from seisflows.workflow.forward.Forward""" - self.evaluate_initial_misfit() + super().evaluate_initial_misfit() def evaluate_gradient(self, path=None): """ @@ -131,7 +131,7 @@ def process_kernels(self): # Runs kernel processing as a single parallel process system.run("postprocess", "sum_smooth_kernels", single=True, - input_path=self.path.GRAD) + path_grad=self.path.GRAD) def write_gradient(self): """ From 6f0576b87ddfc2562b2f41ef939253d84f87dfe3 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Thu, 30 Jun 2022 13:29:04 -0800 Subject: [PATCH 038/195] specfem2d example now working --- seisflows/config.py | 7 +------ seisflows/core.py | 11 +++++++---- seisflows/seisflows.py | 11 +++++++++-- 3 files changed, 17 insertions(+), 12 deletions(-) diff --git a/seisflows/config.py b/seisflows/config.py index 2adc66e8..6811a1a8 100644 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -18,11 +18,9 @@ import types import pickle import copyreg -import logging import traceback from importlib import import_module -from seisflows import logger from seisflows.core import Dict, Null from seisflows.tools import msg, unix from seisflows.tools.wrappers import module_exists @@ -36,7 +34,6 @@ important, and any changes to these will more-than-likely break the underlying mechanics of the package. Do not touch unless you know what you're doing! """ - # List of module names required by SeisFlows for imports. Order-sensitive # In sys.modules these will be prepended by 'seisflows_', e.g., seisflows_system NAMES = ["system", "preprocess", "solver", @@ -50,11 +47,9 @@ # directories will be created relative to the user-defined working directory CFGPATHS = Dict( PAR_FILE="parameters.yaml", # Default SeisFlows parameter file - SCRATCHDIR="scratch", # SeisFlows internal working directory - OUTPUTDIR="output", # Permanent disk storage for state and outputs LOGFILE="sfoutput.txt", # Log files for all system log ERRLOGFILE="sferror.txt", # StdErr dump site for crash messages - LOGDIR="logs", # Dump site for previously created log files + LOGDIR="logs", # Dump site for previously created log files ) """ !!! ^^^ WARNING ^^^ !!! diff --git a/seisflows/core.py b/seisflows/core.py index 71f30e77..93ca61b3 100644 --- a/seisflows/core.py +++ b/seisflows/core.py @@ -1,7 +1,8 @@ #!/usr/bin/env python3 """ Core class definitions for SeisFlows. Defines some unique class objects that -are used heavily during a Seisflows workflow. +define how SeisFlows works internally, or are used heavily during a Seisflows +workflow. """ import sys import logging @@ -160,7 +161,7 @@ def __str__(self): def __repr__(self): """Pretty print when calling an instance of this object""" - return(self.__str__()) + return self.__str__() def __getattr__(self, key): """Attribute-like access of the internal dictionary attributes""" @@ -200,10 +201,12 @@ def __delattr__(self, key): class SeisFlowsPathsParameters: """ A class used to simplify defining required or optional paths and parameters - by enforcing a specific structure to their entry into the environment. + that will be globally accesible through sys.modules. This class enforces a + specific path/parameter structure, and entry point into the environment. .. note:: - if a path or parameter is optional it requires a default value. + if a path or parameter is optional it requires a default value, which is + set at the header of this class """ default_par = "REQUIRED PARAMETER" default_path = "REQUIRED PATH" diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index df2ed5aa..f9883efb 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -647,8 +647,8 @@ def init(self, **kwargs): for NAME in NAMES: sys.modules[f"seisflows_{NAME}"].required.validate() - print(msg.cli(f"instantiating SeisFlows working state in directory: " - f"{CFGPATHS.OUTPUTDIR}")) + print(msg.cli(f"instantiating SeisFlows working state in: " + f"{self._paths.OUTPUT}")) def submit(self, **kwargs): """ @@ -660,6 +660,13 @@ def submit(self, **kwargs): unix.mkdir(self._args.workdir) unix.cd(self._args.workdir) + # If parameter `RESUME_FROM` is set, unset it because submit and restart + # should start from a fresh workflow + try: + self.par(parameter="RESUME_FROM", value="", skip_print=True) + except SystemExit as e: + pass + # Read in the Parameter file and set parameters into sys.modules. self._register_parameters() self._register_modules() From 5076b529778d494ac4519d58ef1beab2208fc4f0 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Thu, 30 Jun 2022 13:47:59 -0800 Subject: [PATCH 039/195] updated test parameter files and fixed all tests with new code structure --- seisflows/tests/test_config.py | 10 +- .../test_data/scripts/make_parameter_files.sh | 5 +- .../tests/test_data/test_conf_parameters.yaml | 169 +++++++++--------- .../test_data/test_filled_parameters.yaml | 153 ++++++++-------- .../test_data/test_setup_parameters.yaml | 6 +- seisflows/tests/test_modules.py | 31 ++-- seisflows/tests/test_preprocess.py | 139 -------------- seisflows/tests/test_seisflows.py | 43 +---- seisflows/tests/test_system.py | 103 ----------- 9 files changed, 189 insertions(+), 470 deletions(-) delete mode 100644 seisflows/tests/test_preprocess.py delete mode 100644 seisflows/tests/test_system.py diff --git a/seisflows/tests/test_config.py b/seisflows/tests/test_config.py index 9fe53aae..51f7f2b0 100644 --- a/seisflows/tests/test_config.py +++ b/seisflows/tests/test_config.py @@ -8,6 +8,7 @@ import pytest from unittest.mock import patch from seisflows import config +from seisflows.core import SeisFlowsPathsParameters from seisflows.seisflows import SeisFlows @@ -37,8 +38,9 @@ def sfinit(tmpdir, copy_par_file): os.chdir(tmpdir) with patch.object(sys, "argv", ["seisflows"]): sf = SeisFlows() - sf._register_parameters(force=True) - sf._register_modules(check=True) + sf._register_parameters() + sf._register_modules() + sf._check_parameters() return sf @@ -100,7 +102,7 @@ def test_seisflows_paths_parameters(sfinit): Recreates the required() function at the top of each class. """ sfinit - sfpp = config.SeisFlowsPathsParameters() + sfpp = SeisFlowsPathsParameters() # All of these parameters are defined in the test parameter file sfpp.par("SOLVER", required=True, par_type=str, @@ -114,7 +116,7 @@ def test_seisflows_paths_parameters(sfinit): # These parameters are not defined and are expected to throw parameter error sfpp.path("UNDEFINED", required=True, docstr="This path is not in the test parameter file") - with pytest.raises(ValueError): + with pytest.raises(KeyError): sfpp.validate() diff --git a/seisflows/tests/test_data/scripts/make_parameter_files.sh b/seisflows/tests/test_data/scripts/make_parameter_files.sh index 28fdf931..d712e063 100644 --- a/seisflows/tests/test_data/scripts/make_parameter_files.sh +++ b/seisflows/tests/test_data/scripts/make_parameter_files.sh @@ -6,14 +6,11 @@ rm *yaml seisflows setup cp parameters.yaml test_setup_parameters.yaml -seisflows configure -r +seisflows configure cp parameters.yaml test_conf_parameters.yaml seisflows par -p materials elastic seisflows par -p density constant -seisflows par -p nt 1000 -seisflows par -p dt .01 -seisflows par -p f0 .084 seisflows par -p format ascii seisflows par -p begin 1 seisflows par -p end 1 diff --git a/seisflows/tests/test_data/test_conf_parameters.yaml b/seisflows/tests/test_data/test_conf_parameters.yaml index 0b08e2d0..36540142 100644 --- a/seisflows/tests/test_data/test_conf_parameters.yaml +++ b/seisflows/tests/test_data/test_conf_parameters.yaml @@ -27,9 +27,9 @@ WORKFLOW: inversion SOLVER: specfem2d SYSTEM: workstation -OPTIMIZE: LBFGS -PREPROCESS: base -POSTPROCESS: base +OPTIMIZE: gradient +PREPROCESS: default +POSTPROCESS: default # ============================================================================= # SYSTEM @@ -37,17 +37,6 @@ POSTPROCESS: base # TITLE (str): # The name used to submit jobs to the system, defaults to the name of the # working directory -# PRECHECK (list): -# A list of parameters that will be displayed to stdout before 'submit' or -# 'resume' is run. Useful for manually reviewing important parameters prior -# to system submission -# LOG_LEVEL (str): -# Verbosity output of SF logger. Available from least to most verbosity: -# 'CRITICAL', 'WARNING', 'INFO', 'DEBUG'; defaults to 'DEBUG' -# VERBOSE (bool): -# Level of verbosity provided to the output log. If True, log statements -# will declare what module/class/function they are being called from. -# Useful for debugging but also very noisy. # MPIEXEC (str): # Function used to invoke executables on the system. For example 'srun' on # SLURM systems, or './' on a workstation. If left blank, will guess based @@ -57,15 +46,20 @@ POSTPROCESS: base # sources in workflow # NPROC (int): # Number of processor to use for each simulation +# LOG_LEVEL (str): +# Verbosity output of SF logger. Available from least to most verbosity: +# 'CRITICAL', 'WARNING', 'INFO', 'DEBUG'; defaults to 'DEBUG' +# VERBOSE (bool): +# Level of verbosity provided to the output log. If True, log statements +# will declare what module/class/function they are being called from. +# Useful for debugging but also very noisy. # ============================================================================= TITLE: test_data -PRECHECK: - - TITLE -LOG_LEVEL: DEBUG -VERBOSE: False MPIEXEC: NTASK: 1 NPROC: 1 +LOG_LEVEL: DEBUG +VERBOSE: False # ============================================================================= # PREPROCESS @@ -125,36 +119,27 @@ MUTE: [] # DENSITY (str): # How to treat density during inversion. Available: ['CONSTANT': Do not # update density, 'VARIABLE': Update density] -# ATTENUATION (str): +# ATTENUATION (bool): # If True, turn on attenuation during forward simulations, otherwise set # attenuation off. Attenuation is always off for adjoint simulations. +# FORMAT (float): +# Format of synthetic waveforms used during workflow, available options: +# ['ASCII', 'SU'] # COMPONENTS (str): # Components used to generate data, formatted as a single string, e.g. ZNE # or NZ or E -# SOLVERIO (int): -# The format external solver files. Available: ['fortran_binary', 'adios'] -# NT (float): -# Number of time steps set in the SPECFEM Par_file -# DT (float): -# Time step or delta set in the SPECFEM Par_file -# F0 (float): -# Dominant source frequency -# FORMAT (float): -# Format of synthetic waveforms used during workflow, available options: -# ['ascii', 'su'] +# SOLVERIO (str): +# The format external solver files. Available: ['fortran_binary'] # SOURCE_PREFIX (str): # Prefix of SOURCE files in path SPECFEM_DATA. By default, 'SOURCE' for # SPECFEM2D # ============================================================================= -MATERIALS: !!! REQUIRED PARAMETER !!! -DENSITY: !!! REQUIRED PARAMETER !!! -ATTENUATION: !!! REQUIRED PARAMETER !!! +MATERIALS: ELASTIC +DENSITY: CONSTANT +ATTENUATION: False +FORMAT: ASCII COMPONENTS: ZNE SOLVERIO: fortran_binary -NT: !!! REQUIRED PARAMETER !!! -DT: !!! REQUIRED PARAMETER !!! -F0: !!! REQUIRED PARAMETER !!! -FORMAT: !!! REQUIRED PARAMETER !!! SOURCE_PREFIX: SOURCE # ============================================================================= @@ -190,62 +175,64 @@ TASKTIME_SMOOTH: 1 # parameters # STEPLENMAX (float): # Max allowable step length, as a fraction of current model parameters -# LBFGSMEM (int): -# Max number of previous gradients to retain in local memory -# LBFGSMAX (int): -# LBFGS periodic restart interval, between 1 and 'inf' -# LBFGSTHRESH (float): -# LBFGS angle restart threshold # ============================================================================= -LINESEARCH: Backtrack +LINESEARCH: Bracket PRECOND: STEPCOUNTMAX: 10 STEPLENINIT: 0.05 STEPLENMAX: 0.5 -LBFGSMEM: 3 -LBFGSMAX: inf -LBFGSTHRESH: 0.0 # ============================================================================= # WORKFLOW # //////// +# SAVETRACES (bool): +# Save waveform traces to disk after they have been generated by the +# external solver +# SAVERESIDUALS (bool): +# Save data-synthetic residuals each time they are caluclated # CASE (str): -# Type of inversion, available: ['data': real data inversion, 'synthetic': -# synthetic-synthetic inversion] -# RESUME_FROM (str): -# Name of task to resume inversion from -# STOP_AFTER (str): -# Name of task to stop inversion after finishing -# SAVEMODEL (bool): -# Save final model files after each iteration +# How to address 'data' in your workflow, available options: 1) 'data': +# Real data inversion. Observed waveforms must be provided by the user in +# PATH.DATA/{SOURCE_NAME}. OR if PAR.PREPROCESS=='pyatoa' data should be +# discoverable via IRIS webservices based on event ID and station codes2) +# 'synthetic': A synthetic-synthetic workflow. 'Data' will be generated as +# synthetics using PATH.MODEL_TRUE. # SAVEGRADIENT (bool): -# Save gradient files after each iteration +# Save gradient files each time the gradient is evaluated # SAVEKERNELS (bool): -# Save event kernel files after each iteration -# SAVETRACES (bool): -# Save waveform traces after each iteration -# SAVERESIDUALS (bool): -# Save waveform residuals after each iteration +# Save event kernel files each time they are evaluated # SAVEAS (str): # Format to save models, gradients, kernels. Available: ['binary': save # files in native SPECFEM .bin format, 'vector': save files as NumPy .npy # files, 'both': save as both binary and vectors] # BEGIN (int): -# First iteration of workflow, 1 <= BEGIN <= inf +# First iteration of an inversion workflow, 1 <= BEGIN <= inf # END (int): -# Last iteration of workflow, BEGIN <= END <= inf +# Last iteration of the inverison workflow,BEGIN <= END <= inf +# RESUME_FROM (str): +# Name of flow task to resume workflow from. Useful for restarting failed +# workflows or re-trying sections of workflows with new parameters. To +# determine available options for your given workflow: > seisflows print +# flow +# STOP_AFTER (str): +# Name of flow task to stop workflow after. Useful for stopping mid- +# workflow to look at results before proceeding (e.g., to look at waveform +# misfits before evaluating the gradient). To determine available options +# for your given workflow: > seisflows print flow +# SAVEMODEL (bool): +# Save updated model files after each iteration # ============================================================================= -CASE: !!! REQUIRED PARAMETER !!! -RESUME_FROM: -STOP_AFTER: -SAVEMODEL: True -SAVEGRADIENT: True -SAVEKERNELS: False SAVETRACES: False SAVERESIDUALS: False +CASE: data +SAVEGRADIENT: True +SAVEKERNELS: False SAVEAS: binary BEGIN: 1 -END: !!! REQUIRED PARAMETER !!! +END: 1 +RESUME_FROM: +STOP_AFTER: +SAVEMODEL: True # ============================================================================= # PATHS @@ -256,33 +243,42 @@ END: !!! REQUIRED PARAMETER !!! # directory to save workflow outputs to disk # SYSTEM: # scratch path to hold any system related data -# LOCAL: -# path to local data to be used during workflow # LOGFILE: # the main output log file where all processes will track their status # PREPROCESS: # scratch path to store any preprocessing outputs # SOLVER: # scratch path to hold solver working directories +# DATA: +# path to observed waveform data available to workflow # SPECFEM_BIN: # path to the SPECFEM binary executables # SPECFEM_DATA: # path to the SPECFEM DATA/ directory containing the 'Par_file', 'STATIONS' # file and 'CMTSOLUTION' files -# DATA: -# path to data available to workflow # MASK: # Directory to mask files for gradient masking # OPTIMIZE: -# scratch path to store data related to nonlinear optimization +# scratch path to store data related to nonlinear optimization library. +# Data stored here include model, gradient, and search direction vectors +# (numpy arrays), andadditional arrays related to specific optimization +# algorithms # MODEL_INIT: -# location of the initial model to be used for workflow +# Path location of the initial model to be used to generate the the first +# evaluation of synthetic seismograms. +# GRAD: +# scratch path to store any models or kernels related to gradient +# evaluations. Sub-directories will be generated inside PATH.GRAD to save +# various stages of gradient manipulation # MODEL_TRUE: -# Target model to be used for PAR.CASE == 'synthetic' +# Target model to be used for PAR.CASE == 'synthetic'. The TRUE model will +# be used to evaluate forward simulations ONCE at the beginning of the +# workflow, to generate 'data'. # FUNC: -# scratch path to store data related to function evaluations -# GRAD: -# scratch path to store data related to gradient evaluations +# scratch path to store data related to misfit function evaluations that +# take place during the line search. Data stored here include residuals +# from data-synthetic misfit, and a given 'try' model being used to +# generate synthetics. # HESS: # scratch path to store data related to Hessian evaluations # ============================================================================= @@ -290,17 +286,16 @@ PATHS: SCRATCH: scratch OUTPUT: output SYSTEM: scratch/system - LOCAL: - LOGFILE: output_sf.txt + LOGFILE: sfoutput.txt PREPROCESS: scratch/preprocess SOLVER: scratch/solver - SPECFEM_BIN: !!! REQUIRED PATH !!! - SPECFEM_DATA: !!! REQUIRED PATH !!! DATA: + SPECFEM_BIN: specfem/bin + SPECFEM_DATA: specfem/DATA MASK: OPTIMIZE: scratch/optimize - MODEL_INIT: !!! REQUIRED PATH !!! - MODEL_TRUE: - FUNC: scratch/evalfunc + MODEL_INIT: specfem/MODEL_INIT GRAD: scratch/evalgrad + MODEL_TRUE: specfem/MODEL_TRUE + FUNC: scratch/evalfunc HESS: scratch/evalhess diff --git a/seisflows/tests/test_data/test_filled_parameters.yaml b/seisflows/tests/test_data/test_filled_parameters.yaml index a33448c0..a25f1ebe 100644 --- a/seisflows/tests/test_data/test_filled_parameters.yaml +++ b/seisflows/tests/test_data/test_filled_parameters.yaml @@ -27,9 +27,9 @@ WORKFLOW: inversion SOLVER: specfem2d SYSTEM: workstation -OPTIMIZE: LBFGS -PREPROCESS: base -POSTPROCESS: base +OPTIMIZE: gradient +PREPROCESS: default +POSTPROCESS: default # ============================================================================= # SYSTEM @@ -37,17 +37,6 @@ POSTPROCESS: base # TITLE (str): # The name used to submit jobs to the system, defaults to the name of the # working directory -# PRECHECK (list): -# A list of parameters that will be displayed to stdout before 'submit' or -# 'resume' is run. Useful for manually reviewing important parameters prior -# to system submission -# LOG_LEVEL (str): -# Verbosity output of SF logger. Available from least to most verbosity: -# 'CRITICAL', 'WARNING', 'INFO', 'DEBUG'; defaults to 'DEBUG' -# VERBOSE (bool): -# Level of verbosity provided to the output log. If True, log statements -# will declare what module/class/function they are being called from. -# Useful for debugging but also very noisy. # MPIEXEC (str): # Function used to invoke executables on the system. For example 'srun' on # SLURM systems, or './' on a workstation. If left blank, will guess based @@ -57,15 +46,20 @@ POSTPROCESS: base # sources in workflow # NPROC (int): # Number of processor to use for each simulation +# LOG_LEVEL (str): +# Verbosity output of SF logger. Available from least to most verbosity: +# 'CRITICAL', 'WARNING', 'INFO', 'DEBUG'; defaults to 'DEBUG' +# VERBOSE (bool): +# Level of verbosity provided to the output log. If True, log statements +# will declare what module/class/function they are being called from. +# Useful for debugging but also very noisy. # ============================================================================= TITLE: test_data -PRECHECK: - - TITLE -LOG_LEVEL: DEBUG -VERBOSE: False MPIEXEC: NTASK: 1 NPROC: 1 +LOG_LEVEL: DEBUG +VERBOSE: False # ============================================================================= # PREPROCESS @@ -125,23 +119,17 @@ MUTE: [] # DENSITY (str): # How to treat density during inversion. Available: ['CONSTANT': Do not # update density, 'VARIABLE': Update density] -# ATTENUATION (str): +# ATTENUATION (bool): # If True, turn on attenuation during forward simulations, otherwise set # attenuation off. Attenuation is always off for adjoint simulations. +# FORMAT (float): +# Format of synthetic waveforms used during workflow, available options: +# ['ASCII', 'SU'] # COMPONENTS (str): # Components used to generate data, formatted as a single string, e.g. ZNE # or NZ or E -# SOLVERIO (int): -# The format external solver files. Available: ['fortran_binary', 'adios'] -# NT (float): -# Number of time steps set in the SPECFEM Par_file -# DT (float): -# Time step or delta set in the SPECFEM Par_file -# F0 (float): -# Dominant source frequency -# FORMAT (float): -# Format of synthetic waveforms used during workflow, available options: -# ['ascii', 'su'] +# SOLVERIO (str): +# The format external solver files. Available: ['fortran_binary'] # SOURCE_PREFIX (str): # Prefix of SOURCE files in path SPECFEM_DATA. By default, 'SOURCE' for # SPECFEM2D @@ -149,12 +137,9 @@ MUTE: [] MATERIALS: elastic DENSITY: constant ATTENUATION: False +FORMAT: ascii COMPONENTS: ZNE SOLVERIO: fortran_binary -NT: 1000 -DT: .01 -F0: .084 -FORMAT: ascii SOURCE_PREFIX: SOURCE # ============================================================================= @@ -190,62 +175,64 @@ TASKTIME_SMOOTH: 1 # parameters # STEPLENMAX (float): # Max allowable step length, as a fraction of current model parameters -# LBFGSMEM (int): -# Max number of previous gradients to retain in local memory -# LBFGSMAX (int): -# LBFGS periodic restart interval, between 1 and 'inf' -# LBFGSTHRESH (float): -# LBFGS angle restart threshold # ============================================================================= -LINESEARCH: Backtrack +LINESEARCH: Bracket PRECOND: STEPCOUNTMAX: 10 STEPLENINIT: 0.05 STEPLENMAX: 0.5 -LBFGSMEM: 3 -LBFGSMAX: inf -LBFGSTHRESH: 0.0 # ============================================================================= # WORKFLOW # //////// +# SAVETRACES (bool): +# Save waveform traces to disk after they have been generated by the +# external solver +# SAVERESIDUALS (bool): +# Save data-synthetic residuals each time they are caluclated # CASE (str): -# Type of inversion, available: ['data': real data inversion, 'synthetic': -# synthetic-synthetic inversion] -# RESUME_FROM (str): -# Name of task to resume inversion from -# STOP_AFTER (str): -# Name of task to stop inversion after finishing -# SAVEMODEL (bool): -# Save final model files after each iteration +# How to address 'data' in your workflow, available options: 1) 'data': +# Real data inversion. Observed waveforms must be provided by the user in +# PATH.DATA/{SOURCE_NAME}. OR if PAR.PREPROCESS=='pyatoa' data should be +# discoverable via IRIS webservices based on event ID and station codes2) +# 'synthetic': A synthetic-synthetic workflow. 'Data' will be generated as +# synthetics using PATH.MODEL_TRUE. # SAVEGRADIENT (bool): -# Save gradient files after each iteration +# Save gradient files each time the gradient is evaluated # SAVEKERNELS (bool): -# Save event kernel files after each iteration -# SAVETRACES (bool): -# Save waveform traces after each iteration -# SAVERESIDUALS (bool): -# Save waveform residuals after each iteration +# Save event kernel files each time they are evaluated # SAVEAS (str): # Format to save models, gradients, kernels. Available: ['binary': save # files in native SPECFEM .bin format, 'vector': save files as NumPy .npy # files, 'both': save as both binary and vectors] # BEGIN (int): -# First iteration of workflow, 1 <= BEGIN <= inf +# First iteration of an inversion workflow, 1 <= BEGIN <= inf # END (int): -# Last iteration of workflow, BEGIN <= END <= inf +# Last iteration of the inverison workflow,BEGIN <= END <= inf +# RESUME_FROM (str): +# Name of flow task to resume workflow from. Useful for restarting failed +# workflows or re-trying sections of workflows with new parameters. To +# determine available options for your given workflow: > seisflows print +# flow +# STOP_AFTER (str): +# Name of flow task to stop workflow after. Useful for stopping mid- +# workflow to look at results before proceeding (e.g., to look at waveform +# misfits before evaluating the gradient). To determine available options +# for your given workflow: > seisflows print flow +# SAVEMODEL (bool): +# Save updated model files after each iteration # ============================================================================= +SAVETRACES: False +SAVERESIDUALS: False CASE: data -RESUME_FROM: -STOP_AFTER: -SAVEMODEL: True SAVEGRADIENT: True SAVEKERNELS: False -SAVETRACES: False -SAVERESIDUALS: False SAVEAS: binary BEGIN: 1 END: 1 +RESUME_FROM: +STOP_AFTER: +SAVEMODEL: True # ============================================================================= # PATHS @@ -256,33 +243,42 @@ END: 1 # directory to save workflow outputs to disk # SYSTEM: # scratch path to hold any system related data -# LOCAL: -# path to local data to be used during workflow # LOGFILE: # the main output log file where all processes will track their status # PREPROCESS: # scratch path to store any preprocessing outputs # SOLVER: # scratch path to hold solver working directories +# DATA: +# path to observed waveform data available to workflow # SPECFEM_BIN: # path to the SPECFEM binary executables # SPECFEM_DATA: # path to the SPECFEM DATA/ directory containing the 'Par_file', 'STATIONS' # file and 'CMTSOLUTION' files -# DATA: -# path to data available to workflow # MASK: # Directory to mask files for gradient masking # OPTIMIZE: -# scratch path to store data related to nonlinear optimization +# scratch path to store data related to nonlinear optimization library. +# Data stored here include model, gradient, and search direction vectors +# (numpy arrays), andadditional arrays related to specific optimization +# algorithms # MODEL_INIT: -# location of the initial model to be used for workflow +# Path location of the initial model to be used to generate the the first +# evaluation of synthetic seismograms. +# GRAD: +# scratch path to store any models or kernels related to gradient +# evaluations. Sub-directories will be generated inside PATH.GRAD to save +# various stages of gradient manipulation # MODEL_TRUE: -# Target model to be used for PAR.CASE == 'synthetic' +# Target model to be used for PAR.CASE == 'synthetic'. The TRUE model will +# be used to evaluate forward simulations ONCE at the beginning of the +# workflow, to generate 'data'. # FUNC: -# scratch path to store data related to function evaluations -# GRAD: -# scratch path to store data related to gradient evaluations +# scratch path to store data related to misfit function evaluations that +# take place during the line search. Data stored here include residuals +# from data-synthetic misfit, and a given 'try' model being used to +# generate synthetics. # HESS: # scratch path to store data related to Hessian evaluations # ============================================================================= @@ -290,17 +286,16 @@ PATHS: SCRATCH: scratch OUTPUT: output SYSTEM: scratch/system - LOCAL: - LOGFILE: output_sf.txt + LOGFILE: sfoutput.txt PREPROCESS: scratch/preprocess SOLVER: scratch/solver + DATA: SPECFEM_BIN: ./bin SPECFEM_DATA: ./DATA - DATA: MASK: OPTIMIZE: scratch/optimize MODEL_INIT: ./MODEL_INIT + GRAD: scratch/evalgrad MODEL_TRUE: ./MODEL_TRUE FUNC: scratch/evalfunc - GRAD: scratch/evalgrad HESS: scratch/evalhess diff --git a/seisflows/tests/test_data/test_setup_parameters.yaml b/seisflows/tests/test_data/test_setup_parameters.yaml index 303931d1..f32a3cb2 100644 --- a/seisflows/tests/test_data/test_setup_parameters.yaml +++ b/seisflows/tests/test_data/test_setup_parameters.yaml @@ -27,6 +27,6 @@ WORKFLOW: inversion SOLVER: specfem2d SYSTEM: workstation -OPTIMIZE: LBFGS -PREPROCESS: base -POSTPROCESS: base +OPTIMIZE: gradient +PREPROCESS: default +POSTPROCESS: default diff --git a/seisflows/tests/test_modules.py b/seisflows/tests/test_modules.py index 16a0dc2b..f0118505 100644 --- a/seisflows/tests/test_modules.py +++ b/seisflows/tests/test_modules.py @@ -21,34 +21,30 @@ required_structure = { "system": { "parameters": [], - "functions": ["required", "check", "setup", "submit", "run", - "taskid", "checkpoint"] + "functions": ["submit", "run", "taskid", "checkpoint"] }, "preprocess": { "parameters": [], - "functions": ["required", "check", "setup", "prepare_eval_grad", - "sum_residuals", "finalize"] + "functions": ["prepare_eval_grad", "sum_residuals", "finalize"] }, "solver": { "parameters": ["MATERIALS", "DENSITY", "ATTENUATION"], - "functions": ["required", "check", "setup", "generate_data", - "generate_mesh", "eval_func", "eval_grad", "load", - "save", "merge", "split", "source_names", "parameters"] + "functions": ["generate_data", "eval_func", + "eval_grad", "load", "save", "merge", "split", + "source_names", "parameters"] }, "postprocess": { "parameters": [], - "functions": ["check", "setup", "write_gradient"] + "functions": ["sum_smooth_kernels", "scale_gradient"] }, "optimize": { "parameters": [], - "functions": ["setup", "check", "compute_direction", - "initialize_search", "update_search", - "finalize_search", "retry_status", - "restart"] + "functions": ["compute_direction", "initialize_search", "update_search", + "finalize_search", "retry_status", "restart"] }, "workflow": { - "parameters": ["CASE"], - "functions": ["check", "main", "checkpoint"] + "parameters": [], + "functions": ["main", "checkpoint"] }, } @@ -83,8 +79,9 @@ def sfinit(tmpdir, copy_par_file): os.chdir(tmpdir) with patch.object(sys, "argv", ["seisflows"]): sf = SeisFlows() - sf._register_parameters(force=True) - sf._register_modules(check=True) + sf._register_parameters() + sf._register_modules() + sf._check_parameters() return sf @@ -175,4 +172,4 @@ def test_setup(sfinit): if os.path.isdir(path_): shutil.rmtree(path_) else: - os.remove(path_) \ No newline at end of file + os.remove(path_) diff --git a/seisflows/tests/test_preprocess.py b/seisflows/tests/test_preprocess.py deleted file mode 100644 index e4a4507c..00000000 --- a/seisflows/tests/test_preprocess.py +++ /dev/null @@ -1,139 +0,0 @@ -""" -Test suite for the SeisFlows system module, which controls interaction with -various compute systems -""" -import os -import sys -import shutil -import pytest -from unittest.mock import patch -from seisflows import config -from seisflows.seisflows import SeisFlows, return_modules - - -# Define some re-used paths -TEST_DIR = os.path.join(config.ROOT_DIR, "tests") -REPO_DIR = os.path.abspath(os.path.join(config.ROOT_DIR, "..")) - - -@pytest.fixture -def copy_par_file(tmpdir): - """ - Copy the template parameter file into the temporary test directory - :rtype: str - :return: location of the parameter file - """ - src = os.path.join(TEST_DIR, "test_data", "test_filled_parameters.yaml") - dst = os.path.join(tmpdir, "parameters.yaml") - shutil.copy(src, dst) - - -@pytest.fixture -def sfinit(tmpdir, copy_par_file): - """ - Re-used function that will initate a SeisFlows working environment in - sys modules - :return: - """ - # Ensure that there is not a currently active working state - config.flush() - - copy_par_file - os.chdir(tmpdir) - with patch.object(sys, "argv", ["seisflows"]): - sf = SeisFlows() - sf._register_parameters(force=True) - sf._register_modules(check=False) - - return sf - - -def test_default_check(sfinit): - """ - Test seisflows.preprocess.default.check() - - :param sfinit: - :param modules: - :return: - """ - sfinit - PAR = sys.modules["seisflows_parameters"] - preprocess = sys.modules["seisflows_preprocess"] - - # Make sure that the check statement catches incorrectly set parameters - incorrect_parameters = { - "NORMALIZE": ["JNORML3"], - "MUTE": ["not_mute"], - "FILTER": "bondpass", - "FORMAT": "not_an_acceptable_format" - } - for key, val in incorrect_parameters.items(): - og_val = PAR[key] - print(key) - with pytest.raises(AssertionError): - PAR[key] = val - preprocess.check() - - PAR[key] = og_val - - # Make sure that parameters set to inappropriate values throw assertions - correct_parameters = { - "FILTER": "BANDPASS", - "WORKFLOW": "INVERSION", - "MIN_FREQ": 1, - } - for key, val in correct_parameters.items(): - PAR[key] = val - - incorrect_values = { - "MAX_FREQ": -1, - } - for key, val in incorrect_values.items(): - og_val = PAR[key] - with pytest.raises(AssertionError): - PAR[key] = val - preprocess.check() - PAR[key] = og_val - - -def test_default_setup(sfinit): - """ - Ensure that default setup correctly sets up the preprocessing machinery - """ - sf = sfinit - PAR = sys.modules["seisflows_parameters"] - preprocess = sys.modules["seisflows_preprocess"] - - # Make sure that preprocess machinery is set up empty - assert(preprocess.misfit is None) - assert(preprocess.adjoint is None) - assert(preprocess.reader is None) - assert(preprocess.writer is None) - - # Set some default parameters to run setup - misfit_name = "waveform" - io_name = "ascii" - PAR["MISFIT"] = misfit_name - PAR["FORMAT"] = io_name - preprocess.setup() - - assert(preprocess.misfit.__name__ == misfit_name) - assert(preprocess.adjoint.__name__ == misfit_name) - assert(preprocess.reader.__name__ == io_name) - assert(preprocess.writer.__name__ == io_name) - - -def test_default_prepare_eval_grad(tmpdir, sfinit): - """ - Ensure that prepare_eval_grad writes out adjoint traces and auxiliary files - """ - sfinit - PAR = sys.modules["seisflows_parameters"] - preprocess = sys.modules["seisflows_preprocess"] - - cwd = tmpdir - taskid = 0 - filenames = [] - preprocess.prepare_eval_grad(cwd=cwd, taskid=taskid, filenames=filenames) - - diff --git a/seisflows/tests/test_seisflows.py b/seisflows/tests/test_seisflows.py index 9ea85647..7ed27ca0 100644 --- a/seisflows/tests/test_seisflows.py +++ b/seisflows/tests/test_seisflows.py @@ -1,8 +1,6 @@ """ Test suite for the SeisFlows command line interface tool and the underlying parser class, ensures that the command line tool works as expected - -!!! TO DO: Finish tests from 'edit' onwards """ import os import io @@ -14,9 +12,9 @@ from unittest.mock import patch from seisflows import logger +from seisflows.core import Dict from seisflows.seisflows import SeisFlows -from seisflows.config import (Dict, ROOT_DIR, NAMES, CFGPATHS, - config_logger, flush) +from seisflows.config import ROOT_DIR, NAMES, CFGPATHS, flush from seisflows.tools.wrappers import loadyaml TEST_DIR = os.path.join(ROOT_DIR, "tests") @@ -126,14 +124,19 @@ def test_register(tmpdir, par_file_dict, copy_par_file): sf = SeisFlows() assert(sf._paths is None) assert(sf._parameters is None) - sf._register_parameters(force=True) + sf._register_parameters() # Check that paths and parameters have been set in sys.modules paths = sys.modules["seisflows_paths"] parameters = sys.modules["seisflows_parameters"] # Check one or two parameters have been set correctly - assert(par_file_dict.LBFGSMAX == parameters.LBFGSMAX) + assert("PATHS" not in parameters) + for key, val in par_file_dict.items(): + if key == "PATHS": + continue + assert(parameters[key] == val) + path_check_full = os.path.abspath(par_file_dict.PATHS["SCRATCH"]) assert(path_check_full == paths.SCRATCH) @@ -235,34 +238,6 @@ def test_cmd_clean(tmpdir): assert(os.path.exists(fid)) -def test_config_logging(tmpdir, copy_par_file): - """ - Test logging configuration to make sure we can print to file - - TODO move this to test_config.py? - :param tmpdir: - :return: - """ - # Run init first to create a working state - os.chdir(tmpdir) - copy_par_file - - msg = "This is an example log that will be checked for test purposes" - with patch.object(sys, "argv", ["seisflows"]): - sf = SeisFlows() - sf._register_parameters(force=True) - config_logger(filename=CFGPATHS.LOGFILE) - logger.debug(msg) - - # Check that we created the log file and wrote the message in - assert(os.path.exists(CFGPATHS.LOGFILE)) - with open(CFGPATHS.LOGFILE, "r") as f: - lines = f.read() - assert(msg in lines) - assert("DEBUG" in lines) # levelname - assert("test_config_logging()" in lines) # funcName - - def test_load_modules(tmpdir, copy_par_file): """ Test if module loading from sys.modules works diff --git a/seisflows/tests/test_system.py b/seisflows/tests/test_system.py deleted file mode 100644 index 28091412..00000000 --- a/seisflows/tests/test_system.py +++ /dev/null @@ -1,103 +0,0 @@ -""" -Test suite for the SeisFlows SYSTEM module, which controls interaction with -various compute systems -""" -import os -import sys -import shutil -import pytest -from glob import glob -from unittest.mock import patch -from seisflows import config -from seisflows.seisflows import SeisFlows - - -TEST_DIR = os.path.join(config.ROOT_DIR, "tests") -REPO_DIR = os.path.abspath(os.path.join(config.ROOT_DIR, "..")) - - -@pytest.fixture -def copy_par_file(tmpdir): - """ - Copy the template parameter file into the temporary test directory - :rtype: str - :return: location of the parameter file - """ - src = os.path.join(TEST_DIR, "test_data", "test_filled_parameters.yaml") - dst = os.path.join(tmpdir, "parameters.yaml") - shutil.copy(src, dst) - - -@pytest.fixture -def sfinit(tmpdir, copy_par_file): - """ - Re-used function that will initate a SeisFlows working environment in - sys modules - :return: - """ - copy_par_file - os.chdir(tmpdir) - with patch.object(sys, "argv", ["seisflows"]): - sf = SeisFlows() - sf._register(force=True) - config.init_seisflows(check=False) - - return sf - - -def test_setup(sfinit): - """ - Make sure that system.base.setup() creates the desired directory structure - and log files. - - :param sfinit: - :param modules: - :return: - """ - sf = sfinit - system = sys.modules["seisflows_system"] - PATH = sys.modules["seisflows_paths"] - - # Make empty text files to check that they will be copied into the logs dir - for fid in [system.output_log, system.error_log]: - open(fid, "w") - - for path in [PATH.SCRATCH, PATH.SYSTEM, PATH.OUTPUT]: - assert(not os.path.exists(path)) - - system.setup() - for path in [PATH.SCRATCH, PATH.SYSTEM, PATH.OUTPUT]: - assert(os.path.exists(path)) - - # Both log files and the parameter file should have been copied - assert(len(glob(os.path.join("logs", "*"))) == 3) - -def test_checkpoint(sfinit): - """ - Check that output pickle files are created during a checkpoint and that - kwargs are saved - """ - sf = sfinit - system = sys.modules["seisflows_system"] - PATH = sys.modules["seisflows_paths"] - classname = "solver" - method = "eval_func" - system.checkpoint(path=PATH.OUTPUT, classname=classname, method=method, - kwargs={"test": 5} - ) - assert(os.path.exists(os.path.join(PATH.OUTPUT, "kwargs", - f"{classname}_{method}.p"))) - assert(len(glob(os.path.join(PATH.OUTPUT, "*.p"))) == len(config.NAMES)) - - -# SYSTEM.WORKSTATION -def test_run_system_workstation(sfinit): - """ - Ensure that workstation.run() simply runs a function as we would expect - """ - sf = sfinit - system = sys.modules["seisflows_system"] - assert(type(system).__name__ == "Workstation") - # We don't care what the function does, just that we can call it - system.run(classname="system", method="taskid") - From 1df4c65911a15584c0efc114454e8d92a1aaee95 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Thu, 30 Jun 2022 15:22:39 -0800 Subject: [PATCH 040/195] new cli function seisflows swap to change out module parameters on an existing parameter file --- seisflows/config.py | 0 seisflows/core.py | 0 seisflows/seisflows.py | 62 +++++++++++++++++++++++++++++++++-- seisflows/tools/specfem.py | 14 ++++++-- seisflows/workflow/forward.py | 1 - 5 files changed, 71 insertions(+), 6 deletions(-) mode change 100644 => 100755 seisflows/config.py mode change 100644 => 100755 seisflows/core.py diff --git a/seisflows/config.py b/seisflows/config.py old mode 100644 new mode 100755 diff --git a/seisflows/core.py b/seisflows/core.py old mode 100644 new mode 100755 diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index f9883efb..27099fe7 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -114,6 +114,18 @@ def _format_action(self, action): configure.add_argument("-a", "--absolute_paths", action="store_true", help="Set default paths relative to cwd") # ========================================================================= + swap = subparser.add_parser( + "swap", help="Swap module parameters in an existing parameter file", + description="""During workflow development, it may be necessary to swap + between different sub-modules (e.g., system.workstation -> + system.cluster). However this would typically involving re-generating + and re-filling a parameter file. The 'swap' function makes it easier + to swap parameters between modules. + """ + ) + swap.add_argument("module", nargs="?", help="Module name to swap") + swap.add_argument("classname", nargs="?", help="Classname to swap to") + # ========================================================================= init = subparser.add_parser( "init", help="Initiate working environment", description="""Establish a SeisFlows working environment but don't @@ -289,7 +301,7 @@ def _format_action(self, action): subparser_dict = {"check": check, "par": par, "inspect": inspect, "sempar": sempar, "clean": clean, "restart": restart, "print": print_, "reset": reset, - "examples": examples} + "examples": examples, "swap": swap} if parser.parse_args().command in subparser_dict: return parser, subparser_dict[parser.parse_args().command] else: @@ -570,7 +582,6 @@ def configure(self, absolute_paths=False, **kwargs): else if False, use path names relative to the working directory. Defaults to False, uses relative paths. """ - print(msg.cli(f"filling {self._args.parameter_file} w/ default values")) self._register_parameters() # Check if the User set turn off any modules (if None, dont instantiate) @@ -626,6 +637,53 @@ def configure(self, absolute_paths=False, **kwargs): else: unix.rm(temp_par_file) + def swap(self, module, classname, **kwargs): + """ + Swap the parameters of an existing parameter file with a new module. + Useful for changing out parameters without having to re-make a + parameter file from scratch. e.g., to swap systems from a workstation + to a cluster + + .. rubric:: + $ seisflows swap system slurm + """ + PAR_FILE = os.path.join(ROOT_DIR, "examples", "parameters.yaml") + + if module not in NAMES: + print(msg.cli(text=f"{module} does not match {NAMES}", + header="error")) + sys.exit(-1) + + # Load in old parameter file and then move it to a hidden file + ogpars = loadyaml(self._args.parameter_file) + ogpaths = Dict(ogpars.pop("PATHS")) + unix.mv(self._args.parameter_file, f"_{self._args.parameter_file}") + + # Create a new parameter file with updated module + unix.cp(PAR_FILE, self._args.workdir) + for name in NAMES: + setpar(key=name, val=ogpars[name.upper()], + file=self._args.parameter_file, delim=":") + + # Overwrite with new parameters + setpar(key=module, val=classname, file=self._args.parameter_file, + delim=":") + self.configure() + for key, val in ogpars.items(): + try: + setpar(key=key, val=val, file=self._args.parameter_file, + delim=":") + except KeyError: + continue + for key, val in ogpaths.items(): + try: + setpar(key=key, val=val, file=self._args.parameter_file, + delim=":", _reverse=True) + except KeyError: + continue + + unix.rm(f"_{self._args.parameter_file}") + def init(self, **kwargs): """ Establish a SeisFlows working environment on disk. Instantiates a diff --git a/seisflows/tools/specfem.py b/seisflows/tools/specfem.py index bfc17db3..9987e3ed 100644 --- a/seisflows/tools/specfem.py +++ b/seisflows/tools/specfem.py @@ -38,7 +38,7 @@ def __init__(self): self.minmax = Minmax() -def getpar(key, file, delim="=", match_partial=False): +def getpar(key, file, delim="=", match_partial=False, _reverse=False): """ Reads and returns parameters from a SPECFEM or SeisFlows parameter file Assumes the parameter file is formatted in the following way: @@ -57,6 +57,9 @@ def getpar(key, file, delim="=", match_partial=False): :param match_partial: allow partial key matches, e.g., allow key='tit' to return value for 'title'. Defaults to False as this can have unintended consequences + :type _reverse: bool + :param _reverse: reverse search for parameters incase there are multiple + matching entries. :rtype: tuple (str, str, int) :return: a tuple of the key, value and line number (indexed from 0). The key will match exactly how it looks in the Par_file @@ -64,6 +67,8 @@ def getpar(key, file, delim="=", match_partial=False): IF no matches found, returns (None, None, None) """ lines = open(file, "r").readlines() + if _reverse: + lines = lines[::-1] for i, line in enumerate(lines): # Find the first occurence, CASE-INSENSITIVE search, strip whitespace @@ -103,7 +108,7 @@ def getpar(key, file, delim="=", match_partial=False): return key_out, val, i -def setpar(key, val, file, delim="=", match_partial=False): +def setpar(key, val, file, delim="=", match_partial=False, _reverse=False): """ Overwrites parameter value to a SPECFEM Par_file. @@ -120,8 +125,11 @@ def setpar(key, val, file, delim="=", match_partial=False): :param match_partial: allow partial key matches, e.g., allow key='tit' to return value for 'title'. Defaults to False as this can have unintended consequences + :type _reverse: bool + :param _reverse: reverse search for parameters incase there are multiple + matching entries. """ - key_out, val_out, i = getpar(key, file, delim, match_partial) + key_out, val_out, i = getpar(key, file, delim, match_partial, _reverse) if key_out is None: return diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index 800c4ea4..8101c1e5 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -7,7 +7,6 @@ """ import os import sys -import numpy as np from glob import glob from seisflows.core import Base From d700407145e62c2f00599c344e11352149adcf0a Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Fri, 1 Jul 2022 12:03:14 -0800 Subject: [PATCH 041/195] removing system self call causing recursion error, removing config logger from submit script --- seisflows/seisflows.py | 3 ++- seisflows/system/runscripts/submit_workflow.py | 3 +-- seisflows/system/slurm.py | 5 ----- 3 files changed, 3 insertions(+), 8 deletions(-) diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 27099fe7..f79aec50 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -675,10 +675,11 @@ def swap(self, module, classname, **kwargs): delim=":") except KeyError: continue + import pdb;pdb.set_trace() for key, val in ogpaths.items(): try: setpar(key=key, val=val, file=self._args.parameter_file, - delim=":", _reverse=True) + delim=":", match_partial=True, _reverse=True) except KeyError: continue diff --git a/seisflows/system/runscripts/submit_workflow.py b/seisflows/system/runscripts/submit_workflow.py index 802ac22e..eb72b06c 100644 --- a/seisflows/system/runscripts/submit_workflow.py +++ b/seisflows/system/runscripts/submit_workflow.py @@ -19,7 +19,7 @@ import argparse from seisflows.tools import unix -from seisflows.config import load, config_logger +from seisflows.config import load def parse_args(): @@ -52,7 +52,6 @@ def parse_args(): # Set up logging on the compute system to print to stdout only PAR = sys.modules["seisflows_parameters"] PATH = sys.modules["seisflows_paths"] - config_logger(level=PAR.LOG_LEVEL, verbose=PAR.VERBOSE) # Execute MASTER JOB as workflow.main() workflow.main() diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index 2b3b7946..5f87d55f 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -188,11 +188,6 @@ def taskid(self): return int(sftaskid) - def checkpoint(self, path, classname, method, kwargs): - """Inherits from workflow.system.workstation.Workstation""" - self.checkpoint(path=path, classname=classname, method=method, - kwargs=kwargs) - def _check_job_status(self, job_ids): """ Repeatedly check the status of a currently running job using 'sacct'. From ac223a18453e9c70def7a1d32f97c81e8bbb72b7 Mon Sep 17 00:00:00 2001 From: bch0w Date: Fri, 1 Jul 2022 20:04:35 +0000 Subject: [PATCH 042/195] updating maui run scripts to get 2d example working on cluster --- seisflows/config.py | 2 ++ seisflows/seisflows.py | 4 +++- seisflows/system/maui.py | 2 ++ seisflows/system/runscripts/run_function.py | 2 +- seisflows/system/runscripts/submit_workflow.py | 3 +-- seisflows/tools/specfem.py | 14 +++----------- 6 files changed, 12 insertions(+), 15 deletions(-) diff --git a/seisflows/config.py b/seisflows/config.py index 6811a1a8..64a4f649 100755 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -46,6 +46,8 @@ # be returned as a Dict() object, defined below. All of these files and # directories will be created relative to the user-defined working directory CFGPATHS = Dict( + SCRATCHDIR="scratch", + OUTPUTDIR="output", PAR_FILE="parameters.yaml", # Default SeisFlows parameter file LOGFILE="sfoutput.txt", # Log files for all system log ERRLOGFILE="sferror.txt", # StdErr dump site for crash messages diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 27099fe7..82588d5d 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -644,6 +644,8 @@ def swap(self, module, classname, **kwargs): parameter file from scratch. e.g., to swap systems from a workstation to a cluster + TODO figure out how to match paths too + .. rubric:: $ seisflows swap system slurm """ @@ -678,7 +680,7 @@ def swap(self, module, classname, **kwargs): for key, val in ogpaths.items(): try: setpar(key=key, val=val, file=self._args.parameter_file, - delim=":", _reverse=True) + delim=":") except KeyError: continue diff --git a/seisflows/system/maui.py b/seisflows/system/maui.py index 1f7ed3df..151b9288 100644 --- a/seisflows/system/maui.py +++ b/seisflows/system/maui.py @@ -161,7 +161,9 @@ def run(self, classname, method, single=False, run_call=None, **kwargs): parameters """ if run_call is None: + # Calculate requested number of nodes based on requested proc count _nodes = np.ceil(self.par.NPROC / float(self.par.NODESIZE)) + _nodes = _nodes.astype(int) run_call = " ".join([ "sbatch", diff --git a/seisflows/system/runscripts/run_function.py b/seisflows/system/runscripts/run_function.py index 064d673e..1c650bc4 100644 --- a/seisflows/system/runscripts/run_function.py +++ b/seisflows/system/runscripts/run_function.py @@ -20,7 +20,7 @@ import pickle import argparse -from seisflows.config import load, config_logger +from seisflows.config import load diff --git a/seisflows/system/runscripts/submit_workflow.py b/seisflows/system/runscripts/submit_workflow.py index 802ac22e..eb72b06c 100644 --- a/seisflows/system/runscripts/submit_workflow.py +++ b/seisflows/system/runscripts/submit_workflow.py @@ -19,7 +19,7 @@ import argparse from seisflows.tools import unix -from seisflows.config import load, config_logger +from seisflows.config import load def parse_args(): @@ -52,7 +52,6 @@ def parse_args(): # Set up logging on the compute system to print to stdout only PAR = sys.modules["seisflows_parameters"] PATH = sys.modules["seisflows_paths"] - config_logger(level=PAR.LOG_LEVEL, verbose=PAR.VERBOSE) # Execute MASTER JOB as workflow.main() workflow.main() diff --git a/seisflows/tools/specfem.py b/seisflows/tools/specfem.py index 9987e3ed..bfc17db3 100644 --- a/seisflows/tools/specfem.py +++ b/seisflows/tools/specfem.py @@ -38,7 +38,7 @@ def __init__(self): self.minmax = Minmax() -def getpar(key, file, delim="=", match_partial=False, _reverse=False): +def getpar(key, file, delim="=", match_partial=False): """ Reads and returns parameters from a SPECFEM or SeisFlows parameter file Assumes the parameter file is formatted in the following way: @@ -57,9 +57,6 @@ def getpar(key, file, delim="=", match_partial=False, _reverse=False): :param match_partial: allow partial key matches, e.g., allow key='tit' to return value for 'title'. Defaults to False as this can have unintended consequences - :type _reverse: bool - :param _reverse: reverse search for parameters incase there are multiple - matching entries. :rtype: tuple (str, str, int) :return: a tuple of the key, value and line number (indexed from 0). The key will match exactly how it looks in the Par_file @@ -67,8 +64,6 @@ def getpar(key, file, delim="=", match_partial=False, _reverse=False): IF no matches found, returns (None, None, None) """ lines = open(file, "r").readlines() - if _reverse: - lines = lines[::-1] for i, line in enumerate(lines): # Find the first occurence, CASE-INSENSITIVE search, strip whitespace @@ -108,7 +103,7 @@ def getpar(key, file, delim="=", match_partial=False, _reverse=False): return key_out, val, i -def setpar(key, val, file, delim="=", match_partial=False, _reverse=False): +def setpar(key, val, file, delim="=", match_partial=False): """ Overwrites parameter value to a SPECFEM Par_file. @@ -125,11 +120,8 @@ def setpar(key, val, file, delim="=", match_partial=False, _reverse=False): :param match_partial: allow partial key matches, e.g., allow key='tit' to return value for 'title'. Defaults to False as this can have unintended consequences - :type _reverse: bool - :param _reverse: reverse search for parameters incase there are multiple - matching entries. """ - key_out, val_out, i = getpar(key, file, delim, match_partial, _reverse) + key_out, val_out, i = getpar(key, file, delim, match_partial) if key_out is None: return From bf36afc4aa2a5842956eb7b836cebf9e4482bcb6 Mon Sep 17 00:00:00 2001 From: bch0w Date: Tue, 5 Jul 2022 20:32:45 +0000 Subject: [PATCH 043/195] fixing logging as it isnt working on cluster, trying to revert back to logging being intitated by the system run scripts rather than inside the core class --- seisflows/config.py | 56 +++++++++ seisflows/core.py | 108 +++++++++--------- seisflows/seisflows.py | 12 +- seisflows/system/cluster.py | 5 - seisflows/system/runscripts/run_function.py | 2 +- .../system/runscripts/submit_workflow.py | 3 +- seisflows/system/slurm.py | 2 +- 7 files changed, 126 insertions(+), 62 deletions(-) diff --git a/seisflows/config.py b/seisflows/config.py index 64a4f649..93893e59 100755 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -17,10 +17,12 @@ import json import types import pickle +import logging import copyreg import traceback from importlib import import_module +from seisflows import logger from seisflows.core import Dict, Null from seisflows.tools import msg, unix from seisflows.tools.wrappers import module_exists @@ -119,6 +121,60 @@ def flush(): del sys.modules[mod_name] +def config_logger(level="DEBUG", filename=None, filemode="a", verbose=True): + """ + Explicitely configure the logging module with some parameters defined + by the user in the System module. Instantiates a stream logger to write + to stdout, and a file logger which writes to `filename`. Two levels of + verbosity and three levels of log messages allow the user to determine + how much output they want to see. + :type level: str + :param level: log level to be passed to logger, available are + 'CRITICAL', 'WARNING', 'INFO', 'DEBUG' + :type filename: str or None + :param filename: name of the log file to write log statements to. If None, + logs will be written to STDOUT ONLY, and `filemode` will not be used. + :type filemode: str + :param filemode: method for opening the log file. defaults to append 'a' + :type verbose: bool + :param verbose: if True, writes a more detailed log message stating the + type of log (warning, info, debug), and the class and method which + called the logger (e.g., seisflows.solver.specfem2d.save()). This + is much more useful for debugging but clutters up the log file. + if False, only write the time and message in the log statement. + """ + # Make sure that we don't already have handlers described, which may happen + # if this function gets run multiple times, and leads to duplicate logs + while logger.hasHandlers() and logger.handlers: + logger.removeHandler(logger.handlers[0]) + + # Two levels of verbosity on log level, triggered with PAR.VERBOSE + if verbose: + # More verbose logging statement with levelname and func name + fmt_str = ( + "%(asctime)s | %(levelname)-5s | %(name)s.%(funcName)s()\n" + "> %(message)s" + ) + else: + # Clean logging statement with only time and message + fmt_str = "%(asctime)s | %(message)s" + + # Instantiate logger during _register() as we now have user-defined pars + logger.setLevel(level) + formatter = logging.Formatter(fmt_str, datefmt="%Y-%m-%d %H:%M:%S") + + # Stream handler to print log statements to stdout + st_handler = logging.StreamHandler(sys.stdout) + st_handler.setFormatter(formatter) + logger.addHandler(st_handler) + + # File handler to print log statements to text file `filename` + if filename is not None: + file_handler = logging.FileHandler(filename, filemode) + file_handler.setFormatter(formatter) + logger.addHandler(file_handler) + + def custom_import(name=None, module=None, classname=None): """ Imports SeisFlows module and extracts class that is the camelcase version diff --git a/seisflows/core.py b/seisflows/core.py index 93ca61b3..961c6f16 100755 --- a/seisflows/core.py +++ b/seisflows/core.py @@ -14,6 +14,8 @@ class Base(object): inherit from the Base object to work properly. This Base class essentially dictates the required structure of a SeisFlows class. """ + logger = logging.getLogger("seisflows") + def __init__(self): """ SeisFlows instantiates its required parameters through the @@ -22,7 +24,7 @@ def __init__(self): then used to build the parameter file dynamically. """ self.required = SeisFlowsPathsParameters() - self._logger = None + # self._logger = None def module(self, name): """ @@ -39,58 +41,58 @@ def module(self, name): mod = None return mod - @property - def logger(self): - """ - An instance specific logger which imprints inheritance information into - the log statements, making it easier to debug functions with - multiple inheritance - """ - if self._logger is None: - self._logger = self._get_logger() - return self._logger - - def _get_logger(self): - """ - Define an instance specific logger at run time which will imprint - inheritance information onto log statements, making it easier to debug - functions that might have multiple points of inheritance. - - All loggers will write to the same main log file and also print to - stdout. PAR.VERBOSE and PAR.LOG_LEVEL both control the amount of - information that gets printed to the log file. - """ - # logger = logging.getLogger( - # self.__class__.__name__).getChild(self.__class__.__qualname__) - logger = logging.getLogger(self.__class__.__name__) - - # Two levels of verbosity on log level, triggered with PAR.VERBOSE - if self.par.VERBOSE: - # More verbose logging statement with levelname and func name - fmt_str = ( - "%(asctime)s | %(levelname)-5s | %(name)s.%(funcName)s()\n" - "> %(message)s" - ) - else: - # Clean logging statement with only time and message - fmt_str = "%(asctime)s | %(message)s" - - # Instantiate logger during _register() as we now have user-defined pars - logger.setLevel(self.par.LOG_LEVEL) - formatter = logging.Formatter(fmt_str, datefmt="%Y-%m-%d %H:%M:%S") - - # Stream handler to print log statements to stdout - st_handler = logging.StreamHandler(sys.stdout) - st_handler.setFormatter(formatter) - logger.addHandler(st_handler) - - # File handler to print log statements to text file `filename` - if self.path.LOGFILE is not None: - file_handler = logging.FileHandler(self.path.LOGFILE, "a") - file_handler.setFormatter(formatter) - logger.addHandler(file_handler) - - return logger + # @property + # def logger(self): + # """ + # An instance specific logger which imprints inheritance information into + # the log statements, making it easier to debug functions with + # multiple inheritance + # """ + # if self._logger is None: + # self._logger = self._get_logger() + # return self._logger + + # def _get_logger(self): + # """ + # Define an instance specific logger at run time which will imprint + # inheritance information onto log statements, making it easier to debug + # functions that might have multiple points of inheritance. + + # All loggers will write to the same main log file and also print to + # stdout. PAR.VERBOSE and PAR.LOG_LEVEL both control the amount of + # information that gets printed to the log file. + # """ + # # logger = logging.getLogger( + # # self.__class__.__name__).getChild(self.__class__.__qualname__) + # logger = logging.getLogger(self.__class__.__name__) + + # # Two levels of verbosity on log level, triggered with PAR.VERBOSE + # if self.par.VERBOSE: + # # More verbose logging statement with levelname and func name + # fmt_str = ( + # "%(asctime)s | %(levelname)-5s | %(name)s.%(funcName)s()\n" + # "> %(message)s" + # ) + # else: + # # Clean logging statement with only time and message + # fmt_str = "%(asctime)s | %(message)s" + + # # Instantiate logger during _register() as we now have user-defined pars + # logger.setLevel(self.par.LOG_LEVEL) + # formatter = logging.Formatter(fmt_str, datefmt="%Y-%m-%d %H:%M:%S") + + # # Stream handler to print log statements to stdout + # st_handler = logging.StreamHandler(sys.stdout) + # st_handler.setFormatter(formatter) + # logger.addHandler(st_handler) + + # # File handler to print log statements to text file `filename` + # if self.path.LOGFILE is not None: + # file_handler = logging.FileHandler(self.path.LOGFILE, "a") + # file_handler.setFormatter(formatter) + # logger.addHandler(file_handler) + + # return logger @property def par(self): diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 82588d5d..5f383738 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -27,7 +27,8 @@ from IPython import embed from seisflows.core import Dict, SeisFlowsPathsParameters -from seisflows.config import custom_import, save, NAMES, ROOT_DIR, CFGPATHS +from seisflows.config import (custom_import, save, NAMES, ROOT_DIR, CFGPATHS, + config_logger) from seisflows.tools import unix, msg from seisflows.tools.specfem import (getpar, setpar, getpar_vel_model, setpar_vel_model) @@ -732,6 +733,10 @@ def submit(self, **kwargs): self._register_modules() self._check_parameters() + config_logger(level=self._parameters.LOG_LEVEL, + verbose=self._parameters.VERBOSE, + filename=self._paths.LOGFILE) + # Submit workflow.main() to the system system = sys.modules["seisflows_system"] system.submit() @@ -784,6 +789,11 @@ def resume(self, **kwargs): """ self._register_parameters() self._load_modules() + + config_logger(level=self._parameters.LOG_LEVEL, + verbose=self._parameters.VERBOSE, + filename=self._paths.LOGFILE) + system = sys.modules["seisflows_system"] system.submit() diff --git a/seisflows/system/cluster.py b/seisflows/system/cluster.py index ef9d87d0..a2a8adb6 100644 --- a/seisflows/system/cluster.py +++ b/seisflows/system/cluster.py @@ -108,8 +108,3 @@ def taskid(self): """ raise NotImplementedError('Must be implemented by subclass.') - def checkpoint(self, path, classname, method, kwargs): - """Inherits from workflow.system.workstation.Workstation""" - self.checkpoint(path=path, classname=classname, method=method, - kwargs=kwargs) - diff --git a/seisflows/system/runscripts/run_function.py b/seisflows/system/runscripts/run_function.py index 1c650bc4..064d673e 100644 --- a/seisflows/system/runscripts/run_function.py +++ b/seisflows/system/runscripts/run_function.py @@ -20,7 +20,7 @@ import pickle import argparse -from seisflows.config import load +from seisflows.config import load, config_logger diff --git a/seisflows/system/runscripts/submit_workflow.py b/seisflows/system/runscripts/submit_workflow.py index eb72b06c..802ac22e 100644 --- a/seisflows/system/runscripts/submit_workflow.py +++ b/seisflows/system/runscripts/submit_workflow.py @@ -19,7 +19,7 @@ import argparse from seisflows.tools import unix -from seisflows.config import load +from seisflows.config import load, config_logger def parse_args(): @@ -52,6 +52,7 @@ def parse_args(): # Set up logging on the compute system to print to stdout only PAR = sys.modules["seisflows_parameters"] PATH = sys.modules["seisflows_paths"] + config_logger(level=PAR.LOG_LEVEL, verbose=PAR.VERBOSE) # Execute MASTER JOB as workflow.main() workflow.main() diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index 336551d7..7337d920 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -213,7 +213,7 @@ def _check_job_status(self, job_ids): # Sometimes states can be something like 'CANCELLED+', so # we can't do exact string matching, check partial matches if any([check in state for check in bad_states]): - return job_id, state + return job_ids[i], state # WAIT CONDITION: if sacct is not working, we'll get stuck in a loop if "UNDEFINED" in states: count += 1 From 3b47faa7abe7a7603690aed6705ecc1b4ba59057 Mon Sep 17 00:00:00 2001 From: bch0w Date: Tue, 5 Jul 2022 21:40:43 +0000 Subject: [PATCH 044/195] logger fixed for submitted processes on system as well as main logger. --- seisflows/config.py | 5 +-- seisflows/core.py | 54 ------------------------------ seisflows/examples/parameters.yaml | 2 +- seisflows/workflow/test.py | 36 ++++++++++++++++---- 4 files changed, 34 insertions(+), 63 deletions(-) diff --git a/seisflows/config.py b/seisflows/config.py index 93893e59..f093cc80 100755 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -150,9 +150,10 @@ def config_logger(level="DEBUG", filename=None, filemode="a", verbose=True): # Two levels of verbosity on log level, triggered with PAR.VERBOSE if verbose: - # More verbose logging statement with levelname and func name + # More verbose logging statement for debugging fmt_str = ( - "%(asctime)s | %(levelname)-5s | %(name)s.%(funcName)s()\n" + "%(asctime)s | %(levelname)-5s | " + "%(filename)s -> %(funcName)s():L%(lineno)s\n" "> %(message)s" ) else: diff --git a/seisflows/core.py b/seisflows/core.py index 961c6f16..99609851 100755 --- a/seisflows/core.py +++ b/seisflows/core.py @@ -24,7 +24,6 @@ def __init__(self): then used to build the parameter file dynamically. """ self.required = SeisFlowsPathsParameters() - # self._logger = None def module(self, name): """ @@ -41,59 +40,6 @@ def module(self, name): mod = None return mod - # @property - # def logger(self): - # """ - # An instance specific logger which imprints inheritance information into - # the log statements, making it easier to debug functions with - # multiple inheritance - # """ - # if self._logger is None: - # self._logger = self._get_logger() - # return self._logger - - # def _get_logger(self): - # """ - # Define an instance specific logger at run time which will imprint - # inheritance information onto log statements, making it easier to debug - # functions that might have multiple points of inheritance. - - # All loggers will write to the same main log file and also print to - # stdout. PAR.VERBOSE and PAR.LOG_LEVEL both control the amount of - # information that gets printed to the log file. - # """ - # # logger = logging.getLogger( - # # self.__class__.__name__).getChild(self.__class__.__qualname__) - # logger = logging.getLogger(self.__class__.__name__) - - # # Two levels of verbosity on log level, triggered with PAR.VERBOSE - # if self.par.VERBOSE: - # # More verbose logging statement with levelname and func name - # fmt_str = ( - # "%(asctime)s | %(levelname)-5s | %(name)s.%(funcName)s()\n" - # "> %(message)s" - # ) - # else: - # # Clean logging statement with only time and message - # fmt_str = "%(asctime)s | %(message)s" - - # # Instantiate logger during _register() as we now have user-defined pars - # logger.setLevel(self.par.LOG_LEVEL) - # formatter = logging.Formatter(fmt_str, datefmt="%Y-%m-%d %H:%M:%S") - - # # Stream handler to print log statements to stdout - # st_handler = logging.StreamHandler(sys.stdout) - # st_handler.setFormatter(formatter) - # logger.addHandler(st_handler) - - # # File handler to print log statements to text file `filename` - # if self.path.LOGFILE is not None: - # file_handler = logging.FileHandler(self.path.LOGFILE, "a") - # file_handler.setFormatter(formatter) - # logger.addHandler(file_handler) - - # return logger - @property def par(self): """ diff --git a/seisflows/examples/parameters.yaml b/seisflows/examples/parameters.yaml index f32a3cb2..003090d1 100644 --- a/seisflows/examples/parameters.yaml +++ b/seisflows/examples/parameters.yaml @@ -24,9 +24,9 @@ # PREPROCESS (str): Preprocessing schema for waveform data # POSTPROCESS (str): Postprocessing schema for kernels and gradients # ============================================================================== +SYSTEM: workstation WORKFLOW: inversion SOLVER: specfem2d -SYSTEM: workstation OPTIMIZE: gradient PREPROCESS: default POSTPROCESS: default diff --git a/seisflows/workflow/test.py b/seisflows/workflow/test.py index 9e80aba4..f4f931c2 100644 --- a/seisflows/workflow/test.py +++ b/seisflows/workflow/test.py @@ -13,7 +13,7 @@ from glob import glob from seisflows.core import Base -from seisflows.config import ROOT_DIR, CFGPATHS, save +from seisflows.config import ROOT_DIR, CFGPATHS, save, config_logger class Test(Base): @@ -58,10 +58,10 @@ def main(self, return_flow=False): """ This controls the main testing workflow """ - FLOW = [#self.test_system, + FLOW = [self.test_system, # self.test_preprocess, # self.test_solver, - self.test_optimize + # self.test_optimize ] if return_flow: return FLOW @@ -78,6 +78,29 @@ def _test_function_print(self, check_value): print(f"Hello world, from taskid {system.taskid()}. " f"Check: {check_value}") + config_logger(level="DEBUG", filemode="a", verbose=False) + + system.logger.info(f"Hello world, from taskid {system.taskid()}. " + f"Logger 'info' message. Check: {check_value}") + + system.logger.debug(f"Hello world, from taskid {system.taskid()}. " + f"Logger 'debug' message. Check: {check_value}") + + system.logger.warning(f"Hello world, from taskid {system.taskid()}. " + f"Logger 'warning' message. Check: {check_value}") + + + config_logger(level="DEBUG", filemode="a", verbose=True) + + system.logger.info(f"Hello world, from taskid {system.taskid()}. " + f"Logger 'info' message. Check: {check_value}") + + system.logger.debug(f"Hello world, from taskid {system.taskid()}. " + f"Logger 'debug' message. Check: {check_value}") + + system.logger.warning(f"Hello world, from taskid {system.taskid()}. " + f"Logger 'warning' message. Check: {check_value}") + def test_system(self): """ Test the system by submitting a simple print statement using the @@ -91,14 +114,15 @@ def test_system(self): # Run a very simple test function using system.run() check_value_1 = 1234.5 - system.run(classname="workflow", method="test_function", + system.run(classname="workflow", method="_test_function_print", check_value=check_value_1) time.sleep(3) # wait a bit for system to catch up check_value_2 = 5432.1 - system.run(classname="workflow", method="test_function", single=True, - check_value=check_value_2) + system.run(classname="workflow", method="_test_function_print", + single=True, check_value=check_value_2) + # Check the output log files to match the check values for fid, check in zip( From 317dc00dbc8ac251b411a327fc353af02d23229c Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Tue, 5 Jul 2022 13:59:43 -0800 Subject: [PATCH 045/195] removing Minmax and Container classes which served very minimal purpose for the amount of abstraction they brought in --- seisflows/solver/specfem.py | 5 +++-- seisflows/system/workstation.py | 10 +++++----- seisflows/tools/specfem.py | 35 --------------------------------- seisflows/workflow/test.py | 7 +++---- 4 files changed, 11 insertions(+), 46 deletions(-) diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index f0448ad0..6b942802 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -14,7 +14,7 @@ from seisflows.core import Base, Dict from seisflows.plugins import solver_io from seisflows.tools import msg, unix -from seisflows.tools.specfem import Container, getpar +from seisflows.tools.specfem import getpar from seisflows.tools.wrappers import diff @@ -604,7 +604,7 @@ def load(self, path, prefix="", suffix="", parameters=None): if parameters is None: parameters = self.parameters - load_dict = Container() + load_dict = Dict() for iproc in range(self.mesh_properties.nproc): for key in parameters: load_dict[key] += self._io.read_slice( @@ -705,6 +705,7 @@ def combine(self, input_path, output_path, parameters=None): Postprocessing wrapper: xcombine_sem Sums kernels from individual source contributions to create gradient. + .. note:: The binary xcombine_sem simply sums matching databases (.bin) diff --git a/seisflows/system/workstation.py b/seisflows/system/workstation.py index 4028e3fe..9cbd409d 100644 --- a/seisflows/system/workstation.py +++ b/seisflows/system/workstation.py @@ -195,12 +195,12 @@ def run(self, classname, method, single=False, **kwargs): if taskid == 0: self.logger.info(f"running task {classname}.{method} " f"{self.par.NTASK} times") - function(**kwargs) - # Redirect output to a log file to mimic cluster runs - # with open(log_file, "w") as f: - # with redirect_stdout(f): - # function(**kwargs) + # Redirect output to a log file to mimic cluster runs where 'run' + # task output logs are sent to different files + with open(log_file, "w") as f: + with redirect_stdout(f): + function(**kwargs) def taskid(self): """ diff --git a/seisflows/tools/specfem.py b/seisflows/tools/specfem.py index bfc17db3..9a1cda5c 100644 --- a/seisflows/tools/specfem.py +++ b/seisflows/tools/specfem.py @@ -2,40 +2,6 @@ Utilities to interact with, manipulate or call on the external solver, i.e., SPECFEM2D/3D/3D_GLOBE """ -import sys -import numpy as np - -from collections import defaultdict -from seisflows.tools import msg -from seisflows.tools.math import poissons_ratio -from seisflows.tools.wrappers import iterable - - -class Minmax(defaultdict): - """ - Keeps track of min, max values of model or kernel - """ - def __init__(self): - super(Minmax, self).__init__(lambda: [+np.inf, -np.inf]) - - def update(self, keys, vals): - for key, val in zip(iterable(keys), iterable(vals)): - if min(val) < self.dict[key][0]: - self.dict[key][0] = min(val) - if max(val) > self.dict[key][1]: - self.dict[key][1] = max(val) - - def __call__(self, key): - return self.dict[key] - - -class Container(defaultdict): - """ - Dictionary-like object for holding models or kernels - """ - def __init__(self): - super(Container, self).__init__(lambda: []) - self.minmax = Minmax() def getpar(key, file, delim="=", match_partial=False): @@ -225,4 +191,3 @@ def setpar_vel_model(file, model): # Set nbmodels to the correct value setpar(key="nbmodels", val=len(model), file=file) - diff --git a/seisflows/workflow/test.py b/seisflows/workflow/test.py index f4f931c2..ace180d8 100644 --- a/seisflows/workflow/test.py +++ b/seisflows/workflow/test.py @@ -89,7 +89,6 @@ def _test_function_print(self, check_value): system.logger.warning(f"Hello world, from taskid {system.taskid()}. " f"Logger 'warning' message. Check: {check_value}") - config_logger(level="DEBUG", filemode="a", verbose=True) system.logger.info(f"Hello world, from taskid {system.taskid()}. " @@ -122,7 +121,6 @@ def test_system(self): check_value_2 = 5432.1 system.run(classname="workflow", method="_test_function_print", single=True, check_value=check_value_2) - # Check the output log files to match the check values for fid, check in zip( @@ -135,8 +133,9 @@ def test_system(self): # Check that MPI Exec works assert("MPIEXEC" in self.par), f"MPIEXEC is not defined for this system" - stdout = subprocess.run(self.par.MPIEXEC, shell=True, check=True, - stdout=subprocess.PIPE) + if self.par.MPIEXEC: + stdout = subprocess.run(self.par.MPIEXEC, shell=True, check=True, + stdout=subprocess.PIPE) def test_preprocess(self): """ From 9a63fa59b1c5959e18f5a9459c6f57ea1aca61f5 Mon Sep 17 00:00:00 2001 From: bch0w Date: Tue, 5 Jul 2022 22:25:56 +0000 Subject: [PATCH 046/195] fix small bug in solver trying to load model values into a non-instantiated dictionary --- seisflows/solver/specfem.py | 11 +++++++---- seisflows/system/slurm.py | 2 +- 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 6b942802..5802650a 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -604,14 +604,17 @@ def load(self, path, prefix="", suffix="", parameters=None): if parameters is None: parameters = self.parameters - load_dict = Dict() + # Initiate empty dictionary to hold model values + model_dict = Dict({key: [] for key in self.parameters}) + for iproc in range(self.mesh_properties.nproc): for key in parameters: - load_dict[key] += self._io.read_slice( + _model_slice_values = self._io.read_slice( path=path, parameters=f"{prefix}{key}{suffix}", iproc=iproc - ) + ) + model_dict[key].extend(_model_slice_values) - return load_dict + return model_dict def save(self, save_dict, path, parameters=None, prefix="", suffix=""): """ diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index 7337d920..e2f22a81 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -165,7 +165,7 @@ def run(self, classname, method, single=False, run_call=None, **kwargs): header="slurm run error", border="=")) sys.exit(-1) - self.logger.info(f"Task {classname}.{method} finished successfully") + self.logger.info(f"task {classname}.{method} finished successfully") def taskid(self): """ From 7a567230b0407f57c30aab288a449a1fb43100a9 Mon Sep 17 00:00:00 2001 From: bch0w Date: Tue, 5 Jul 2022 23:44:33 +0000 Subject: [PATCH 047/195] starting to separate solver model manipulation functions from the class (e.g., load, save, merge) --- seisflows/solver/specfem.py | 8 ++++---- seisflows/solver/specfem3d_globe.py | 15 +++++++++++++-- 2 files changed, 17 insertions(+), 6 deletions(-) diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 5802650a..25c3f37d 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -620,8 +620,8 @@ def save(self, save_dict, path, parameters=None, prefix="", suffix=""): """ Solver I/O: Saves SPECFEM2D/3D models or kernels - :type save_dict: dict or Container - :param save_dict: model stored as a dictionary or Container + :type save_dict: Dict + :param save_dict: model stored by parameter key :type path: str :param path: directory from which model is read :type parameters: list @@ -692,14 +692,14 @@ def split(self, m, parameters=None): nproc = self.mesh_properties.nproc ngll = self.mesh_properties.ngll - model = Container() + model = Dict() for idim, key in enumerate(parameters): model[key] = [] for iproc in range(nproc): imin = sum(ngll) * idim + sum(ngll[:iproc]) imax = sum(ngll) * idim + sum(ngll[:iproc + 1]) - model[key] += [m[imin:imax]] + model[key].extend([m[imin:imax]]) return model diff --git a/seisflows/solver/specfem3d_globe.py b/seisflows/solver/specfem3d_globe.py index 1e6fb021..b2033383 100644 --- a/seisflows/solver/specfem3d_globe.py +++ b/seisflows/solver/specfem3d_globe.py @@ -4,6 +4,9 @@ This class provides utilities for the Seisflows solver interactions with Specfem3D Globe. It inherits all attributes from seisflows.solver.specfem3d, and overwrites these functions to provide specified interaction with Specfem3D. + +SPECFEM3D_Globe specfic notes: + - does not allow SU seismogram outputs, only ASCII, SAC, ASDF, 3D_Array """ import os from glob import glob @@ -44,8 +47,16 @@ def load(self, path, prefix="reg1_", suffix="", parameters=None): """ Reads SPECFEM model or kernel - Models are stored in Fortran binary format and separated into - multiple files according to material parameter and processor rank. + .. note:: + SPECFEM3D_Globe meshes are broken into 3 regions. + Region 1 == Crust + Mantle + Region 2 == Outer core + Region 3 == Inner core + + .. warning:: + Currently SeisFlows only considers the crust + mantle in Globe + simulations + :type path: str :param path: directory from which model is read From 6fc60ca1256f48a0fa0588056e32efca24fd34cd Mon Sep 17 00:00:00 2001 From: bch0w Date: Tue, 5 Jul 2022 23:49:49 +0000 Subject: [PATCH 048/195] bug fixing missing imports --- seisflows/solver/specfem3d_globe.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/seisflows/solver/specfem3d_globe.py b/seisflows/solver/specfem3d_globe.py index b2033383..9b02c757 100644 --- a/seisflows/solver/specfem3d_globe.py +++ b/seisflows/solver/specfem3d_globe.py @@ -12,9 +12,7 @@ from glob import glob from seisflows.solver.specfem3d import Specfem3D -from seisflows.tools.specfem import Minmax from seisflows.tools import unix -from seisflows.tools.wrappers import Struct, exists class Specfem3DGlobe(Specfem3D): @@ -142,7 +140,7 @@ def check_mesh_properties(self, path=None, parameters=None): dummy = loadbin(path, nproc, 'reg1_' + parameters[0]) ngll += [len(dummy)] nproc += 1 - if not exists( + if not os.path.exists( os.path.join(path, f"proc{nrpoc}_reg1_{parameters[0]}.bin")): break From 918ef00ff1f937d4e2d0dd2a41a3606f01b071d9 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Wed, 6 Jul 2022 10:41:55 -0800 Subject: [PATCH 049/195] working to separate solver from model manipulation functinoalities --- seisflows/solver/specfem.py | 67 +++++++++++++++++------------------ seisflows/workflow/forward.py | 2 +- 2 files changed, 34 insertions(+), 35 deletions(-) diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 25c3f37d..e6496528 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -331,10 +331,8 @@ def check(self, validate=True): self.parameters.append("rho") assert hasattr(solver_io, self.par.SOLVERIO) - assert hasattr(self._io, "read_slice"), \ - "IO method has no attribute 'read_slice'" - assert hasattr(self._io, "write_slice"), \ - "IO method has no attribute 'write_slice'" + assert hasattr(self._io, "read"), "IO method has no attribute 'read'" + assert hasattr(self._io, "write"), "IO method has no attribute 'write'" def setup(self): """ @@ -584,37 +582,38 @@ def _call_solver(self, executable, output="solver.log"): ) sys.exit(-1) - def load(self, path, prefix="", suffix="", parameters=None): - """ - Solver I/O: Loads SPECFEM2D/3D models or kernels - - :type path: str - :param path: directory from which model is read - :type prefix: str - :param prefix: optional filename prefix - :type suffix: str - :param suffix: optional filename suffix, eg '_kernel' - :type parameters: list - :param parameters: material parameters to be read - (if empty, defaults to self.parameters) - :rtype: dict - :return: model or kernels indexed by material parameter and - processor rank, ie dict[parameter][iproc] - """ - if parameters is None: - parameters = self.parameters - - # Initiate empty dictionary to hold model values - model_dict = Dict({key: [] for key in self.parameters}) - - for iproc in range(self.mesh_properties.nproc): - for key in parameters: - _model_slice_values = self._io.read_slice( - path=path, parameters=f"{prefix}{key}{suffix}", iproc=iproc - ) - model_dict[key].extend(_model_slice_values) - return model_dict + # def load(self, path, prefix="", suffix="", parameters=None): + # """ + # Solver I/O: Loads SPECFEM2D/3D models or kernels + # + # :type path: str + # :param path: directory from which model is read + # :type prefix: str + # :param prefix: optional filename prefix + # :type suffix: str + # :param suffix: optional filename suffix, eg '_kernel' + # :type parameters: list + # :param parameters: material parameters to be read + # (if empty, defaults to self.parameters) + # :rtype: dict + # :return: model or kernels indexed by material parameter and + # processor rank, ie dict[parameter][iproc] + # """ + # if parameters is None: + # parameters = self.parameters + # + # # Initiate empty dictionary to hold model values + # model_dict = Dict({key: [] for key in self.parameters}) + # + # for iproc in range(self.mesh_properties.nproc): + # for key in parameters: + # _model_slice_values = self._io.read_slice( + # path=path, parameters=f"{prefix}{key}{suffix}", iproc=iproc + # ) + # model_dict[key].extend(_model_slice_values) + # + # return model_dict def save(self, save_dict, path, parameters=None, prefix="", suffix=""): """ diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index 8101c1e5..4b38bb65 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -19,7 +19,7 @@ class Forward(Base): Workflow abstract base class representing an en-masse forward solver and misfit calculator. """ - def __init__(self): + def __init__(self, path_data): """ These parameters should not be set by the user. Attributes are initialized as NoneTypes for clarity and docstrings. From 458b1db05f0cd57fbb96008305c008159ae1b0b7 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Wed, 6 Jul 2022 16:17:28 -0800 Subject: [PATCH 050/195] created a new specfem Model class which is in charge of reading, checking, vectorizing and saving specfem models/kernels and gradients. This replaces the solver.merge, split and load functionalities which were difficult to split away from the solver moduel, making it less flexible --- seisflows/config.py | 15 - seisflows/optimize/gradient.py | 82 ++-- seisflows/solver/specfem.py | 65 ++- .../proc000000_rho_vp_vs.dat | 3 + .../test_file_formats/proc000000_vp.bin | Bin 0 -> 160008 bytes .../test_file_formats/proc000000_vs.bin | Bin 0 -> 160008 bytes seisflows/tests/test_seisflows.py | 3 +- seisflows/tests/test_tools.py | 15 + seisflows/tools/specfem.py | 424 ++++++++++++++++++ 9 files changed, 516 insertions(+), 91 deletions(-) create mode 100644 seisflows/tests/test_data/test_file_formats/proc000000_rho_vp_vs.dat create mode 100644 seisflows/tests/test_data/test_file_formats/proc000000_vp.bin create mode 100644 seisflows/tests/test_data/test_file_formats/proc000000_vs.bin create mode 100644 seisflows/tests/test_tools.py diff --git a/seisflows/config.py b/seisflows/config.py index f093cc80..c844e55f 100755 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -106,21 +106,6 @@ def load(path): sys.modules[f"seisflows_{name}"] = pickle.load(f) -def flush(): - """ - It is sometimes necessary to flush the currently active working state to - avoid affecting subsequent working states (e.g., running tests back to back) - This command will flush sys.modules of all `seisflows_{}` modules that are - typically instantiated using load(), or init_seisflows() - - https://stackoverflow.com/questions/1668223/how-to-de-import-a-python-module - """ - for name in NAMES: - mod_name = f"seisflows_{name}" - if mod_name in sys.modules: - del sys.modules[mod_name] - - def config_logger(level="DEBUG", filename=None, filemode="a", verbose=True): """ Explicitely configure the logging module with some parameters defined diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index 7df3507a..78186ebc 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -421,44 +421,44 @@ def _write_stats(self): f"{step_length:6.3E}," f"{theta:6.3E}\n" ) - - def check_model(self, m, min_pr=-1., max_pr=0.5): - """ - Check to ensure that the model parameters fall within the guidelines - of the solver. Print off min/max model parameters for the User. - - :type m: np.array - :param m: model to check parameters of - :type min_pr: float - :param min_pr: minimum allowable Poisson's ratio value dictated by - SPECFEM - :type max_pr: float - :param max_pr: maximum allowable Poisson's ratio value dictated by - SPECFEM - """ - solver = self.module("solver") - - # Dynamic way to split up the model based on number of params - pars = {} - for i, par in enumerate(solver.parameters): - pars[par] = np.split(m, len(solver.parameters))[i] - - # Check Poisson's ratio, which will error our SPECFEM if outside limits - if (pars["vp"] is not None) and (pars["vs"] is not None): - pars["pr"] = poissons_ratio(vp=pars["vp"], vs=pars["vs"]) - if pars["pr"].min() < 0: - self.logger.warning("minimum poisson's ratio is negative") - if pars["pr"].max() < min_pr: - self.logger.warning(f"maximum poisson's ratio out of bounds: " - f"{pars['pr'].max():.2f} > {max_pr}") - if pars["pr"].min() > max_pr: - self.logger.warning(f"minimum poisson's ratio out of bounds: " - f"{pars['pr'].min():.2f} < {min_pr}") - - # Tell the User min and max values of the updated model - self.logger.info(f"model parameters") - parts = "{minval:.2f} <= {key} <= {maxval:.2f}" - for key, vals in pars.items(): - self.logger.info(parts.format(minval=vals.min(), key=key, - maxval=vals.max()) - ) + # + # def check_model(self, m, min_pr=-1., max_pr=0.5): + # """ + # Check to ensure that the model parameters fall within the guidelines + # of the solver. Print off min/max model parameters for the User. + # + # :type m: np.array + # :param m: model to check parameters of + # :type min_pr: float + # :param min_pr: minimum allowable Poisson's ratio value dictated by + # SPECFEM + # :type max_pr: float + # :param max_pr: maximum allowable Poisson's ratio value dictated by + # SPECFEM + # """ + # solver = self.module("solver") + # + # # Dynamic way to split up the model based on number of params + # pars = {} + # for i, par in enumerate(solver.parameters): + # pars[par] = np.split(m, len(solver.parameters))[i] + # + # # Check Poisson's ratio, which will error our SPECFEM if outside limits + # if (pars["vp"] is not None) and (pars["vs"] is not None): + # pars["pr"] = poissons_ratio(vp=pars["vp"], vs=pars["vs"]) + # if pars["pr"].min() < 0: + # self.logger.warning("minimum poisson's ratio is negative") + # if pars["pr"].max() < min_pr: + # self.logger.warning(f"maximum poisson's ratio out of bounds: " + # f"{pars['pr'].max():.2f} > {max_pr}") + # if pars["pr"].min() > max_pr: + # self.logger.warning(f"minimum poisson's ratio out of bounds: " + # f"{pars['pr'].min():.2f} < {min_pr}") + # + # # Tell the User min and max values of the updated model + # self.logger.info(f"model parameters") + # parts = "{minval:.2f} <= {key} <= {maxval:.2f}" + # for key, vals in pars.items(): + # self.logger.info(parts.format(minval=vals.min(), key=key, + # maxval=vals.max()) + # ) diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index e6496528..adca3ea4 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -331,8 +331,8 @@ def check(self, validate=True): self.parameters.append("rho") assert hasattr(solver_io, self.par.SOLVERIO) - assert hasattr(self._io, "read"), "IO method has no attribute 'read'" - assert hasattr(self._io, "write"), "IO method has no attribute 'write'" + assert hasattr(self._io, "read_slice"), "IO method has no attribute 'read'" + assert hasattr(self._io, "write_slice"), "IO method has no attribute 'write'" def setup(self): """ @@ -582,38 +582,37 @@ def _call_solver(self, executable, output="solver.log"): ) sys.exit(-1) + def load(self, path, prefix="", suffix="", parameters=None): + """ + Solver I/O: Loads SPECFEM2D/3D models or kernels - # def load(self, path, prefix="", suffix="", parameters=None): - # """ - # Solver I/O: Loads SPECFEM2D/3D models or kernels - # - # :type path: str - # :param path: directory from which model is read - # :type prefix: str - # :param prefix: optional filename prefix - # :type suffix: str - # :param suffix: optional filename suffix, eg '_kernel' - # :type parameters: list - # :param parameters: material parameters to be read - # (if empty, defaults to self.parameters) - # :rtype: dict - # :return: model or kernels indexed by material parameter and - # processor rank, ie dict[parameter][iproc] - # """ - # if parameters is None: - # parameters = self.parameters - # - # # Initiate empty dictionary to hold model values - # model_dict = Dict({key: [] for key in self.parameters}) - # - # for iproc in range(self.mesh_properties.nproc): - # for key in parameters: - # _model_slice_values = self._io.read_slice( - # path=path, parameters=f"{prefix}{key}{suffix}", iproc=iproc - # ) - # model_dict[key].extend(_model_slice_values) - # - # return model_dict + :type path: str + :param path: directory from which model is read + :type prefix: str + :param prefix: optional filename prefix + :type suffix: str + :param suffix: optional filename suffix, eg '_kernel' + :type parameters: list + :param parameters: material parameters to be read + (if empty, defaults to self.parameters) + :rtype: dict + :return: model or kernels indexed by material parameter and + processor rank, ie dict[parameter][iproc] + """ + if parameters is None: + parameters = self.parameters + + # Initiate empty dictionary to hold model values + model_dict = Dict({key: [] for key in self.parameters}) + + for iproc in range(self.mesh_properties.nproc): + for key in parameters: + _model_slice_values = self._io.read_slice( + path=path, parameters=f"{prefix}{key}{suffix}", iproc=iproc + ) + model_dict[key].extend(_model_slice_values) + + return model_dict def save(self, save_dict, path, parameters=None, prefix="", suffix=""): """ diff --git a/seisflows/tests/test_data/test_file_formats/proc000000_rho_vp_vs.dat b/seisflows/tests/test_data/test_file_formats/proc000000_rho_vp_vs.dat new file mode 100644 index 00000000..1d964709 --- /dev/null +++ b/seisflows/tests/test_data/test_file_formats/proc000000_rho_vp_vs.dat @@ -0,0 +1,3 @@ + 0.00000E+0000 0.00000E+0000 0.26000E+0004 0.58000E+0004 0.35000E+0004 + 0.20721E+0004 0.00000E+0000 0.26000E+0004 0.58000E+0004 0.35000E+0004 + 0.60000E+0004 0.00000E+0000 0.26000E+0004 0.58000E+0004 0.35000E+0004 diff --git a/seisflows/tests/test_data/test_file_formats/proc000000_vp.bin b/seisflows/tests/test_data/test_file_formats/proc000000_vp.bin new file mode 100644 index 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ROOT_DIR, NAMES, CFGPATHS from seisflows.tools.wrappers import loadyaml TEST_DIR = os.path.join(ROOT_DIR, "tests") diff --git a/seisflows/tests/test_tools.py b/seisflows/tests/test_tools.py new file mode 100644 index 00000000..23eff2fe --- /dev/null +++ b/seisflows/tests/test_tools.py @@ -0,0 +1,15 @@ +""" +Test any of the utility functions defined in the Tools directory +""" +import os +import pytest +from seisflows.config import ROOT_DIR, NAMES, CFGPATHS + + +TEST_DIR = os.path.join(ROOT_DIR, "tests") + + +def test_load_specfem_model(): + """ + Make sure we can dynamically load SPECFEM models in various formats + """ diff --git a/seisflows/tools/specfem.py b/seisflows/tools/specfem.py index 9a1cda5c..fbd643f6 100644 --- a/seisflows/tools/specfem.py +++ b/seisflows/tools/specfem.py @@ -2,6 +2,385 @@ Utilities to interact with, manipulate or call on the external solver, i.e., SPECFEM2D/3D/3D_GLOBE """ +import os +import numpy as np +from glob import glob +from seisflows import logger +from seisflows.core import Dict +from seisflows.tools import unix +from seisflows.tools.math import poissons_ratio + + +class Model: + """ + A container for reading, storing and manipulating model/gradient/kernel + parameters from SPECFEM2D/3D/3D_GLOBE. + Stores metadata information alongside model data allowing models to be + converted to back and forth between vector representations required by the + optimization library. + Also contains utility functions to read/write itself so that models can be + saved alongside their metadata. + """ + def __init__(self, path, fmt=None, read=True, load=False): + """ + Model only needs path to model to determine model parameters. Format + `fmt` can be provided by the user or guessed based on available file + formats + + :type path: str + :param path: path to SPECFEM model/kernel/gradient files + :type fmt: str + :param fmt: expected format of the files (e.g., '.bin'), if None, will + attempt to guess based on the file extensions found in `path` + Available formats are: .bin, .dat + :type load: bool + :param load: load the model into disk as dictionary and vector + representations. If False, will only collect some metadata as a + non-cpu intensive operation + """ + assert os.path.exists(path), f"specfem model path {path} does not exist" + self.path = path + + if read: + if fmt is None: + self.fmt = self._guess_file_format() + else: + self.fmt = fmt + self.nproc, self.available_parameters = self._get_nproc_parameters() + self.model, self.ngll = self.read() + elif load: + self.model, self.ngll = self.load(file=self.path) + _first_key = list(self.model.keys())[0] + self.nproc = len(self.model[_first_key]) + + self.parameters = self.model.keys() + self.vector = self.merge() + self.check() + + @staticmethod + def fnfmt(i="*", val="*", ext="*"): + """ + Expected SPECFEM filename format with some checks to ensure that wildcards + and numbers are accepted. An example filename is: 'proc000001_vs.bin' + + :type i: int or str + :param i: processor number or wildcard. If given as an integer, will be + converted to a 6 digit zero-leading value to match SPECFEM format + :type val: str + :param val: parameter value (e.g., 'vs') or wildcard + :type ext: str + :param ext: the file format (e.g., '.bin'). If NOT preceded by a leading '.' + will have one prepended + :rtype: str + :return: filename formatter for use in model manipulation + """ + if not ext.startswith("."): + ext = f".{ext}" + if isinstance(i, int): + filename_format = f"proc{i:0>6}_{val}{ext}" + else: + filename_format = f"proc{i}_{val}{ext}" + return filename_format + + def read(self, parameters=None): + """ + Utility function to load in SPECFEM models/kernels/gradients saved in + various formats. Will try to guess format of model. Assumes that models are + saved using the following filename format: + + proc{num}_{val}.{format} where `num` is usually a 6 digit number + representing the processor number (e.g., 000000), `val` is the parameter + value of the model/kernel/gradient and `format` is the format of the file + (e.g., bin) + + :type parameters: list of str + :param parameters: unique parameters to load model for, if None will load + all available parameters found in `path` + :rtype: Dict of np arrays + :return: Dictionary where keys correspond to model parameters and values + are vectors (np.arrays) representing the model + """ + if parameters is None: + parameters = self.available_parameters + else: + assert(set(parameters).union(set(self.available_parameters))), ( + f"user-chosen parameters not in available: " + f"{self.available_parameters}" + ) + + # Pick the correct read function based on the file format + load_fx = {".bin": self._read_model_fortran_binary, + ".dat": self._read_model_ascii, + ".adios": self._read_model_adios # TODO Check if this is right + }[self.fmt] + + # Create a dictionary object containing all parameters and respective + # models, save to internal attribute + parameter_dict = Dict({key: [] for key in parameters}) + for parameter in parameters: + parameter_dict[parameter] = load_fx(parameter=parameter) + + # Save some metadata to be able to manipulate model slices freely + ngll = [] + for array in parameter_dict[parameters[0]]: + ngll.append(len(array)) + + return parameter_dict, ngll + + def merge(self, parameter=None): + """ + Convert dictionary representation `model` to vector representation `m` + where all parameters and processors are stored as a single 1D vector. + This vector representation is used by the optimization library. + + :type parameter: str + :param parameter: single parameter to retrieve model vector from, + otherwise returns all parameters merged into single vector + :rtype: np.array + :return: vector representation of the model + """ + m = np.array([]) + if parameter is None: + parameters = self.parameters + else: + parameters = [parameter] + + for parameter in parameters: + for iproc in range(self.nproc): + m = np.append(m, self.model[parameter][iproc]) + + return m + + def write(self, path, fmt=None): + """ + Save a SPECFEM model/gradient/kernel vector loaded into memory back to + disk in the appropriate format expected by SPECFEM + """ + unix.mkdir(path) + if fmt is None: + assert(self.fmt is not None), f"must specifiy model format: `fmt`" + fmt = self.fmt + + # Pick the correct read function based on the file format + save_fx = {".bin": self._write_model_fortran_binary, + # ".dat": _write_model_ascii, + # ".adios": _write_model_adios # TODO Check if right + }[fmt] + + save_fx(path=path) + + def split(self): + """ + Converts internal vector representation `m` to dictionary representation + `model`. Does this by separating the vector based on how it was + constructed, parameter-wise and processor-wise + + :rtype: Dict of np.array + :return: dictionary of model parameters split up by number of processors + """ + model = Dict({key: [] for key in self.parameters}) + for idim, key in enumerate(self.parameters): + for iproc in range(self.nproc): + imin = sum(self.ngll) * idim + sum(self.ngll[:iproc]) + imax = sum(self.ngll) * idim + sum(self.ngll[:iproc + 1]) + model[key].extend([self.vector[imin:imax]]) + + model[key] = np.array(model[key]) + return model + + def check(self, min_pr=-1., max_pr=0.5): + """ + Checks parameters in the model. If Vs and Vp present, checks poissons + ratio. Checks for negative velocity values. And prints out model + min/max values + """ + if "vs" in self.parameters and "vp" in self.parameters: + pr = poissons_ratio(vp=self.merge(parameter="vp"), + vs=self.merge(parameter="vs")) + if pr.min() < 0: + logger.warning("minimum poisson's ratio is negative") + if pr.max() < min_pr: + logger.warning(f"maximum poisson's ratio out of bounds: " + f"{pr.max():.2f} > {max_pr}") + if pr.min() > max_pr: + logger.warning(f"minimum poisson's ratio out of bounds: " + f"{pr.min():.2f} < {min_pr}") + + if "vs" in self.model and self.model.vs.min() < 0: + logger.warning(f"Vs minimum is negative {self.model.vs.min()}") + + if "vp" in self.model and self.model.vp.min() < 0: + logger.warning(f"Vp minimum is negative {self.model.vp.min()}") + + # Tell the User min and max values of the updated model + logger.info(f"model parameters") + parts = "{minval:.2f} <= {key} <= {maxval:.2f}" + for key, vals in self.model.items(): + logger.info(parts.format(minval=vals.min(), key=key, + maxval=vals.max())) + + def save(self, file): + """ + Save instance attributes (model, vector, metadata) to disk as an + .npz array so that it can be loaded in at a later time for future use + """ + model = self.split() + np.savez(file=file, **model) + + def load(self, file): + """ + Load in a previously saved .npz file containing model information + """ + model = Dict() + ngll = [] + data = np.load(file=file) + for i, key in enumerate(data.files): + model[key] = data[key] + if i == 0: + for array in model[key]: + ngll.append(len(array)) + + return model, ngll + + def _get_nproc_parameters(self): + """ + Get the number of processors and the available parameters from a list of + output SPECFEM model files. + """ + fids = glob(os.path.join(self.path, self.fnfmt(val="*", ext=self.fmt))) + fids = [os.path.basename(_) for _ in fids] # drop full path + fids = [os.path.splitext(_)[0] for _ in fids] # drop extension + + if self.fmt == ".bin": + avail_par = list(set([_.split("_")[-1] for _ in fids])) + nproc = len(glob(os.path.join( + self.path, self.fnfmt(val=avail_par[0], ext=self.fmt))) + ) + elif self.fmt == ".dat": + # e.g., 'proc000000_rho_vp_vs' + _, *avail_par = fids[0].split("_") + nproc = len(fids) + 1 + else: + raise NotImplementedError(f"{self.fmt} is not yet supported by " + f"SeisFlows") + + return nproc, avail_par + + def _guess_file_format(self): + """ + Guess the file format of model/kernel/gradient files if none provided by + the user. Does so by checking file formats against formats expected from + SPECFEM2D/3D/3D_GLOBE + """ + acceptable_formats = {".bin", ".dat"} + + files = glob(os.path.join(self.path, "*")) + suffixes = set([os.path.splitext(_)[1] for _ in files]) + fmt = acceptable_formats.intersection(suffixes) + assert (len(fmt) == 1), ( + f"cannot guess model format, multiple matching acceptable formats " + f"found: {list(suffixes)}" + ) + return list(fmt)[0] # pulling single entry from set + + def _read_model_fortran_binary(self, parameter): + """ + Load Fortran binary models into disk. This is the preferred model format + for SeisFlows <-> SPECFEM interaction + + :type parameter: str + :param parameter: chosen parameter to load model for + :rtype: np.array + :return: vector of model values for given `parameter` + """ + def _read(filename): + """Read a single slice (e.g., proc000000_vs.bin) binary file""" + nbytes = os.path.getsize(filename) + with open(filename, 'rb') as file: + # read size of record + file.seek(0) + n = np.fromfile(file, dtype='int32', count=1)[0] + + if n == nbytes-8: + file.seek(4) + data = np.fromfile(file, dtype='float32') + return data[:-1] + else: + file.seek(0) + data = np.fromfile(file, dtype='float32') + return data + + array = [] + fids = glob(os.path.join( + self.path, self.fnfmt(val=parameter, ext=".bin")) + ) + for fid in sorted(fids): # make sure were going in numerical order + array.append(_read(fid)) + + array = np.array(array) + + return array + + def _read_model_adios(self, parameter): + """ + Load ADIOS models into disk + + :type parameter: str + :param parameter: chosen parameter to load model for + :rtype: np.array + :return: vector of model values for given `parameter` + """ + raise NotImplementedError("ADIOS file formats are not currently " + "implemented into SeisFlows") + + def _read_model_ascii(self, parameter): + """ + Load ASCII SPECFEM2D models into disk. ASCII models are generally saved + all in a single file with all parameters together as a N column ASCII file + where columns 1 and 2 are the coordinates of the mesh, and the remainder + columns are data corresponding to the filenames + e.g., proc000000_rho_vp_vs.dat, rho is column 3, vp is 4 etc. + + :type parameter: str + :param parameter: chosen parameter to load model for + :rtype: np.array + :return: vector of model values for given `parameter` + """ + fids = glob(os.path.join(self.path, self.fnfmt(val="*", ext=".dat"))) + _, *available_parameters = fids[0].split("_") + assert(parameter in available_parameters), ( + f"{parameter} not available for ASCII model" + ) + # +2 because first 2 columns are the X and Z coordinates in the mesh + array = [] + column_idx = available_parameters.index(parameter) + 2 + for fid in sorted(fids): + array.append(np.loadtxt(fid).T[:, column_idx]) + + return np.array(array) + + def _write_model_fortran_binary(self, path): + """ + Save a SPECFEM model back to Fortran binary format. + Data are written as single precision floating point numbers + + .. note:: + FORTRAN unformatted binaries are bounded by an INT*4 byte count. + This function mimics that behavior by tacking on the boundary data + as 'int32' at the top and bottom of the data array. + https://docs.oracle.com/cd/E19957-01/805-4939/6j4m0vnc4/index.html + """ + for parameter in self.parameters: + for i, data in enumerate(self.model[parameter]): + filename = self.fnfmt(i=i, val=parameter, ext=".bin") + filepath = os.path.join(path, filename) + buffer = np.array([4 * len(data)], dtype="int32") + data = data.astype("float32") + + with open(filepath, 'wb') as f: + buffer.tofile(f) + data.tofile(f) + buffer.tofile(f) def getpar(key, file, delim="=", match_partial=False): @@ -191,3 +570,48 @@ def setpar_vel_model(file, model): # Set nbmodels to the correct value setpar(key="nbmodels", val=len(model), file=file) + + +def _read(filename): + """ + Legacy code: Reads Fortran style binary data into numpy array. + + .. note:: + Has been rewritten into the Model class but left here if useful + """ + nbytes = os.path.getsize(filename) + with open(filename, 'rb') as file: + # read size of record + file.seek(0) + n = np.fromfile(file, dtype='int32', count=1)[0] + + if n == nbytes-8: + file.seek(4) + data = np.fromfile(file, dtype='float32') + return data[:-1] + else: + file.seek(0) + data = np.fromfile(file, dtype='float32') + return data + + +def _write(v, filename): + """ + Legacy code: Writes Fortran style binary files + Data are written as single precision floating point numbers + + .. note:: + Has been rewritten into the Model class but left here if useful + + .. note:: + FORTRAN unformatted binaries are bounded by an INT*4 byte count. This + function mimics that behavior by tacking on the boundary data. + https://docs.oracle.com/cd/E19957-01/805-4939/6j4m0vnc4/index.html + """ + n = np.array([4 * len(v)], dtype='int32') + v = np.array(v, dtype='float32') + + with open(filename, 'wb') as file: + n.tofile(file) + v.tofile(file) + n.tofile(file) From dc4aac328c7875b2935d8338ebe44ddd0063d517 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Wed, 6 Jul 2022 17:07:54 -0800 Subject: [PATCH 051/195] starting to replace the old model implementation with the new one throughout the package --- seisflows/optimize/gradient.py | 94 ++++----------- seisflows/postprocess/default.py | 20 ++-- seisflows/seisflows.py | 40 +------ seisflows/solver/specfem.py | 200 ++----------------------------- seisflows/solver/specfem2d.py | 11 -- seisflows/tools/specfem.py | 16 +-- seisflows/workflow/forward.py | 10 +- seisflows/workflow/migration.py | 13 +- 8 files changed, 57 insertions(+), 347 deletions(-) diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index 78186ebc..5c08c393 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -16,8 +16,8 @@ from seisflows.core import Base from seisflows.tools import msg, unix from seisflows.tools.math import angle, dot +from seisflows.tools.specfem import Model from seisflows.plugins import line_search -from seisflows.tools.math import poissons_ratio class Gradient(Base): @@ -59,7 +59,7 @@ def __init__(self): been restarted recently """ super().__init__() - + # Define the Parameters required by this module self.required.par( "LINESEARCH", required=False, default="Bracket", par_type=str, @@ -132,7 +132,6 @@ def setup(self): Sets up nonlinear optimization machinery """ super().setup() - solver = self.module("solver") unix.mkdir(self.path.OPTIMIZE) # Line search machinery is defined externally as a plugin class @@ -143,9 +142,8 @@ def setup(self): ) # Read in initial model as a vector and ensure it is a valid model if os.path.exists(self.path.MODEL_INIT): - m_new = solver.merge(solver.load(self.path.MODEL_INIT)) - self.save("m_new", m_new) - self.check_model(m_new) + m_new = Model(path=self.path.MODEL_INIT) + m_new.save(path=os.path.join(self.path.OPTIMIZE, "m_new")) else: self.logger.warning( "PATH.MODEL_INIT not found, cannot save 'm_new'. Either ensure " @@ -181,11 +179,9 @@ def load(self, name): :param name: name of the vector, acceptable: m, g, p, f, alpha """ assert(name in self.acceptable_vectors) - vector = np.load(os.path.join(self.path.OPTIMIZE, f"{name}.npy")) - # Allow single length vectors, which alpha and misfit (f) are - if vector.size == 1: - vector = float(vector) - return vector + model = Model(os.path.join(self.path.OPTIMIZE, f"{name}.npz"), + load=True) + return model def save(self, name, vector): """ @@ -209,13 +205,11 @@ def _precondition(self, q): :rtype: np.array :return: preconditioned vector """ - solver = self.module("solver") - if self.par.PRECOND is not None: - p = solver.merge(solver.load(self.path.PRECOND)) + p = Model(path=self.path.PRECOND) if self.par.PRECOND == "DIAGONAL": self.logger.info("applying diagonal preconditioner") - return p * q + return p.vector * q else: return q @@ -273,7 +267,7 @@ def initialize_search(self): m_try = m + alpha * p self.save("m_try", m_try) - self.save("alpha", alpha) + np.savetxt("alpha", alpha) self.check_model(m_try) def update_search(self): @@ -286,7 +280,7 @@ def update_search(self): status == 0 : not finished status == -1 : failed """ - self.line_search.update(step_len=self.load("alpha"), + self.line_search.update(step_len=np.loadtxt("alpha"), func_val=self.load("f_try")) alpha, status = self.line_search.calculate_step() @@ -294,7 +288,7 @@ def update_search(self): if status in [0, 1]: m = self.load("m_new") p = self.load("p_new") - self.save("alpha", alpha) + np.savetxt("alpha", alpha) m_try = m + alpha * p self.save("m_try", m_try) @@ -410,55 +404,15 @@ def _write_stats(self): theta = 180. * np.pi ** -1 * angle(p, -g) with open(fid, "a") as f: - f.write(f"{self.iter:0>2}," - f"{factor:6.3E}," - f"{grad_norm_L1:6.3E}," - f"{grad_norm_L2:6.3E}," - f"{misfit:6.3E}," - f"{restarted:6.3E}," - f"{slope:6.3E}," - f"{step_count:0>2}," - f"{step_length:6.3E}," - f"{theta:6.3E}\n" - ) - # - # def check_model(self, m, min_pr=-1., max_pr=0.5): - # """ - # Check to ensure that the model parameters fall within the guidelines - # of the solver. Print off min/max model parameters for the User. - # - # :type m: np.array - # :param m: model to check parameters of - # :type min_pr: float - # :param min_pr: minimum allowable Poisson's ratio value dictated by - # SPECFEM - # :type max_pr: float - # :param max_pr: maximum allowable Poisson's ratio value dictated by - # SPECFEM - # """ - # solver = self.module("solver") - # - # # Dynamic way to split up the model based on number of params - # pars = {} - # for i, par in enumerate(solver.parameters): - # pars[par] = np.split(m, len(solver.parameters))[i] - # - # # Check Poisson's ratio, which will error our SPECFEM if outside limits - # if (pars["vp"] is not None) and (pars["vs"] is not None): - # pars["pr"] = poissons_ratio(vp=pars["vp"], vs=pars["vs"]) - # if pars["pr"].min() < 0: - # self.logger.warning("minimum poisson's ratio is negative") - # if pars["pr"].max() < min_pr: - # self.logger.warning(f"maximum poisson's ratio out of bounds: " - # f"{pars['pr'].max():.2f} > {max_pr}") - # if pars["pr"].min() > max_pr: - # self.logger.warning(f"minimum poisson's ratio out of bounds: " - # f"{pars['pr'].min():.2f} < {min_pr}") - # - # # Tell the User min and max values of the updated model - # self.logger.info(f"model parameters") - # parts = "{minval:.2f} <= {key} <= {maxval:.2f}" - # for key, vals in pars.items(): - # self.logger.info(parts.format(minval=vals.min(), key=key, - # maxval=vals.max()) - # ) + pass + f.write(f"{self.iter:0>2}," + f"{factor:6.3E}," + f"{grad_norm_L1:6.3E}," + f"{grad_norm_L2:6.3E}," + f"{misfit:6.3E}," + f"{restarted:6.3E}," + f"{slope:6.3E}," + f"{step_count:0>2}," + f"{step_length:6.3E}," + f"{theta:6.3E}\n" + ) diff --git a/seisflows/postprocess/default.py b/seisflows/postprocess/default.py index 4cfc2e6a..6c298d13 100644 --- a/seisflows/postprocess/default.py +++ b/seisflows/postprocess/default.py @@ -5,10 +5,9 @@ kernel summation """ import os -import sys from seisflows.core import Base -from seisflows.tools import msg +from seisflows.tools.specfem import Model class Default(Base): @@ -84,34 +83,31 @@ def scale_gradient(self, input_path): :rtype: np.array :return: scaled gradient as a vector """ - solver = self.module("solver") - # Postprocess file structure defined here once-and-for-all path_grad_nomask = os.path.join(input_path, "gradient_nomask") path_model = os.path.join(input_path, "model") path_kernels_sum = os.path.join(input_path, "kernels", "sum") # Access the gradient information stored in as kernel files - gradient = solver.load(path_kernels_sum, suffix="_kernel") - + gradient = Model(path=path_kernels_sum) + model = Model(path=path_model) # Merge to vector and convert to absolute perturbations: # log dm --> dm (see Eq.13 Tromp et al 2005) - gradient = solver.merge(gradient) - gradient *= solver.merge(solver.load(path_model)) + gradient.vector *= model.vector if self.path.MASK: self.logger.info(f"masking gradient") # to scale the gradient, users can supply "masks" by exactly # mimicking the file format in which models are stored - mask = solver.merge(solver.load(self.path.MASK)) + mask = Model(self.path.MASK) # While both masking and preconditioning involve scaling the # gradient, they are fundamentally different operations: # masking is ad hoc, preconditioning is a change of variables; # For more info, see Modrak & Tromp 2016 GJI - solver.save(solver.split(gradient), path=path_grad_nomask, - suffix="_kernel") - gradient *= mask + gradient.write(path=path_grad_nomask) + + gradient.vector *= mask.vector return gradient diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 5f383738..1bf83f98 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -259,8 +259,6 @@ def _format_action(self, action): print_.add_argument("args", type=str, nargs="*", help="Generic arguments passed to check functions") # ========================================================================= - subparser.add_parser("convert", help="Convert model file format", ) - # ========================================================================= reset = subparser.add_parser( "reset", formatter_class=argparse.RawDescriptionHelpFormatter, help="Reset modules within an active state", description=""" @@ -272,7 +270,7 @@ def _format_action(self, action): reset.add_argument("choice", type=str, nargs="?", default=None, help="Choice of module/component to reset") reset.add_argument("args", type=str, nargs="*", - help="Generic arguments passed to reset functions") + help="Generic arguments passed to reset functions") # ========================================================================= subparser.add_parser( "debug", help="Start interactive debug environment", @@ -1139,42 +1137,6 @@ def reset(self, choice=None, **kwargs): self._load_modules() acceptable_args[choice](*self._args.args, **kwargs) - def convert(self, name, path=None, **kwargs): - """ - Convert a model in the OUTPUT directory between vector to binary - representation. Kwargs are passed through to solver.save() - - USAGE - - seisflows convert [name] [path] [**kwargs] - - To convert the vector model 'm_try' to binary representation in the - output directory - - seisflows convert m_try - - :type name: str - :param name: name of the model to convert, e.g. 'm_try' - :type path: str - :param path: path and file id to save the output model. if None, will - default to saving in the output directory under the name of the - model - """ - self._load_modules() - - solver = sys.modules["seisflows_solver"] - optimize = sys.modules["seisflows_optimize"] - PATH = sys.modules["seisflows_paths"] - - if path is None: - path = os.path.join(PATH.OUTPUT, name) - if os.path.exists(path): - print(msg.cli("The following file exists and will be overwritten. " - "Please rename or move this file and re-try: {path}")) - sys.exit(-1) - - solver.save(solver.split(optimize.load(name)), path=path, **kwargs ) - @staticmethod def _inspect_class_that_defined_method(name, func, **kwargs): """ diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index adca3ea4..aa181c21 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -8,14 +8,12 @@ import os import sys import subprocess -import numpy as np from glob import glob -from seisflows.core import Base, Dict +from seisflows.core import Base from seisflows.plugins import solver_io from seisflows.tools import msg, unix -from seisflows.tools.specfem import getpar -from seisflows.tools.wrappers import diff +from seisflows.tools.specfem import getpar, Model class Specfem(Base): @@ -55,20 +53,6 @@ class Specfem(Base): and particular file formats for models, data, and parameter files. These methods help put in place all these prerequisites - load, save - - For reading and writing SPECFEM2D/3D models and kernels. On the disk, - models and kernels are stored as binary files, and in memory, as - dictionaries with different keys corresponding to different material - parameters - - split, merge - - Within the solver routines, it is natural to store models as - dictionaries. Within the optimization routines, it is natural to store - models as vectors. Two methods, 'split' and 'merge', are used to convert - back and forth between these two representations - combine, smooth Utilities for combining and smoothing kernels @@ -87,9 +71,6 @@ def __init__(self): :type parameters: list of str :param parameters: a list detailing the parameters to be used to define the model, available: ['vp', 'vs', 'rho'] - :type _mesh_properties: Dict - :param _mesh_properties: hidden attribute, a dictionary of mesh - properties, including the ngll points, nprocs, and mesh coordinates :type _source_names: hidden attribute, :param _source_names: the names of all the sources that are being used by the solver @@ -156,7 +137,6 @@ def __init__(self): ) self.parameters = [] - self._mesh_properties = None self._source_names = None @property @@ -226,25 +206,6 @@ def cwd(self): """ return os.path.join(self.path.SOLVER, self.source_name) - @property - def mesh_properties(self): - """ - Returns mesh properties including number of GLL points and number of - processors (NPROC). - - .. note:: - We assume that there are only two available models MODEL_INIT and - MODEL_TRUE, and that both models have the SAME underlying mesh - structure. That way we can only check MODEL_INIT and be sure that - MODEL_TRUE has the same structure. - - :rtype: Dict - :return: Dictionary containing information on mesh properties - """ - if self._mesh_properties is None: - self._check_mesh_properties(model_path=self.path.MODEL_INIT) - return self._mesh_properties - def data_wildcard(self, comp="?"): """ Provide a wildcard string that will match the name of the output @@ -382,7 +343,6 @@ def _set_model(self, model_name, model_type=None): assert(os.path.exists(model_path)), f"model {model_path} does not exist" if model_type == "gll": - self._check_mesh_properties(model_path=model_path) # Copy the model files (ex: proc000023_vp.bin ...) into database dir src = glob(os.path.join(model_path, "*")) dst = self.model_databases @@ -582,125 +542,6 @@ def _call_solver(self, executable, output="solver.log"): ) sys.exit(-1) - def load(self, path, prefix="", suffix="", parameters=None): - """ - Solver I/O: Loads SPECFEM2D/3D models or kernels - - :type path: str - :param path: directory from which model is read - :type prefix: str - :param prefix: optional filename prefix - :type suffix: str - :param suffix: optional filename suffix, eg '_kernel' - :type parameters: list - :param parameters: material parameters to be read - (if empty, defaults to self.parameters) - :rtype: dict - :return: model or kernels indexed by material parameter and - processor rank, ie dict[parameter][iproc] - """ - if parameters is None: - parameters = self.parameters - - # Initiate empty dictionary to hold model values - model_dict = Dict({key: [] for key in self.parameters}) - - for iproc in range(self.mesh_properties.nproc): - for key in parameters: - _model_slice_values = self._io.read_slice( - path=path, parameters=f"{prefix}{key}{suffix}", iproc=iproc - ) - model_dict[key].extend(_model_slice_values) - - return model_dict - - def save(self, save_dict, path, parameters=None, prefix="", suffix=""): - """ - Solver I/O: Saves SPECFEM2D/3D models or kernels - - :type save_dict: Dict - :param save_dict: model stored by parameter key - :type path: str - :param path: directory from which model is read - :type parameters: list - :param parameters: list of material parameters to be read - :type prefix: str - :param prefix: optional filename prefix - :type suffix: str - :param suffix: optional filename suffix, eg '_kernel' - """ - unix.mkdir(path) - - if parameters is None: - parameters = self.parameters - - # Fill in any missing parameters - missing_keys = diff(parameters, save_dict.keys()) - for iproc in range(self.mesh_properties.nproc): - for key in missing_keys: - save_dict[key] += self._io.read_slice( - path=self.path.MODEL_INIT, - parameters=f"{prefix}{key}{suffix}", - iproc=iproc - ) - - # Write slices to disk - for iproc in range(self.mesh_properties.nproc): - for key in parameters: - self._io.write_slice(data=save_dict[key][iproc], path=path, - parameters=f"{prefix}{key}{suffix}", - iproc=iproc) - - def merge(self, model, parameters=None): - """ - Convert dictionary representation `model` to vector representation `m` - - :type model: dict - :param model: model to be converted - :type parameters: list - :param parameters: optional list of parameters, - defaults to `self.parameters` - :rtype: np.ndarray - :return: model as a vector - """ - if parameters is None: - parameters = self.parameters - - m = np.array([]) - for key in parameters: - for iproc in range(self.mesh_properties.nproc): - m = np.append(m, model[key][iproc]) - - return m - - def split(self, m, parameters=None): - """ - Converts vector representation `m` to dictionary representation `model` - - :type m: np.ndarray - :param m: model to be converted - :type parameters: list - :param parameters: optional list of parameters, - defaults to `self.parameters` - :rtype: dict - :return: model as a dictionary - """ - if parameters is None: - parameters = self.parameters - - nproc = self.mesh_properties.nproc - ngll = self.mesh_properties.ngll - model = Dict() - - for idim, key in enumerate(parameters): - model[key] = [] - for iproc in range(nproc): - imin = sum(ngll) * idim + sum(ngll[:iproc]) - imax = sum(ngll) * idim + sum(ngll[:iproc + 1]) - model[key].extend([m[imin:imax]]) - - return model - def combine(self, input_path, output_path, parameters=None): """ Postprocessing wrapper: xcombine_sem @@ -800,13 +641,16 @@ def smooth(self, input_path, output_path, parameters=None, span_h=0., def _import_model(self, path): """ - File transfer utility. Import the model into the workflow. + File transfer utility to move a SPEFEM2D model into the correct location + for a workflow. :type path: str - :param path: path to model + :param path: path to the SPECFEM2D model + :return: """ - model = self.load(path=os.path.join(path, "model")) - self.save(model, self.model_databases) + unix.cp(src=glob(os.path.join(path, "model", "*")), + dst=os.path.join(self.cwd, "DATA") + ) def _import_traces(self, path): """ @@ -1006,32 +850,6 @@ def _initialize_adjoint_traces(self): path=os.path.join(self.cwd, "traces", "adj") ) - def _check_mesh_properties(self, model_path): - """ - Determine if Mesh properties are okay for workflow. - - :type model_path: str - :param model_path: path to the mesh file - """ - if os.path.exists(model_path): - # Count the number of .bin files and the number of grid points - key = self.parameters[0] - bin_files = glob(os.path.join(model_path, f"proc*_{key}.bin")) - nproc = len(bin_files) - ngll = [] - for i in range(0, len(bin_files)): - ngll.append( - len(self._io.read_slice(path=model_path, - parameters=key, iproc=i)[0]) - ) - - # Define internal mesh properties - self._mesh_properties = Dict(nproc=nproc, ngll=ngll, - path=model_path) - else: - self.logger.warning("solver cannot find mesh and will not have " - "access to mesh properties") - def _check_source_names(self): """ Determines names of sources by applying wildcard rule to diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index 7cb7b29d..6f5d771f 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -211,18 +211,7 @@ def smooth(self, input_path, output_path, parameters=None, span_h=0., parameters=parameters, span_h=span_h, span_v=span_v, output=output) - def _import_model(self, path): - """ - File transfer utility to move a SPEFEM2D model into the correct location - for a workflow. - :type path: str - :param path: path to the SPECFEM2D model - :return: - """ - unix.cp(src=glob(os.path.join(path, "model", "*")), - dst=os.path.join(self.cwd, "DATA") - ) def _initialize_adjoint_traces(self): """ diff --git a/seisflows/tools/specfem.py b/seisflows/tools/specfem.py index fbd643f6..1d3d5484 100644 --- a/seisflows/tools/specfem.py +++ b/seisflows/tools/specfem.py @@ -21,7 +21,7 @@ class Model: Also contains utility functions to read/write itself so that models can be saved alongside their metadata. """ - def __init__(self, path, fmt=None, read=True, load=False): + def __init__(self, path, fmt=None, read=False, load=False): """ Model only needs path to model to determine model parameters. Format `fmt` can be provided by the user or guessed based on available file @@ -53,9 +53,10 @@ def __init__(self, path, fmt=None, read=True, load=False): _first_key = list(self.model.keys())[0] self.nproc = len(self.model[_first_key]) - self.parameters = self.model.keys() - self.vector = self.merge() - self.check() + if read or load: + self.parameters = self.model.keys() + self.vector = self.merge() + self.check() @staticmethod def fnfmt(i="*", val="*", ext="*"): @@ -219,13 +220,13 @@ def check(self, min_pr=-1., max_pr=0.5): logger.info(parts.format(minval=vals.min(), key=key, maxval=vals.max())) - def save(self, file): + def save(self, path): """ Save instance attributes (model, vector, metadata) to disk as an .npz array so that it can be loaded in at a later time for future use """ model = self.split() - np.savez(file=file, **model) + np.savez(file=path, fmt=self.fmt, **model) def load(self, file): """ @@ -236,9 +237,10 @@ def load(self, file): data = np.load(file=file) for i, key in enumerate(data.files): model[key] = data[key] - if i == 0: + if not ngll: for array in model[key]: ngll.append(len(array)) + model.fmt = str(model.fmt) return model, ngll diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index 4b38bb65..651d7ef3 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -12,6 +12,7 @@ from seisflows.core import Base from seisflows.tools import msg from seisflows.config import save +from seisflows.tools.specfem import Model class Forward(Base): @@ -175,12 +176,9 @@ def _write_model(self, path, model_tag): a suffix depending on where in the inversion we are. e.g., 'm_try'. Expected that these tags are defined in OPTIMIZE module """ - solver = self.module("solver") - optimize = self.module("optimize") - - dst = os.path.join(path, "model") - self.logger.debug(f"saving model '{model_tag}' to:\n{dst}") - solver.save(solver.split(optimize.load(model_tag)), dst) + m = Model(path=os.path.join(self.path.OPTIMIZE, model_tag)) + m.save(path=os.path.join(path, "model")) + self.logger.debug(f"saving model '{model_tag}'") def _write_misfit(self, path, misfit_tag): """ diff --git a/seisflows/workflow/migration.py b/seisflows/workflow/migration.py index b24f803c..2e28467b 100644 --- a/seisflows/workflow/migration.py +++ b/seisflows/workflow/migration.py @@ -138,21 +138,12 @@ def write_gradient(self): Uses the optimization and postprocess modules to scale the gradient to the given model, write the gradient in vector form and model form, and apply an optional mask to the gradient - - .. note:: - """ postprocess = self.module("postprocess") - optimize = self.module("optimize") - solver = self.module("solver") # Scale the gradient by a mask and by the model gradient = postprocess.scale_gradient(input_path=self.path.GRAD) - # Save the new gradient as a vector in PATH.OPTIMIZE - optimize.save("g_new", gradient) - # Save the new gradient as a set of model files (i.e., proc*_kernel.bin) - solver.save(solver.split(gradient), - path=os.path.join(self.path.GRAD, "gradient"), - suffix="_kernel") + gradient.write(path=os.path.join(self.path.GRAD, "gradient")) + gradient.save(path=os.path.join(self.path.OPTIMIZE, "g_new")) From 7b97ac3b4ab084fb08a74916c9c8040b58363cfe Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Thu, 7 Jul 2022 13:11:55 -0800 Subject: [PATCH 052/195] finished new Model class which has ability to IO and manipulate SPECFEM models, working with fortran binary format. Also including unit test for class with example data --- seisflows/tests/test_tools.py | 33 ++++++++++++++++++++++++++++++++- seisflows/tools/specfem.py | 7 ++++--- 2 files changed, 36 insertions(+), 4 deletions(-) diff --git a/seisflows/tests/test_tools.py b/seisflows/tests/test_tools.py index 23eff2fe..a5188056 100644 --- a/seisflows/tests/test_tools.py +++ b/seisflows/tests/test_tools.py @@ -3,13 +3,44 @@ """ import os import pytest +from glob import glob +from seisflows.tools.specfem import Model from seisflows.config import ROOT_DIR, NAMES, CFGPATHS TEST_DIR = os.path.join(ROOT_DIR, "tests") -def test_load_specfem_model(): +def test_specfem_model(tmpdir): """ Make sure we can dynamically load SPECFEM models in various formats + + TODO eventually we want to split this into multiple tests that test each + TODO of the IO formats (binary, adios etc.). Currently only testing binary. """ + # Make sure that multiple acceptable file extensions throw error + with pytest.raises(AssertionError): + m = Model(path=os.path.join(TEST_DIR, "test_data", "test_file_formats"), + read=True) + # Check that model values are read in correctly + m = Model(path=os.path.join(TEST_DIR, "test_data", "test_file_formats"), + read=True, fmt=".bin") + assert(m.ngll[0] == 40000) + assert(m.nproc == 1) + assert("vp" in m.model.keys()) + assert("vs" in m.model.keys()) + assert(m.model.vp[0][0] == 5800.) + assert(m.model.vs[0][0] == 3500.) + + assert(len(m.merge() == len(m.model.vs[0]) + len(m.model.vp[0]))) + assert(len(m.split()) == len(m.parameters)) + + # Check that saving and loading npz file works + m.save(path=os.path.join(tmpdir, "test.npz")) + m_new = Model(path=os.path.join(tmpdir, "test.npz"), load=True) + assert(m_new.ngll[0] == m.ngll[0]) + assert(m_new.fmt == m.fmt) + + # Check that writing fortran binary works + m.write(path=tmpdir) + assert(len(glob(os.path.join(tmpdir, f"*{m.fmt}"))) == 2) diff --git a/seisflows/tools/specfem.py b/seisflows/tools/specfem.py index 1d3d5484..5aa47d94 100644 --- a/seisflows/tools/specfem.py +++ b/seisflows/tools/specfem.py @@ -49,7 +49,7 @@ def __init__(self, path, fmt=None, read=False, load=False): self.nproc, self.available_parameters = self._get_nproc_parameters() self.model, self.ngll = self.read() elif load: - self.model, self.ngll = self.load(file=self.path) + self.model, self.ngll, self.fmt = self.load(file=self.path) _first_key = list(self.model.keys())[0] self.nproc = len(self.model[_first_key]) @@ -236,13 +236,14 @@ def load(self, file): ngll = [] data = np.load(file=file) for i, key in enumerate(data.files): + if key == "fmt": + continue model[key] = data[key] if not ngll: for array in model[key]: ngll.append(len(array)) - model.fmt = str(model.fmt) - return model, ngll + return model, ngll, str(data["fmt"]) def _get_nproc_parameters(self): """ From 12fda4a5fdd78cec4ee4595deafc1dcd383b92e7 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Fri, 8 Jul 2022 09:14:05 -0800 Subject: [PATCH 053/195] fixing tests, fully changed out new model implementation throughout package --- seisflows/optimize/gradient.py | 63 ++++++++++++++----------- seisflows/postprocess/default.py | 1 + seisflows/seisflows.py | 2 +- seisflows/solver/specfem.py | 4 +- seisflows/tests/test_modules.py | 3 +- seisflows/tools/specfem.py | 81 +++++++++++++++++++++----------- seisflows/workflow/forward.py | 7 +-- seisflows/workflow/inversion.py | 22 ++------- 8 files changed, 101 insertions(+), 82 deletions(-) diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index 5c08c393..f1e9b696 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -179,22 +179,27 @@ def load(self, name): :param name: name of the vector, acceptable: m, g, p, f, alpha """ assert(name in self.acceptable_vectors) - model = Model(os.path.join(self.path.OPTIMIZE, f"{name}.npz"), + model = Model(path=os.path.join(self.path.OPTIMIZE, f"{name}.npz"), load=True) return model - def save(self, name, vector): + def save(self, name, m): """ Convenience function to save/overwrite vectors on disk :type name: str :param name: name of the vector to overwrite - :type vector: np.array - :param vector: vector to save to name + :type m: seisflows.tools.cspefem.Model or float + :param m: Model to save to disk as npz array """ assert(name in self.acceptable_vectors) - vector_path = os.path.join(self.path.OPTIMIZE, f"{name}.npy") - np.save(vector_path, vector) + if isinstance(m, Model): + path = os.path.join(self.path.OPTIMIZE, f"{name}.npz") + m.model = m.split() # overwrite m representation + m.save(path=path) + elif isinstance(m, (float, int)): + path = os.path.join(self.path.OPTIMIZE, f"{name}.txt") + np.savetxt(path, [m]) def _precondition(self, q): """ @@ -213,7 +218,7 @@ def _precondition(self, q): else: return q - def compute_direction(self): + def compute_direction(self, save_to): """ Computes a steepest descent search direction (inverse gradient) with an optional user-defined preconditioner. @@ -224,7 +229,7 @@ def compute_direction(self): self.logger.info(f"computing search direction with {self.par.OPTIMIZE}") g_new = self.load("g_new") - p_new = -1 * self._precondition(g_new) + p_new = -1 * self._precondition(g_new.vector) self.save("p_new", p_new) def initialize_search(self): @@ -237,11 +242,11 @@ def initialize_search(self): p = self.load("p_new") f = self.load("f_new") - norm_m = max(abs(m)) - norm_p = max(abs(p)) + norm_m = max(abs(m.vector)) + norm_p = max(abs(p.vector)) - gtg = dot(g, g) - gtp = dot(g, p) + gtg = dot(g.vector, g.vector) + gtp = dot(g.vector, p.vector) # Restart plugin line search if the optimization library restarts if self.restarted: @@ -264,11 +269,10 @@ def initialize_search(self): # The new model is the old model, scaled by the step direction and # gradient threshold to remove any outlier values - m_try = m + alpha * p + m_try = m.vector + alpha * p.vector self.save("m_try", m_try) - np.savetxt("alpha", alpha) - self.check_model(m_try) + self.save("alpha", alpha) def update_search(self): """ @@ -288,11 +292,10 @@ def update_search(self): if status in [0, 1]: m = self.load("m_new") p = self.load("p_new") - np.savetxt("alpha", alpha) + self.save("alpha", alpha) - m_try = m + alpha * p + m_try = m.vector + alpha * p.vector self.save("m_try", m_try) - self.check_model(m_try) return status @@ -316,14 +319,15 @@ def finalize_search(self): self._write_stats() self.logger.info("shifting current model (new) to previous model (old)") - unix.mv("m_new.npy", "m_old.npy") - unix.mv("f_new.npy", "f_old.npy") - unix.mv("g_new.npy", "g_old.npy") - unix.mv("p_new.npy", "p_old.npy") + unix.mv("m_new.npz", "m_old.npz") + unix.mv("f_new.npz", "f_old.npz") + unix.mv("g_new.npz", "g_old.npz") + unix.mv("p_new.npz", "p_old.npz") self.logger.info("setting accepted line search model as current model") - unix.mv("m_try.npy", "m_new.npy") + unix.mv("m_try.npz", "m_new.npz") + # Choose minimum misfit value as final misfit/model f = self.line_search.search_history()[1] self.save("f_new", f.min()) self.logger.info(f"current misfit is {f.min():.3E}") @@ -339,7 +343,7 @@ def retry_status(self): """ g = self.load("g_new") p = self.load("p_new") - theta = angle(p, -g) + theta = angle(p.vector, -1 * g.vector) self.logger.debug(f"theta: {theta:6.3f}") @@ -363,7 +367,7 @@ def restart(self): return g = self.load("g_new") - self.save("p_new", -g) + self.save("p_new", -1 * g.vector) self.line_search.clear_history() self.restarted = 1 @@ -393,9 +397,12 @@ def _write_stats(self): f = self.line_search.search_history()[1] # Calculated stats factors - factor = -dot(g, g) ** -0.5 * (f[1] - f[0]) / (x[1] - x[0]) - grad_norm_L1 = np.linalg.norm(g, 1) - grad_norm_L2 = np.linalg.norm(g, 2) + factor = -dot(g.vector, g.vector) + factor = factor ** -0.5 * (f[1] - f[0]) / (x[1] - x[0]) + + grad_norm_L1 = np.linalg.norm(g.vector, 1) + grad_norm_L2 = np.linalg.norm(g.vector, 2) + misfit = f[0] restarted = self.restarted slope = (f[1] - f[0]) / (x[1] - x[0]) diff --git a/seisflows/postprocess/default.py b/seisflows/postprocess/default.py index 6c298d13..52697869 100644 --- a/seisflows/postprocess/default.py +++ b/seisflows/postprocess/default.py @@ -91,6 +91,7 @@ def scale_gradient(self, input_path): # Access the gradient information stored in as kernel files gradient = Model(path=path_kernels_sum) model = Model(path=path_model) + # Merge to vector and convert to absolute perturbations: # log dm --> dm (see Eq.13 Tromp et al 2005) gradient.vector *= model.vector diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 1bf83f98..d272b348 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -1330,7 +1330,7 @@ def _check_model_parameters(self, src=None, **kwargs): srcs = [src] for tag in srcs: m = optimize.load(tag) - optimize.check_model(m, tag) + m.check() def _check_current_iteration(self, **kwargs): """ diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index aa181c21..ed12964e 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -453,9 +453,9 @@ def eval_grad(self, path, export_traces=False): if export_traces: self._export_traces(path=os.path.join(path, "traces", "syn"), - prefix="traces/syn") + prefix="traces/syn") self._export_traces(path=os.path.join(path, "traces", "adj"), - prefix="traces/adj") + prefix="traces/adj") # def apply_hess(self, path): # """ diff --git a/seisflows/tests/test_modules.py b/seisflows/tests/test_modules.py index f0118505..3b77581a 100644 --- a/seisflows/tests/test_modules.py +++ b/seisflows/tests/test_modules.py @@ -30,8 +30,7 @@ "solver": { "parameters": ["MATERIALS", "DENSITY", "ATTENUATION"], "functions": ["generate_data", "eval_func", - "eval_grad", "load", "save", "merge", "split", - "source_names", "parameters"] + "eval_grad", "source_names", "parameters"] }, "postprocess": { "parameters": [], diff --git a/seisflows/tools/specfem.py b/seisflows/tools/specfem.py index 5aa47d94..26c8b0fc 100644 --- a/seisflows/tools/specfem.py +++ b/seisflows/tools/specfem.py @@ -21,7 +21,13 @@ class Model: Also contains utility functions to read/write itself so that models can be saved alongside their metadata. """ - def __init__(self, path, fmt=None, read=False, load=False): + # Dictate the parameters that Model can handle, which does not cover all + # files created by SPECFEM, which includes things like 'ibool', 'info' etc. + acceptable_parameters = ["vp", "vs", "rho", + "vpv", "vph", "vsv", "vsh", "eta"] + acceptable_parameters.extend([f"{_}_kernel" for _ in acceptable_parameters]) + + def __init__(self, path, fmt=None, parameters=None, load=False): """ Model only needs path to model to determine model parameters. Format `fmt` can be provided by the user or guessed based on available file @@ -35,34 +41,37 @@ def __init__(self, path, fmt=None, read=False, load=False): Available formats are: .bin, .dat :type load: bool :param load: load the model into disk as dictionary and vector - representations. If False, will only collect some metadata as a - non-cpu intensive operation + representations. If False, will guess how to import data based on + file extensions or lack thereof """ assert os.path.exists(path), f"specfem model path {path} does not exist" self.path = path + self.parameters = parameters - if read: + # Load an existing model + if os.path.splitext(path)[-1] == ".npz" or load: + self.model, self.ngll, self.fmt = self.load(file=self.path) + _first_key = list(self.model.keys())[0] + self.nproc = len(self.model[_first_key]) + # Read a model from files + else: if fmt is None: self.fmt = self._guess_file_format() else: self.fmt = fmt self.nproc, self.available_parameters = self._get_nproc_parameters() - self.model, self.ngll = self.read() - elif load: - self.model, self.ngll, self.fmt = self.load(file=self.path) - _first_key = list(self.model.keys())[0] - self.nproc = len(self.model[_first_key]) + self.model, self.ngll = self.read(parameters=parameters) - if read or load: - self.parameters = self.model.keys() - self.vector = self.merge() - self.check() + self.parameters = self.model.keys() + self.vector = self.merge() + self.check() @staticmethod def fnfmt(i="*", val="*", ext="*"): """ - Expected SPECFEM filename format with some checks to ensure that wildcards - and numbers are accepted. An example filename is: 'proc000001_vs.bin' + Expected SPECFEM filename format with some checks to ensure that + wildcards and numbers are accepted. An example filename is: + 'proc000001_vs.bin' :type i: int or str :param i: processor number or wildcard. If given as an integer, will be @@ -70,7 +79,7 @@ def fnfmt(i="*", val="*", ext="*"): :type val: str :param val: parameter value (e.g., 'vs') or wildcard :type ext: str - :param ext: the file format (e.g., '.bin'). If NOT preceded by a leading '.' + :param ext: the file format (e.g., '.bin'). If NOT preceded by a '.' will have one prepended :rtype: str :return: filename formatter for use in model manipulation @@ -86,13 +95,13 @@ def fnfmt(i="*", val="*", ext="*"): def read(self, parameters=None): """ Utility function to load in SPECFEM models/kernels/gradients saved in - various formats. Will try to guess format of model. Assumes that models are - saved using the following filename format: + various formats. Will try to guess format of model. Assumes that models + are saved using the following filename format: proc{num}_{val}.{format} where `num` is usually a 6 digit number representing the processor number (e.g., 000000), `val` is the parameter - value of the model/kernel/gradient and `format` is the format of the file - (e.g., bin) + value of the model/kernel/gradient and `format` is the format of the + file (e.g., bin) :type parameters: list of str :param parameters: unique parameters to load model for, if None will load @@ -112,11 +121,10 @@ def read(self, parameters=None): # Pick the correct read function based on the file format load_fx = {".bin": self._read_model_fortran_binary, ".dat": self._read_model_ascii, - ".adios": self._read_model_adios # TODO Check if this is right + ".adios": self._read_model_adios # TODO Check if this okay }[self.fmt] - # Create a dictionary object containing all parameters and respective - # models, save to internal attribute + # Create a dictionary object containing all parameters and their models parameter_dict = Dict({key: [] for key in parameters}) for parameter in parameters: parameter_dict[parameter] = load_fx(parameter=parameter) @@ -132,7 +140,8 @@ def merge(self, parameter=None): """ Convert dictionary representation `model` to vector representation `m` where all parameters and processors are stored as a single 1D vector. - This vector representation is used by the optimization library. + This vector representation is used by the optimization library during + model perturbation. :type parameter: str :param parameter: single parameter to retrieve model vector from, @@ -215,7 +224,7 @@ def check(self, min_pr=-1., max_pr=0.5): # Tell the User min and max values of the updated model logger.info(f"model parameters") - parts = "{minval:.2f} <= {key} <= {maxval:.2f}" + parts = "{minval:.2E} <= {key} <= {maxval:.2E}" for key, vals in self.model.items(): logger.info(parts.format(minval=vals.min(), key=key, maxval=vals.max())) @@ -231,6 +240,13 @@ def save(self, path): def load(self, file): """ Load in a previously saved .npz file containing model information + and re-create a Model instance matching the one that was `save`d + + :type file: str + :param file: .npz file to load data from. Must have been created by + Model.save() + :rtype: tuple (Dict, list, str) + :return: (Model Dictionary, ngll points for each slice, file format) """ model = Dict() ngll = [] @@ -249,13 +265,16 @@ def _get_nproc_parameters(self): """ Get the number of processors and the available parameters from a list of output SPECFEM model files. + + :rtype: tuple (int, list) + :return: (number of processors, list of available parameters in dir) """ fids = glob(os.path.join(self.path, self.fnfmt(val="*", ext=self.fmt))) fids = [os.path.basename(_) for _ in fids] # drop full path fids = [os.path.splitext(_)[0] for _ in fids] # drop extension - + if self.fmt == ".bin": - avail_par = list(set([_.split("_")[-1] for _ in fids])) + avail_par = list(set(["_".join(_.split("_")[1:]) for _ in fids])) nproc = len(glob(os.path.join( self.path, self.fnfmt(val=avail_par[0], ext=self.fmt))) ) @@ -267,13 +286,19 @@ def _get_nproc_parameters(self): raise NotImplementedError(f"{self.fmt} is not yet supported by " f"SeisFlows") - return nproc, avail_par + # Remove any parameters not accepted by Model + avail_par = set(avail_par).intersection(set(self.acceptable_parameters)) + + return nproc, list(avail_par) def _guess_file_format(self): """ Guess the file format of model/kernel/gradient files if none provided by the user. Does so by checking file formats against formats expected from SPECFEM2D/3D/3D_GLOBE + + :rtype: str + :return: file format suffix with a leading '.' e.g., '.bin' """ acceptable_formats = {".bin", ".dat"} diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index 651d7ef3..a4d2aefa 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -20,7 +20,7 @@ class Forward(Base): Workflow abstract base class representing an en-masse forward solver and misfit calculator. """ - def __init__(self, path_data): + def __init__(self): """ These parameters should not be set by the user. Attributes are initialized as NoneTypes for clarity and docstrings. @@ -176,8 +176,9 @@ def _write_model(self, path, model_tag): a suffix depending on where in the inversion we are. e.g., 'm_try'. Expected that these tags are defined in OPTIMIZE module """ - m = Model(path=os.path.join(self.path.OPTIMIZE, model_tag)) - m.save(path=os.path.join(path, "model")) + m = Model(path=os.path.join(self.path.OPTIMIZE, f"{model_tag}.npz")) + m.write(path=os.path.join(path, "model")) + self.logger.debug(f"saving model '{model_tag}'") def _write_misfit(self, path, misfit_tag): diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index 898e5a16..8888faeb 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -272,15 +272,15 @@ def export(self): optimize = self.module("optimize") if self.par.SAVEMODEL: - src = optimize.load("m_new") + src = os.path.join(self.path.OPTIMIZE, "m_new") dst = os.path.join(self.path.OUTPUT, f"model_{optimize.iter:04d}") self.logger.debug(f"exporting model 'm_new' to disk") - self._write_vector(src, dst) + unix.cp(src, dst) if self.par.SAVEGRADIENT: - src = optimize.load("g_old") + src = os.path.join(self.path.OPTIMIZE, "g_old") dst = os.path.join(self.path.OUTPUT, f"grad_{optimize.iter:04d}") - self._write_vector(src, dst) + unix.cp(src, dst) if self.par.SAVEKERNELS: src = os.path.join(self.path.GRAD, "kernels") @@ -288,23 +288,9 @@ def export(self): self.logger.debug(f"saving kernels to path:\n{dst}") unix.mv(src, dst) - # if self.par.SAVETRACES: - # do some stuff - if self.par.SAVERESIDUALS: src = os.path.join(self.path.GRAD, "residuals") dst = os.path.join(self.path.OUTPUT, f"residuals_{optimize.iter:04d}") unix.mv(src, dst) - def _write_vector(self, vector, path): - """ - Convenience function to write vectors as numpy arrays or as model files - to a given path - """ - solver = self.module("solver") - - if self.par.SAVEAS in ["binary", "both"]: - solver.save(solver.split(vector), path) - if self.par.SAVEAS in ["vector", "both"]: - np.save(file=path, arr=vector) From 521b0286ada51d6cd7f542e7c54236e677e791fc Mon Sep 17 00:00:00 2001 From: bch0w Date: Fri, 8 Jul 2022 12:14:10 -0800 Subject: [PATCH 054/195] scrapping the 'postprocess' module as it was really not carrying its weight compared to the others. postprocessing functionality has now been moved to 'workflow' (scale_gradient) and 'solver' (postprocess_kernels) --- seisflows/solver/specfem.py | 58 +++++++++++++++++++++++++++++++++ seisflows/workflow/inversion.py | 2 +- seisflows/workflow/migration.py | 38 ++++++++++++++++----- 3 files changed, 88 insertions(+), 10 deletions(-) diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index ed12964e..3006490a 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -113,6 +113,22 @@ def __init__(self): docstr="The format external solver files. Available: " "['fortran_binary']" ) + self.required.par( + "SMOOTH_H", required=False, default=0., par_type=float, + docstr="Gaussian half-width for horizontal smoothing in units of " + "meters. If 0., no smoothing applied" + ) + self.required.par( + "SMOOTH_V", required=False, default=0., par_type=float, + docstr="Gaussian half-width for vertical smoothing in units of " + "meters" + ) + self.required.par( + "TASKTIME_SMOOTH", required=False, default=1, par_type=int, + docstr="Large radii smoothing may take longer than normal tasks. " + "Allocate additional smoothing task time as a multiple of " + "TASKTIME" + ) self.required.path( "SOLVER", required=False, default=os.path.join(self.path.WORKDIR, "scratch", "solver"), @@ -135,6 +151,11 @@ def __init__(self): docstr="path to the SPECFEM DATA/ directory containing the " "'Par_file', 'STATIONS' file and 'CMTSOLUTION' files" ) + # Define the Paths required by this module + self.required.path( + "MASK", required=False, docstr="Directory to mask files for " + "gradient masking" + ) self.parameters = [] self._source_names = None @@ -457,6 +478,43 @@ def eval_grad(self, path, export_traces=False): self._export_traces(path=os.path.join(path, "traces", "adj"), prefix="traces/adj") + def postprocess_kernels(self, path_grad): + """ + Sums kernels from individual sources, with optional smoothing + + .. note:: + This function needs to be run on system, i.e., called by + system.run(single=True) + + :type path_grad: str + :param path_grad: directory containing sensitivity kernels in the + scratch directory to be summed and smoothed. Output summed and + summed + smoothed kernels will be saved here as well. + """ + # If specified, smooth the kernels in the vertical and horizontal and + # save both (summed, summed+smoothed) to separate output directories + kernel_path = os.path.join(path_grad, "kernels") + + path_sum_nosmooth = os.path.join(kernel_path, "sum_nosmooth") + path_sum = os.path.join(kernel_path, "sum") + + if (self.par.SMOOTH_H > 0) or (self.par.SMOOTH_V > 0): + self.logger.debug(f"saving un-smoothed and summed kernels to:\n" + f"{path_sum_nosmooth}") + self.combine(input_path=kernel_path, output_path=path_sum_nosmooth) + + self.logger.info(f"smoothing gradient: H={self.par.SMOOTH_H}m, " + f"V={self.par.SMOOTH_V}m") + self.logger.debug(f"saving smoothed kernels to:\n{path_sum}") + self.smooth(input_path=path_sum_nosmooth, output_path=path_sum, + span_h=self.par.SMOOTH_H, span_v=self.par.SMOOTH_V) + + # Combine all the input kernels, generating the unscaled gradient + else: + self.logger.debug(f"saving summed kernels to:\n{path_sum}") + self.combine(input_path=kernel_path, output_path=path_sum) + + # def apply_hess(self, path): # """ # High level solver interface that computes action of Hessian on a given diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index 8888faeb..d29df7a1 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -156,7 +156,7 @@ def main(self, flow=None, return_flow=False): flow = (self.evaluate_initial_misfit, self.evaluate_gradient, self.process_kernels, - self.write_gradient, + self.scale_gradient, self.compute_direction, self.line_search, self.export, diff --git a/seisflows/workflow/migration.py b/seisflows/workflow/migration.py index 2e28467b..54e23bce 100644 --- a/seisflows/workflow/migration.py +++ b/seisflows/workflow/migration.py @@ -10,6 +10,7 @@ from seisflows.workflow.forward import Forward from seisflows.tools import msg +from seisflows.tools.specfem import Model class Migration(Forward): @@ -96,7 +97,7 @@ def main(self, flow=None, return_flow=False): flow = (self.evaluate_initial_misfit, self.evaluate_gradient, self.process_kernels, - self.write_gradient + self.scale_gradient ) self.main(flow=flow, return_flow=return_flow) @@ -130,19 +131,38 @@ def process_kernels(self): self.logger.info(msg.mnr("PROCESSING KERNELS")) # Runs kernel processing as a single parallel process - system.run("postprocess", "sum_smooth_kernels", single=True, + system.run("solver", "postprocess_kernels", single=True, path_grad=self.path.GRAD) - def write_gradient(self): + def scale_gradient(self): """ - Uses the optimization and postprocess modules to scale the gradient - to the given model, write the gradient in vector form and model form, - and apply an optional mask to the gradient + Scale the gradient magnitude by the given model, write the gradient in + vector form and model form, and apply an optional mask to the gradient + + .. note:: + While both masking and preconditioning involve scaling the + gradient, they are fundamentally different operations: + masking is ad hoc, preconditioning is a change of variables; + For more info, see Modrak & Tromp 2016 GJI """ - postprocess = self.module("postprocess") + model = Model(path=os.path.join(self.path.GRAD)) + gradient = Model(path=os.path.join(self.path.GRAD, "kernels", "sum")) + + # Merge to vector and convert to absolute perturbations: + # log dm --> dm (see Eq.13 Tromp et al 2005) + gradient.vector *= model.vector + + if self.path.MASK: + mask = Model(os.path.join(self.path.MASK)) + # Write out a non-masked gradient incase masking is not wanted + gradient.write(path=os.path.join(self.path.GRAD, "gradient_nomask")) + + gradient.vector *= mask.vector + + # Update the model values based on the vector manipulation + gradient.model = gradient.split() - # Scale the gradient by a mask and by the model - gradient = postprocess.scale_gradient(input_path=self.path.GRAD) + # Write the gradient out gradient.write(path=os.path.join(self.path.GRAD, "gradient")) gradient.save(path=os.path.join(self.path.OPTIMIZE, "g_new")) From 03189f604edf2a1df3abb9d9d590f0ca6da10859 Mon Sep 17 00:00:00 2001 From: bch0w Date: Fri, 8 Jul 2022 13:01:47 -0800 Subject: [PATCH 055/195] testing out a new workflow schema involving functions and a script, NOT classes. Hopefully makes it clearer and easier to edit a workflow --- seisflows/seisflows.py | 6 +-- seisflows/solver/specfem.py | 1 - seisflows/tests/test_seisflows.py | 8 ++-- seisflows/tools/wrappers.py | 76 ++++++++++++++---------------- seisflows/workflow/forward_test.py | 50 ++++++++++++++++++++ 5 files changed, 92 insertions(+), 49 deletions(-) create mode 100644 seisflows/workflow/forward_test.py diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index d272b348..861dc21b 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -32,7 +32,7 @@ from seisflows.tools import unix, msg from seisflows.tools.specfem import (getpar, setpar, getpar_vel_model, setpar_vel_model) -from seisflows.tools.wrappers import loadyaml +from seisflows.tools.wrappers import load_yaml def sfparser(): @@ -412,7 +412,7 @@ def _register_parameters(self): # Register parameters from the parameter file try: - parameters = loadyaml(self._args.parameter_file) + parameters = load_yaml(self._args.parameter_file) except Exception as e: print(msg.cli(f"Please check that your parameter file is properly " f"formatted in the YAML format. The read error is:", @@ -656,7 +656,7 @@ def swap(self, module, classname, **kwargs): sys.exit(-1) # Load in old parameter file and then move it to a hidden file - ogpars = loadyaml(self._args.parameter_file) + ogpars = load_yaml(self._args.parameter_file) ogpaths = Dict(ogpars.pop("PATHS")) unix.mv(self._args.parameter_file, f"_{self._args.parameter_file}") diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 3006490a..f4998536 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -494,7 +494,6 @@ def postprocess_kernels(self, path_grad): # If specified, smooth the kernels in the vertical and horizontal and # save both (summed, summed+smoothed) to separate output directories kernel_path = os.path.join(path_grad, "kernels") - path_sum_nosmooth = os.path.join(kernel_path, "sum_nosmooth") path_sum = os.path.join(kernel_path, "sum") diff --git a/seisflows/tests/test_seisflows.py b/seisflows/tests/test_seisflows.py index c738658e..cd69e5ca 100644 --- a/seisflows/tests/test_seisflows.py +++ b/seisflows/tests/test_seisflows.py @@ -14,7 +14,7 @@ from seisflows.core import Dict from seisflows.seisflows import SeisFlows from seisflows.config import ROOT_DIR, NAMES, CFGPATHS -from seisflows.tools.wrappers import loadyaml +from seisflows.tools.wrappers import load_yaml TEST_DIR = os.path.join(ROOT_DIR, "tests") @@ -56,7 +56,7 @@ def par_file_dict(filled_par_file): :rtype: seisflows.config.Dict :return: dictionary of parameters """ - return Dict(loadyaml(filled_par_file)) + return Dict(load_yaml(filled_par_file)) def test_call_seisflows(tmpdir, par_file_dict, copy_par_file): @@ -185,7 +185,7 @@ def test_cmd_init(tmpdir, copy_par_file): copy_par_file # Create necessary paths to get past some assertion errors - parameters = loadyaml("parameters.yaml") + parameters = load_yaml("parameters.yaml") paths = parameters.pop("PATHS") for key in ["MODEL_INIT", "MODEL_TRUE"]: @@ -249,7 +249,7 @@ def test_load_modules(tmpdir, copy_par_file): copy_par_file # Create necessary paths to get past some assertion errors - parameters = loadyaml("parameters.yaml") + parameters = load_yaml("parameters.yaml") paths = parameters.pop("PATHS") for key in ["MODEL_INIT", "MODEL_TRUE"]: diff --git a/seisflows/tools/wrappers.py b/seisflows/tools/wrappers.py index 6b8051d2..e9bdd7e9 100644 --- a/seisflows/tools/wrappers.py +++ b/seisflows/tools/wrappers.py @@ -11,15 +11,45 @@ import numpy as np from importlib import import_module from pkgutil import find_loader +from seisflows.core import Dict -class Struct(dict): +def load_yaml(filename): """ - Revised dictionary structure + Define how the PyYaml yaml loading function behaves. + Replaces None and inf strings with NoneType and numpy.inf respectively + + :type filename: str + :param filename: .yaml file to load in + :rtype: Dict + :return: Dictionary containing all parameters in a YAML file """ - def __init__(self, *args, **kwargs): - super(Struct, self).__init__(*args, **kwargs) - self.__dict__ = self + # work around PyYAML bugs + yaml.SafeLoader.add_implicit_resolver( + u'tag:yaml.org,2002:float', + re.compile(u'''^(?: + [-+]?(?:[0-9][0-9_]*)\\.[0-9_]*(?:[eE][-+]?[0-9]+)? + |[-+]?(?:[0-9][0-9_]*)(?:[eE][-+]?[0-9]+) + |\\.[0-9_]+(?:[eE][-+][0-9]+)? + |[-+]?[0-9][0-9_]*(?::[0-5]?[0-9])+\\.[0-9_]* + |[-+]?\\.(?:inf|Inf|INF) + |\\.(?:nan|NaN|NAN))$''', re.X), + list(u'-+0123456789.')) + + with open(filename, 'r') as f: + mydict = Dict(yaml.safe_load(f)) + + if mydict is None: + mydict = Dict() + + # Replace 'None' and 'inf' values to match expectations + for key, val in mydict.items(): + if val == "None": + mydict[key] = None + if val == "inf": + mydict[key] = np.inf + + return mydict def diff(list1, list2): @@ -143,42 +173,6 @@ def timestamp(): return time.strftime('%H:%M:%S') -def loadyaml(filename): - """ - Define how the PyYaml yaml loading function behaves. - Replaces None and inf strings with NoneType and numpy.inf respectively - - :type filename: str - :param filename: .yaml file to load in - """ - # work around PyYAML bugs - yaml.SafeLoader.add_implicit_resolver( - u'tag:yaml.org,2002:float', - re.compile(u'''^(?: - [-+]?(?:[0-9][0-9_]*)\\.[0-9_]*(?:[eE][-+]?[0-9]+)? - |[-+]?(?:[0-9][0-9_]*)(?:[eE][-+]?[0-9]+) - |\\.[0-9_]+(?:[eE][-+][0-9]+)? - |[-+]?[0-9][0-9_]*(?::[0-5]?[0-9])+\\.[0-9_]* - |[-+]?\\.(?:inf|Inf|INF) - |\\.(?:nan|NaN|NAN))$''', re.X), - list(u'-+0123456789.')) - - with open(filename, 'r') as f: - mydict = yaml.safe_load(f) - - if mydict is None: - mydict = dict() - - # Replace 'None' and 'inf' values to match expectations - for key, val in mydict.items(): - if val == "None": - mydict[key] = None - if val == "inf": - mydict[key] = np.inf - - return mydict - - def getset(arg): """ Return a set object diff --git a/seisflows/workflow/forward_test.py b/seisflows/workflow/forward_test.py new file mode 100644 index 00000000..464acf19 --- /dev/null +++ b/seisflows/workflow/forward_test.py @@ -0,0 +1,50 @@ +""" +Test workflow to see if a new form of seisflows workflow can be used +""" +import os +from seisflows.core import Dict +from seisflows.config import custom_import, config_logger, NAMES +from seisflows.tools.wrappers import load_yaml +from seisflows.tools.specfem import Model + + +def setup(instances): + """ + Run the .setup() function for each of the instances, which + """ + + + + + +if __name__ == "__main__": + # Standard SeisFlows Workflow setup block + # ========================================================================== + cwd = os.getcwd() + pars = load_yaml("parameters.yaml") + paths = Dict(pars.pop("paths")) + logger = config_logger(level=pars.log_level, filename=paths.log_file, + verbose=pars.verbose) + + # Dynamically create module instances, instantiated with parameters + classes = [custom_import(name, pars[name.upper()]) for name in NAMES] + instances = [cls(**pars) for cls in classes] + # Check that parameters have been set correctly + for instance in instances: + instance.check() + + # Distribute instances to their respective namesakes + system, preprocess, solver, postprocess, optimize, workflow = instances + # ========================================================================== + + + # Begin workflow + logger.info("Starting forward simulation workflow") + for module in modules: + module.setup() + + m = Model(paths.model_init) + m.write(path=os.path.join(paths.eval_fun, "model")) + + system.run(solver.eval_func) + From 32c0ccc5955e5f07863048439fdcb927bdc3c583 Mon Sep 17 00:00:00 2001 From: bch0w Date: Fri, 8 Jul 2022 14:00:22 -0800 Subject: [PATCH 056/195] gradient and LBFGS now have new implementation of parameter system. internal parameters are now prefixed by leading underscore, which will be useful for future rewrite of 'seisflows configure' --- seisflows/optimize/LBFGS.py | 167 +++++++++--------- seisflows/optimize/gradient.py | 275 ++++++++++++++--------------- seisflows/workflow/forward_test.py | 18 +- 3 files changed, 217 insertions(+), 243 deletions(-) diff --git a/seisflows/optimize/LBFGS.py b/seisflows/optimize/LBFGS.py index 5e9ed721..1c9b4b3f 100644 --- a/seisflows/optimize/LBFGS.py +++ b/seisflows/optimize/LBFGS.py @@ -6,10 +6,12 @@ import os import numpy as np +from seisflows import logger from seisflows.optimize.gradient import Gradient from seisflows.tools import unix from seisflows.tools.msg import DEG from seisflows.tools.math import angle +from seisflows.plugins import line_search as line_search_dir class LBFGS(Gradient): @@ -45,14 +47,22 @@ class LBFGS(Gradient): status == 0 : not finished status < 0 : failed """ - def __init__(self): + def __init__(self, lbfgs_mem=3, lbfgs_max=np.inf, lbfgs_thresh=0., + **kwargs): """ These parameters should not be set by the user. Attributes are initialized as NoneTypes for clarity and docstrings. - :type LBFGS: Class - :param LBFGS: plugin LBFGS class that controls the machinery of the - L-BFGS optimization schema + :type lbfgs_mem: int + :param lbfgs_mem: L-BFGS memory. Max number of previous gradients to + retain in local memory for approximating the objective function. + :type lbfgs_max: L-BFGS periodic restart interval. Must be + 1 <= lbfgs_max <= infinity. + :type lbfgs_thresh: L-BFGS angle restart threshold. If the angle between + the current and previous search direction exceeds this value, + optimization algorithm will be restarted. + + :type LBFGS_iter: int :param LBFGS_iter: an internally used iteration that differs from optimization iter. Keeps track of internal LBFGS memory of previous @@ -71,51 +81,34 @@ def __init__(self): :param s_file: path to store memory of the model differences i.e., `m_new - m_old` """ - super().__init__() - - # Define the Parameters required by this module - self.required.par( - "LINESEARCH", required=False, default="Backtrack", par_type=str, - docstr="Algorithm to use for line search, see " - "seisflows.plugins.line_search for available choices" - ) - self.required.par( - "LBFGSMEM", required=False, default=3, par_type=int, - docstr="Max number of previous gradients to retain in local memory" - ) - self.required.par( - "LBFGSMAX", required=False, par_type=int, default="inf", - docstr="LBFGS periodic restart interval, between 1 and 'inf'" - ) - self.required.par( - "LBFGSTHRESH", required=False, default=0., par_type=float, - docstr="LBFGS angle restart threshold" - ) - - self.LBFGS_iter = 0 - self.memory_used = 0 - self.LBFGS_dir = "LBFGS" - self.y_file = os.path.join(self.LBFGS_dir, "Y") - self.s_file = os.path.join(self.LBFGS_dir, "S") - - def check(self, validate=True): - """ - Checks parameters, paths, and dependencies - """ - super().check(validate=validate) - - assert(self.par.LINESEARCH.upper() == "BACKTRACK"), \ - "LBFGS requires a Backtracking line search" + super().__init__(**kwargs) + + # Overwrite user-chosen line search. L-BFGS requires 'Backtrack'ing LS + if self._line_search.title != "Backtrack": + logger.warning(f"L-BFGS optimization requires 'backtrack'ing line " + f"search. Overwritng {self._line_search}") + self._line_search = "Backtrack" + self.line_search = getattr(line_search_dir, self._line_search)( + step_count_max=self.step_count_max, step_len_max=self.step_len_max + ) + + self.LBFGS_mem = lbfgs_mem + self.LBFGS_max = lbfgs_max + self.LBFGS_thresh = lbfgs_thresh + + # Internally used memory and path parameters + self._LBFGS_iter = 0 + self._memory_used = 0 + self._LBFGS_dir = os.path.join(self.path, "LBFGS") + self._y_file = os.path.join(self.path, "LBFGS", "Y") + self._s_file = os.path.join(self.path, "LBFGS", "S") def setup(self): """ Set up the LBFGS optimization schema """ super().setup() - - # Create a separate directory for LBFGS matters - unix.cd(self.path.OPTIMIZE) - unix.mkdir(self.LBFGS_dir) + unix.mkdir(self._LBFGS_dir) def compute_direction(self): """ @@ -135,45 +128,45 @@ def compute_direction(self): 4. New search direction is acceptably angled from previous, becomes the new search direction """ - self.logger.info(f"computing search direction with L-BFGS") - self.LBFGS_iter += 1 + unix.cd(self.path) - unix.cd(self.path.OPTIMIZE) + logger.info(f"computing search direction with L-BFGS") + self._LBFGS_iter += 1 # Load the current gradient direction, which is the L-BFGS search # direction if this is the first iteration g = self.load("g_new") - if self.LBFGS_iter == 1: - self.logger.info("first L-BFGS iteration, setting search direction " + if self._LBFGS_iter == 1: + logger.info("first L-BFGS iteration, setting search direction " "as inverse gradient") - p_new = -g + p_new = -1 * g.vector restarted = 0 + # Restart condition or first iteration lead to setting search direction # as the inverse gradient (i.e., default to steepest descent) - elif self.LBFGS_iter > self.par.LBFGSMAX: - self.logger.info("restarting L-BFGS due to periodic restart " - "condition. setting search direction as" - "inverse gradient") + elif self._LBFGS_iter > self.LBFGS_max: + logger.info("restarting L-BFGS due to periodic restart condition. " + "setting search direction as inverse gradient") self.restart() - p_new = -g + p_new = -1 * g.vector restarted = 1 # Normal LBFGS direction computation else: # Update the search direction, apply the inverse Hessian such that # 'q' becomes the new search direction 'g' - self.logger.info("applying inverse Hessian to gradient") + logger.info("applying inverse Hessian to gradient") s, y = self._update() - q = self._apply(g, s, y) + q = self._apply(g.vector, s, y) # Determine if the new search direction is appropriate by checking # its angle to the previous search direction if self._check_status(g, q): - self.logger.info("new L-BFGS search direction found") + logger.info("new L-BFGS search direction found") p_new = -q restarted = 0 else: - self.logger.info("new search direction not appropriate, " - "defaulting to inverse gradient") + logger.info("new search direction not appropriate, defaulting " + "to inverse gradient") self.restart() p_new = -g restarted = 1 @@ -188,14 +181,14 @@ def restart(self): """ super().restart() - self.logger.info("restarting L-BFGS optimization algorithm by clearing " + logger.info("restarting L-BFGS optimization algorithm by clearing " "internal memory") - self.LBFGS_iter = 1 - self.memory_used = 0 + self._LBFGS_iter = 1 + self._memory_used = 0 unix.cd(self.path.OPTIMIZE) - s = np.memmap(filename=self.s_file, mode="r+") - y = np.memmap(filename=self.y_file, mode="r+") + s = np.memmap(filename=self._s_file, mode="r+") + y = np.memmap(filename=self._y_file, mode="r+") s[:] = 0. y[:] = 0. @@ -218,31 +211,31 @@ def _update(self): :rtype y: np.memmap :return y: memory of the gradient differences `g_new - g_old` """ - unix.cd(self.path.OPTIMIZE) + unix.cd(self.path) # Determine the iterates for model m and gradient g - s_k = self.load("m_new") - self.load("m_old") - y_k = self.load("g_new") - self.load("g_old") + s_k = self.load("m_new").vector - self.load("m_old").vector + y_k = self.load("g_new").vector - self.load("g_old").vector # Determine the shape of the memory map (length of model, length of mem) m = len(s_k) - n = self.par.LBFGSMEM + n = self.LBFGS_mem # Initial iteration, need to create the memory map - if self.memory_used == 0: - s = np.memmap(filename=self.s_file, mode="w+", dtype="float32", + if self._memory_used == 0: + s = np.memmap(filename=self._s_file, mode="w+", dtype="float32", shape=(m, n)) - y = np.memmap(filename=self.y_file, mode="w+", dtype="float32", + y = np.memmap(filename=self._y_file, mode="w+", dtype="float32", shape=(m, n)) # Store the model and gradient differences in memmaps s[:, 0] = s_k y[:, 0] = y_k - self.memory_used = 1 + self._memory_used = 1 # Subsequent iterations will append to memory maps else: - s = np.memmap(filename=self.s_file, mode="r+", dtype="float32", + s = np.memmap(filename=self._s_file, mode="r+", dtype="float32", shape=(m, n)) - y = np.memmap(filename=self.y_file, mode="r+", dtype="float32", + y = np.memmap(filename=self._y_file, mode="r+", dtype="float32", shape=(m, n)) # Shift all stored memory by one index to make room for latest mem s[:, 1:] = s[:, :-1] @@ -252,8 +245,8 @@ def _update(self): y[:, 0] = y_k # Keep track of the memory used - if self.memory_used < self.par.LBFGSMEM: - self.memory_used += 1 + if self._memory_used < self.LBFGS_mem: + self._memory_used += 1 return s, y @@ -271,20 +264,20 @@ def _apply(self, q, s=None, y=None): :rtype r: np.array :return r: new search direction from application of L-BFGS """ - unix.cd(self.path.OPTIMIZE) + unix.cd(self.path) # If no memmaps are given as arguments, instantiate them if s is None or y is None: m = len(q) - n = self.par.LBFGSMEM - s = np.memmap(filename=self.s_file, mode="w+", dtype="float32", + n = self.LBFGS_mem + s = np.memmap(filename=self._s_file, mode="w+", dtype="float32", shape=(m, n)) - y = np.memmap(filename=self.y_file, mode="w+", dtype="float32", + y = np.memmap(filename=self._y_file, mode="w+", dtype="float32", shape=(m, n)) # First matrix product # Recursion step 2 from appendix A of Modrak & Tromp 2016 - kk = self.memory_used + kk = self._memory_used rh = np.zeros(kk) al = np.zeros(kk) for ii in range(kk): @@ -293,8 +286,8 @@ def _apply(self, q, s=None, y=None): q = q - al[ii] * y[:, ii] # Apply a preconditioner if available - if self.precond: - r = self.precond(q) + if self.preconditioner: + r = self._precondition(q) else: r = q @@ -324,13 +317,13 @@ def _check_status(self, g, r): :return: okay status based on status check (False==bad, True==good) """ theta = 180. * np.pi ** -1 * angle(g, r) - self.logger.info(f"new search direction: {theta:.2f}{DEG} from current") + logger.info(f"new search direction: {theta:.2f}{DEG} from current") if not 0. < theta < 90.: - self.logger.info("restarting L-BFGS, theta not a descent direction") + logger.info("restarting L-BFGS, theta not a descent direction") okay = False - elif theta > 90. - self.par.LBFGSTHRESH: - self.logger.info("restarting L-BFGS due to practical safeguard") + elif theta > 90. - self.LBFGS_thresh: + logger.info("restarting L-BFGS due to practical safeguard") okay = False else: okay = True diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index f1e9b696..3b2b522d 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -13,14 +13,14 @@ import os import numpy as np -from seisflows.core import Base +from seisflows import logger from seisflows.tools import msg, unix from seisflows.tools.math import angle, dot from seisflows.tools.specfem import Model -from seisflows.plugins import line_search +from seisflows.plugins import line_search as line_search_dir -class Gradient(Base): +class Gradient: """ Nonlinear optimization abstract base class poviding a gradient/steepest descent optimization algorithm. @@ -45,117 +45,102 @@ class Gradient(Base): algorithm. """ - def __init__(self): + def __init__(self, begin=1, line_search="bracket", preconditioner=False, + step_count_max=10, step_len_init=0.05, step_len_max=0.5, + path_optimize=None, path_output=None, path_preconditioner=None, + **kwargs): """ - Initialize internally used variables for optimization workflow - - :type iter: int - :param iter: the current iteration of the workflow - :type line_search: Class - :param line_search: a class controlling the line search functionality - for determining step length - :type restarted: bool - :param restarted: a flag signalling if the optimization algorithm has - been restarted recently + Optimization algorithm variables + + :type line_search: str + :param line_search: chosen line_search algorithm. Currently available + are 'bracket' and 'backtrack'. See seisflows.plugins.line_search + for all available options + :type preconditioner: str + :param preconditioner: algorithm for preconditioning gradients + :type step_count_max: int + :param step_count_max: maximum number of trial steps to perform during + the line search before a change in line search behavior is + considered + :type step_len_init: float + :param step_len_init: initial line search step length as a fraction of + current model parameters. + :type step_len_max: float + :param step_len_max: maximum allowable step length during the line + search. Set as a fraction of the current model parameters + :type path: str + :param path: scratch path for all optimization related procedures. + if None, defaults to $PWD/scratch/optimize """ super().__init__() - # Define the Parameters required by this module - self.required.par( - "LINESEARCH", required=False, default="Bracket", par_type=str, - docstr="Algorithm to use for line search, see " - "seisflows.plugins.line_search for available choices" - ) - self.required.par( - "PRECOND", required=False, par_type=str, - docstr="Algorithm to use for preconditioning gradients, see " - "seisflows.plugins.preconds for available choices" - ) - self.required.par( - "STEPCOUNTMAX", required=False, default=10, par_type=int, - docstr="Max number of trial steps in line search before a " - "change in line search behavior" - ) - self.required.par( - "STEPLENINIT", required=False, default=0.05, par_type=float, - docstr="Initial line search step length, as a fraction of current " - "model parameters" - ) - self.required.par( - "STEPLENMAX", required=False, default=0.5, par_type=float, - docstr="Max allowable step length, as a fraction of current model " - "parameters" - ) - self.required.path( - "OPTIMIZE", required=False, - default=os.path.join(self.path.WORKDIR, "scratch", "optimize"), - docstr="scratch path for nonlinear optimization data" + self.iteration = begin # to match PAR.BEGIN + self.preconditioner = preconditioner + + self.step_count_max = step_count_max + self.step_len_init = step_len_init + self.step_len_max = step_len_max + + # Internal check to see if the chosen line search algorithm exists + if not hasattr(line_search_dir, line_search): + logger.warning(f"{line_search} is not a valid line search " + f"algorithm, defaulting to 'bracket'") + line_search = "bracket" + + self._line_search = line_search.title() + self.line_search = getattr(line_search_dir, self._line_search)( + step_count_max=step_count_max, step_len_max=step_len_max ) - # Internally used parameters - self.iter = 1 - self.line_search = None - self.precond = None - self.restarted = False - self.acceptable_vectors = ["m_new", "m_old", "m_try", - "g_new", "g_old", "g_try", - "p_new", "p_old", "alpha", - "f_new", "f_old", "f_try"] - - def check(self, validate=True): + # Set required path structure + self.path = path_optimize or \ + os.path.join(os.getcwd(), "scratch", "optimize") + self.path_output = path_output or os.path.join(os.getcwd(), "output") + self.path_preconditioner = path_preconditioner + + # Internally used parameters for checking validity + self._acceptable_vectors = ["m_new", "m_old", "m_try", + "g_new", "g_old", "g_try", + "p_new", "p_old", "alpha", + "f_new", "f_old", "f_try"] + self._acceptable_preconditioners = ["diagonal"] + + def check(self): """ Checks parameters, paths, and dependencies """ - super().check(validate=validate) + if self.preconditioner: + # This list should match the logic in self.precondition() - if self.par.LINESEARCH: - assert self.par.LINESEARCH in dir(line_search), \ - f"LINESEARCH parameter must be in {dir(line_search)}" + assert self.preconditioner in self._acceptable_preconditioners, \ + f"PRECOND must be in {self._acceptable_preconditioners}" - if self.par.PRECOND: - # This list should match the logic in self.precondition() - acceptable_preconditioners = ["diagonal"] - assert self.par.PRECOND in acceptable_preconditioners, \ - f"PRECOND must be in {acceptable_preconditioners}" - assert(os.path.exists(self.path.PRECOND)), ( + assert(os.path.exists(self.path_preconditioner)), ( f"preconditioner requires PATH.PRECOND pointing to a array-like" f"weight file" ) - assert 0. < self.par.STEPLENINIT, f"STEPLENINIT must be >= 0." - assert 0. < self.par.STEPLENMAX, f"STEPLENMAX must be >= 0." - assert self.par.STEPLENINIT < self.par.STEPLENMAX, \ - f"STEPLENINIT must be < STEPLENMAX" + assert 0. < self.step_len_init, f"optimize.step_len_init must be >= 0." + assert 0. < self.step_len_max, f"optimize.step_len_max must be >= 0." + assert self.step_len_init < self.step_len_max, \ + f"optimize.step_len_init must be < optimize.step_len_max" def setup(self): """ Sets up nonlinear optimization machinery """ - super().setup() - unix.mkdir(self.path.OPTIMIZE) - - # Line search machinery is defined externally as a plugin class - if self.par.LINESEARCH: - self.line_search = getattr(line_search, self.par.LINESEARCH)( - step_count_max=self.par.STEPCOUNTMAX, - step_len_max=self.par.STEPLENMAX, - ) - # Read in initial model as a vector and ensure it is a valid model - if os.path.exists(self.path.MODEL_INIT): - m_new = Model(path=self.path.MODEL_INIT) - m_new.save(path=os.path.join(self.path.OPTIMIZE, "m_new")) - else: - self.logger.warning( - "PATH.MODEL_INIT not found, cannot save 'm_new'. Either ensure " - "that 'm_new' is present in PATH.OPTIMIZE or restart with a " - "valid PATH.MODEL_INIT" - ) - - def finalize(self): - """ - Finalization tasks - """ - super().finalize() + unix.mkdir(self.path) + + # # Read in initial model as a vector and ensure it is a valid model + # if os.path.exists(self.path.MODEL_INIT): + # m_new = Model(path=self.path.MODEL_INIT) + # m_new.save(path=os.path.join(self.path.OPTIMIZE, "m_new")) + # else: + # logger.warning( + # "PATH.MODEL_INIT not found, cannot save 'm_new'. Either ensure " + # "that 'm_new' is present in PATH.OPTIMIZE or restart with a " + # "valid PATH.MODEL_INIT" + # ) def load(self, name): """ @@ -178,9 +163,8 @@ def load(self, name): :type name: str :param name: name of the vector, acceptable: m, g, p, f, alpha """ - assert(name in self.acceptable_vectors) - model = Model(path=os.path.join(self.path.OPTIMIZE, f"{name}.npz"), - load=True) + assert(name in self._acceptable_vectors) + model = Model(path=os.path.join(self.path, f"{name}.npz"), load=True) return model def save(self, name, m): @@ -192,13 +176,14 @@ def save(self, name, m): :type m: seisflows.tools.cspefem.Model or float :param m: Model to save to disk as npz array """ - assert(name in self.acceptable_vectors) + assert(name in self._acceptable_vectors) + if isinstance(m, Model): - path = os.path.join(self.path.OPTIMIZE, f"{name}.npz") + path = os.path.join(self.path, f"{name}.npz") m.model = m.split() # overwrite m representation m.save(path=path) elif isinstance(m, (float, int)): - path = os.path.join(self.path.OPTIMIZE, f"{name}.txt") + path = os.path.join(self.path, f"{name}.txt") np.savetxt(path, [m]) def _precondition(self, q): @@ -210,26 +195,31 @@ def _precondition(self, q): :rtype: np.array :return: preconditioned vector """ - if self.par.PRECOND is not None: - p = Model(path=self.path.PRECOND) - if self.par.PRECOND == "DIAGONAL": - self.logger.info("applying diagonal preconditioner") + if self.preconditioner is not None: + p = Model(path=self.path_preconditioner) + if self.preconditioner.upper() == "DIAGONAL": + logger.info("applying diagonal preconditioner") return p.vector * q + else: + raise NotImplementedError( + f"preconditioner {self.preconditioner} not supported" + ) else: return q - def compute_direction(self, save_to): + def compute_direction(self): """ - Computes a steepest descent search direction (inverse gradient) + Computes steepest descent search direction (inverse gradient) with an optional user-defined preconditioner. .. note:: Other optimization algorithms must overload this method """ - self.logger.info(f"computing search direction with {self.par.OPTIMIZE}") + logger.info(f"computing search direction") g_new = self.load("g_new") p_new = -1 * self._precondition(g_new.vector) + self.save("p_new", p_new) def initialize_search(self): @@ -237,6 +227,7 @@ def initialize_search(self): Initialize the plugin line search machinery. Should only be run at the beginning of line search, by the main workflow module. """ + # Vectors required to initialize a line search m = self.load("m_new") g = self.load("g_new") p = self.load("p_new") @@ -253,19 +244,18 @@ def initialize_search(self): self.line_search.clear_history() # Optional safeguard to prevent step length from getting too large - if self.par.STEPLENMAX: - self.line_search.step_len_max = \ - self.par.STEPLENMAX * norm_m / norm_p - self.logger.debug(f"max step length safeguard is: " - f"{self.line_search.step_len_max:.2E}") + if self.step_len_max: + new_step_len_max = self.step_len_max * norm_m / norm_p + self.line_search.step_len_max = new_step_len_max + logger.debug(f"max step length safeguard = {new_step_len_max:.2E}") self.line_search.initialize(step_len=0., func_val=f, gtg=gtg, gtp=gtp) alpha, _ = self.line_search.calculate_step() # Alpha defines the trial step length. Optional step length override - if self.par.STEPLENINIT and len(self.line_search.step_lens) <= 1: - alpha = self.par.STEPLENINIT * norm_m / norm_p - self.logger.debug(f"overwrite initial step length: {alpha:.2E}") + if self.step_len_init and len(self.line_search.step_lens) <= 1: + alpha = self.step_len_init * norm_m / norm_p + logger.debug(f"overwrite initial step length: {alpha:.2E}") # The new model is the old model, scaled by the step direction and # gradient threshold to remove any outlier values @@ -306,33 +296,34 @@ def finalize_search(self): Removes old model/search parameters, moves current parameters to old, sets up new current parameters and writes statistic outputs """ - unix.cd(self.path.OPTIMIZE) - self.logger.info(msg.sub("FINALIZING LINE SEARCH")) + unix.cd(self.path) + + logger.info(msg.sub("FINALIZING LINE SEARCH")) # Remove the old model parameters - if self.iter > 1: - self.logger.info("removing previously accepted model files (old)") + if self.iteration > 1: + logger.info("removing previously accepted model files (old)") for fid in ["m_old", "f_old", "g_old", "p_old"]: unix.rm(fid) # Needs to be run before shifting model in next step self._write_stats() - self.logger.info("shifting current model (new) to previous model (old)") + logger.info("shifting current model (new) to previous model (old)") unix.mv("m_new.npz", "m_old.npz") unix.mv("f_new.npz", "f_old.npz") unix.mv("g_new.npz", "g_old.npz") unix.mv("p_new.npz", "p_old.npz") - self.logger.info("setting accepted line search model as current model") + logger.info("setting accepted line search model as current model") unix.mv("m_try.npz", "m_new.npz") # Choose minimum misfit value as final misfit/model f = self.line_search.search_history()[1] self.save("f_new", f.min()) - self.logger.info(f"current misfit is {f.min():.3E}") + logger.info(f"current misfit is {f.min():.3E}") - self.logger.info("resetting line search step count to 0") + logger.info("resetting line search step count to 0") self.line_search.step_count = 0 def retry_status(self): @@ -345,7 +336,7 @@ def retry_status(self): p = self.load("p_new") theta = angle(p.vector, -1 * g.vector) - self.logger.debug(f"theta: {theta:6.3f}") + logger.debug(f"theta: {theta:6.3f}") thresh = 1.e-3 if abs(theta) < thresh: @@ -363,8 +354,9 @@ def restart(self): numerical stagnation. """ # Steepest descent (base) does not need to be restarted - if self.par.OPTIMIZE.capitalize() == "Gradient": - return + # TODO figure out how to deal with this noting inheritance from others + # if self.par.OPTIMIZE.capitalize() == "Gradient": + # return g = self.load("g_new") self.save("p_new", -1 * g.vector) @@ -379,8 +371,8 @@ def _write_stats(self): by subsequent iterations so we need to append values to text files if they should be retained. """ - self.logger.info(f"writing optimization stats") - fid = os.path.join(self.path.OUTPUT, f"optim_stats.txt") + logger.info(f"writing optimization stats") + fid = os.path.join(self.path_output, f"optim_stats.txt") # First time, write header information if not os.path.exists(fid): @@ -397,7 +389,7 @@ def _write_stats(self): f = self.line_search.search_history()[1] # Calculated stats factors - factor = -dot(g.vector, g.vector) + factor = -1 * dot(g.vector, g.vector) factor = factor ** -0.5 * (f[1] - f[0]) / (x[1] - x[0]) grad_norm_L1 = np.linalg.norm(g.vector, 1) @@ -408,18 +400,17 @@ def _write_stats(self): slope = (f[1] - f[0]) / (x[1] - x[0]) step_count = self.line_search.step_count step_length = x[f.argmin()] - theta = 180. * np.pi ** -1 * angle(p, -g) + theta = 180. * np.pi ** -1 * angle(p, -1 * g.vector) with open(fid, "a") as f: - pass - f.write(f"{self.iter:0>2}," - f"{factor:6.3E}," - f"{grad_norm_L1:6.3E}," - f"{grad_norm_L2:6.3E}," - f"{misfit:6.3E}," - f"{restarted:6.3E}," - f"{slope:6.3E}," - f"{step_count:0>2}," - f"{step_length:6.3E}," - f"{theta:6.3E}\n" - ) + f.write(f"{self.iteration:0>2}," + f"{factor:6.3E}," + f"{grad_norm_L1:6.3E}," + f"{grad_norm_L2:6.3E}," + f"{misfit:6.3E}," + f"{restarted:6.3E}," + f"{slope:6.3E}," + f"{step_count:0>2}," + f"{step_length:6.3E}," + f"{theta:6.3E}\n" + ) diff --git a/seisflows/workflow/forward_test.py b/seisflows/workflow/forward_test.py index 464acf19..ad002cd2 100644 --- a/seisflows/workflow/forward_test.py +++ b/seisflows/workflow/forward_test.py @@ -8,22 +8,13 @@ from seisflows.tools.specfem import Model -def setup(instances): - """ - Run the .setup() function for each of the instances, which - """ - - - - if __name__ == "__main__": # Standard SeisFlows Workflow setup block # ========================================================================== cwd = os.getcwd() pars = load_yaml("parameters.yaml") - paths = Dict(pars.pop("paths")) - logger = config_logger(level=pars.log_level, filename=paths.log_file, + logger = config_logger(level=pars.log_level, filename=pars.path_log_file, verbose=pars.verbose) # Dynamically create module instances, instantiated with parameters @@ -40,11 +31,10 @@ def setup(instances): # Begin workflow logger.info("Starting forward simulation workflow") - for module in modules: - module.setup() - m = Model(paths.model_init) - m.write(path=os.path.join(paths.eval_fun, "model")) + + m = Model(pars.path_model_init) + m.write(path=os.path.join(paths.eval_func, "model")) system.run(solver.eval_func) From 906a5bcc18bb05c7a49c05b995dc0595be8d2f9c Mon Sep 17 00:00:00 2001 From: bch0w Date: Fri, 8 Jul 2022 14:09:50 -0800 Subject: [PATCH 057/195] finished NLCG new parameter system, will need to go back and make sure vector notation is correct --- seisflows/optimize/LBFGS.py | 3 +- seisflows/optimize/NLCG.py | 105 +++++++++++++++++++++--------------- 2 files changed, 64 insertions(+), 44 deletions(-) diff --git a/seisflows/optimize/LBFGS.py b/seisflows/optimize/LBFGS.py index 1c9b4b3f..4ae6921c 100644 --- a/seisflows/optimize/LBFGS.py +++ b/seisflows/optimize/LBFGS.py @@ -89,7 +89,8 @@ def __init__(self, lbfgs_mem=3, lbfgs_max=np.inf, lbfgs_thresh=0., f"search. Overwritng {self._line_search}") self._line_search = "Backtrack" self.line_search = getattr(line_search_dir, self._line_search)( - step_count_max=self.step_count_max, step_len_max=self.step_len_max + step_count_max=self.step_count_max, + step_len_max=self.step_len_max ) self.LBFGS_mem = lbfgs_mem diff --git a/seisflows/optimize/NLCG.py b/seisflows/optimize/NLCG.py index 891c12bf..19911d0a 100644 --- a/seisflows/optimize/NLCG.py +++ b/seisflows/optimize/NLCG.py @@ -3,9 +3,13 @@ This is the custom class for an NLCG optimization schema. It inherits from the `seisflows.optimize.gradient.Gradient` class """ +import numpy as np + +from seisflows import logger from seisflows.optimize.gradient import Gradient from seisflows.tools import unix from seisflows.tools.math import dot +from seisflows.plugins import line_search as line_search_dir class NLCG(Gradient): @@ -31,38 +35,54 @@ class NLCG(Gradient): status == 0 : not finished status < 0 : failed """ - def __init__(self): + def __init__(self, nlcg_max=np.inf, nlcg_thresh=np.inf, + calc_beta="pollak_ribere", **kwargs): """ These parameters should not be set by the user. Attributes are initialized as NoneTypes for clarity and docstrings. - TODO allow user to choose the calc_beta function - :type NLCG_iter: Class - :param NLCG_iter: an internally used iteration that differs from + :type nlcg_max: int + :param nlcg_max: NLCG periodic restart interval, should be between 1 + and infinity + :type nlcg_thresh: NLCG conjugacy restart threshold, should be + between 1 and infinity + :type calc_beta: str + :param calc_beta: method to calculate the parameter 'beta' in the + NLCG algorithm. Available: 'pollak_ribere', 'fletcher_reeves' + + + :type _NLCG_iter: Class + :param _NLCG_iter: an internally used iteration that differs from optimization iter. Keeps track of internal NLCG memory. """ - super().__init__() - - self.required.par( - "NLCGMAX", required=False, default="null", par_type=float, - docstr="NLCG periodic restart interval, between 1 and inf" + super().__init__(**kwargs) + + # Overwrite user-chosen line search. L-BFGS requires 'Backtrack'ing LS + if self._line_search.title != "Bracket": + logger.warning(f"NLCG optimization requires 'bracket'ing line " + f"search. Overwritng {self._line_search}") + self._line_search = "Bracket" + self.line_search = getattr(line_search_dir, self._line_search)( + step_count_max=self.step_count_max, + step_len_max=self.step_len_max + ) + + + self.NLCG_max = nlcg_max + self.NLCG_thresh = nlcg_thresh + + # Check paramter validity + _acceptable_calc_beta = ["pollak_ribere", "fletcher_reeves"] + assert(calc_beta in _acceptable_calc_beta), ( + f"unacceptable `calc_beta`, must be in {_acceptable_calc_beta}" ) - self.required.par( - "NLCGTHRESH", required=False, default="null", par_type=float, - docstr="NLCG conjugacy restart threshold, between 1 and inf" - ) - self.NLCG_iter = 0 - self.calc_beta = self._pollak_ribere + self.calc_beta = calc_beta - def check(self, validate=True): - """ - Checks parameters, paths, and dependencies - """ - super().check(validate=validate) + # Internally used parameters + self._NLCG_iter = 0 + self._calc_beta = getattr(self, f"_{calc_beta}") - assert(self.par.LINESEARCH.upper() == "BRACKET"), \ - f"NLCG requires a bracketing line search algorithm" def compute_direction(self): """ @@ -81,53 +101,52 @@ def compute_direction(self): 5. New NLCG search direction has conjugacy and is a descent direction and is set as the new search direction. """ - self.logger.debug(f"computing search direction with NLCG") - self.NLCG_iter += 1 - - unix.cd(self.path.OPTIMIZE) + unix.cd(self.path) + logger.debug(f"computing search direction with NLCG") + self._NLCG_iter += 1 # Load the current gradient direction g_new = self.load("g_new") # CASE 1: If first iteration, search direction is the current gradient - if self.NLCG_iter == 1: - self.logger.info("first NLCG iteration, setting search direction" + if self._NLCG_iter == 1: + logger.info("first NLCG iteration, setting search direction" "as inverse gradient") - p_new = -g_new + p_new = -1 * g_new.vector restarted = 0 # CASE 2: Force restart if the iterations have surpassed the maximum # number of allowable iter - elif self.NLCG_iter > self.par.NLCGMAX: - self.logger.info("restarting NLCG due to periodic restart " + elif self._NLCG_iter > self.NLCG_max: + logger.info("restarting NLCG due to periodic restart " "condition. setting search direction as inverse " "gradient") self.restart() - p_new = -g_new + p_new = -1 * g_new.vector restarted = 1 # Normal NLCG direction compuitation else: # Compute search direction - g_old = self.load("g_old") - p_old = self.load("p_old") + g_old = self.load("g_old").vector + p_old = self.load("p_old").vector # Apply preconditioner and calc. scale factor for search dir. (beta) - if self.precond is not None: - beta = self.calc_beta(g_new, g_old) + if self.preconditioner is not None: + beta = self._calc_beta(g_new, g_old) p_new = -1 * self._precondition(g_new) + beta * p_old else: - beta = self.calc_beta(g_new, g_old) + beta = self._calc_beta(g_new, g_old) p_new = -g_new + beta * p_old # Check restart conditions, return search direction and status - if check_conjugacy(g_new, g_old) > self.par.NLCGTHRESH: - self.logger.info("restarting NLCG due to loss of conjugacy") + if check_conjugacy(g_new, g_old) > self.NLCG_thresh: + logger.info("restarting NLCG due to loss of conjugacy") self.restart() - p_new = -g_new + p_new = -1 * g_new.vector restarted = 1 elif check_descent(p_new, g_new) > 0.: - self.logger.info("restarting NLCG, not a descent direction") + logger.info("restarting NLCG, not a descent direction") self.restart() - p_new = -g_new + p_new = -1 * g_new.vector restarted = 1 else: p_new = p_new @@ -142,7 +161,7 @@ def restart(self): Overwrite the Base restart class and include a restart of the NLCG """ super().restart() - self.NLCG_iter = 1 + self._NLCG_iter = 1 def _fletcher_reeves(self, g_new, g_old): """ From 4f5a8d6cb1f5eb4767ba030a3fabaef25478d1b1 Mon Sep 17 00:00:00 2001 From: bch0w Date: Fri, 8 Jul 2022 14:19:56 -0800 Subject: [PATCH 058/195] converted default postprocess module to new paramter system --- seisflows/optimize/gradient.py | 6 ++ seisflows/postprocess/default.py | 132 ++++++++++++++----------------- 2 files changed, 67 insertions(+), 71 deletions(-) diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index 3b2b522d..06d0cdae 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -142,6 +142,12 @@ def setup(self): # "valid PATH.MODEL_INIT" # ) + def finalize(self): + """ + Finalization tasks + """ + pass + def load(self, name): """ Convenience function to access the full paths of model and gradient diff --git a/seisflows/postprocess/default.py b/seisflows/postprocess/default.py index 52697869..8184d376 100644 --- a/seisflows/postprocess/default.py +++ b/seisflows/postprocess/default.py @@ -6,68 +6,68 @@ """ import os -from seisflows.core import Base +from seisflows import logger from seisflows.tools.specfem import Model -class Default(Base): +class Default: """ Postprocessing in a Seisflows workflow includes tasks such as regularization, smoothing, sharpening, masking and related operations on models or gradients """ - def __init__(self): + def __init__(self, smooth_h=0., smooth_v=0., tasktime_smooth=1, + path_postprocess=None, path_mask=None): """ - These parameters should not be set by __init__! - Attributes are just initialized as NoneTypes for clarity and docstrings + Establish Postprocessing parameters + + :type smooth_h: float + :param smooth_h: Gaussian half-width for horizontal smoothing in units + of meters. If 0., no smoothing applied + :type smooth_h: float + :param smooth_v: Gaussian half-width for vertical smoothing in units + of meters. + :type tasktime_smooth: float + :param tasktime_smooth: Large radii smoothing may take longer than + normal tasks. Allocate additional smoothing task time as a multiple + of system.tasktime + :type path_postprocess: str + :param path_postprocess: scratch path to perform all postprocessing + tasks such as smoothing, kernel combination etc. + :type path_mask: str + :param path_mask: Directory to mask files for gradient masking. Format + of the mask files MUST match the format of the input model. """ super().__init__() - self.required.par( - "SMOOTH_H", required=False, default=0., par_type=float, - docstr="Gaussian half-width for horizontal smoothing in units of " - "meters. If 0., no smoothing applied" - ) - self.required.par( - "SMOOTH_V", required=False, default=0., par_type=float, - docstr="Gaussian half-width for vertical smoothing in units of " - "meters" - ) - self.required.par( - "TASKTIME_SMOOTH", required=False, default=1, par_type=int, - docstr="Large radii smoothing may take longer than normal tasks. " - "Allocate additional smoothing task time as a multiple of " - "TASKTIME" - ) - # Define the Paths required by this module - self.required.path( - "MASK", required=False, docstr="Directory to mask files for " - "gradient masking" - ) - - def check(self, validate=True): + self.smooth_h = smooth_h + self.smooth_v = smooth_v + self.tasktime_smooth = tasktime_smooth + self.path = path_postprocess or \ + os.path.join(os.getcwd(), "scratch", "evalgrad") + self.path_mask = path_mask + + def check(self): """ Checks parameters and paths """ - super().check(validate=validate) - - if self.path.MASK: - assert os.path.exists(self.path.MASK), \ - f"PATH.MASK provided but does not exist" + if self.path_mask: + assert os.path.exists(self.path_mask), \ + "`postprocess.path_mask` provided but does not exist" def setup(self): """ Setup tasks """ - super().setup() + pass def finalize(self): """ Finalization tasks """ - super().finalize() + pass - def scale_gradient(self, input_path): + def scale_gradient(self): """ Combines contributions from individual sources and material parameters to get the gradient, and optionally applies user-supplied scaling @@ -83,36 +83,31 @@ def scale_gradient(self, input_path): :rtype: np.array :return: scaled gradient as a vector """ - # Postprocess file structure defined here once-and-for-all - path_grad_nomask = os.path.join(input_path, "gradient_nomask") - path_model = os.path.join(input_path, "model") - path_kernels_sum = os.path.join(input_path, "kernels", "sum") - # Access the gradient information stored in as kernel files - gradient = Model(path=path_kernels_sum) - model = Model(path=path_model) + gradient = Model(path=os.path.join(self.path, "model")) + model = Model(path=os.path.join(self.path, "kernels", "sum")) # Merge to vector and convert to absolute perturbations: # log dm --> dm (see Eq.13 Tromp et al 2005) gradient.vector *= model.vector - if self.path.MASK: - self.logger.info(f"masking gradient") + if self.path_mask: + logger.info(f"masking gradient") # to scale the gradient, users can supply "masks" by exactly # mimicking the file format in which models are stored - mask = Model(self.path.MASK) + mask = Model(self.path_mask) # While both masking and preconditioning involve scaling the # gradient, they are fundamentally different operations: # masking is ad hoc, preconditioning is a change of variables; # For more info, see Modrak & Tromp 2016 GJI - gradient.write(path=path_grad_nomask) + gradient.write(path=os.path.join(self.path, "gradient_nomask")) gradient.vector *= mask.vector return gradient - def sum_smooth_kernels(self, path_grad): + def sum_smooth_kernels(self, solver): """ Sums kernels from individual sources, with optional smoothing @@ -120,33 +115,28 @@ def sum_smooth_kernels(self, path_grad): This function needs to be run on system, i.e., called by system.run(single=True) - :type path_grad: str - :param path_grad: directory containing sensitivity kernels in the - scratch directory to be summed and smoothed. Output summed and - summed + smoothed kernels will be saved here as well. + :type solver: solver instance + :param solver: SeisFlows solver which will be used for its combine and + smooth functions """ - solver = self.module("solver") - # If specified, smooth the kernels in the vertical and horizontal and # save both (summed, summed+smoothed) to separate output directories - kernel_path = os.path.join(path_grad, "kernels") - - path_sum_nosmooth = os.path.join(kernel_path, "sum_nosmooth") - path_sum = os.path.join(kernel_path, "sum") - - if (self.par.SMOOTH_H > 0) or (self.par.SMOOTH_V > 0): - self.logger.debug(f"saving un-smoothed and summed kernels to:\n" - f"{path_sum_nosmooth}") - solver.combine(input_path=kernel_path, - output_path=path_sum_nosmooth) - - self.logger.info(f"smoothing gradient: H={self.par.SMOOTH_H}m, " - f"V={self.par.SMOOTH_V}m") - self.logger.debug(f"saving smoothed kernels to:\n{path_sum}") + path_kernel = os.path.join(self.path, "kernels") + path_sum_nosmooth = os.path.join(path_kernel, "sum_nosmooth") + path_sum = os.path.join(path_kernel, "sum") + + if (self.smooth_h > 0) or (self.smooth_v > 0): + logger.debug(f"saving un-smoothed and summed kernels to:\n" + f"{path_sum_nosmooth}") + solver.combine(input_path=path_kernel, output_path=path_sum_nosmooth) + + logger.info(f"smoothing gradient: " + f"H={self.smooth_h}m; V={self.smooth_v}m") + logger.debug(f"saving smoothed kernels to:\n{path_sum}") solver.smooth(input_path=path_sum_nosmooth, output_path=path_sum, - span_h=self.par.SMOOTH_H, span_v=self.par.SMOOTH_V) + span_h=self.smooth_h, span_v=self.smooth_v) # Combine all the input kernels, generating the unscaled gradient else: - self.logger.debug(f"saving summed kernels to:\n{path_sum}") - solver.combine(input_path=kernel_path, output_path=path_sum) + logger.debug(f"saving summed kernels to:\n{path_sum}") + solver.combine(input_path=path_kernel, output_path=path_sum) From 6d903e2b8571ecd30145c8eda719fe9576f0eca3 Mon Sep 17 00:00:00 2001 From: bch0w Date: Fri, 8 Jul 2022 14:39:26 -0800 Subject: [PATCH 059/195] preprocess default converrted to new paramter system but will require some work --- seisflows/preprocess/default.py | 341 ++++++++++++++++---------------- 1 file changed, 166 insertions(+), 175 deletions(-) diff --git a/seisflows/preprocess/default.py b/seisflows/preprocess/default.py index fe8cde68..6b5290f4 100644 --- a/seisflows/preprocess/default.py +++ b/seisflows/preprocess/default.py @@ -9,186 +9,176 @@ import obspy import numpy as np -from seisflows.core import Base +from seisflows import logger from seisflows.tools import signal, unix from seisflows.plugins.preprocess import adjoint, misfit, readers, writers -class Default(Base): +class Default: """ Default SeisFlows preprocessing class Provides data processing functions for seismic traces, with options for data misfit, filtering, normalization and muting """ - def __init__(self): + def __init__(self, data_format="ascii", misfit="waveform", backproject=None, + normalize=None, filter=None, min_period=None, max_period=None, + min_freq=None, max_freq=None, mute=None, path_preprocess=None): """ - These parameters should not be set by __init__! - Attributes are just initialized as NoneTypes for clarity and docstrings + Preprocessing module parameters + + :type data_format: str + :param data_format: data format for reading traces into memory. For + available see: seisflows.plugins.preprocess.readers + :type misfit: str + :param misfit: misfit function for waveform comparisons. For available + see seisflows.plugins.preprocess.misfit + :type backproject: str + :param backproject: backprojection function for migration, or the + objective function in FWI. For available see + seisflows.plugins.preprocess.adjoint + :type normalize: str + :param normalize: Data normalization parameters used to normalize the + amplitudes of waveforms. Choose from two sets: + ENORML1: normalize per event by L1 of traces; OR + ENORML2: normalize per event by L2 of traces; + & + TNORML1: normalize per trace by L1 of itself; OR + TNORML2: normalize per trace by L2 of itself + :type filter: str + :param filter: Data filtering type, available options are: + BANDPASS (req. MIN/MAX PERIOD/FREQ); + LOWPASS (req. MAX_FREQ or MIN_PERIOD); + HIGHPASS (req. MIN_FREQ or MAX_PERIOD) + :type min_period: float + :param min_period: Minimum filter period applied to time series. + See also MIN_FREQ, MAX_FREQ, if User defines FREQ parameters, they + will overwrite PERIOD parameters. + :type max_period: float + :param max_period: Maximum filter period applied to time series. See + also MIN_FREQ, MAX_FREQ, if User defines FREQ parameters, they will + overwrite PERIOD parameters. + :type min_freq: float + :param min_freq: Maximum filter frequency applied to time series, + See also MIN_PERIOD, MAX_PERIOD, if User defines FREQ parameters, + they will overwrite PERIOD parameters. + :type max_freq: float + :param max_freq: Maximum filter frequency applied to time series, + See also MIN_PERIOD, MAX_PERIOD, if User defines FREQ parameters, + they will overwrite PERIOD parameters. + :type mute: list + :param mute: Data mute parameters used to zero out early / late + arrivals or offsets. Choose any number of: + EARLY: mute early arrivals; + LATE: mute late arrivals; + SHORT: mute short source-receiver distances; + LONG: mute long source-receiver distances + :type path_preprocess: str + :param path_preprocess: scratch path for all preprocessing processes, + including saving files """ - super().__init__() - - # Define the Parameters required by this module - self.required.par( - "MISFIT", required=False, default="waveform", par_type=str, - docstr="Misfit function for waveform comparisons, for available " - "see seisflows.plugins.misfit" - ) - self.required.par( - "BACKPROJECT", required=False, default="null", par_type=str, - docstr="Backprojection function for migration, for available see " - "seisflows.plugins.adjoint" - ) - self.required.par( - "NORMALIZE", required=False, default="null", par_type=str, - docstr="Data normalization option" - ) - self.required.par( - "FILTER", required=False, default="null", par_type=str, - docstr="Data filtering type, available options are:" - "BANDPASS (req. MIN/MAX PERIOD/FREQ);" - "LOWPASS (req. MAX_FREQ or MIN_PERIOD); " - "HIGHPASS (req. MIN_FREQ or MAX_PERIOD) " - ) - self.required.par( - "MIN_PERIOD", required=False, par_type=float, - docstr="Minimum filter period applied to time series." - "See also MIN_FREQ, MAX_FREQ, if User defines FREQ " - "parameters, they will overwrite PERIOD parameters." - ) - self.required.par( - "MAX_PERIOD", required=False, par_type=float, - docstr="Maximum filter period applied to time series." - "See also MIN_FREQ, MAX_FREQ, if User defines FREQ " - "parameters, they will overwrite PERIOD parameters." - ) - self.required.par( - "MIN_FREQ", required=False, par_type=float, - docstr="Maximum filter frequency applied to time series." - "See also MIN_PERIOD, MAX_PERIOD, if User defines FREQ " - "parameters, they will overwrite PERIOD parameters." - ) - self.required.par( - "MAX_FREQ", required=False, par_type=float, - docstr="Maximum filter frequency applied to time series," - "See also MIN_PERIOD, MAX_PERIOD, if User defines FREQ " - "parameters, they will overwrite PERIOD parameters." - ) - self.required.par( - "MUTE", required=False, par_type=list, default=[], - docstr="Data mute parameters used to zero out early / late " - "arrivals or offsets. Choose any number of: " - "EARLY: mute early arrivals; " - "LATE: mute late arrivals; " - "SHORT: mute short source-receiver distances; " - "LONG: mute long source-receiver distances" - ) - self.required.par( - "NORMALIZE", required=False, par_type=list, default=[], - docstr="Data normalization parameters used to normalize the " - "amplitudes of waveforms. Choose from two sets: " - "ENORML1: normalize per event by L1 of traces; OR " - "ENORML2: normalize per event by L2 of traces; AND " - "TNORML1: normalize per trace by L1 of itself; OR " - "TNORML2: normalize per trace by L2 of itself" - ) - # TODO: Add the mute parameters here, const, slope and dist + self.data_format = data_format.title() + self.misfit = misfit + self.backproject = backproject, + self.normalize = normalize + self.filter = filter + self.min_period = min_period + self.max_period = max_period + self.min_freq = min_freq + self.max_freq = max_freq + self.mute = mute or [] + self.normalize = normalize or [] + self.path = path_preprocess or \ + os.path.join(os.getcwd(), "scratch", "preprocess") - # Define the Paths required by this module - self.required.path( - "PREPROCESS", required=False, - default=os.path.join(self.path.WORKDIR, "scratch", "preprocess"), - docstr="scratch path to store any preprocessing outputs" - ) + # TODO: Add the mute parameters here, const, slope and dist - self.misfit = None - self.adjoint = None - self.reader = None - self.writer = None + self._misfit = None + self._adjoint = None + self._reader = None + self._writer = None def check(self, validate=True): """ Checks parameters and paths """ - super().check(validate=validate) - # Data normalization option - if self.par.NORMALIZE: + if self.normalize: acceptable_norms = {"TNORML1", "TNORML2", "ENORML1", "ENORML2"} - chosen_norms = [_.upper() for _ in self.par.NORMALIZE] + chosen_norms = [_.upper() for _ in self.normalize] assert(set(chosen_norms).issubset(acceptable_norms)) # Data muting options - if self.par.MUTE: + if self.mute: acceptable_mutes = {"EARLY", "LATE", "LONG", "SHORT"} - chosen_mutes = [_.upper() for _ in self.par.MUTE] + chosen_mutes = [_.upper() for _ in self.mute] assert(set(chosen_mutes).issubset(acceptable_mutes)) if "EARLY" in chosen_mutes: - assert(self.par.EARLY_SLOPE is not None) - assert(self.par.EARLY_SLOPE >= 0.) - assert(self.par.EARLY_CONST is not None) + assert(self.early_slope is not None) + assert(self.early_slope >= 0.) + assert(self.early_const is not None) if "LATE" in chosen_mutes: - assert(self.par.LATE_SLOPE is not None) - assert(self.par.LATE_SLOPE >= 0.) - assert(self.par.LATE_CONST is not None) + assert(self.late_slope is not None) + assert(self.late_slope >= 0.) + assert(self.late_const is not None) if "SHORT" in chosen_mutes: - assert(self.par.SHORT_DIST is not None) - assert (self.par.SHORT_DIST > 0) + assert(self.short_dist is not None) + assert (self.short_dist > 0) if "LONG" in chosen_mutes: - assert(self.par.LONG_DIST is not None) - assert (self.par.LONG_DIST > 0) + assert(self.long_dist is not None) + assert (self.long_dist > 0) # Data filtering options that will be passed to ObsPy filters - if self.par.FILTER: + if self.filter: acceptable_filters = ["BANDPASS", "LOWPASS", "HIGHPASS"] - assert self.par.FILTER.upper() in acceptable_filters, \ - f"self.par.FILTER must be in {acceptable_filters}" + assert self.filter.upper() in acceptable_filters, \ + f"self.filter must be in {acceptable_filters}" # Set the min/max frequencies and periods, frequency takes priority - if self.par.MIN_FREQ is not None: - self.par.MAX_PERIOD = 1 / self.par.MIN_FREQ - elif self.par.MAX_PERIOD is not None: - self.par.MIN_FREQ = 1 / self.par.MAX_PERIOD + if self.min_freq is not None: + self.max_period = 1 / self.min_freq + elif self.max_period is not None: + self.min_freq = 1 / self.max_period - if self.par.MAX_FREQ is not None: - self.par.MIN_PERIOD = 1 / self.par.MAX_FREQ - elif self.par.MIN_PERIOD is not None: - self.par.MAX_FREQ = 1 / self.par.MIN_PERIOD + if self.max_freq is not None: + self.min_period = 1 / self.max_freq + elif self.min_period is not None: + self.max_freq = 1 / self.min_period # Check that the correct filter bounds have been set - if self.par.FILTER.upper() == "BANDPASS": - assert(self.par.MIN_FREQ is not None and - self.par.MAX_FREQ is not None), \ + if self.filter.upper() == "BANDPASS": + assert(self.min_freq is not None and + self.max_freq is not None), \ ("BANDPASS filter PAR.MIN_PERIOD and PAR.MAX_PERIOD or " "PAR.MIN_FREQ and PAR.MAX_FREQ") - elif self.par.FILTER.upper() == "LOWPASS": - assert(self.par.MAX_FREQ is not None or - self.par.MIN_PERIOD is not None),\ + elif self.filter.upper() == "LOWPASS": + assert(self.max_freq is not None or + self.min_period is not None),\ "LOWPASS requires PAR.MAX_FREQ or PAR.MIN_PERIOD" - elif self.par.FILTER.upper() == "HIGHPASS": - assert(self.par.MIN_FREQ is not None or - self.par.MAX_PERIOD is not None),\ + elif self.filter.upper() == "HIGHPASS": + assert(self.min_freq is not None or + self.max_period is not None),\ "HIGHPASS requires PAR.MIN_FREQ or PAR.MAX_PERIOD" # Check that filter bounds make sense, by this point, MIN and MAX # FREQ and PERIOD should be set, so we just check the FREQ - assert(0 < self.par.MIN_FREQ < np.inf), "0 < PAR.MIN_FREQ < inf" - assert(0 < self.par.MAX_FREQ < np.inf), "0 < PAR.MAX_FREQ < inf" - assert(self.par.MIN_FREQ < self.par.MAX_FREQ), ( + assert(0 < self.min_freq < np.inf), "0 < PAR.MIN_FREQ < inf" + assert(0 < self.max_freq < np.inf), "0 < PAR.MAX_FREQ < inf" + assert(self.min_freq < self.max_freq), ( "PAR.MIN_FREQ < PAR.MAX_FREQ" ) # Assert that readers and writers available # TODO | This is a bit vague as dir contains imported modules and hidden # TODO | variables (e.g., np, __name__) - assert(self.par.FORMAT.lower() in dir(readers)), ( - f"Reader {self.par.FORMAT} not found") - assert(self.par.FORMAT.lower() in dir(writers)), ( - f"Writer {self.par.FORMAT} not found") + assert(self.data_format.lower() in dir(readers)), ( + f"Reader {self.data_format} not found") + assert(self.data_format.lower() in dir(writers)), ( + f"Writer {self.data_format} not found") # Assert that either misfit or backproject exists if self.par.WORKFLOW.upper() == "INVERSION": - assert(self.par.MISFIT is not None) + assert(self.misfit is not None) def setup(self): """ @@ -196,29 +186,29 @@ def setup(self): misfit, adjoint source type, and specifying the expected file type for input and output seismic data. """ - unix.mkdir(self.path.PREPROCESS) + unix.mkdir(self.path) # Define misfit function and adjoint trace generator - if self.par.MISFIT: - self.logger.debug(f"misfit function is: '{self.par.MISFIT}'") - self.misfit = getattr(misfit, self.par.MISFIT.lower()) - self.adjoint = getattr(adjoint, self.par.MISFIT.lower()) - elif self.par.BACKPROJECT: - self.logger.debug( - f"backproject function is: '{self.par.BACKPROJECT}'" + if self.misfit: + logger.debug(f"misfit function is: '{self.misfit}'") + self._misfit = getattr(misfit, self.misfit.lower()) + self._adjoint = getattr(adjoint, self.misfit.lower()) + elif self.backproject: + logger.debug( + f"backproject function is: '{self.backproject}'" ) - self.adjoint = getattr(adjoint, self.par.BACKPROJECT.lower()) + self._adjoint = getattr(adjoint, self.backproject.lower()) # Define seismic data reader and writer - self.reader = getattr(readers, self.par.FORMAT.lower()) - self.writer = getattr(writers, self.par.FORMAT.lower()) + self._reader = getattr(readers, self.data_format.lower()) + self._writer = getattr(writers, self.data_format.lower()) def finalize(self): """ Any finalization processes that need to take place at the end of an iteration """ - super().finalize() + pass def prepare_eval_grad(self, cwd, taskid, filenames, **kwargs): """ @@ -241,42 +231,42 @@ def prepare_eval_grad(self, cwd, taskid, filenames, **kwargs): :param filenames: list of filenames defining the files in traces """ if taskid == 0: - self.logger.debug("preparing files for gradient evaluation") + logger.debug("preparing files for gradient evaluation") for filename in filenames: - obs = self.reader(path=os.path.join(cwd, "traces", "obs"), + obs = self._reader(path=os.path.join(cwd, "traces", "obs"), filename=filename) - syn = self.reader(path=os.path.join(cwd, "traces", "syn"), + syn = self._reader(path=os.path.join(cwd, "traces", "syn"), filename=filename) # Process observations and synthetics identically - if self.par.FILTER: + if self.filter: if taskid == 0: - self.logger.debug(f"applying {self.par.FILTER} filter to data") + logger.debug(f"applying {self.filter} filter to data") obs = self._apply_filter(obs) syn = self._apply_filter(syn) - if self.par.MUTE: + if self.mute: if taskid == 0: - self.logger.debug(f"applying {self.par.MUTE} mutes to data") + logger.debug(f"applying {self.mute} mutes to data") obs = self._apply_mute(obs) syn = self._apply_mute(syn) - if self.par.NORMALIZE: + if self.normalize: if taskid == 0: - self.logger.debug( - f"normalizing data with: {self.par.NORMALIZE}" + logger.debug( + f"normalizing data with: {self.normalize}" ) obs = self._apply_normalize(obs) syn = self._apply_normalize(syn) - if self.par.MISFIT is not None: + if self.misfit is not None: self._write_residuals(cwd, syn, obs) # Write the adjoint traces. Rename file extension for Specfem - if self.par.FORMAT.upper() == "ASCII": + if self.data_format.upper() == "ASCII": # Change the extension to '.adj' from whatever it is ext = os.path.splitext(filename)[-1] filename_out = filename.replace(ext, ".adj") - elif self.par.FORMAT.upper() == "SU": + elif self.data_format.upper() == "SU": # TODO implement this raise NotImplementedError @@ -285,6 +275,7 @@ def prepare_eval_grad(self, cwd, taskid, filenames, **kwargs): # Copy over the STATIONS file to STATIONS_ADJOINT required by Specfem # ASSUMING that all stations are used in adjoint simulation + # TODO !!! This is SPECFEM dependent? Belongs in solver.specfem? src = os.path.join(cwd, "DATA", "STATIONS") dst = os.path.join(cwd, "DATA", "STATIONS_ADJOINT") unix.cp(src, dst) @@ -307,7 +298,7 @@ def sum_residuals(self, files): def _write_residuals(self, path, syn, obs): """ Computes residuals between observed and synthetic seismogram based on - the misfit function self.par.MISFIT. Saves the residuals for each + the misfit function self.misfit. Saves the residuals for each data-synthetic pair into a text file located at: ./scratch/solver/*/residuals @@ -324,9 +315,9 @@ def _write_residuals(self, path, syn, obs): """ residuals = [] for tr_obs, tr_syn in zip(obs, syn): - residual = self.misfit(syn=tr_syn.data, obs=tr_obs.data, - nt=tr_syn.stats.npts, - dt=tr_syn.stats.delta + residual = self._misfit(syn=tr_syn.data, obs=tr_obs.data, + nt=tr_syn.stats.npts, + dt=tr_syn.stats.delta ) residuals.append(residual) @@ -352,12 +343,12 @@ def _write_adjoint_traces(self, path, syn, obs, filename): # Use the synthetics as a template for the adjoint sources adj = syn.copy() for tr_adj, tr_obs, tr_syn in zip(adj, obs, syn): - tr_adj.data = self.adjoint(syn=tr_syn.data, obs=tr_obs.data, - nt=tr_syn.stats.npts, - dt=tr_syn.stats.delta - ) + tr_adj.data = self._adjoint(syn=tr_syn.data, obs=tr_obs.data, + nt=tr_syn.stats.npts, + dt=tr_syn.stats.delta + ) - self.writer(adj, path, filename) + self._writer(adj, path, filename) def _apply_filter(self, st): """ @@ -373,13 +364,13 @@ def _apply_filter(self, st): st.detrend("linear") st.taper(0.05, type="hann") - if self.par.FILTER.upper() == "BANDPASS": - st.filter("bandpass", zerophase=True, freqmin=self.par.MIN_FREQ, - freqmax=self.par.FREQMAX) - elif self.par.FILTER.upper() == "LOWPASS": - st.filter("lowpass", zerophase=True, freq=self.par.MAX_FREQ) - elif self.par.FILTER.upper() == "HIGHPASS": - st.filter("highpass", zerophase=True, freq=self.par.MIN_FREQ) + if self.filter.upper() == "BANDPASS": + st.filter("bandpass", zerophase=True, freqmin=self.min_freq, + freqmax=self.max_freq) + elif self.filter.upper() == "LOWPASS": + st.filter("lowpass", zerophase=True, freq=self.max_freq) + elif self.filter.upper() == "HIGHPASS": + st.filter("highpass", zerophase=True, freq=self.min_freq) return st @@ -398,20 +389,20 @@ def _apply_mute(self, st): :rtype: obspy.core.stream.Stream :return: muted stream object """ - mute_choices = [_.upper() for _ in self.par.MUTE] + mute_choices = [_.upper() for _ in self.mute] if "EARLY" in mute_choices: - st = signal.mute_arrivals(st, slope=self.par.EARLY_SLOPE, - const=self.par.EARLY_CONST, + st = signal.mute_arrivals(st, slope=self.early_slope, + const=self.early_const, choice="EARLY") if "LATE" in mute_choices: - st = signal.mute_arrivals(st, slope=self.par.LATE_SLOPE, - const=self.par.LATE_CONST, + st = signal.mute_arrivals(st, slope=self.late_slope, + const=self.late_const, choice="LATE") if "SHORT" in mute_choices: - st = signal.mute_offsets(st, dist=self.par.SHORT_DIST, + st = signal.mute_offsets(st, dist=self.short_dist, choice="SHORT") if "LONG" in mute_choices: - st = signal.mute_arrivals(st, dist=self.par.LONG_DIST, + st = signal.mute_arrivals(st, dist=self.long_dist, choice="LONG") return st @@ -431,7 +422,7 @@ def _apply_normalize(self, st): :return: stream with normalized traces """ st_out = st.copy() - norm_choices = [_.upper() for _ in self.par.NORMALIZE] + norm_choices = [_.upper() for _ in self.normalize] # Normalize an event by the L1 norm of all traces if 'ENORML1' in norm_choices: From d2474af4ba70996011d146b74f5011fa34b7a9c4 Mon Sep 17 00:00:00 2001 From: bch0w Date: Fri, 8 Jul 2022 15:59:18 -0800 Subject: [PATCH 060/195] solver up to specfem3d_globe converted to new parameter system --- seisflows/preprocess/default.py | 3 +- seisflows/preprocess/pyatoa.py | 313 ++++++++++------------- seisflows/solver/specfem.py | 373 +++++++++++----------------- seisflows/solver/specfem2d.py | 82 +++--- seisflows/solver/specfem3d.py | 52 ++-- seisflows/solver/specfem3d_globe.py | 13 + 6 files changed, 375 insertions(+), 461 deletions(-) diff --git a/seisflows/preprocess/default.py b/seisflows/preprocess/default.py index 6b5290f4..0a11fa80 100644 --- a/seisflows/preprocess/default.py +++ b/seisflows/preprocess/default.py @@ -23,7 +23,8 @@ class Default: """ def __init__(self, data_format="ascii", misfit="waveform", backproject=None, normalize=None, filter=None, min_period=None, max_period=None, - min_freq=None, max_freq=None, mute=None, path_preprocess=None): + min_freq=None, max_freq=None, mute=None, path_preprocess=None, + **kwargs): """ Preprocessing module parameters diff --git a/seisflows/preprocess/pyatoa.py b/seisflows/preprocess/pyatoa.py index 8b4537b0..4b99ee51 100644 --- a/seisflows/preprocess/pyatoa.py +++ b/seisflows/preprocess/pyatoa.py @@ -14,181 +14,149 @@ from glob import glob from pyatoa import Pyaflowa, Inspector -from seisflows.core import Base +from seisflows import logger from seisflows.tools import unix, msg from seisflows.config import CFGPATHS -class Pyatoa(Base): +class Pyatoa: """ Data preprocessing class using the Pyaflowa class within the Pyatoa package. In charge of data discovery, preprocessing, filtering, misfiti quantification and data storage. The User does not need to implement Pyatoa, but rather interacts with it via the parameters and paths of SeisFlows. """ - def __init__(self): + def __init__(self, data_format="ascii", components=None, ntask=1, nproc=1, + min_period=None, max_period=None, filter_corners=4, + client=None, rotate=False, pyflex_preset="default", + fix_windows=False, adj_src_type="cc", plot=True, + pyatoa_log_level="DEBUG", unit_output="VEL", + start_pad_s=0., end_pad_s=None, path_preprocess=None, + path_data=None, path_output=None, save_datasets=True, + save_figures=True, save_log_files=True, **kwargs): """ - These parameters should not be set by __init__! - Attributes are just initialized as NoneTypes for clarity and docstrings - - :param logger: Class-specific logging module, log statements pushed - from this logger will be tagged by its specific module/classname + Pyatoa preprocessing parameters that will be passed to Pyaflowa + + :type data_format: str + :param data_format: data format for reading traces into memory. Pyatoa + only works with 'ASCII' currently. + :type components: str + :param components: components to consider and tag data with. Should be + string of letters such as 'RTZ' + :type min_period: float + :param min_period: Minimum filter corner in unit seconds. Bandpass + filter if set with `max_period`, highpass filter if set without + `max_period`, no filtering if not set and `max_period also not set + :type max_period: float + :param max_period: Maximum filter corner in unit seconds. Bandpass + filter if set with `min_period`, lowpass filter if set without + `min_period`, no filtering if not set and `min_period also not set + :type filter_corners: int + :param filter_corners: number of filter corners applied to filtering + :type client: str + :param client: Client name for ObsPy FDSN data gathering. Pyatoa will + attempt to collect waveform and metadata based on network and + station codes provided in the SPECFEM STATIONS file. If set None, + no FDSN gathering will be attempted + :type rotate: bool + :param rotate: Attempt to rotate waveform components from NEZ -> RTZ + :type pyflex_preset: str + :param pyflex_preset: Parameter map for misfit window configuration + defined by Pyflex. IF None, misfit and adjoint sources will be + calculated on whole traces. For available choices, see Pyatoa docs + page (pyatoa.rtfd.io) + :type fix_windows: bool or str + :param fix_windows: How to address misfit window evaluation at each + evaluation. Options to re-use misfit windows collected during an + inversion, available options: + [True, False, 'ITER', 'ONCE'] + True: Re-use windows after first evaluation (i01s00); + False: Calculate new windows each evaluation; + 'ITER': Calculate new windows at first evaluation of + each iteration (e.g., i01s00... i02s00... + 'ONCE': Calculate new windows at first evaluation of + the workflow, i.e., at self.par.BEGIN + :type adj_src_type: str + :param adj_src_type: Adjoint source type to evaluate misfit, defined by + Pyadjoint. Currently available options: ['cc': cross-correlation, + 'mt': multitaper, 'wav': waveform'] + :type plot: bool + :param plot: plot waveform figures and source receiver maps during + the preprocessing stage + :type pyatoa_log_level: str + :param pyatoa_log_level: Log level to set Pyatoa, Pyflex, Pyadjoint. + Available: ['null': no logging, 'warning': warnings only, + 'info': task tracking, 'debug': log all small details (recommended)] + :type start_pad_s: int + :param start_pad_s: seconds BEFORE origin time to gather data. Must be + >= T_0 specificed in SPECFEM constants.h. Positive values only + :type end_pad_s: int + :param end_pad_s: seconds AFTER origin time to gather data. Must be + >= NT * DT (from SPECFEM Par_file) postive values only. + :type unit_output: str + :param unit_output: Data units. Must match the synthetic output of + external solver. Available: ['DISP': displacement, 'VEL': velocity, + 'ACC': acceleration] + :type save_datasets: bool + :param save_datasets: periodically save the output ASDFDataSets which + contain data, metadata and results collected during the + preprocessing procedure + :type save_figures: bool + :param save_figures: periodically save the output basemaps and + data-synthetic waveform comparison figures + :type save_log_files: bool + :param save_log_files: periodically save log files created by Pyatoa + :type path_preprocess: str + :param path_preprocess: scratch path for preprocessing related steps + :type path_data: str + :param path_data: optional path for preprocessing module to discover + waveform and meta-data. """ - super().__init__() - - # Define the Parameters required by this module - self.required.par( - "UNIT_OUTPUT", required=True, par_type=str, - docstr="Data units. Must match the synthetic output of external " - "solver. Available: ['DISP': displacement, " - "'VEL': velocity, 'ACC': acceleration]" - ) - # TODO Check this against T0 in check() - self.required.par( - "START_PAD", required=False, default=0, par_type=float, - docstr="For data gathering; time before origin time to gather. " - "START_PAD >= T_0 in SPECFEM constants.h.in. " - "Positive values only" - ) - # TODO set this automatically by setting equal NT * DT - self.required.par( - "END_PAD", required=True, par_type=float, - docstr="For data gathering; time after origin time to gather. " - "END_PAD >= NT * DT (of Par_file). Positive values only" - ) - self.required.par( - "MIN_PERIOD", required=False, default="", par_type=float, - docstr="Minimum filter corner in unit seconds. Bandpass filter " - "if set with `MAX_PERIOD`, highpass filter if set " - "without `MAX_PERIOD`, no filtering if not set and " - "`MAX_PERIOD also not set" - ) - self.required.par( - "MAX_PERIOD", required=False, default="", par_type=float, - docstr="Maximum filter corner in unit seconds. Bandpass filter " - "if set with `MIN_PERIOD`, lowpass filter if set " - "without `MIN_PERIOD`, no filtering if not set and " - "`MIN_PERIOD also not set" - ) - self.required.par( - "CORNERS", required=False, default=4, par_type=int, - docstr="Number of filter corners applied to filtering" - ) - self.required.par( - "CLIENT", required=False, par_type=str, - docstr="Client name for ObsPy FDSN data gathering. Pyatoa will " - "attempt to collect waveform and metadata based on " - "network and station codes provided in the SPECFEM " - "STATIONS file. If set None, no FDSN gathering will be " - "attempted" - ) - self.required.par( - "ROTATE", required=False, default=False, par_type=bool, - docstr="Attempt to rotate waveform components from NEZ -> RTZ" - ) - self.required.par( - "PYFLEX_PRESET", required=False, default="default", par_type=str, - docstr="Parameter map for misfit window configuration defined " - "by Pyflex. IF None, misfit and adjoint sources will be " - "calculated on whole traces. For available choices, " - "see Pyatoa docs page (pyatoa.rtfd.io)" - ) - self.required.par( - "FIX_WINDOWS", required=False, default=False, \ - par_type="bool or str", - docstr="How to address misfit window evaluation at each " - "evaluation. Options to re-use misfit windows collected " - "during an inversion, available options: " - "[True, False, 'ITER', 'ONCE'] " - "True: Re-use windows after first evaluation (i01s00); " - "False: Calculate new windows each evaluation; " - "'ITER': Calculate new windows at first evaluation of " - "each iteration (e.g., i01s00... i02s00..." - "'ONCE': Calculate new windows at first evaluation of " - "the workflow, i.e., at self.par.BEGIN" - ) - self.required.par( - "ADJ_SRC_TYPE", required=False, default="cc", par_type=str, - docstr="Adjoint source type to evaluate misfit, defined by " - "Pyadjoint. Currently available options: " - "['cc': cross-correlation, 'mt': multitaper, " - "wav: waveform']" - ) - self.required.par( - "PLOT", required=False, default=True, par_type=bool, - docstr="Attempt to plot waveforms and maps as PDF files at each " - "function evaluation" - ) - - self.required.par( - "PYATOA_LOG_LEVEL", required=False, default="DEBUG", par_type=str, - docstr="Log level to set Pyatoa, Pyflex, Pyadjoint. Available: " - "['null': no logging, 'warning': warnings only, " - "'info': task tracking, " - "'debug': log all small details (recommended)]" - ) - # Parameters to control saving scratch/preprocess files to work dir. - self.required.par( - "SAVE_DATASETS", required=False, default=True, par_type=bool, - docstr="Save PyASDF HDF5 datasets to disk. These datasets store " - "waveform data, metadata, misfit windows, adjoint " - "sources and configuration parameters" - ) - self.required.par( - "SAVE_FIGURES", required=False, default=True, par_type=bool, - docstr="Save output waveform figures to disk as PDFs" - ) - self.required.par( - "SAVE_LOGS", required=False, default=True, par_type=bool, - docstr="Save event-specific Pyatoa logs to disk as .txt files" - ) - # Define the Paths required by this module - self.required.path( - "PREPROCESS", required=False, - default=os.path.join(self.path.WORKDIR, "scratch", "preprocess"), - docstr="scratch/ path to store waveform data and figures. " - "Pyatoa will generate an internal directory structure " - "here" - ) - self.required.path( - "DATA", required=False, - docstr="Directory to locally stored data. Pyatoa looks for " - "waveform and metadata in the 'self.path.DATA/mseed' and " - "'self.path.DATA/seed', directories respectively." - ) - - def check(self, validate=True): + # Shared SeisFlows parameters + self.data_format = data_format + self.components = components + self.ntask = ntask + self.nproc = nproc + + # Pyatoa specific parameters + self.min_period = min_period + self.max_period = max_period + self.filter_corners = filter_corners + self.client = client + self.rotate = rotate + self.pyflex_preset = pyflex_preset + self.fix_windows = fix_windows + self.adj_src_type = adj_src_type + self.plot = plot + self.pyatoa_log_level = pyatoa_log_level + self.unit_output = unit_output + self.start_pad_s = start_pad_s + self.end_pad_s = end_pad_s + + self.path = path_preprocess or \ + os.path.join(os.getcwd(), "scratch", "preprocess") + self.path_output = path_output or os.path.join(os.getcwd(), "output") + self.path_data = path_data + + self.save_datasets = save_datasets + self.save_figures = save_figures + self.save_log_files = save_log_files + + def check(self): """ Checks Parameter and Path files, will be run at the start of a Seisflows workflow to ensure that things are set appropriately. """ - super().check(validate=validate) - - # Check that other modules have set parameters that will be used here - for required_parameter in ["COMPONENTS", "FORMAT"]: - assert(required_parameter in self.par), \ - f"Pyatoa requires {required_parameter}" - - assert(self.par.FORMAT.upper() == "ASCII"), \ - "Pyatoa preprocess requires self.par.FORMAT=='ASCII'" - - # assert((self.par.DT * self.par.NT) <= (self.par.START_PAD + self.par.END_PAD)), \ - # ("Pyatoa preprocess must have (self.par.START_PAD + self.par.END_PAD) >= " - # "(self.par.DT * self.par.NT), current values will not provide sufficiently " - # f"long data traces (DT*NT={self.par.DT * self.par.NT}; " - # f"START+END={self.par.START_PAD + self.par.END_PAD}") + assert(self.data_format.upper() == "ASCII"), \ + "Pyatoa preprocess requires `data_format`=='ASCII'" def setup(self): """ Sets up data preprocessing machinery by establishing an internally defined directory structure that will be used to store the outputs of the preprocessing workflow - - Akin to an __init__ class, but to be called externally by the workflow. """ - super().setup() - - unix.mkdir(self.path.PREPROCESS) + unix.mkdir(self.path) def finalize(self): """ @@ -201,8 +169,6 @@ def finalize(self): * Aggregate event-specific PDFs into a single evaluation PDF * Save scratch/ data into output/ if requested """ - super().finalize() - # Initiate Pyaflowa to get access to path structure pyaflowa = Pyaflowa(sfpar=self.par, sfpath=self.path) unix.cd(pyaflowa.paths.datasets) @@ -210,29 +176,29 @@ def finalize(self): # Generate the Inspector from existing datasets and save to disk # Allow this is fail, which might happen if we don't have enough data # or the Dataset is not formatted as expected - insp = Inspector(self.par.TITLE, verbose=False) + insp = Inspector(self.par.TITLE, verbose=False) # !!! TODO try: insp.discover() insp.save() except Exception as e: - self.logger.warning(f"Uncontrolled exception in Inspector creation " + logger.warning(f"Uncontrolled exception in Inspector creation " f"will not create inspector:\n{e}") # Make the final PDF for easier User ingestion of waveform/map figures pyaflowa.make_evaluation_composite_pdf() # Move scratch/ directory results into more permanent storage - if self.par.SAVE_DATASETS: + if self.save_datasets: datasets = glob(os.path.join(pyaflowa.paths.datasets, "*.h5")) self._save_quantity(datasets, tag="datasets") - if self.par.SAVE_FIGURES: + if self.save_figures: figures = glob(os.path.join(pyaflowa.paths.figures, "*.pdf")) self._save_quantity(figures, tag="figures") - if self.par.SAVE_LOGS: + if self.save_log_files: logs = glob(os.path.join(pyaflowa.paths.logs, "*.txt")) - path_out = os.path.join(self.path.WORKDIR, CFGPATHS.LOGDIR) + path_out = os.path.join(self.path_output, CFGPATHS.LOGDIR) self._save_quantity(logs, path_out=path_out) def prepare_eval_grad(self, cwd, taskid, source_name, **kwargs): @@ -263,12 +229,12 @@ def prepare_eval_grad(self, cwd, taskid, source_name, **kwargs): traces """ if taskid == 0: - self.logger.debug("preparing files for gradient evaluation with " + logger.debug("preparing files for gradient evaluation with " "Pyaflowa") # Process all the stations for a given event using Pyaflowa pyaflowa = self._setup_event_pyaflowa(source_name) - scaled_misfit = pyaflowa.process(nproc=self.par.NPROC) + scaled_misfit = pyaflowa.process(nproc=self.nproc) if scaled_misfit is None: print(msg.cli(f"Event {source_name} returned no misfit, you may " @@ -282,7 +248,7 @@ def prepare_eval_grad(self, cwd, taskid, source_name, **kwargs): # Event misfit defined by Tape et al. (2010) written to solver dir. self._write_residuals(path=cwd, scaled_misfit=scaled_misfit) - def _setup_event_pyaflowa(self, source_name=None): + def _setup_event_pyaflowa(self, source_name, iteration, step_count=""): """ A convenience function to set up a Pyaflowa processing instance for a specific event. @@ -296,19 +262,6 @@ def _setup_event_pyaflowa(self, source_name=None): :param source_name: solver source name to evaluate setup for. Must match from list defined by: solver.source_names """ - solver = self.module("solver") - optimize = self.module("optimize") - - iteration = optimize.iter - if source_name is None: - source_name = solver.source_names[0] - - # Deal with the migration case where no step count given - try: - step_count = optimize.line_search.step_count - except AttributeError: - step_count = "" - # Outsource data processing to an event-specfic Pyaflowa instance pyaflowa = Pyaflowa(sfpar=self.par, sfpath=self.path) pyaflowa.setup(source_name=source_name, iteration=iteration, @@ -330,7 +283,7 @@ def _save_quantity(self, filepaths, tag="", path_out=""): :param path_out: overwrite the default output path file naming """ if not path_out: - path_out = os.path.join(self.path.OUTPUT, tag) + path_out = os.path.join(self.path_output, tag) if not os.path.exists(path_out): unix.mkdir(path_out) @@ -367,10 +320,10 @@ def sum_residuals(self, files): :rtype: float :return: average misfit """ - if len(files) != self.par.NTASK: + if len(files) != self.ntask: print(msg.cli(f"Pyatoa preprocessing module did not recover the " f"correct number of residual files " - f"({len(files)}/{self.par.NTASK}). Please check that " + f"({len(files)}/{self.ntask}). Please check that " f"the preprocessing logs", header="error") ) sys.exit(-1) @@ -379,7 +332,7 @@ def sum_residuals(self, files): for filename in files: total_misfit += np.sum(np.loadtxt(filename)) - total_misfit /= self.par.NTASK + total_misfit /= self.ntask return total_misfit diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index f4998536..e511cb75 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -10,13 +10,13 @@ import subprocess from glob import glob -from seisflows.core import Base +from seisflows import logger from seisflows.plugins import solver_io from seisflows.tools import msg, unix from seisflows.tools.specfem import getpar, Model -class Specfem(Base): +class Specfem: """ This base class provides an interface through which solver simulations can be set up and run. It should not be used by itself, but rather it is meant @@ -63,10 +63,41 @@ class Specfem(Base): !!! Required functions which must be implemented by subclass !!! """ - def __init__(self): - """ - These parameters should not be set by the User_! - Attributes are just initialized as NoneTypes for clarity and docstrings + def __init__(self, case="data", data_format="ascii", materials="elastic", + density=False, nproc=1, ntask=1, attenuation=False, + components="ZNE", + solver_io="fortran_binary", mpiexec=None, path_solver=None, + path_data=None, path_specfem_bin=None, path_specfem_data=None, + path_model_init=None, path_model_true=None, path_output=None, + save_traces=False, **kwargs): + """ + SPECFEM Solver parameters + + :type data_format: str + :param data_format: data format for reading traces into memory. + Availalble: ['SU' seismic unix format, 'ASCII' human-readable ascii] + :type materials: str + :param materials: Material parameters used to define model. Available: + ['ELASTIC': Vp, Vs, 'ACOUSTIC': Vp, 'ISOTROPIC', 'ANISOTROPIC'] + :type density: bool + :param density: How to treat density during inversion. If True, updates + density during inversion. If False, keeps it constant. + TODO Add density scaling based on Vp? + :type attenuation: bool + :param attenuation: How to treat attenuation during inversion. + if True, turns on attenuation during forward simulations only. If + False, attenuation is always set to False. Requires underlying + attenution (Q_mu, Q_kappa) model + :type components: str + :param components: components to consider and tag data with. Should be + string of letters such as 'RTZ' + :type path_specfem_bin: str + :param path_specfem_bin: path to SPECFEM bin/ directory which + contains binary executables for running SPECFEM + :type path_specfem_data: str + :param path_specfem_data: path to SPECFEM DATA/ directory which must + contain the CMTSOLUTION, STATIONS and Par_file files used for + running SPECFEM :type parameters: list of str :param parameters: a list detailing the parameters to be used to @@ -78,97 +109,47 @@ def __init__(self): :param logger: Class-specific logging module, log statements pushed from this logger will be tagged by its specific module/classname """ - super().__init__() - - self.required.par( - "MATERIALS", required=False, par_type=str, default="ELASTIC", - docstr="Material parameters used to define model. Available: " - "['ELASTIC': Vp, Vs, 'ACOUSTIC': Vp, 'ISOTROPIC', " - "'ANISOTROPIC']" - ) - self.required.par( - "DENSITY", required=False, par_type=str, default="CONSTANT", - docstr="How to treat density during inversion. Available: " - "['CONSTANT': Do not update density, " - "'VARIABLE': Update density]" - ) - self.required.par( - "ATTENUATION", required=False, par_type=bool, default=False, - docstr="If True, turn on attenuation during forward " - "simulations, otherwise set attenuation off. Attenuation " - "is always off for adjoint simulations." - ) - self.required.par( - "FORMAT", required=False, par_type=float, default="ASCII", - docstr="Format of synthetic waveforms used during workflow, " - "available options: ['ASCII', 'SU']" - ) - self.required.par( - "COMPONENTS", required=False, default="ZNE", par_type=str, - docstr="Components used to generate data, formatted as a single " - "string, e.g. ZNE or NZ or E" - ) - self.required.par( - "SOLVERIO", required=False, default="fortran_binary", par_type=str, - docstr="The format external solver files. Available: " - "['fortran_binary']" - ) - self.required.par( - "SMOOTH_H", required=False, default=0., par_type=float, - docstr="Gaussian half-width for horizontal smoothing in units of " - "meters. If 0., no smoothing applied" - ) - self.required.par( - "SMOOTH_V", required=False, default=0., par_type=float, - docstr="Gaussian half-width for vertical smoothing in units of " - "meters" - ) - self.required.par( - "TASKTIME_SMOOTH", required=False, default=1, par_type=int, - docstr="Large radii smoothing may take longer than normal tasks. " - "Allocate additional smoothing task time as a multiple of " - "TASKTIME" - ) - self.required.path( - "SOLVER", required=False, - default=os.path.join(self.path.WORKDIR, "scratch", "solver"), - docstr="scratch path to hold solver working directories" - ) - self.required.path( - "DATA", required=False, - docstr="path to a directory containing any external data " - "required by the workflow. Catch all directory that " - "can be accessed by all modules" - ) - self.required.path( - "SPECFEM_BIN", required=False, - default=os.path.join(self.path.WORKDIR, "specfem", "bin"), - docstr="path to the SPECFEM binary executables" - ) - self.required.path( - "SPECFEM_DATA", required=False, - default=os.path.join(self.path.WORKDIR, "specfem", "DATA"), - docstr="path to the SPECFEM DATA/ directory containing the " - "'Par_file', 'STATIONS' file and 'CMTSOLUTION' files" - ) - # Define the Paths required by this module - self.required.path( - "MASK", required=False, docstr="Directory to mask files for " - "gradient masking" - ) - - self.parameters = [] + self.case = case + self.data_format = data_format + self.materials = materials + self.nproc = nproc + self.ntask = ntask + self.density = density + self.attenuation = attenuation + self.components = components + self.solver_io = solver_io + self.mpiexec = mpiexec + + # Define internally used directory structure + self.path = path_solver or \ + os.path.join(os.getcwd(), "scratch", "solver") + self.path_data = path_data + self.path_specfem_bin = path_specfem_bin + self.path_specfem_data = path_specfem_data + self.path_model_init = path_model_init + self.path_model_true = path_model_true + self.path_output = path_output + + self.save_traces = save_traces + + # Establish internally defined parameter system + self._parameters = [] + if self.density: + self._parameters.append("rho") + self._source_names = None + self._io = getattr(solver_io, self.solver_io) - @property - def _io(self): - """ - Solver IO module set by User. Located in seisflows.plugins.solver_io + # Define available choices for check parameter + self._available_model_types = ["gll"] + self._available_materials = [ + "ELASTIC", "ACOUSTIC", # specfem2d, specfem3d + "ISOTROPIC", "ANISOTROPIC" # specfem3d_globe + ] + self._available_data_formats = ["ASCII", "SU"] - :rtype: module - :return: module containing solver input/output options - """ - return getattr(solver_io, self.par.SOLVERIO) + # Parameters that NEED to be filled out by child classes + self.source_prefix = None @property def taskid(self): @@ -205,18 +186,6 @@ def source_name(self): """ return self.source_names[self.taskid] - @property - def source_prefix(self): - """ - Preferred source prefix - - TODO remove this and replace all instances with the self.par parameter - - :rtype: str - :return: source prefix - """ - return self.par.SOURCE_PREFIX.upper() - @property def cwd(self): """ @@ -225,7 +194,7 @@ def cwd(self): :rtype: str :return: current solver working directory """ - return os.path.join(self.path.SOLVER, self.source_name) + return os.path.join(self.path, self.source_name) def data_wildcard(self, comp="?"): """ @@ -238,7 +207,7 @@ def data_wildcard(self, comp="?"): :rtype: str :return: a wildcard string that can be used to search for data """ - return NotImplementedError + return NotImplementedError("must be implemented by child class") @property def data_filenames(self): @@ -249,7 +218,7 @@ def data_filenames(self): :rtype: list :return: list of data filenames """ - return NotImplementedError + return NotImplementedError("must be implemented by child class") @property def model_databases(self): @@ -257,7 +226,7 @@ def model_databases(self): SPECFEM directory where model database files are saved. This directory is SPECFEM version dep. """ - return NotImplementedError + return NotImplementedError("must be implemented by child class") @property def kernel_databases(self): @@ -265,56 +234,29 @@ def kernel_databases(self): SPECFEM directory where kernel database files are saved. This directory is SPECFEM version dep. """ - return NotImplementedError + return NotImplementedError("must be implemented by child class") - def check(self, validate=True): + def check(self): """ Checks parameters and paths """ - super().check(validate=validate) - - # Check that other modules have set parameters that will be used here - for required_parameter in ["NPROC"]: - assert (required_parameter in self.par), \ - f"Solver requires {required_parameter}" - - available_materials = ["ELASTIC", "ACOUSTIC", # specfem2d, specfem3d - "ISOTROPIC", "ANISOTROPIC"] # specfem3d_globe - assert(self.par.MATERIALS.upper() in available_materials), \ - f"MATERIALS must be in {available_materials}" - - acceptable_densities = ["CONSTANT", "VARIABLE"] - assert(self.par.DENSITY.upper() in acceptable_densities), \ - f"DENSITY must be in {acceptable_densities}" - - acceptable_formats = ["SU", "ASCII"] - if self.par.FORMAT.upper() not in acceptable_formats: - raise Exception(f"'FORMAT' must be {acceptable_formats}") - - # Internal parameter list based on user-input material choices - # Important to reset parameters to a blank list and let the check - # statements fill it. If not, each time workflow is resumed, parameters - # list will append redundant parameters and things stop working - self.parameters = [] - if self.par.MATERIALS.upper() == "ELASTIC": - assert(self.par.SOLVER.lower() in ["specfem2d", "specfem3d"]) - self.parameters += ["vp", "vs"] - elif self.par.MATERIALS.upper() == "ACOUSTIC": - assert(self.par.SOLVER.lower() in ["specfem2d", "specfem3d"]) - self.parameters += ["vp"] - elif self.par.MATERIALS.upper() == "ISOTROPIC": - assert(self.par.SOLVER.lower() in ["specfem3d_globe"]) - self.parameters += ["vp", "vs"] - elif self.par.MATERIALS.upper() == "ANISOTROPIC": - assert(self.par.SOLVER.lower() in ["specfem3d_globe"]) - self.parameters += ["vpv", "vph", "vsv", "vsh", "eta"] - - if self.par.DENSITY.upper() == "VARIABLE": - self.parameters.append("rho") - - assert hasattr(solver_io, self.par.SOLVERIO) - assert hasattr(self._io, "read_slice"), "IO method has no attribute 'read'" - assert hasattr(self._io, "write_slice"), "IO method has no attribute 'write'" + assert(self.materials.upper() in self._available_materials), \ + f"solver.materials must be in {self._available_materials}" + + if self.data_format.upper() not in self._available_data_formats: + raise NotImplementedError( + f"solver.data_format must be {self._available_data_formats}" + ) + + assert hasattr(solver_io, self.solver_io) + assert hasattr(self._io, "read_slice"), \ + "IO method has no attribute 'read'" + assert hasattr(self._io, "write_slice"), \ + "IO method has no attribute 'write'" + + # TODO Check path data, model_true and case combination + # TODO Check SPECFEM_DATA available files + # TODO Check SPECFEM_BIN available executables def setup(self): """ @@ -356,9 +298,9 @@ def _set_model(self, model_name, model_type=None): # Determine which model will be set as the starting model if model_name.upper() == "INIT": - model_path = self.path.MODEL_INIT + model_path = self.path_model_init elif model_name.upper() == "TRUE": - model_path = self.path.MODEL_TRUE + model_path = self.path_model_true else: raise ValueError(f"model name must be 'INIT' or 'TRUE'") assert(os.path.exists(model_path)), f"model {model_path} does not exist" @@ -390,29 +332,31 @@ def generate_data(self): Also exports observed data to OUTPUT if desired """ # If synthetic inversion, generate 'data' with solver - if self.par.CASE.upper() == "SYNTHETIC": - if self.path.MODEL_TRUE is not None: + if self.case.upper() == "SYNTHETIC": + if self.path_model_true is not None: if self.taskid == 0: - self.logger.info("generating 'data' with MODEL_TRUE") + logger.info("generating 'data' with MODEL_TRUE") # Generate synthetic data on the fly using the true model self._set_model(model_name="true", model_type="gll") self._forward(output_path=os.path.join("traces", "obs")) # If Data provided by user, copy directly into the solver directory - elif self.path.DATA is not None and os.path.exists(self.path.DATA): + elif self.path_data is not None and os.path.exists(self.path_data): unix.cp( - src=glob(os.path.join(self.path.DATA, self.source_name, "*")), + src=glob(os.path.join(self.path_data, self.source_name, "*")), dst=os.path.join("traces", "obs") ) # Save observation data to disk - if self.par.SAVETRACES: + if self.save_traces: self._export_traces( - path=os.path.join(self.path.OUTPUT, "traces", "obs") + path=os.path.join(self.path_output, "traces", "obs") ) - def eval_func(self, path, write_residuals=True): + def eval_func(self, path, preprocess=None): """ Performs forward simulations and evaluates the misfit function using - the preprocess module. + the preprocess module. solver.eval_func is bundled with + preprocess.prepare_eval_grad because they are meant to be run serially + so it is better to lump them together into a single allocation. .. note:: This task should be run in parallel by system.run() @@ -420,21 +364,23 @@ def eval_func(self, path, write_residuals=True): :type path: str :param path: directory from which model is imported and where residuals will be exported - :type write_residuals: bool - :param write_residuals: calculate and export residuals + :type preprocess: instance + :param preprocess: SeisFlows preprocess module which can be used to + prepare gradient evaluation by comparing misfit and creating + adjoint sources. If None, only forward simulations will be + performed """ - preprocess = self.module("preprocess") + unix.cd(self.cwd) if self.taskid == 0: - self.logger.info("running forward simulations") + logger.info("running forward simulations") - unix.cd(self.cwd) self._import_model(path) self._forward(output_path=os.path.join("traces", "syn")) - if write_residuals: + if preprocess: if self.taskid == 0: - self.logger.debug("calling preprocess.prepare_eval_grad()") + logger.debug("call preprocess to prepare gradient evaluation") preprocess.prepare_eval_grad(cwd=self.cwd, taskid=self.taskid, source_name=self.source_name, filenames=self.data_filenames @@ -456,8 +402,9 @@ def eval_grad(self, path, export_traces=False): if False, discard traces """ unix.cd(self.cwd) + if self.taskid == 0: - self.logger.debug("running adjoint simulations") + logger.debug("running adjoint simulations") # Check to make sure that preprocessing module created adjoint traces adjoint_traces_wildcard = os.path.join("traces", "adj", "*") @@ -478,42 +425,6 @@ def eval_grad(self, path, export_traces=False): self._export_traces(path=os.path.join(path, "traces", "adj"), prefix="traces/adj") - def postprocess_kernels(self, path_grad): - """ - Sums kernels from individual sources, with optional smoothing - - .. note:: - This function needs to be run on system, i.e., called by - system.run(single=True) - - :type path_grad: str - :param path_grad: directory containing sensitivity kernels in the - scratch directory to be summed and smoothed. Output summed and - summed + smoothed kernels will be saved here as well. - """ - # If specified, smooth the kernels in the vertical and horizontal and - # save both (summed, summed+smoothed) to separate output directories - kernel_path = os.path.join(path_grad, "kernels") - path_sum_nosmooth = os.path.join(kernel_path, "sum_nosmooth") - path_sum = os.path.join(kernel_path, "sum") - - if (self.par.SMOOTH_H > 0) or (self.par.SMOOTH_V > 0): - self.logger.debug(f"saving un-smoothed and summed kernels to:\n" - f"{path_sum_nosmooth}") - self.combine(input_path=kernel_path, output_path=path_sum_nosmooth) - - self.logger.info(f"smoothing gradient: H={self.par.SMOOTH_H}m, " - f"V={self.par.SMOOTH_V}m") - self.logger.debug(f"saving smoothed kernels to:\n{path_sum}") - self.smooth(input_path=path_sum_nosmooth, output_path=path_sum, - span_h=self.par.SMOOTH_H, span_v=self.par.SMOOTH_V) - - # Combine all the input kernels, generating the unscaled gradient - else: - self.logger.debug(f"saving summed kernels to:\n{path_sum}") - self.combine(input_path=kernel_path, output_path=path_sum) - - # def apply_hess(self, path): # """ # High level solver interface that computes action of Hessian on a given @@ -576,11 +487,11 @@ def _call_solver(self, executable, output="solver.log"): sys.exit(-1) # mpiexec is None when running in serial mode, so e.g., ./xmeshfem2D - if self.par.SYSTEM in ["workstation"]: + if not self.mpiexec: exc_cmd = f"./{executable}" # Otherwise mpiexec is system dependent (e.g., srun, mpirun) else: - exc_cmd = f"{self.par.MPIEXEC} {executable}" + exc_cmd = f"{self.mpiexec} {executable}" # Run solver. Write solver stdout (log files) to text file try: @@ -615,7 +526,7 @@ def combine(self, input_path, output_path, parameters=None): :type input_path: str :param input_path: path to data - :type output_path: str + :type output_path: strs :param output_path: path to export the outputs of xcombine_sem :type parameters: list :param parameters: optional list of parameters, @@ -624,7 +535,7 @@ def combine(self, input_path, output_path, parameters=None): unix.cd(self.cwd) if parameters is None: - parameters = self.parameters + parameters = self._parameters if not os.path.exists(output_path): unix.mkdir(output_path) @@ -677,7 +588,7 @@ def smooth(self, input_path, output_path, parameters=None, span_h=0., unix.cd(self.cwd) if parameters is None: - parameters = self.parameters + parameters = self._parameters if not os.path.exists(output_path): unix.mkdir(output_path) @@ -732,7 +643,7 @@ def _export_model(self, path, parameters=None): :param parameters: list of parameters that define the model """ if parameters is None: - parameters = self.parameters + parameters = self._parameters if self.taskid == 0: unix.mkdir(path) @@ -748,7 +659,7 @@ def _export_kernels(self, path): :param path: path to save kernels """ if self.taskid == 0: - self.logger.debug(f"exporting kernels to:\n{path}") + logger.debug(f"exporting kernels to:\n{path}") unix.cd(self.kernel_databases) @@ -768,7 +679,7 @@ def _export_residuals(self, path): :param path: path to save residuals """ if self.taskid == 0: - self.logger.debug(f"exporting residuals to:\n{path}") + logger.debug(f"exporting residuals to:\n{path}") unix.mkdir(os.path.join(path, "residuals")) src = os.path.join(self.cwd, "residuals") @@ -796,7 +707,7 @@ def _export_traces(self, path, prefix="traces/obs"): :param prefix: location of traces w.r.t self.cwd """ if self.taskid == 0: - self.logger.debug("exporting traces to {path} {prefix}") + logger.debug("exporting traces to {path} {prefix}") unix.mkdir(os.path.join(path)) @@ -831,7 +742,7 @@ def _initialize_solver_directories(self): extra files. """ if self.taskid == 0: - self.logger.info(f"initializing {self.par.NTASK} solver directories") + logger.info(f"initializing {self.ntask} solver directories") unix.rm(self.cwd) unix.mkdir(self.cwd) @@ -844,19 +755,19 @@ def _initialize_solver_directories(self): unix.mkdir(cwd_dir) # Copy exectuables into the bin/ directory - src = glob(os.path.join(self.path.SPECFEM_BIN, "*")) + src = glob(os.path.join(self.path_specfem_bin, "*")) dst = os.path.join("bin", "") unix.cp(src, dst) # Copy all input files except source files - src = glob(os.path.join(self.path.SPECFEM_DATA, "*")) + src = glob(os.path.join(self.path_specfem_data, "*")) src = [_ for _ in src if self.source_prefix not in _] dst = os.path.join("DATA", "") unix.cp(src, dst) # Symlink event source specifically, strip the source name as SPECFEM # just expects `source_name` - src = os.path.join(self.path.SPECFEM_DATA, + src = os.path.join(self.path_specfem_data, f"{self.source_prefix}_{self.source_name}") dst = os.path.join("DATA", self.source_prefix) unix.ln(src, dst) @@ -866,11 +777,11 @@ def _initialize_solver_directories(self): # Symlink taskid_0 as mainsolver in solver directory for convenience if not os.path.exists(mainsolver): unix.ln(self.cwd, mainsolver) - self.logger.debug(f"source {self.source_name} symlinked as " - f"mainsolver") + logger.debug(f"symlink {self.source_name} as 'mainsolver'") else: # Copy the initial model from mainsolver into current directory # Avoids the need to run multiple instances of xgenerate_databases + # TODO race condition if things havent been written? Sleep? src = os.path.join(self.path.SOLVER, "mainsolver", "OUTPUT_FILES", "DATABASES_MPI") dst = os.path.join(self.cwd, "OUTPUT_FILES", "DATABASES_MPI") @@ -881,6 +792,8 @@ def _initialize_adjoint_traces(self): Setup utility: Creates the "adjoint traces" expected by SPECFEM. This is only done for the 'base' the Preprocess class. + TODO move this into workflow setup + .. note:: Adjoint traces are initialized by writing zeros for all channels. Channels actually in use during an inversion or migration will be @@ -890,7 +803,7 @@ def _initialize_adjoint_traces(self): if self.par.PREPROCESS.upper() == "DEFAULT": if self.taskid == 0: - self.logger.debug(f"intializing {len(self.data_filenames)} " + logger.debug(f"intializing {len(self.data_filenames)} " f"empty adjoint traces per event") for filename in self.data_filenames: @@ -918,11 +831,11 @@ def _check_source_names(self): # Apply wildcard rule and check for available sources, exit if no # sources found because then we can't proceed wildcard = f"{self.source_prefix}_*" - fids = sorted(glob(os.path.join(self.path.SPECFEM_DATA, wildcard))) + fids = sorted(glob(os.path.join(self.path_specfem_data, wildcard))) if not fids: print(msg.cli("No matching source files when searching PATH for" "the given WILDCARD", - items=[f"PATH: {self.path.SPECFEM_DATA}", + items=[f"PATH: {self.path_specfem_data}", f"WILDCARD: {wildcard}"], header="error" ) ) @@ -930,7 +843,7 @@ def _check_source_names(self): # Create internal definition of sources names by stripping prefixes names = [os.path.basename(fid).split("_")[-1] for fid in fids] - self._source_names = names[:self.par.NTASK] + self._source_names = names[:self.ntask] diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index 6f5d771f..eb4cc511 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -18,20 +18,31 @@ class Specfem2D(Specfem): """ Python interface to Specfem2D. """ - def __init__(self): + def __init__(self, source_prefix="SOURCE", multiples=False, **kwargs): """ - These parameters should not be set by the user. - Attributes are initialized as NoneTypes for clarity and docstrings. + SPECFEM2D specific parameters + + :type source_prefix: str + :param source_prefix: Prefix of SOURCE files in path SPECFEM_DATA. + :type multiples: bool + :param multiples: set an absorbing top-boundary condition """ - super().__init__() + super().__init__(**kwargs) + + self.source_prefix = source_prefix + self.multiples = multiples + self._f0 = None + + # Define parameters based on material type + if self.materials.upper() == "ACOUSTIC": + self._parameters.append("vp") + elif self.materials.upper() == "ELASTIC": + self._parameters.append("vp") + self._parameters.append("vs") + + self._acceptable_source_prefix = ["FORCE", "FORCESOLUTION"] - self.required.par( - "SOURCE_PREFIX", required=False, default="SOURCE", par_type=str, - docstr="Prefix of SOURCE files in path SPECFEM_DATA. By " - "default, 'SOURCE' for SPECFEM2D" - ) - self.f0 = None def data_wildcard(self, comp="?"): """ @@ -44,10 +55,10 @@ def data_wildcard(self, comp="?"): :rtype: str :return: wildcard identifier for channels """ - if self.par.FORMAT.upper() == "SU": + if self.data_format.upper() == "SU": # return f"*.su" # too vague but maybe for a reason? -bryant return f"U{comp}_file_single.su" - elif self.par.FORMAT.upper() == "ASCII": + elif self.data_format.upper() == "ASCII": return f"*.?X{comp}.sem?" @property @@ -64,16 +75,15 @@ def data_filenames(self): :rtype: list :return: list of data filenames """ - unix.cd(self.cwd) - unix.cd(os.path.join("traces", "obs")) + unix.cd(os.path.join(self.cwd, "traces", "obs")) - if self.par.COMPONENTS: + if self.components: filenames = [] - if self.par.FORMAT.upper() == "SU": - for comp in self.par.COMPONENTS: + if self.data_format.upper() == "SU": + for comp in self.components: filenames += [self.data_wildcard(comp=comp.lower())] - elif self.par.FORMAT.upper() == "ASCII": - for comp in self.par.COMPONENTS: + elif self.data_format.upper() == "ASCII": + for comp in self.components: filenames += glob(self.data_wildcard(comp=comp.upper())) else: filenames = glob(self.data_wildcard()) @@ -112,11 +122,18 @@ def setup(self): self.f0 = getpar(key="f0", file=os.path.join(self.cwd, "DATA/SOURCE"))[1] - if "MULTIPLES" in self.par: - if self.par.MULTIPLES: - setpar(key="absorbtop", val=".false.", file="DATA/Par_file") - else: - setpar(key="absorbtop", val=".true.", file="DATA/Par_file") + if self.multiples: + setpar(key="absorbtop", val=".false.", file="DATA/Par_file") + else: + setpar(key="absorbtop", val=".true.", file="DATA/Par_file") + + def check(self): + """ + + """ + super().check() + assert(self.source_prefix in self._acceptable_source_prefix) + def _forward(self, output_path): """ @@ -136,7 +153,7 @@ def _forward(self, output_path): self._call_solver(executable="bin/xmeshfem2D", output="fwd_mesher.log") self._call_solver(executable="bin/xspecfem2D", output="fwd_solver.log") - if self.par.FORMAT.upper() == "SU": + if self.data_format.upper() == "SU": # Work around SPECFEM2D's version dependent file names for tag in ["d", "v", "a", "p"]: unix.rename(old=f"single_{tag}.su", new="single.su", @@ -160,7 +177,7 @@ def _adjoint(self): # Deal with different SPECFEM2D name conventions for regular traces and # "adjoint" traces - if self.par.FORMAT.upper == "SU": + if self.data_format.upper == "SU": unix.rename(old=".su", new=".su.adj", names=glob(os.path.join("traces", "adj", "*.su"))) @@ -225,15 +242,14 @@ def _initialize_adjoint_traces(self): """ super()._initialize_adjoint_traces() - unix.cd(self.cwd) - unix.cd(os.path.join("traces", "adj")) + unix.cd(os.path.join(self.cwd, "traces", "adj")) # work around SPECFEM2D's use of different name conventions for # regular traces and 'adjoint' traces - if self.par.FORMAT.upper() == "SU": + if self.data_format.upper() == "SU": files = glob("*SU") unix.rename(old="_SU", new="_SU.adj", names=files) - elif self.par.FORMAT.upper() == "ASCII": + elif self.data_format.upper() == "ASCII": files = glob("*sem?") # Get the available extensions, which are named based on unit @@ -244,13 +260,13 @@ def _initialize_adjoint_traces(self): # SPECFEM2D requires that all components exist even if ununsed components = ["x", "y", "z", "p"] - if self.par.FORMAT.upper() == "SU": + if self.data_format.upper() == "SU": for comp in components: - src = f"U{self.par.COMPONENTS[0]}_file_single.su.adj" + src = f"U{self.components[0]}_file_single.su.adj" dst = f"U{comp.lower()}s_file_single.su.adj" if not os.path.exists(dst): unix.cp(src, dst) - elif self.par.FORMAT.upper() == "ASCII": + elif self.data_format.upper() == "ASCII": for fid in glob("*.adj"): net, sta, cha, ext = fid.split(".") for comp in components: diff --git a/seisflows/solver/specfem3d.py b/seisflows/solver/specfem3d.py index 0be10ffc..43f7b557 100644 --- a/seisflows/solver/specfem3d.py +++ b/seisflows/solver/specfem3d.py @@ -18,17 +18,35 @@ class Specfem3D(Specfem): """ Python interface to Specfem3D Cartesian. """ - def __init__(self): + def __init__(self, source_prefix="CMTSOLUTION", **kwargs): """ - Initiate parameters required for Specfem3D Cartesian + SPECFEM2D specific parameters + + :type source_prefix: str + :param source_prefix: Prefix of SOURCE files in path SPECFEM_DATA. + :type multiples: bool + :param multiples: set an absorbing top-boundary condition """ - super().__init__() + super().__init__(**kwargs) + + self.source_prefix = source_prefix + self._f0 = None + + # Define parameters based on material type + if self.materials.upper() == "ACOUSTIC": + self._parameters.append("vp") + elif self.materials.upper() == "ELASTIC": + self._parameters.append("vp") + self._parameters.append("vs") + + self._acceptable_source_prefix = ["CMTSOLUTION", "FORCESOLUTION"] - self.required.par( - "SOURCE_PREFIX", required=False, default="CMTSOLUTION", - par_type=str, - docstr="Prefix of SOURCE files in path SPECFEM_DATA. Available " - "['CMTSOLUTION', FORCESOLUTION']") + def check(self): + """ + Check parameter validitiy + """ + super().check() + assert(self.source_prefix in self._acceptable_source_prefix) def data_wildcard(self, comp="?"): """ @@ -53,8 +71,8 @@ def data_filenames(self): """ unix.cd(os.path.join(self.cwd, "traces", "obs")) - if self.par.COMPONENTS: - files = glob(self.data_wildcard(comp=self.par.COMPONENTS.lower())) + if self.components: + files = glob(self.data_wildcard(comp=self.components.lower())) else: files = glob(self.data_wildcard(comp="?")) return sorted(files) @@ -77,7 +95,7 @@ def kernel_databases(self): """ return self.model_databases - def eval_func(self, path, write_residuals=True): + def eval_func(self, path, preprocess=None): """ Performs forward simulations and evaluates the misfit function using the preprocess module. Overrides to add a data renaming call @@ -90,7 +108,7 @@ def eval_func(self, path, write_residuals=True): will be exported :type write_residuals: bool :param write_residuals: calculate and export residuals """ - super().eval_func(path=path, write_residuals=write_residuals) + super().eval_func(path=path, preprocess=preprocess) # Work around SPECFEM3D conflicting name conventions of SU data self._rename_data() @@ -108,7 +126,7 @@ def _forward(self, output_path): # Set parameters and run forward simulation setpar(key="SIMULATION_TYPE", val="1", file="DATA/Par_file") setpar(key="SAVE_FORWARD", val=".true.", file="DATA/Par_file") - if self.par.ATTENUATION: + if self.attenuation: setpar(key="ATTENUATION", val=".true.", file="DATA/Par_file") else: setpar(key="ATTENUATION", val=".false`.", file="DATA/Par_file") @@ -156,14 +174,14 @@ def _initialize_adjoint_traces(self): # Workaround for Specfem3D's requirement that all components exist, # even ones not in use as adjoint traces - if self.par.FORMAT.upper() == "SU": + if self.data_format.upper() == "SU": unix.cd(os.path.join(self.cwd, "traces", "adj")) - for iproc in range(self.par.NPROC): + for iproc in range(self.nproc): for channel in ["x", "y", "z"]: dst = f"{iproc:d}_d{channel}_SU.adj" if not exists(dst): - src = f"{iproc:d}_d{self.par.COMPONENTS[0]}_SU.adj" + src = f"{iproc:d}_d{self.components[0]}_SU.adj" unix.cp(src, dst) def _rename_data(self): @@ -173,7 +191,7 @@ def _rename_data(self): Specfem3D's uses different name conventions for regular traces and 'adjoint' traces """ - if self.par.FORMAT.upper() == "SU": + if self.data_format.upper() == "SU": files = glob(os.path.join(self.cwd, "traces", "adj", "*SU")) unix.rename(old='_SU', new='_SU.adj', names=files) diff --git a/seisflows/solver/specfem3d_globe.py b/seisflows/solver/specfem3d_globe.py index 9b02c757..37897fcc 100644 --- a/seisflows/solver/specfem3d_globe.py +++ b/seisflows/solver/specfem3d_globe.py @@ -28,6 +28,19 @@ def __init__(self): Attributes are initialized as NoneTypes for clarity and docstrings. """ super().__init__() + if self.materials.upper() == "ELASTIC": + assert(self.par.SOLVER.lower() in ["specfem2d", "specfem3d"]) + self.parameters += ["vp", "vs"] + elif self.materials.upper() == "ACOUSTIC": + assert(self.par.SOLVER.lower() in ["specfem2d", "specfem3d"]) + self.parameters += ["vp"] + elif self.materials.upper() == "ISOTROPIC": + assert(self.par.SOLVER.lower() in ["specfem3d_globe"]) + self.parameters += ["vp", "vs"] + elif self.materials.upper() == "ANISOTROPIC": + assert(self.par.SOLVER.lower() in ["specfem3d_globe"]) + self.parameters += ["vpv", "vph", "vsv", "vsh", "eta"] + def data_wildcard(self, comp="?"): """ From b17bc736bd2556e88d89ac8cfc711d87bbcd798b Mon Sep 17 00:00:00 2001 From: bch0w Date: Fri, 8 Jul 2022 16:59:30 -0800 Subject: [PATCH 061/195] finished refactoring all system modules up to maui (what it inherited from). will need to address other modules after --- seisflows/solver/specfem3d.py | 8 +- seisflows/system/cluster.py | 54 ++++------ seisflows/system/maui.py | 163 +++++++++++++----------------- seisflows/system/slurm.py | 98 +++++++++--------- seisflows/system/workstation.py | 172 ++++++++++++++------------------ 5 files changed, 219 insertions(+), 276 deletions(-) diff --git a/seisflows/solver/specfem3d.py b/seisflows/solver/specfem3d.py index 43f7b557..bad6f5f6 100644 --- a/seisflows/solver/specfem3d.py +++ b/seisflows/solver/specfem3d.py @@ -55,9 +55,9 @@ def data_wildcard(self, comp="?"): :rtype: str :return: wildcard identifier for channels """ - if self.par.FORMAT.upper() == "SU": + if self.data_format.upper() == "SU": return f"*_d?_SU" - elif self.par.FORMAT.upper() == "ASCII": + elif self.data_format.upper() == "ASCII": return f"*.?X{comp}.sem?" @property @@ -123,6 +123,8 @@ def _forward(self, output_path): data (i.e., synthetics generated by the TRUE model), or 'traces/syn', for synthetics generated during function evaluations """ + unix.cd(self.cwd) + # Set parameters and run forward simulation setpar(key="SIMULATION_TYPE", val="1", file="DATA/Par_file") setpar(key="SAVE_FORWARD", val=".true.", file="DATA/Par_file") @@ -144,6 +146,8 @@ def _adjoint(self): Calls SPECFEM3D adjoint solver, creates the `SEM` folder with adjoint traces which is required by the adjoint solver """ + unix.cd(self.cwd) + setpar(key="SIMULATION_TYPE", val="3", file="DATA/Par_file") setpar(key="SAVE_FORWARD", val=".false.", file="DATA/Par_file") diff --git a/seisflows/system/cluster.py b/seisflows/system/cluster.py index a2a8adb6..83888596 100644 --- a/seisflows/system/cluster.py +++ b/seisflows/system/cluster.py @@ -5,6 +5,7 @@ specific clusters. """ import subprocess +from seisflows.config import save from seisflows.system.workstation import Workstation @@ -13,45 +14,29 @@ class Cluster(Workstation): Abstract base class for the Systems module which controls interaction with compute systems such as HPC clusters. """ - def __init__(self): + def __init__(self, walltime=10, tasktime=1, environs="", **kwargs): """ Instantiate the Cluster System class - """ - super().__init__() - - self.required.par( - "WALLTIME", required=True, par_type=float, - docstr="Maximum job time in minutes for main SeisFlows job" - ) - self.required.par( - "TASKTIME", required=True, par_type=float, - docstr="Maximum job time in minutes for each SeisFlows task" - ) - # note: OVERLOADS the Workstation `NTASK` parameter - self.required.par( - "NTASK", required=True, par_type=int, - docstr="Number of separate, individual tasks. Also equal to " - "the number of desired sources in workflow" - ) - # note: OVERLOADS the Workstation `NPROC` parameter - self.required.par( - "NPROC", required=True, par_type=int, - docstr="Number of processor to use for each simulation" - ) - self.required.par( - "ENVIRONS", required=False, default="", par_type=str, - docstr="Optional environment variables to be provided in the" - "following format VAR1=var1,VAR2=var2... Will be set" - "using os.environs" - ) - def check(self, validate=True): + :type walltime: int + :param walltime: maximum job time in minutes for the master SeisFlows + job submitted to cluster + :type tasktime: int + :param tasktime: maximum job time in minutes for each job spawned by + the SeisFlows master job during a workflow. These include, e.g., + running the forward solver + :type environs: str + :param environs: Optional environment variables to be provided in the + following format VAR1=var1,VAR2=var2... Will be set using + os.environs """ - Checks parameters and paths - """ - super().check(validate=validate) + super().__init__(**kwargs) + + self.walltime = walltime + self.tasktime = tasktime + self.environs = environs - def submit(self, submit_call=None): + def submit(self, workflow, submit_call=None): """ Main insertion point of SeisFlows onto the compute system. @@ -69,7 +54,6 @@ def submit(self, submit_call=None): subclasses. """ self.setup() - workflow = self.module("workflow") workflow.checkpoint() # check==True: subprocess will wait for workflow.main() to finish subprocess.run(submit_call, shell=True, check=True) diff --git a/seisflows/system/maui.py b/seisflows/system/maui.py index 151b9288..dafaaa36 100644 --- a/seisflows/system/maui.py +++ b/seisflows/system/maui.py @@ -19,6 +19,7 @@ """ import os import numpy as np +from seisflows import logger from seisflows.system.slurm import Slurm from seisflows.config import ROOT_DIR @@ -27,62 +28,43 @@ class Maui(Slurm): """ System interface for Maui, which operates on a SLURM system """ - def __init__(self): + def __init__(self, account=None, cpus_per_task=1, cluster="maui", + partition="nesi_research", ancil_cluster="maui_ancil", + ancil_partition="nesi_prepost", ancil_tasktime=1, **kwargs): """ - These parameters should not be set by the user. - Attributes are initialized as NoneTypes for clarity and docstrings. - - :type partitions: dict - :param partitions: Maui has various partitions which each have their - own number of cores per compute node, defined here + Maui parameters + + :type account: str + :param account: Maui account to submit jobs under, will be used for the + '--account' sbatch argument + :type cpus_per_task: int + :param cpus_per_task: allow for multiple cpus per task, i.e,. + multithreaded jobs + :type cluster: str + :param cluster: cluster to submit jobs to. Available are Maui and + Mahuika + :type partition: str + :param partition: partition of the cluster to submit jobs to. + :type ancil_cluster: str + :param ancil_cluster: name of the ancilary cluster used for pre- + post-processing tasks. + :type ancil_partition: name of the partition of the ancilary cluster + :type ancil_tasktime: int + :param ancil_tasktime: Tasktime in minutes for pre and post-processing + jobs submitted to Maui ancil. """ - super().__init__() - - self.required.par( - "ACCOUNT", required=True, par_type=str, - docstr="Maui account name to submit jobs under" - ) - self.required.par( - "NODESIZE", required=False, default=40, par_type=int, - docstr="The number of cores per node defined by the Maui cluster. " - "Assumed to be 40 cores per node." - ) - self.required.par( - "MPIEXEC", required=False, default="srun", par_type=str, - docstr="MPI call function used to invoke parallel executables, " - "defaults to 'srun'" - ) - self.required.par( - "CPUS_PER_TASK", required=False, default=1, par_type=int, - docstr="Multiple CPUS per task allows for multithreading jobs" - ) - self.required.par( - "CLUSTER", required=False, default="maui", par_type=str, - docstr="Name of main cluster for job submission. Available options: " - "'maui', 'maui_ancil', 'mahuika'. Note Mahuika untested" - ) - self.required.par( - "PARTITION", required=False, default="nesi_research", - par_type=str, docstr="Name of cluster partition to submit job to" - ) - self.required.par( - "ANCIL_CLUSTER", required=False, default="maui_ancil", par_type=str, - docstr="Ancillary cluster for pre- and post-processing tasks." - "Defaults to 'maui_ancil'") - - self.required.par( - "ANCIL_PARTITION", required=False, default="nesi_prepost", - par_type=str, - docstr="Name of ancillary partition for prepost tasks. Defaults to" - "'nesi_prepost'" - ) - self.required.par( - "ANCIL_TASKTIME", required=False, default="null", par_type=float, - docstr="Tasktime for prepost jobs submitted to ancillary nodes " - "matching 'ANCIL_CLUSTER' and 'ANCIL_PARTITION'" - ) - - self.partitions = {"nesi_research": 40} + super().__init__(**kwargs) + + self.account = account + self.cluster = cluster + self.partition = partition + self.cpus_per_task = cpus_per_task + self.ancil_cluster = ancil_cluster + self.ancil_partition = ancil_partition + self.ancil_tasktime = ancil_tasktime + + self._partitions = {"nesi_research": 40} + self.node_size = self._partitions[self.partition] def check(self, validate=True): """ @@ -90,12 +72,7 @@ def check(self, validate=True): """ super().check(validate=validate) - assert(self.par.NODESIZE == self.partitions[self.par.PARTITION]), \ - (f"PARTITION {self.par.PARTITION} is expected to have NODESIZE=" - f"{self.partitions[self.par.PARTITION]}, not current " - f"{self.par.NODESIZE}") - - assert("SLURM_MEM_PER_CPU" in (self.par.ENVIRONS or "")), \ + assert("SLURM_MEM_PER_CPU" in (self.environs or "")), \ ("Maui runs Slurm>=21 which enforces mutually exclusivity of Slurm " "memory environment variables SLURM_MEM_PER_CPU and " "SLURM_MEM_PER_NODE. Due to the cross-cluster nature of " @@ -124,17 +101,17 @@ def submit(self, submit_call=None): if submit_call is None: submit_call = " ".join([ f"sbatch", - f"--account={self.par.ACCOUNT}", - f"--cluster={self.par.ANCIL_CLUSTER}", - f"--partition={self.par.ANCIL_PARTITION}", - f"--job-name={self.par.TITLE}", - f"--output={self.output_log}", - f"--error={self.error_log}", + f"--account={self.account}", + f"--cluster={self.ancil_cluster}", + f"--partition={self.ancil_partition}", + f"--job-name={self.title}", + f"--output={self.path_output_log}", + f"--error={self.path_error_log}", f"--ntasks=1", f"--cpus-per-task=1", - f"--time={self.par.WALLTIME:d}", + f"--time={self.walltime:d}", f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'submit')}", - f"--output {self.path.OUTPUT}" + f"--output {self.path_output}" ]) super().submit(submit_call=submit_call) @@ -162,27 +139,27 @@ def run(self, classname, method, single=False, run_call=None, **kwargs): """ if run_call is None: # Calculate requested number of nodes based on requested proc count - _nodes = np.ceil(self.par.NPROC / float(self.par.NODESIZE)) + _nodes = np.ceil(self.nproc / float(self.node_size)) _nodes = _nodes.astype(int) run_call = " ".join([ "sbatch", - f"{self.par.SLURMARGS or ''}", - f"--account={self.par.ACCOUNT}", - f"--job-name={self.par.TITLE}", - f"--clusters={self.par.CLUSTER}", - f"--partition={self.par.PARTITION}", - f"--cpus-per-task={self.par.CPUS_PER_TASK}", + f"{self.slurm_args or ''}", + f"--account={self.account}", + f"--job-name={self.title}", + f"--clusters={self.cluster}", + f"--partition={self.partition}", + f"--cpus-per-task={self.cpus_per_task}", f"--nodes={_nodes:d}", - f"--ntasks={self.par.NPROC:d}", - f"--time={self.par.TASKTIME:d}", - f"--output={os.path.join(self.path.WORKDIR, 'logs', '%A_%a')}", - f"--array=0-{self.par.NTASK-1 % self.par.NTASKMAX}", + f"--ntasks={self.nproc:d}", + f"--time={self.tasktime:d}", + f"--output={os.path.join(self.path_log_files, '%A_%a')}", + f"--array=0-{self.ntask-1 % self.ntask_max}", f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'run')}", - f"--output {self.path.OUTPUT}", + f"--output {self.path_output}", f"--classname {classname}", f"--funcname {method}", - f"--environment {self.par.ENVIRONS or ''}" + f"--environment {self.environs or ''}" ]) super().run(classname, method, single, run_call=run_call, **kwargs) @@ -199,21 +176,21 @@ def run_ancil(self, classname, method, **kwargs): """ ancil_run_call = " ".join([ "sbatch", - f"{self.par.SLURMARGS or ''}", - f"--account={self.par.ACCOUNT}", - f"--job-name={self.par.TITLE}", - f"--clusters={self.par.ANCIL_CLUSTER}", - f"--partition={self.par.ANCIL_PARTITION}", - f"--cpus-per-task={self.par.CPUS_PER_TASK}", - f"--time={self.par.ANCIL_TASKTIME:d}", - f"--output={os.path.join(self.path.WORKDIR, 'logs', '%A_%a')}", - f"--array=0-{self.par.NTASK-1 % self.par.NTASKMAX}", + f"{self.slurm_args or ''}", + f"--account={self.account}", + f"--job-name={self.title}", + f"--clusters={self.ancil_cluster}", + f"--partition={self.ancil_partition}", + f"--cpus-per-task={self.cpus_per_task}", + f"--time={self.ancil_tasktime:d}", + f"--output={os.path.join(self.path_log_files, '%A_%a')}", + f"--array=0-{self.ntask-1 % self.ntask_max}", f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'run')}", - f"--output {self.path.OUTPUT}", + f"--output {self.path_output}", f"--classname {classname}", f"--funcname {method}", - f"--environment {self.par.ENVIRONS or ''}" + f"--environment {self.environs or ''}" ]) - self.logger.debug(ancil_run_call) + logger.debug(ancil_run_call) super().run(classname, method, single=False, run_call=ancil_run_call, **kwargs) diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index e2f22a81..0d274faa 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -19,6 +19,7 @@ import time import subprocess +from seisflows import logger from seisflows.system.cluster import Cluster from seisflows.tools import msg from seisflows.config import ROOT_DIR @@ -29,40 +30,35 @@ class Slurm(Cluster): Generalized interface for submitting jobs to and interfacing with a SLURM workload management system. """ - def __init__(self): + def __init__(self, ntask_max=100, slurm_args="", **kwargs): """ - These parameters should not be set by the user. - Attributes are initialized as NoneTypes for clarity and docstrings. + Slurm-specific setup parameters + + :type ntask_max: int + :param ntask_max: limit the number of concurrent tasks in a given + array job + :type slurm_args: str + :param slurm_args: Any optional, additional SLURM arguments that will + be passed to the SBATCH scripts. Should be in the form: + '--key1=value1 --key2=value2" """ - super().__init__() - - self.required.par( - "MPIEXEC", required=False, default="srun -u", par_type=str, - docstr="Function used to invoke executables on the system. " - "For example 'srun' on SLURM systems, or './' on a " - "workstation. If left blank, will guess based on the " - "system." - ) - self.required.par( - "NTASKMAX", required=False, default=100, par_type=int, - docstr="Limit on the number of concurrent tasks in array" - ) - self.required.par( - "NODESIZE", required=True, par_type=int, - docstr="The number of cores per node defined by the system" - ) - - self.required.par( - "SLURMARGS", required=False, default="", par_type=str, - docstr="Any optional, additional SLURM arguments that will be " - "passed to the SBATCH scripts" - ) + super().__init__(**kwargs) + + # Overwrite the existing 'mpiexec' + self.mpiexec = "srun -u" + self.ntask_max = ntask_max + self.slurm_args = slurm_args + + # Must be overwritten by child class + self.node_size = None def check(self, validate=True): """ Checks parameters and paths """ - super().check(validate=validate) + assert(self.node_size is not None), ( + f"Slurm system child classes require defining the `node_size` or " + f"the number of cores per node inherent to the compute system") def submit(self, submit_call=None): """ @@ -76,18 +72,18 @@ def submit(self, submit_call=None): if submit_call is None: submit_call = " ".join([ f"sbatch", - f"{self.par.SLURMARGS or ''}", - f"--job-name={self.par.TITLE}", - f"--output={self.output_log}", - f"--error={self.error_log}", - f"--ntasks-per-node={self.par.NODESIZE}", + f"{self.slurmargs or ''}", + f"--job-name={self.title}", + f"--output={self.path_output_log}", + f"--error={self.path_error_log}", + f"--ntasks-per-node={self.node_size}", f"--nodes=1", - f"--time={self.par.WALLTIME:d}", + f"--time={self.walltime:d}", f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'submit')}", - f"--output {self.path.OUTPUT}" + f"--output {self.path_output}" ]) - self.logger.debug(submit_call) + logger.debug(submit_call) super().submit(submit_call=submit_call) def run(self, classname, method, single=False, run_call=None, **kwargs): @@ -115,35 +111,35 @@ def run(self, classname, method, single=False, run_call=None, **kwargs): can overload the sbatch command line input by setting run_call. If set to None, default run_call will be set here. """ - self.checkpoint(self.path.OUTPUT, classname, method, kwargs) + self.save_kwargs_to_disk(self.path_output, classname, method, kwargs) # Default sbatch command line input, can be overloaded by subclasses # Copy-paste this default run_call and adjust accordingly for subclass if run_call is None: run_call = " ".join([ "sbatch", - f"{self.par.SLURMARGS or ''}", - f"--job-name={self.par.TITLE}", - f"--nodes={math.ceil(self.par.NPROC/float(self.par.NODESIZE)):d}", - f"--ntasks-per-node={self.par.NODESIZE:d}", - f"--ntasks={self.par.NPROC:d}", - f"--time={self.par.TASKTIME:d}", - f"--output={os.path.join(self.path.WORKDIR, 'logs', '%A_%a')}", - f"--array=0-{self.par.NTASK-1 % self.par.NTASKMAX}", + f"{self.slurm_args or ''}", + f"--job-name={self.title}", + f"--nodes={math.ceil(self.nproc/float(self.node_size)):d}", + f"--ntasks-per-node={self.node_size:d}", + f"--ntasks={self.nproc:d}", + f"--time={self.tasktime:d}", + f"--output={os.path.join(self.path_log_files, '%A_%a')}", + f"--array=0-{self.natsk-1 % self.ntaskmax}", f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'run')}", - f"--output {self.path.OUTPUT}", + f"--output {self.path_output}", f"--classname {classname}", f"--funcname {method}", - f"--environment {self.par.ENVIRONS or ''}" + f"--environment {self.environs or ''}" ]) - self.logger.debug(run_call) + logger.debug(run_call) # Single-process jobs simply need to replace a few sbatch arguments. # Do it AFTER `run_call` has been defined so that subclasses submitting # custom run calls can still benefit from this if single: - self.logger.info("replacing parts of sbatch run call for single " + logger.info("replacing parts of sbatch run call for single " "process job") run_call = _modify_run_call_single_proc(run_call) @@ -165,7 +161,7 @@ def run(self, classname, method, single=False, run_call=None, **kwargs): header="slurm run error", border="=")) sys.exit(-1) - self.logger.info(f"task {classname}.{method} finished successfully") + logger.info(f"task {classname}.{method} finished successfully") def taskid(self): """ @@ -220,7 +216,7 @@ def _check_job_status(self, job_ids): # Every 10 counts, warn the user this is unexpected behavior if not count % 10: job_id = job_ids[states.index("UNDEFINED")] - self.logger.warning(f"SLURM command 'sacct {job_id}' has " + logger.warning(f"SLURM command 'sacct {job_id}' has " f"returned unexpected response {count} " f"times. This job may have failed " f"unexpectedly. Consider checking " @@ -260,7 +256,7 @@ def _job_id_list(self, stdout, single): if single: ntask = 1 else: - ntask = self.par.NTASK + ntask = self.ntask # Splitting e.g.,: 'Submitted batch job 441636\n' for part in stdout.strip().split(): diff --git a/seisflows/system/workstation.py b/seisflows/system/workstation.py index 9cbd409d..e394cf78 100644 --- a/seisflows/system/workstation.py +++ b/seisflows/system/workstation.py @@ -4,94 +4,79 @@ Provides utilities for submitting jobs in serial on a single machine """ import os +import sys import pickle from contextlib import redirect_stdout -from seisflows.core import Base +from seisflows import logger from seisflows.config import CFGPATHS, save from seisflows.tools import msg, unix from seisflows.tools.wrappers import number_fid -class Workstation(Base): +class Workstation: """ Run tasks in a serial fashion on a single local machine. Also serves as the Base System module, upon which all other System classes should be built. """ - def __init__(self): + def __init__(self, title=None, mpiexec=None, ntask=1, nproc=1, + log_level="DEBUG", verbose=False, path_output=None, + path_system=None, path_output_log=None, path_error_log=None, + path_log_files=None, path_par_file=None): """ Instantiate the Workstation base class + + :type title: str + :param title: The name used to submit jobs to the system, defaults + to the name of the current working directory + :type mpiexec: str + :param mpiexec: Function used to invoke executables on the system. + For example 'srun' on SLURM systems. If None this will default to + './' for calling executables. + :type ntask: int + :param ntask: number of individual tasks/events to run during workflow + :type nproc: int + :param nproc: number of processors to use for each simulation + :type log_level: str + :param log_level: logger level to pass to logging module. + Available: 'debug', 'info', 'warning' + :type verbose: bool + :param verbose: if True, formats the log messages to include the file + name, line number and message type. Useful for debugging but + also very verbose + :type path_output: str + :param path_output: path to save files permanently to disk + :type path_system: str + :param path_system: scratch path to save any system related files """ - super().__init__() - - self.output_log = os.path.join(self.path.WORKDIR, CFGPATHS.LOGFILE) - self.error_log = os.path.join(self.path.WORKDIR, CFGPATHS.ERRLOGFILE) - - self.required.par( - "TITLE", required=False, - default=os.path.basename(os.path.abspath(".")), par_type=str, - docstr="The name used to submit jobs to the system, defaults " - "to the name of the working directory" - ) - self.required.par( - "MPIEXEC", required=False, default=None, par_type=str, - docstr="Function used to invoke executables on the system. " - "For example 'srun' on SLURM systems, or './' on a " - "workstation. If left blank, will guess based on the " - "system." - ) - self.required.par( - "NTASK", required=False, default=1, par_type=int, - docstr="Number of separate, individual tasks. Also equal to " - "the number of desired sources in workflow" - ) - self.required.par( - "NPROC", required=False, default=1, par_type=int, - docstr="Number of processor to use for each simulation" - ) - self.required.par( - "LOG_LEVEL", required=False, par_type=str, default="DEBUG", - docstr="Verbosity output of SF logger. Available from least to " - "most verbosity: 'CRITICAL', 'WARNING', 'INFO', 'DEBUG'; " - "defaults to 'DEBUG'" - ) - self.required.par( - "VERBOSE", required=False, default=False, par_type=bool, - docstr="Level of verbosity provided to the output log. If True, " - "log statements will declare what module/class/function " - "they are being called from. Useful for debugging but " - "also very noisy." - ) - # note: self.path.WORKDIR has been set by the entry point seisflows.setup() - self.required.path( - "SCRATCH", required=False, - default=os.path.join(self.path.WORKDIR, "scratch"), - docstr="scratch path to hold temporary data during workflow" - ) - self.required.path( - "OUTPUT", required=False, - default=os.path.join(self.path.WORKDIR, "output"), - docstr="directory to save workflow outputs to disk" - ) - self.required.path( - "SYSTEM", required=False, - default=os.path.join(self.path.WORKDIR, "scratch", "system"), - docstr="scratch path to hold any system related data" - ) - self.required.path( - "LOGFILE", required=False, default=self.output_log, - docstr="the main output log file where all processes will track " - "their status" - ) - - def check(self, validate=True): + self.title = title + self.mpiexec = mpiexec + self.ntask = ntask + self.nproc = nproc + self.log_level = log_level + self.verbose = verbose + + # Define internal path system + self.path = path_system or \ + os.path.join(os.getcwd(), "scratch", "system") + self.path_par_file = path_par_file or \ + os.path.join(os.getcwd(), "parameters.yaml") + self.path_output = path_output + + # Define where to write logs + self.path_log_files = path_log_files or \ + os.path.join(os.getcwd(), "logs") + self.path_output_log = path_output_log or \ + os.path.join(os.getcwd(), "sfoutput.log") + self.path_error_log = path_error_log or \ + os.path.join(os.getcwd(), "sferror.log") + + + def check(self): """ Checks parameters and paths """ - super().check(validate=validate) - - if self.output_log != self.path.LOGFILE: - self.output_log = self.path.LOGFILE + pass def setup(self): """ @@ -111,39 +96,36 @@ def setup(self): :rtype: tuple of str :return: (path to output log, path to error log) """ - # Create scratch directories - unix.mkdir(self.path.SCRATCH) - unix.mkdir(self.path.SYSTEM) - - # Create output directories - unix.mkdir(self.path.OUTPUT) - log_files = os.path.join(self.path.WORKDIR, CFGPATHS.LOGDIR) - unix.mkdir(log_files) + for path in [self.path, self.path_output, self.path_log_files]: + unix.mkdir(path) # If resuming, move old log files to keep them out of the way. Number # in ascending order, so we don't end up overwriting things - for src in [self.output_log, self.error_log, self.path.PAR_FILE]: + for src in [self.path_output_log, self.path_error_log, + self.path_par_file]: i = 1 if os.path.exists(src): - dst = os.path.join(log_files, number_fid(src, i)) + dst = os.path.join(self.path_log_files, number_fid(src, i)) while os.path.exists(dst): i += 1 - dst = os.path.join(log_files, number_fid(src, i)) - self.logger.debug(f"copying par/log file to: {dst}") + dst = os.path.join(self.path_log_files, number_fid(src, i)) + logger.debug(f"copying par/log file to: {dst}") unix.cp(src=src, dst=dst) - def submit(self, submit_call=None): + def submit(self, workflow, submit_call=None): """ Submits the main workflow job as a serial job submitted directly to the compute node that is running the master job + TO DO fix this + :type submit_call: str or None :param submit_call: the command line workload manager call to be run by subprocess. This is only needed for overriding classes, it has no effect on the Workstation class """ self.setup() - workflow = self.module("workflow") + workflow = sys.modules["seisflows_workflow"] workflow.checkpoint() workflow.main() @@ -165,18 +147,17 @@ def run(self, classname, method, single=False, **kwargs): defined, such that the job is submitted as a single-core job to the system. """ - self.checkpoint(self.path.OUTPUT, classname, method, kwargs) + self.save_kwargs_to_disk(self.path_output, classname, method, kwargs) # Allows dynamic retrieval of any function from within package, e.g., # 4}_{taskid:0>2}.log") + log_file = os.path.join(self.path_log_files, + f"{idx:0>4}_{taskid:0>2}.log") if os.path.exists(log_file): idx += 1 else: break if taskid == 0: - self.logger.info(f"running task {classname}.{method} " - f"{self.par.NTASK} times") + logger.info(f"running task {classname}.{method} " + f"{self.ntask} times") # Redirect output to a log file to mimic cluster runs where 'run' # task output logs are sent to different files @@ -221,10 +203,9 @@ def taskid(self): sftaskid = 0 return int(sftaskid) - def checkpoint(self, path, classname, method, kwargs): + def save_kwargs_to_disk(self, path, classname, method, kwargs): """ - Writes the SeisFlows working environment to disk so that new tasks can - be executed in a separate/new/restarted working environment. + Writes keyword arguments for a given method to disk :type path: str :param path: path to save the checkpointed pickle files to @@ -241,4 +222,5 @@ def checkpoint(self, path, classname, method, kwargs): unix.mkdir(argspath) with open(argsfile, "wb") as f: pickle.dump(kwargs, f) - save(path=self.path.OUTPUT) \ No newline at end of file + + save(path=self.path_output) \ No newline at end of file From 832a6759db8a4225e468bc87879c5f58dabe1190 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Mon, 11 Jul 2022 16:13:06 -0800 Subject: [PATCH 062/195] refactoring solver and preprocess to fit the new workflow style where less of the structure of the code is assumed and is rather stated more explicitely. working to get en-masse forward simulation workflow going --- seisflows/config.py | 42 ++++- seisflows/core.py | 2 - seisflows/plugins/preprocess/readers.py | 20 +- seisflows/plugins/preprocess/writers.py | 18 +- seisflows/postprocess/default.py | 2 +- seisflows/preprocess/default.py | 74 +++++++- seisflows/solver/specfem.py | 171 ++++++++---------- seisflows/solver/specfem2d.py | 34 ++-- seisflows/system/workstation.py | 2 +- .../test_solver/sources/CMTSOLUTION_001 | 1 + .../test_solver/sources/CMTSOLUTION_002 | 1 + .../test_data/test_solver/sources/SOURCE_001 | 1 + .../test_data/test_solver/sources/SOURCE_002 | 1 + seisflows/tests/test_solver.py | 11 ++ seisflows/workflow/base.py | 80 ++++++++ seisflows/workflow/forward.py | 15 +- seisflows/workflow/forward_test.py | 67 ++++--- 17 files changed, 355 insertions(+), 187 deletions(-) create mode 100644 seisflows/tests/test_data/test_solver/sources/CMTSOLUTION_001 create mode 100644 seisflows/tests/test_data/test_solver/sources/CMTSOLUTION_002 create mode 100644 seisflows/tests/test_data/test_solver/sources/SOURCE_001 create mode 100644 seisflows/tests/test_data/test_solver/sources/SOURCE_002 create mode 100644 seisflows/tests/test_solver.py create mode 100644 seisflows/workflow/base.py diff --git a/seisflows/config.py b/seisflows/config.py index c844e55f..702a26d2 100755 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -25,7 +25,7 @@ from seisflows import logger from seisflows.core import Dict, Null from seisflows.tools import msg, unix -from seisflows.tools.wrappers import module_exists +from seisflows.tools.wrappers import module_exists, load_yaml """ @@ -38,8 +38,7 @@ """ # List of module names required by SeisFlows for imports. Order-sensitive # In sys.modules these will be prepended by 'seisflows_', e.g., seisflows_system -NAMES = ["system", "preprocess", "solver", - "postprocess", "optimize", "workflow"] +NAMES = ["system", "preprocess", "solver", "postprocess", "optimize"] #, "workflow"] # The location of this config file, which is the main repository ROOT_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__))) @@ -106,6 +105,43 @@ def load(path): sys.modules[f"seisflows_{name}"] = pickle.load(f) +def import_seisflows(workdir=os.getcwd(), parameter_file="parameters.yaml"): + """ + Standard SeisFlows workflow setup block which runs some standard setup + tasks including: setting the working directory, instantiating the logging + module and dynmically importing each of the SeisFlows modules by specific + class names. + + .. note:: + This should be called in the exact way each time: + > pars, modules = import_seisflows() + > system, preprocess, solver, postprocess, optimize = modules + + :type workdir: str + :param workdir: the current working directory in which to perform a + SeisFlows workflow. Defaults to the current working directory + :type parameter_file: str + :param parameter_file: the YAML formatted parameter file that is used to + instantiate each of the SeisFlows modules and run the workflow. This + should be created by the command line argument 'seisflows configure'. + Defaults to 'parameters.yaml' + :rtype: list + :return: instantiated modules that are returned in the following order + 'system', 'preprcess', 'solver', 'postprocess', 'optimize' + """ + parameters = load_yaml(os.path.join(workdir, parameter_file)) + config_logger(level=parameters.log_level, filename=parameters.path_log_file, + verbose=parameters.verbose) + + classes = [custom_import(name, parameters[name]) for name in NAMES] + modules = [cls(**parameters) for cls in classes] + # Check that parameters have been set correctly by running their check funcs + for module in modules: + module.check() + + return parameters, modules + + def config_logger(level="DEBUG", filename=None, filemode="a", verbose=True): """ Explicitely configure the logging module with some parameters defined diff --git a/seisflows/core.py b/seisflows/core.py index 99609851..cff4917b 100755 --- a/seisflows/core.py +++ b/seisflows/core.py @@ -14,8 +14,6 @@ class Base(object): inherit from the Base object to work properly. This Base class essentially dictates the required structure of a SeisFlows class. """ - logger = logging.getLogger("seisflows") - def __init__(self): """ SeisFlows instantiates its required parameters through the diff --git a/seisflows/plugins/preprocess/readers.py b/seisflows/plugins/preprocess/readers.py index 2ba08930..1a54e5ea 100644 --- a/seisflows/plugins/preprocess/readers.py +++ b/seisflows/plugins/preprocess/readers.py @@ -11,35 +11,31 @@ from obspy.core import Stream, Stats, Trace -def su(path, filename): +def su(filename): """ Reads seismic unix files outputted by Specfem, using Obspy - :type path: str - :param path: path to datasets :type filename: str - :param filename: file to read + :param filename: full path to data file to read """ - st = read(os.path.join(path, filename), format='SU', byteorder='<') + st = read(os.path.join(filename), format='SU', byteorder='<') return st -def ascii(path, filename): +def ascii(filename): """ Reads SPECFEM3D-style ASCII data - :type path: str - :param path: path to datasets - :type filenames: list - :param filenames: files to read + :type filename: str + :param filename: full path to data file to read """ st = Stream() stats = Stats() - time, data = loadtxt(os.path.join(path, filename)).T + time, data = loadtxt(filename).T - stats.filename = filename + stats.filename = os.path.basename(filename) stats.starttime = time[0] stats.delta = time[1] - time[0] stats.npts = len(data) diff --git a/seisflows/plugins/preprocess/writers.py b/seisflows/plugins/preprocess/writers.py index 37858928..44cff58a 100644 --- a/seisflows/plugins/preprocess/writers.py +++ b/seisflows/plugins/preprocess/writers.py @@ -9,16 +9,14 @@ import numpy as np -def su(st, path, filename): +def su(st, filename): """ Writes seismic unix files outputted by Specfem, using Obspy :type st: obspy.core.stream.Stream :param st: stream to write - :type path: str - :param path: path to datasets :type filename: str - :param filename: file to read + :param filename: full path to filename to write data to """ for tr in st: # Work around obspy data type conversion @@ -32,29 +30,27 @@ def su(st, path, filename): tr.stats.delta = dummy_delta # Write data to file - st.write(os.path.join(path, filename), format='SU') + st.write(filename, format='SU') -def ascii(st, path, filename=None): +def ascii(st, filename=None): """ Writes seismic traces as ascii files. Kwargs are left to keep structure of inputs compatible with other input formats. :type st: obspy.core.stream.Stream :param st: stream to write - :type path: str - :param path: path to datasets + :type filename: str + :param filename: full path to filename to write data to """ for tr in st: if filename is None: filename = tr.stats.filename - fid_out = os.path.join(path, filename) - # Float provides the time difference between starttime and default time time_offset = float(tr.stats.starttime) data_out = np.vstack((tr.times() + time_offset, tr.data)).T - np.savetxt(fid_out, data_out, ["%13.7f", "%17.7f"]) + np.savetxt(filename, data_out, ["%13.7f", "%17.7f"]) diff --git a/seisflows/postprocess/default.py b/seisflows/postprocess/default.py index 8184d376..4e53e229 100644 --- a/seisflows/postprocess/default.py +++ b/seisflows/postprocess/default.py @@ -17,7 +17,7 @@ class Default: on models or gradients """ def __init__(self, smooth_h=0., smooth_v=0., tasktime_smooth=1, - path_postprocess=None, path_mask=None): + path_postprocess=None, path_mask=None, **kwargs): """ Establish Postprocessing parameters diff --git a/seisflows/preprocess/default.py b/seisflows/preprocess/default.py index 0a11fa80..4a46ffdd 100644 --- a/seisflows/preprocess/default.py +++ b/seisflows/preprocess/default.py @@ -177,10 +177,6 @@ def check(self, validate=True): assert(self.data_format.lower() in dir(writers)), ( f"Writer {self.data_format} not found") - # Assert that either misfit or backproject exists - if self.par.WORKFLOW.upper() == "INVERSION": - assert(self.misfit is not None) - def setup(self): """ Sets up data preprocessing machinery by dynamicalyl loading the @@ -211,6 +207,66 @@ def finalize(self): """ pass + def quantify_misfit(self, observed, synthetic, + write_residuals=False, write_adjsrcs=False, **kwargs): + """ + Prepares solver for gradient evaluation by writing residuals and + adjoint traces. Meant to be called by solver.eval_func(). + + Reads in observed and synthetic waveforms, applies optional + preprocessing, assesses misfit, and writes out adjoint sources and + STATIONS_ADJOINT file. + + .. note:: + Meant to be called by solver.eval_func(), may have unused arguments + to keep functions general across subclasses. + + :type cwd: str + :param cwd: current specfem working directory containing observed and + synthetic seismic data to be read and processed. Should be defined + by solver.cwd + :type filenames: list of str + :param filenames: list of filenames defining the files in traces + """ + for obs_fid, syn_fid in zip(observed, synthetic): + obs = self._reader(filename=obs_fid) + syn = self._reader(filename=syn_fid) + + # Process observations and synthetics identically + if self.filter: + obs = self._apply_filter(obs) + syn = self._apply_filter(syn) + if self.mute: + obs = self._apply_mute(obs) + syn = self._apply_mute(syn) + if self.normalize: + obs = self._apply_normalize(obs) + syn = self._apply_normalize(syn) + + # Write the residuals/misfit and adjoint sources for each component + for tr_obs, tr_syn in zip(obs, syn): + if write_residuals: + residual = self._misfit(obs=tr_obs.data, syn=tr_syn.data, + nt=tr_syn.stats.npts, + dt=tr_syn.stats.delta) + with open(write_residuals, "a") as f: + f.write(f"{residual:.2E}\n") + if write_adjsrcs: + adjsrc = syn.copy() + adjsrc.data = self._adjoint(obs=tr_obs.data, syn=tr_syn.data, + nt=tr_syn.stats.npts, + dt=tr_syn.stats.delta) + if self.data_format.upper() == "ASCII": + # Change the extension to '.adj' from whatever it is + ext = os.path.splitext(os.path.basename(obs))[-1] + filename = os.path.basename(obs).replace(ext, ".adj") + elif self.data_format.upper() == "SU": + # TODO implement this + raise NotImplementedError + self._writer(st=adjsrc, + filename=os.path.join(write_adjsrcs, filename) + ) + def prepare_eval_grad(self, cwd, taskid, filenames, **kwargs): """ Prepares solver for gradient evaluation by writing residuals and @@ -236,9 +292,9 @@ def prepare_eval_grad(self, cwd, taskid, filenames, **kwargs): for filename in filenames: obs = self._reader(path=os.path.join(cwd, "traces", "obs"), - filename=filename) + filename=filename) syn = self._reader(path=os.path.join(cwd, "traces", "syn"), - filename=filename) + filename=filename) # Process observations and synthetics identically if self.filter: @@ -296,7 +352,7 @@ def sum_residuals(self, files): return total_misfit - def _write_residuals(self, path, syn, obs): + def _write_residuals(self, obs, syn, output): """ Computes residuals between observed and synthetic seismogram based on the misfit function self.misfit. Saves the residuals for each @@ -319,10 +375,10 @@ def _write_residuals(self, path, syn, obs): residual = self._misfit(syn=tr_syn.data, obs=tr_obs.data, nt=tr_syn.stats.npts, dt=tr_syn.stats.delta - ) + ) residuals.append(residual) - filename = os.path.join(path, "residuals") + filename = os.path.join(output, "residuals") if os.path.exists(filename): residuals = np.append(residuals, np.loadtxt(filename)) diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index e511cb75..67422547 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -11,7 +11,7 @@ from glob import glob from seisflows import logger -from seisflows.plugins import solver_io +from seisflows.plugins import solver_io as solver_io_dir from seisflows.tools import msg, unix from seisflows.tools.specfem import getpar, Model @@ -65,10 +65,16 @@ class Specfem: """ def __init__(self, case="data", data_format="ascii", materials="elastic", density=False, nproc=1, ntask=1, attenuation=False, - components="ZNE", - solver_io="fortran_binary", mpiexec=None, path_solver=None, - path_data=None, path_specfem_bin=None, path_specfem_data=None, - path_model_init=None, path_model_true=None, path_output=None, + components="ZNE", solver_io="fortran_binary", mpiexec=None, + path_solver=os.path.join(os.getcwd(), "scratch", "solver"), + path_data=os.path.join(os.getcwd(), "SFDATA"), + path_specfem_bin=os.path.join(os.getcwd(), "specfem", "bin"), + path_specfem_data=os.path.join(os.getcwd(), "specfem", "DATA"), + path_model_init=os.path.join(os.getcwd(), "specfem", + "MODEL_INIT"), + path_model_true=os.path.join(os.getcwd(), "specfem", + "MODEL_TRUE"), + path_output=os.path.join(os.getcwd(), "output"), save_traces=False, **kwargs): """ SPECFEM Solver parameters @@ -121,14 +127,14 @@ def __init__(self, case="data", data_format="ascii", materials="elastic", self.mpiexec = mpiexec # Define internally used directory structure - self.path = path_solver or \ - os.path.join(os.getcwd(), "scratch", "solver") + self.path = path_solver self.path_data = path_data self.path_specfem_bin = path_specfem_bin self.path_specfem_data = path_specfem_data self.path_model_init = path_model_init self.path_model_true = path_model_true self.path_output = path_output + self.path_mainsolver = os.path.join(self.path, "mainsolver") self.save_traces = save_traces @@ -138,7 +144,7 @@ def __init__(self, case="data", data_format="ascii", materials="elastic", self._parameters.append("rho") self._source_names = None - self._io = getattr(solver_io, self.solver_io) + self._io = getattr(solver_io_dir, self.solver_io) # Define available choices for check parameter self._available_model_types = ["gll"] @@ -155,12 +161,17 @@ def __init__(self, case="data", data_format="ascii", materials="elastic", def taskid(self): """ Returns the currently running process for embarassingly parallelized - tasks. + tasks. Task IDs are assigned to the environment by system.run(). + Task IDs are simply integer values from 0 to the number of + simultaneously running tasks. :rtype: int :return: task id for given solver """ - return self.module("system").taskid() + _taskid = os.getenv("SEISFLOWS_TASKID") + if _taskid is None: + _taskid = 0 + return int(_taskid) @property def source_names(self): @@ -248,7 +259,7 @@ def check(self): f"solver.data_format must be {self._available_data_formats}" ) - assert hasattr(solver_io, self.solver_io) + assert hasattr(solver_io_dir, self.solver_io) assert hasattr(self._io, "read_slice"), \ "IO method has no attribute 'read'" assert hasattr(self._io, "write_slice"), \ @@ -271,50 +282,12 @@ def setup(self): In the former case, a value for PATH.DATA must be supplied; in the latter case, a value for PATH.MODEL_TRUE must be provided. """ - self._initialize_solver_directories() - self.generate_data() - self._set_model(model_name="init", model_type="gll") + self._initialize_working_directories() + self._import_starting_model(path_model=self.path_model_init, + model_type="gll") + self._export_model() self._initialize_adjoint_traces() - def _set_model(self, model_name, model_type=None): - """ - Mesh and database files should have been created during the manual set - up phase. This function simply checks the mesh properties of that mesh - and ensures that it is locatable by future SeisFlows processes. - - :type model_name: str - :param model_name: name of the model to be used as identification - :type model_type: str - :param model_type: available model types to be passed to the Specfem3D - Par_file. See Specfem3D Par_file for available options. - """ - unix.cd(self.cwd) - available_model_types = ["gll"] - - # Check the type of model. So far SeisFlows only accepts GLL models - model_type = model_type or getpar(key="MODEL", file="DATA/Par_file")[1] - assert(model_type in available_model_types), \ - f"{model_type} not in available types {available_model_types}" - - # Determine which model will be set as the starting model - if model_name.upper() == "INIT": - model_path = self.path_model_init - elif model_name.upper() == "TRUE": - model_path = self.path_model_true - else: - raise ValueError(f"model name must be 'INIT' or 'TRUE'") - assert(os.path.exists(model_path)), f"model {model_path} does not exist" - - if model_type == "gll": - # Copy the model files (ex: proc000023_vp.bin ...) into database dir - src = glob(os.path.join(model_path, "*")) - dst = self.model_databases - unix.cp(src, dst) - - # Export the model into output folder, ready to be used by other tasks - if self.taskid == 0: - self._export_model(os.path.join(self.path.OUTPUT, model_name)) - def generate_data(self): """ Generates observation data to be compared to synthetics. This must @@ -337,7 +310,8 @@ def generate_data(self): if self.taskid == 0: logger.info("generating 'data' with MODEL_TRUE") # Generate synthetic data on the fly using the true model - self._set_model(model_name="true", model_type="gll") + self._import_starting_model(path_model=self.path_model_true, + model_type="gll") self._forward(output_path=os.path.join("traces", "obs")) # If Data provided by user, copy directly into the solver directory elif self.path_data is not None and os.path.exists(self.path_data): @@ -345,6 +319,7 @@ def generate_data(self): src=glob(os.path.join(self.path_data, self.source_name, "*")), dst=os.path.join("traces", "obs") ) + # Save observation data to disk if self.save_traces: self._export_traces( @@ -446,20 +421,21 @@ def eval_grad(self, path, export_traces=False): # self.adjoint() # self.export_kernels(path) - def _forward(self, output_path): + def forward_simulation(self, output_path): """ Calls forward solver with the appropriate parameters in the Par_file set Also exports data to the correct output_path - :type output_path: str - :param output_path: path to export traces to after completion of + :type output_seismograms: str + :param output_seismograms: path to export traces to after completion of simulation expected values are either 'traces/obs' for 'observation' data (i.e., synthetics generated by the TRUE model), or - 'traces/syn', for synthetics generated during function evaluations + 'traces/syn', for synthetics generated during function evaluations. + If False, will leave seismograms in OUTPUT_FILES/ """ raise NotImplementedError("must be implemented by solver subclass") - def _adjoint(self): + def adjoint_simulation(self): """ Calls adjoint solver with the appropriate parameters in the Par_file set Also takes care of setting up the SEM/ directory where SPECFEM expects @@ -620,6 +596,31 @@ def _import_model(self, path): dst=os.path.join(self.cwd, "DATA") ) + def _import_starting_model(self, path_model, model_type=None, save_as=None): + """ + Mesh and database files should have been created during the manual set + up phase. This function simply checks the mesh properties of that mesh + and ensures that it is locatable by future SeisFlows processes. + + :type path_model: str + :param path_model: path to an existing starting model + :type model_type: str + :param model_type: available model types to be passed to the Specfem3D + Par_file. See Specfem3D Par_file for available options. + """ + unix.cd(self.cwd) + # Check type/existence of model. SeisFlows only accepts GLL models + model_type = model_type or getpar(key="MODEL", file="DATA/Par_file")[1] + assert(model_type in self._available_model_types), \ + f"{model_type} not in available types {self._available_model_types}" + assert(os.path.exists(path_model)), f"model {path_model} does not exist" + + if model_type == "gll": + # Copy the model files (ex: proc000023_vp.bin ...) into database dir + src = glob(os.path.join(path_model, "*")) + dst = self.model_databases + unix.cp(src, dst) + def _import_traces(self, path): """ File transfer utility. Import traces into the workflow. @@ -631,43 +632,26 @@ def _import_traces(self, path): dst = os.path.join(self.cwd, 'traces', 'obs') unix.cp(src, dst) - def _export_model(self, path, parameters=None): + def _export_model(self): """ - File transfer utility. Export the model to disk. - - Performed by master solver. - - :type path: str - :param path: path to save model - :type parameters: list - :param parameters: list of parameters that define the model + File transfer utility. Export the model to disk. Run from master solver. """ - if parameters is None: - parameters = self._parameters + unix.mkdir(self.path_output) + for key in self._parameters: + files = glob(os.path.join(self.model_databases, f"*{key}.bin")) + unix.cp(files, self.path_output) - if self.taskid == 0: - unix.mkdir(path) - for key in parameters: - files = glob(os.path.join(self.model_databases, f"*{key}.bin")) - unix.cp(files, path) - - def _export_kernels(self, path): + def _export_kernels(self): """ File transfer utility. Export kernels to disk - - :type path: str - :param path: path to save kernels """ - if self.taskid == 0: - logger.debug(f"exporting kernels to:\n{path}") - unix.cd(self.kernel_databases) # Work around conflicting name conventions self._rename_kernels() src = glob("*_kernel.bin") - dst = os.path.join(path, "kernels", self.source_name) + dst = os.path.join(self.path_output, "kernels", self.source_name) unix.mkdir(dst) unix.mv(src, dst) @@ -730,7 +714,7 @@ def _rename_kernels(self): names = glob(f"*proc??????_{tag}_kernel.bin") unix.rename(old="beta", new="vs", names=names) - def _initialize_solver_directories(self): + def _initialize_working_directories(self): """ Creates directory structure expected by SPECFEM3D (bin/, DATA/) copies executables, and prepares input files. Executables must be supplied @@ -773,17 +757,17 @@ def _initialize_solver_directories(self): unix.ln(src, dst) if self.taskid == 0: - mainsolver = os.path.join(self.path.SOLVER, "mainsolver") # Symlink taskid_0 as mainsolver in solver directory for convenience - if not os.path.exists(mainsolver): - unix.ln(self.cwd, mainsolver) + if not os.path.exists(self.path_mainsolver): + unix.ln(self.cwd, self.path_mainsolver) logger.debug(f"symlink {self.source_name} as 'mainsolver'") else: # Copy the initial model from mainsolver into current directory # Avoids the need to run multiple instances of xgenerate_databases # TODO race condition if things havent been written? Sleep? - src = os.path.join(self.path.SOLVER, "mainsolver", "OUTPUT_FILES", - "DATABASES_MPI") + src = os.path.join( + self.path_mainsolver, "OUTPUT_FILES", "DATABASES_MPI" + ) dst = os.path.join(self.cwd, "OUTPUT_FILES", "DATABASES_MPI") unix.cp(src, dst) @@ -804,7 +788,7 @@ def _initialize_adjoint_traces(self): if self.par.PREPROCESS.upper() == "DEFAULT": if self.taskid == 0: logger.debug(f"intializing {len(self.data_filenames)} " - f"empty adjoint traces per event") + f"empty adjoint traces per event") for filename in self.data_filenames: st = preprocess.reader( @@ -833,7 +817,7 @@ def _check_source_names(self): wildcard = f"{self.source_prefix}_*" fids = sorted(glob(os.path.join(self.path_specfem_data, wildcard))) if not fids: - print(msg.cli("No matching source files when searching PATH for" + print(msg.cli("No matching source files when searching PATH for " "the given WILDCARD", items=[f"PATH: {self.path_specfem_data}", f"WILDCARD: {wildcard}"], header="error" @@ -844,6 +828,3 @@ def _check_source_names(self): # Create internal definition of sources names by stripping prefixes names = [os.path.basename(fid).split("_")[-1] for fid in fids] self._source_names = names[:self.ntask] - - - diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index eb4cc511..c5f66abe 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -40,9 +40,7 @@ def __init__(self, source_prefix="SOURCE", multiples=False, **kwargs): self._parameters.append("vp") self._parameters.append("vs") - self._acceptable_source_prefix = ["FORCE", "FORCESOLUTION"] - - + self._acceptable_source_prefix = ["SOURCE", "FORCE", "FORCESOLUTION"] def data_wildcard(self, comp="?"): """ @@ -61,21 +59,22 @@ def data_wildcard(self, comp="?"): elif self.data_format.upper() == "ASCII": return f"*.?X{comp}.sem?" - @property - def data_filenames(self): + def data_filenames(self, choice="obs"): """ Returns the filenames of all data, either by the requested components or by all available files in the directory. .. note:: If the glob returns an empty list, this function exits the - workflow because filenames should not be empty is they're being + workflow because filenames should not be empty is they're being queried :rtype: list :return: list of data filenames """ - unix.cd(os.path.join(self.cwd, "traces", "obs")) + assert(choice in ["obs", "syn", "adj"]), \ + f"choice must be: 'obs', 'syn' or 'adj'" + unix.cd(os.path.join(self.cwd, "traces", choice)) if self.components: filenames = [] @@ -134,16 +133,16 @@ def check(self): super().check() assert(self.source_prefix in self._acceptable_source_prefix) - - def _forward(self, output_path): + def forward_simulation(self, output_seismograms=False): """ Calls SPECFEM2D forward solver, exports solver outputs to traces dir - :type output_path: str - :param output_path: path to export traces to after completion of + :type output_seismograms: str + :param output_seismograms: path to export traces to after completion of simulation expected values are either 'traces/obs' for 'observation' data (i.e., synthetics generated by the TRUE model), or - 'traces/syn', for synthetics generated during function evaluations + 'traces/syn', for synthetics generated during function evaluations. + If False, will leave seismograms in OUTPUT_FILES/ """ unix.cd(self.cwd) @@ -159,10 +158,13 @@ def _forward(self, output_path): unix.rename(old=f"single_{tag}.su", new="single.su", names=glob(os.path.join("OUTPUT_FILES", "*.su"))) - unix.mv(src=glob(os.path.join("OUTPUT_FILES", self.data_wildcard())), - dst=output_path) + if output_seismograms: + unix.mv( + src=glob(os.path.join("OUTPUT_FILES", self.data_wildcard())), + dst=output_seismograms + ) - def _adjoint(self): + def adjoint_simulation(self): """ Calls SPECFEM2D adjoint solver, creates the `SEM` folder with adjoint traces which is required by the adjoint solver @@ -228,8 +230,6 @@ def smooth(self, input_path, output_path, parameters=None, span_h=0., parameters=parameters, span_h=span_h, span_v=span_v, output=output) - - def _initialize_adjoint_traces(self): """ Setup utility: Creates the "adjoint traces" expected by SPECFEM. diff --git a/seisflows/system/workstation.py b/seisflows/system/workstation.py index e394cf78..8b8e93a4 100644 --- a/seisflows/system/workstation.py +++ b/seisflows/system/workstation.py @@ -22,7 +22,7 @@ class Workstation: def __init__(self, title=None, mpiexec=None, ntask=1, nproc=1, log_level="DEBUG", verbose=False, path_output=None, path_system=None, path_output_log=None, path_error_log=None, - path_log_files=None, path_par_file=None): + path_log_files=None, path_par_file=None, **kwargs): """ Instantiate the Workstation base class diff --git a/seisflows/tests/test_data/test_solver/sources/CMTSOLUTION_001 b/seisflows/tests/test_data/test_solver/sources/CMTSOLUTION_001 new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/sources/CMTSOLUTION_001 @@ -0,0 +1 @@ + diff --git a/seisflows/tests/test_data/test_solver/sources/CMTSOLUTION_002 b/seisflows/tests/test_data/test_solver/sources/CMTSOLUTION_002 new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/sources/CMTSOLUTION_002 @@ -0,0 +1 @@ + diff --git a/seisflows/tests/test_data/test_solver/sources/SOURCE_001 b/seisflows/tests/test_data/test_solver/sources/SOURCE_001 new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/sources/SOURCE_001 @@ -0,0 +1 @@ + diff --git a/seisflows/tests/test_data/test_solver/sources/SOURCE_002 b/seisflows/tests/test_data/test_solver/sources/SOURCE_002 new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/sources/SOURCE_002 @@ -0,0 +1 @@ + diff --git a/seisflows/tests/test_solver.py b/seisflows/tests/test_solver.py new file mode 100644 index 00000000..d054d47c --- /dev/null +++ b/seisflows/tests/test_solver.py @@ -0,0 +1,11 @@ +""" +Test the ability of the Solver module to interact with various versions of +SPECFEM +""" +import os +import pytest +from glob import glob +from seisflows.tools.specfem import Model +from seisflows.config import ROOT_DIR, NAMES, CFGPATHS + + diff --git a/seisflows/workflow/base.py b/seisflows/workflow/base.py new file mode 100644 index 00000000..aa2f96c3 --- /dev/null +++ b/seisflows/workflow/base.py @@ -0,0 +1,80 @@ +#!/usr/bin/env python3 +""" +The simplest simulation workflow you can run is a large number of forward +simulations to generate synthetics from a velocity model. Therefore the +Forward class represents the BASE workflow. All other workflows will build off +of the scaffolding defined by the Forward class. +""" +from seisflows.config import import_seisflows + + +class Base(object): + """ + Defines the core Base object for all SeisFlows modules. All modules MUST + inherit from the Base object to work properly. This Base class essentially + dictates the required structure of a SeisFlows class. + """ + def __init__(self): + """ + SeisFlows instantiates its required parameters through the + SeisFlowsPathsParameters class, which scaffolds a rigid framework of + how parameters and paths should be defined by the program. This is + then used to build the parameter file dynamically. + """ + self.parameters = self.modules = import_seisflows() + (self.system, self.preprocess, self.solver, + self.postprocess, self.optimize) = self.modules + + def setup(self): + """ + + """ + + @property + def par(self): + """ + Quick access SeisFlows parameters from sys.modules. Throws a warning + if parameters have not been instantiated + + :rtype: Dict or None + :return: Returns a Dictionary with instantiated parameters, or None if + parameters have not been instantiated + """ + return self.module("parameters") + + @property + def path(self): + """ + Quick access SeisFlows paths from sys.modules. Throws a warning + if paths have not been instantiated + + :rtype: Dict or None + :return: Returns a Dictionary with instantiated paths, or None if + parameters have not been instantiated + """ + return self.module("paths") + + def check(self, validate=True): + """ + General check() function for each module to check the validity of the + user-input parameters and paths + """ + if validate: + self.required.validate() + + # Example of a check statement + # assert(self.par.PARAMETER == example_value), f"Parameter != example" + + def setup(self): + """ + A placeholder function for any initialization or setup tasks that + need to be run once at the beginning of any workflow. + """ + return + + def finalize(self): + """ + A placeholder function for any finalization or tear-down tasks that + need to be run at the end of any iteration or workflow. + """ + return \ No newline at end of file diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index a4d2aefa..373d2c11 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -9,23 +9,24 @@ import sys from glob import glob -from seisflows.core import Base from seisflows.tools import msg -from seisflows.config import save +from seisflows.config import save, import_seisflows from seisflows.tools.specfem import Model -class Forward(Base): +class Forward: """ Workflow abstract base class representing an en-masse forward solver and misfit calculator. """ - def __init__(self): + def __init__(self, save_traces=False, save_residuals=False, + path_eval_grad=None): """ - These parameters should not be set by the user. - Attributes are initialized as NoneTypes for clarity and docstrings. + En-masse forward simulation parameters """ - super().__init__() + self.save_traces = save_traces + self.save_residuals = save_residuals + self.path_eval_grad = self.path_eval_grad self.required.par( "SAVETRACES", required=False, default=False, par_type=bool, diff --git a/seisflows/workflow/forward_test.py b/seisflows/workflow/forward_test.py index ad002cd2..d346cf07 100644 --- a/seisflows/workflow/forward_test.py +++ b/seisflows/workflow/forward_test.py @@ -2,39 +2,48 @@ Test workflow to see if a new form of seisflows workflow can be used """ import os -from seisflows.core import Dict -from seisflows.config import custom_import, config_logger, NAMES -from seisflows.tools.wrappers import load_yaml -from seisflows.tools.specfem import Model - +from seisflows import logger +from seisflows.config import import_seisflows +from seisflows.tools import msg + +# Standard SeisFlows setup, makes modules global variables to the workflow +pars, modules = import_seisflows() +system, preprocess, solver, postprocess, optimize = modules + + +def evaluate_objective_function(path_model): + """ + Performs forward simulation for a single given event. Also evaluates the + objective function and writes residuals and adjoint sources for later tasks. + """ + if system.taskid == 0: + logger.info(msg.sub("EVALUATING OBJECTIVE FUNCTION")) + + # Run the forward simulation with the given input model + solver.import_model(path=path_model) + solver.forward_simulation( + output_seismograms=os.path.join(solver.cwd, "traces", "syn") + ) + + # Perform data-synthetic misfit quantification + if preprocess is not None: + preprocess.quantify_misfit( + observed=solver.data_filenames(choice="obs"), + synthetics=solver.data_filenames(choice="syn"), + output=os.path.join(solver.cwd, "traces", "adj") + ) if __name__ == "__main__": - # Standard SeisFlows Workflow setup block - # ========================================================================== - cwd = os.getcwd() - pars = load_yaml("parameters.yaml") - logger = config_logger(level=pars.log_level, filename=pars.path_log_file, - verbose=pars.verbose) - - # Dynamically create module instances, instantiated with parameters - classes = [custom_import(name, pars[name.upper()]) for name in NAMES] - instances = [cls(**pars) for cls in classes] - # Check that parameters have been set correctly - for instance in instances: - instance.check() - - # Distribute instances to their respective namesakes - system, preprocess, solver, postprocess, optimize, workflow = instances - # ========================================================================== - - - # Begin workflow - logger.info("Starting forward simulation workflow") + # Begin the forward simulation workflow + logger.info(msg.mjr("Starting forward simulation workflow")) + for module in modules: + module.setup() - m = Model(pars.path_model_init) - m.write(path=os.path.join(paths.eval_func, "model")) + # Run objective function evaluation NTASK times + system.run(evaluate_objective_function, path_model=pars.path_model_init, + suffix="new", system=system) - system.run(solver.eval_func) + logger.info(msg.mjr("Finished forward simulation workflow")) From 67749849bd1e1f5b7d413f4cb446191638740629 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Tue, 12 Jul 2022 16:17:25 -0800 Subject: [PATCH 063/195] refactoring solver.specfem to be more pythonic, moving a lot of the logic contained in the solver module into the workflow module to keep things more isolated. --- seisflows/preprocess/default.py | 17 + seisflows/solver/specfem.py | 666 +++++++++++++++++------------ seisflows/solver/specfem2d.py | 142 +----- seisflows/solver/specfem3d.py | 50 +-- seisflows/workflow/forward_test.py | 12 +- 5 files changed, 451 insertions(+), 436 deletions(-) diff --git a/seisflows/preprocess/default.py b/seisflows/preprocess/default.py index 4a46ffdd..35fc0f0b 100644 --- a/seisflows/preprocess/default.py +++ b/seisflows/preprocess/default.py @@ -207,6 +207,23 @@ def finalize(self): """ pass + def initialize_adjoint_traces(self, filenames=None): + """ + TO DO + """ + for filename in self.data_filenames: + st = self.preprocess.reader(path=os.path.join(self.cwd, "traces", "obs"), + filename=filename + ) + # Zero out data just so we have empty adjoint traces as SPECFEM + # will expect all adjoint sources to have all components + st *= 0 + + # Write traces back to the adjoint trace directory + preprocess.writer(st=st, filename=filename, + path=os.path.join(self.cwd, "traces", "adj") + ) + def quantify_misfit(self, observed, synthetic, write_residuals=False, write_adjsrcs=False, **kwargs): """ diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 67422547..8bf71d4e 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -4,6 +4,9 @@ such as SPECFEM (2D/3D/3D_GLOBE). This SPECFEM base class provides general functions that work with all versions of SPECFEM. Subclasses will provide additional capabilities unique to each version of SPECFEM. + +TODO add in `apply_hess` functionality that was partially written in legacy code +TODO move `_initialize_adjoint_traces` to workflow.migration """ import os import sys @@ -11,71 +14,34 @@ from glob import glob from seisflows import logger +from seisflows.core import Dict from seisflows.plugins import solver_io as solver_io_dir from seisflows.tools import msg, unix -from seisflows.tools.specfem import getpar, Model +from seisflows.tools.specfem import getpar, setpar class Specfem: """ This base class provides an interface through which solver simulations can be set up and run. It should not be used by itself, but rather it is meant - to provide the foundation for the following child classes: + to provide the foundation for: SPECFEM2D/3D/3D_GLOBE - SPECFEM2D - SPECFEM3D - SPECFEM3D_GLOBE + .. note:: + This Base class implementation is almost completely SPECFEM2D related. + However, SPECFEM2D requires a few unique parameters that 3D/3D_GLOBE + do not. Because of the inheritance architecture of SeisFlows, we do not + want the 3D and 3D_GLOBE versions to inherit 2D-specific parameters, so + we need this this more generalized SPECFEM base class. .. note::: This class supports only acoustic and isotropic elastic inversions. - - Function descriptors: - - eval_func, eval_grad, apply_hess - - These methods deal with evaluation of the misfit function or its - derivatives. Together, they provide the primary interface through which - SeisFlows interacts with SPECFEM2D/3D - - forward, adjoint - - These methods allow direct access to low-level SPECFEM2D/3D components, - providing an alternative interface through which to interact with the - solver - - steup, generate_data, generate_model - - One-time operations performed at the beginning of inversion or migration - - initialize_solver_directories, initialize_adjoint_traces - - SPECFEM2D/3D requires a particular directory structure in which to run - and particular file formats for models, data, and parameter files. These - methods help put in place all these prerequisites - - combine, smooth - - Utilities for combining and smoothing kernels - - generate_data, generate_mesh, eval_fwd, forward, adjoint, - data_filenames, model_databases, kernel_databases, source_prefix - - !!! Required functions which must be implemented by subclass !!! - """ def __init__(self, case="data", data_format="ascii", materials="elastic", density=False, nproc=1, ntask=1, attenuation=False, components="ZNE", solver_io="fortran_binary", mpiexec=None, - path_solver=os.path.join(os.getcwd(), "scratch", "solver"), - path_data=os.path.join(os.getcwd(), "SFDATA"), - path_specfem_bin=os.path.join(os.getcwd(), "specfem", "bin"), - path_specfem_data=os.path.join(os.getcwd(), "specfem", "DATA"), - path_model_init=os.path.join(os.getcwd(), "specfem", - "MODEL_INIT"), - path_model_true=os.path.join(os.getcwd(), "specfem", - "MODEL_TRUE"), - path_output=os.path.join(os.getcwd(), "output"), - save_traces=False, **kwargs): + path_solver=None, path_data=None, path_specfem_bin=None, + path_specfem_data=None, path_model_init=None, + path_model_true=None, path_output=None, **kwargs): """ SPECFEM Solver parameters @@ -97,11 +63,11 @@ def __init__(self, case="data", data_format="ascii", materials="elastic", :type components: str :param components: components to consider and tag data with. Should be string of letters such as 'RTZ' - :type path_specfem_bin: str - :param path_specfem_bin: path to SPECFEM bin/ directory which + :type path.specfem_bin: str + :param path.specfem_bin: path to SPECFEM bin/ directory which contains binary executables for running SPECFEM - :type path_specfem_data: str - :param path_specfem_data: path to SPECFEM DATA/ directory which must + :type path.specfem_data: str + :param path.specfem_data: path to SPECFEM DATA/ directory which must contain the CMTSOLUTION, STATIONS and Par_file files used for running SPECFEM @@ -127,16 +93,17 @@ def __init__(self, case="data", data_format="ascii", materials="elastic", self.mpiexec = mpiexec # Define internally used directory structure - self.path = path_solver - self.path_data = path_data - self.path_specfem_bin = path_specfem_bin - self.path_specfem_data = path_specfem_data - self.path_model_init = path_model_init - self.path_model_true = path_model_true - self.path_output = path_output - self.path_mainsolver = os.path.join(self.path, "mainsolver") - - self.save_traces = save_traces + _cwd = os.getcwd() + self.path = Dict( + scratch=path_solver or os.path.join(_cwd, "scratch", "solver"), + data=path_data or os.path.join(_cwd, "SFDATA"), + output=path_output or os.path.join(_cwd, "output"), + mainsolver=os.path.join(_cwd, "scratch", "mainsolver"), + specfem_bin=path_specfem_bin, + specfem_data=path_specfem_data, + model_init=path_model_init, + model_true=path_model_true, + ) # Establish internally defined parameter system self._parameters = [] @@ -153,9 +120,12 @@ def __init__(self, case="data", data_format="ascii", materials="elastic", "ISOTROPIC", "ANISOTROPIC" # specfem3d_globe ] self._available_data_formats = ["ASCII", "SU"] + self._required_binaries = ["xspecfem2d", "xmeshfem2d", "xcombine_sem", + "xsmooth_sem"] - # Parameters that NEED to be filled out by child classes + # These are parameters that need to be established by child classes self.source_prefix = None + self._acceptable_source_prefixes = [] @property def taskid(self): @@ -165,12 +135,18 @@ def taskid(self): Task IDs are simply integer values from 0 to the number of simultaneously running tasks. + .. note:: + Dependent on environment variable 'SEISFLOWS_TASKID' which is + assigned by system.run() to each individually running process. + :rtype: int :return: task id for given solver """ _taskid = os.getenv("SEISFLOWS_TASKID") if _taskid is None: _taskid = 0 + logger.warning("Environment variable 'SEISFLOWS_TASKID' not found. " + "Assigning Task ID == 0") return int(_taskid) @property @@ -180,11 +156,15 @@ def source_names(self): Source names are expected to match the following wildcard, 'PREFIX_*' where PREFIX is something like 'CMTSOLUTION' or 'FORCE' + .. note:: + Dependent on environment variable 'SEISFLOWS_TASKID' which is + assigned by system.run() to each individually running process. + :rtype: list :return: list of source names """ if self._source_names is None: - self._check_source_names() + self._source_names = self._check_source_names() return self._source_names @property @@ -192,6 +172,10 @@ def source_name(self): """ Returns name of source currently under consideration + .. note:: + Dependent on environment variable 'SEISFLOWS_TASKID' which is + assigned by system.run() to each individually running process. + :rtype: str :return: given source name for given task id """ @@ -202,6 +186,10 @@ def cwd(self): """ Returns working directory currently in use by a running solver instance + .. note:: + Dependent on environment variable 'SEISFLOWS_TASKID' which is + assigned by system.run() to each individually running process. + :rtype: str :return: current solver working directory """ @@ -209,48 +197,98 @@ def cwd(self): def data_wildcard(self, comp="?"): """ - Provide a wildcard string that will match the name of the output - synthetic seismograms + Returns a wildcard identifier for synthetic data based on SPECFEM2D + file naming schema. Allows formatting dcomponent e.g., + when called by solver.data_filenames. + + .. note:: + SPECFEM3D/3D_GLOBE versions must overwrite this function :type comp: str - :param comp: single letter defining the component that can be inserted - into the wildcard. Defaults to '?' + :param comp: component formatter, defaults to wildcard '?' :rtype: str - :return: a wildcard string that can be used to search for data + :return: wildcard identifier for channels """ - return NotImplementedError("must be implemented by child class") + if self.data_format.upper() == "SU": + return f"U{comp}_file_single.su" + elif self.data_format.upper() == "ASCII": + return f"*.?X{comp}.sem?" - @property - def data_filenames(self): + def data_filenames(self, choice="obs"): """ - A list of waveform files matching the `data_wildcard` which is used - to keep track of data + Returns the filenames of SPECFEM2D data, either by the requested + components or by all available files in the directory. + + .. note:: + SPECFEM3D/3D_GLOBE versions must overwrite this function + + .. note:: + If the glob returns an empty list, this function exits the + workflow because filenames should not be empty is they're being + queried :rtype: list :return: list of data filenames """ - return NotImplementedError("must be implemented by child class") + assert(choice in ["obs", "syn", "adj"]), \ + f"choice must be: 'obs', 'syn' or 'adj'" + unix.cd(os.path.join(self.cwd, "traces", choice)) + + filenames = [] + if self.components: + for comp in self.components: + filenames = glob(self.data_wildcard(comp=comp.lower())) + else: + filenames = glob(self.data_wildcard(comp="?")) + + if not filenames: + print(msg.cli("The property solver.data_filenames, used to search " + "for traces in 'scratch/solver/*/traces' is empty " + "and should not be. Please check solver parameters: ", + items=[f"data_wildcard: {self.data_wildcard()}"], + header="data filenames error", border="=") + ) + sys.exit(-1) + + return filenames @property def model_databases(self): """ - SPECFEM directory where model database files are saved. This directory - is SPECFEM version dep. + The location of model inputs and outputs as defined by SPECFEM2D. + This is RELATIVE to a SPECFEM2D working directory. + + .. note:: + This path is SPECFEM version dependent so SPECFEM3D/3D_GLOBE + versions must overwrite this function + + :rtype: str + :return: path where SPECFEM2D database files are stored """ - return NotImplementedError("must be implemented by child class") + return "DATA" @property def kernel_databases(self): """ - SPECFEM directory where kernel database files are saved. This directory - is SPECFEM version dep. + The location of kernel inputs and outputs as defined by SPECFEM2D + This is RELATIVE to a SPECFEM2D working directory. + + .. note:: + This path is SPECFEM version dependent so SPECFEM3D/3D_GLOBE + versions must overwrite this function + + :rtype: str + :return: path where SPECFEM2D database files are stored """ - return NotImplementedError("must be implemented by child class") + return "OUTPUT_FILES" def check(self): """ - Checks parameters and paths + Checks parameter validity for SPECFEM input files and model parameters """ + assert(self.case.upper() in ["DATA", "SYNTHETIC"]), \ + f"solver.case must be 'DATA' or 'SYNTHETIC'" + assert(self.materials.upper() in self._available_materials), \ f"solver.materials must be in {self._available_materials}" @@ -259,36 +297,113 @@ def check(self): f"solver.data_format must be {self._available_data_formats}" ) + # Make sure we can read in the model/kernel/gradient files assert hasattr(solver_io_dir, self.solver_io) assert hasattr(self._io, "read_slice"), \ "IO method has no attribute 'read'" assert hasattr(self._io, "write_slice"), \ "IO method has no attribute 'write'" - # TODO Check path data, model_true and case combination - # TODO Check SPECFEM_DATA available files - # TODO Check SPECFEM_BIN available executables + # Check that User has provided appropriate bin/ and DATA/ directories + for name, dir_ in zip(["bin/", "DATA/"], + [self.path.specfem_bin, self.path.specfem_data]): + assert(dir_ is not None), f"SPECFEM path '{name}' cannot be None" + assert(os.path.exists(dir_)), f"SPECFEM path '{name}' must exist" + + # Check that the required SPECFEM files are available + for fid in [self.source_prefix, "STATIONS", "Par_file"]: + assert(os.path.exists(os.path.join(self.path.specfem_data, fid))), ( + f"DATA/{fid} does not exist but is required by SeisFlows solver" + ) + + # Check that required binary files exist which are called upon by solver + for fid in self._required_binaries: + assert(os.path.exists(os.path.join(self.path.specfem_bin, fid))), ( + f"bin/{fid} does not exist but is required by SeisFlows solver" + ) + + # Check that the 'case' variable matches required models + if self.case.upper() == "SYNTHETIC": + assert(os.path.exists(self.path.model_true)), ( + f"solver.case == 'synthetic' requires `path.model_true`" + ) + + # Make sure source files exist and are appropriately labeled + assert(self.source_prefix in self._acceptable_source_prefixes) + assert(glob(os.path.join(self.path.specfem_data, + f"{self.source_prefix}*"))), ( + f"No source files with prefix {self.source_prefix} found in DATA/") + + # Check that model type is set correctly in the Par_file + model_type = getpar(key="MODEL", + file=os.path.join(self.path.specfem_data, + "Par_file"))[1] + assert(model_type in self._available_model_types), \ + f"{model_type} not in available types {self._available_model_types}" def setup(self): """ - Prepares solver for inversion or migration. + Prepares solver scratch directories for an impending workflow. + Sets up directory structure expected by SPECFEM and copies or generates seismic data to be inverted or migrated - .. note:; - As input for an inversion or migration, users can choose between - providing data, or providing a target model from which data are - generated on the fly. - In the former case, a value for PATH.DATA must be supplied; - in the latter case, a value for PATH.MODEL_TRUE must be provided. + TODO the .bin during model export assumes GLL file format, more general? """ self._initialize_working_directories() - self._import_starting_model(path_model=self.path_model_init, - model_type="gll") - self._export_model() - self._initialize_adjoint_traces() - def generate_data(self): + # Export the initial model to the SeisFlows output directory + unix.mkdir(self.path.output) + for key in self._parameters: + src = glob(os.path.join(self.path.model_init, f"*{key}.bin")) + dst = os.path.join(self.path.output, "MODEL_INIT", "") + unix.cp(src, dst) + + # TODO move this into workflow.migration + # self._initialize_adjoint_traces() + + # def generate_data(self, save_traces=False): + # """ + # Generates observation data to be compared to synthetics. This must + # only be run once. If `PAR.CASE`=='data', then real data will be copied + # over. + # TODO move this to workflow + # + # If `PAR.CASE`=='synthetic' then the external solver will use the + # True model to generate 'observed' synthetics. Finally exports traces to + # 'cwd/traces/obs' + # + # Elif `PAR.CASE`=='DATA', will look in PATH.DATA for directories matching + # the given source name and copy ANY files that exist there. e.g., if + # source name is '001', you must store waveform data in PATH.DATA/001/* + # + # Also exports observed data to OUTPUT if desired + # """ + # # If synthetic inversion, generate 'data' with solver + # if self.case.upper() == "SYNTHETIC": + # if self.path.model_true is not None: + # if self.taskid == 0: + # logger.info("generating 'data' with MODEL_TRUE") + # + # # Generate synthetic data on the fly using the true model + # self.import_model(path_model=self.path.model_true) + # self.forward_simulation( + # save_traces=os.path.join("traces", "obs") + # ) + # # If Data provided by user, copy directly into the solver directory + # elif self.path.data is not None and os.path.exists(self.path.data): + # unix.cp( + # src=glob(os.path.join(self.path.data, self.source_name, "*")), + # dst=os.path.join(self.cwd, "traces", "obs") + # ) + # + # # Save observation data to disk + # if save_traces: + # self._export_traces( + # path=os.path.join(self.path.output, "traces", "obs") + # ) + + def generate_data(self, export_traces=False): """ Generates observation data to be compared to synthetics. This must only be run once. If `PAR.CASE`=='data', then real data will be copied @@ -302,29 +417,40 @@ def generate_data(self): the given source name and copy ANY files that exist there. e.g., if source name is '001', you must store waveform data in PATH.DATA/001/* - Also exports observed data to OUTPUT if desired + :type export_traces: str + :param export_traces: path to copy and save traces to a more permament + storage location as waveform stored in scratch/ are liable to be + deleted or overwritten """ - # If synthetic inversion, generate 'data' with solver - if self.case.upper() == "SYNTHETIC": - if self.path_model_true is not None: - if self.taskid == 0: - logger.info("generating 'data' with MODEL_TRUE") - # Generate synthetic data on the fly using the true model - self._import_starting_model(path_model=self.path_model_true, - model_type="gll") - self._forward(output_path=os.path.join("traces", "obs")) - # If Data provided by user, copy directly into the solver directory - elif self.path_data is not None and os.path.exists(self.path_data): - unix.cp( - src=glob(os.path.join(self.path_data, self.source_name, "*")), - dst=os.path.join("traces", "obs") - ) + # Basic checks to make sure there are True model files to copy + assert(self.case.upper() == "SYNTHETIC") + assert(os.path.exists(self.path.model_true)) + assert(glob(os.path.join(self.path.model_true, "*"))) - # Save observation data to disk - if self.save_traces: - self._export_traces( - path=os.path.join(self.path_output, "traces", "obs") - ) + # Generate synthetic data on the fly using the true model + self.import_model(path_model=self.path.model_true) + self.forward_simulation( + save_traces=os.path.join(self.cwd, "traces", "obs"), + export_traces=export_traces + ) + + def import_data(self, path_data): + """ + Import data from an existing directory into the current working + directory, required if 'observed' waveform data will be provided by + the User rather than automatically collected (with Pyatoa) or generated + synthetically (with external solver) + """ + # Simple checks to make sure we can actually import data + assert(self.case.upper() == "DATA") + assert(self.path.data is not None) + assert(os.path.exists(os.path.join(self.path.data, self.source_name))) + assert(glob(os.path.join(self.path.data, self.source_name, "*"))) + + src = os.path.join(self.path.data, self.source_name, "*") + dst = os.path.join(self.cwd, "traces", "obs") + + unix.cp(src, dst) def eval_func(self, path, preprocess=None): """ @@ -400,48 +526,73 @@ def eval_grad(self, path, export_traces=False): self._export_traces(path=os.path.join(path, "traces", "adj"), prefix="traces/adj") - # def apply_hess(self, path): - # """ - # High level solver interface that computes action of Hessian on a given - # model vector. A gradient evaluation must have already been carried out. - # - # TODO preprocess has no function prepare_apply_hess() - # - # :type path: str - # :param path: directory to which output files are exported - # """ - # raise NotImplementedError("must be implemented by solver subclass") - # - # unix.cd(self.cwd) - # self.import_model(path) - # unix.mkdir("traces/lcg") - # - # self.forward("traces/lcg") - # preprocess.prepare_apply_hess(self.cwd) - # self.adjoint() - # self.export_kernels(path) - - def forward_simulation(self, output_path): + def forward_simulation(self, save_traces=False, export_traces=False): """ - Calls forward solver with the appropriate parameters in the Par_file set - Also exports data to the correct output_path + Calls SPECFEM2D forward solver, exports solver outputs to traces dir - :type output_seismograms: str - :param output_seismograms: path to export traces to after completion of - simulation expected values are either 'traces/obs' for 'observation' - data (i.e., synthetics generated by the TRUE model), or - 'traces/syn', for synthetics generated during function evaluations. - If False, will leave seismograms in OUTPUT_FILES/ + .. note:: + SPECFEM3D/3D_GLOBE versions must overwrite this function + + :type save_traces: str + :param save_traces: move files from their native SPECFEM output location + to another directory. This is used to move output waveforms to + 'traces/obs' or 'traces/syn' so that SeisFlows knows where to look + for them, and so that SPECFEM doesn't overwrite existing files + during subsequent forward simulations + :type export_traces: str + :param export_traces: export traces from the scratch directory to a more + permanent storage location. i.e., copy files from their original + location """ - raise NotImplementedError("must be implemented by solver subclass") + unix.cd(self.cwd) + + setpar(key="SIMULATION_TYPE", val="1", file="DATA/Par_file") + setpar(key="SAVE_FORWARD", val=".true.", file="DATA/Par_file") + + self._call_solver(executable="bin/xmeshfem2D", output="fwd_mesher.log") + self._call_solver(executable="bin/xspecfem2D", output="fwd_solver.log") + + # Work around SPECFEM2D's version dependent file names + if self.data_format.upper() == "SU": + for tag in ["d", "v", "a", "p"]: + unix.rename(old=f"single_{tag}.su", new="single.su", + names=glob(os.path.join("OUTPUT_FILES", "*.su"))) + + if export_traces: + unix.cp( + src=glob(os.path.join("OUTPUT_FILES", self.data_wildcard())), + dst=export_traces + ) + + if save_traces: + unix.mv( + src=glob(os.path.join("OUTPUT_FILES", self.data_wildcard())), + dst=save_traces + ) def adjoint_simulation(self): """ - Calls adjoint solver with the appropriate parameters in the Par_file set - Also takes care of setting up the SEM/ directory where SPECFEM expects - adjoint sources to be + Calls SPECFEM2D adjoint solver, creates the `SEM` folder with adjoint + traces which is required by the adjoint solver. + + .. note:: + SPECFEM3D/3D_GLOBE versions must overwrite this function """ - raise NotImplementedError("must be implemented by solver subclass") + unix.cd(self.cwd) + + setpar(key="SIMULATION_TYPE", val="3", file="DATA/Par_file") + setpar(key="SAVE_FORWARD", val=".false.", file="DATA/Par_file") + + unix.rm("SEM") + unix.ln("traces/adj", "SEM") + + # Deal with different SPECFEM2D name conventions for regular traces and + # "adjoint" traces + if self.data_format.upper == "SU": + unix.rename(old=".su", new=".su.adj", + names=glob(os.path.join("traces", "adj", "*.su"))) + + self._call_solver(executable="bin/xspecfem2D", output="adj_solver.log") def _call_solver(self, executable, output="solver.log"): """ @@ -525,23 +676,16 @@ def combine(self, input_path, output_path, parameters=None): # Call on xcombine_sem to combine kernels into a single file for name in parameters: - # e.g.: mpiexec ./bin/xcombine_sem alpha_kernel kernel_paths output - self._call_solver(executable=" ".join([f"bin/xcombine_sem", - f"{name}_kernel", - "kernel_paths", - output_path] - ) - ) + # e.g.: mpiexec bin/xcombine_sem alpha_kernel kernel_paths output/ + exc = f"bin/xcombine_sem {name}_kernel kernel_paths {output_path}" + self._call_solver(executable=exc) def smooth(self, input_path, output_path, parameters=None, span_h=0., - span_v=0., output="smooth.log"): + span_v=0., use_gpu=False, output="smooth.log"): """ Postprocessing wrapper: xsmooth_sem Smooths kernels by convolving them with a Gaussian. - .. note:: - paths require a trailing `/` character when calling xsmooth_sem - .. note:: It is ASSUMED that this function is being called by system.run(single=True) so that we can use the main solver @@ -560,6 +704,9 @@ def smooth(self, input_path, output_path, parameters=None, span_h=0., :param span_v: vertical smoothing length in meters :type output: str :param output: file to output stdout to + :type use_gpu: bool + :param use_gpu: whether to use GPU acceleration for smoothing. Requires + GPU compiled binaries and GPU compute node. """ unix.cd(self.cwd) @@ -569,77 +716,48 @@ def smooth(self, input_path, output_path, parameters=None, span_h=0., if not os.path.exists(output_path): unix.mkdir(output_path) + # Ensure trailing '/' character, required by xsmooth_sem + input_path = os.path.join(input_path, "") + output_path = os.path.join(output_path, "") + if use_gpu: + use_gpu = ".true" + else: + use_gpu = ".false" # mpiexec ./bin/xsmooth_sem SMOOTH_H SMOOTH_V name input output use_gpu for name in parameters: - self._call_solver(executable=" ".join(["bin/xsmooth_sem", - str(span_h), str(span_v), - f"{name}_kernel", - os.path.join(input_path, ""), - os.path.join(output_path, ""), - ".false"]), - output=output) - - # Rename output files + exc = (f"bin/xsmooth_sem {str(span_h)} {str(span_v)} {name}_kernel " + f"{input_path} {output_path} {use_gpu}") + self._call_solver(executable=exc, output=output) + + # Rename output files to remove the '_smooth' suffix which SeisFlows + # will not recognize files = glob(os.path.join(output_path, "*")) unix.rename(old="_smooth", new="", names=files) - def _import_model(self, path): + def import_model(self, path_model): """ - File transfer utility to move a SPEFEM2D model into the correct location - for a workflow. - - :type path: str - :param path: path to the SPECFEM2D model - :return: - """ - unix.cp(src=glob(os.path.join(path, "model", "*")), - dst=os.path.join(self.cwd, "DATA") - ) - - def _import_starting_model(self, path_model, model_type=None, save_as=None): - """ - Mesh and database files should have been created during the manual set - up phase. This function simply checks the mesh properties of that mesh - and ensures that it is locatable by future SeisFlows processes. + Copy files from given `path_model` into the current working directory + model database :type path_model: str :param path_model: path to an existing starting model - :type model_type: str - :param model_type: available model types to be passed to the Specfem3D - Par_file. See Specfem3D Par_file for available options. """ - unix.cd(self.cwd) - # Check type/existence of model. SeisFlows only accepts GLL models - model_type = model_type or getpar(key="MODEL", file="DATA/Par_file")[1] - assert(model_type in self._available_model_types), \ - f"{model_type} not in available types {self._available_model_types}" assert(os.path.exists(path_model)), f"model {path_model} does not exist" + unix.cd(self.cwd) - if model_type == "gll": - # Copy the model files (ex: proc000023_vp.bin ...) into database dir - src = glob(os.path.join(path_model, "*")) - dst = self.model_databases - unix.cp(src, dst) - - def _import_traces(self, path): - """ - File transfer utility. Import traces into the workflow. - - :type path: str - :param path: path to traces - """ - src = glob(os.path.join(path, 'traces', self.source_name, '*')) - dst = os.path.join(self.cwd, 'traces', 'obs') + # Copy the model files (ex: proc000023_vp.bin ...) into database dir + src = glob(os.path.join(path_model, "*")) + dst = os.path.join(self.cwd, self.model_databases, "") unix.cp(src, dst) def _export_model(self): """ File transfer utility. Export the model to disk. Run from master solver. """ - unix.mkdir(self.path_output) + unix.mkdir(self.path.output) for key in self._parameters: files = glob(os.path.join(self.model_databases, f"*{key}.bin")) - unix.cp(files, self.path_output) + unix.cp(files, self.path.output) def _export_kernels(self): """ @@ -651,7 +769,7 @@ def _export_kernels(self): self._rename_kernels() src = glob("*_kernel.bin") - dst = os.path.join(self.path_output, "kernels", self.source_name) + dst = os.path.join(self.path.output, "kernels", self.source_name) unix.mkdir(dst) unix.mv(src, dst) @@ -699,7 +817,8 @@ def _export_traces(self, path, prefix="traces/obs"): dst = os.path.join(path, self.source_name) unix.cp(src, dst) - def _rename_kernels(self): + @staticmethod + def _rename_kernels(): """ Works around conflicting kernel filename conventions by renaming `alpha` to `vp` and `beta` to `vs` @@ -716,60 +835,79 @@ def _rename_kernels(self): def _initialize_working_directories(self): """ - Creates directory structure expected by SPECFEM3D (bin/, DATA/) copies - executables, and prepares input files. Executables must be supplied - by user as there is no mechanism for automatically compiling from source + Serial task used to initialize working directories for each of the a + available sources - Directories will act as completely independent Specfem run directories. - This allows for embarrassing parallelization while avoiding the need - for intra-directory communications, at the cost of redundancy and - extra files. + TODO run this with concurrent futures for speedup? """ - if self.taskid == 0: - logger.info(f"initializing {self.ntask} solver directories") + logger.info(f"initializing {self.ntask} solver directories") + for source_name in self.source_names: + cwd = os.path.join(self.path, source_name) + self._initialize_working_directory(cwd=cwd) - unix.rm(self.cwd) - unix.mkdir(self.cwd) - unix.cd(self.cwd) + def _initialize_working_directory(self, cwd=None): + """ + Creates directory structure expected by SPECFEM + (i.e., bin/, DATA/, OUTPUT_FILES/). Copies executables and prepares + input files. + + Each directory will act as completely independent Specfem working dir. + This allows for embarrassing parallelization while avoiding the need + for intra-directory communications, at the cost of temporary disk space. - # Create directory structure - for cwd_dir in ["bin", "DATA", "OUTPUT_FILES/DATABASES_MPI", - "traces/obs", "traces/syn", "traces/adj", - self.model_databases, self.kernel_databases]: - unix.mkdir(cwd_dir) + .. note:: + Path to binary executables must be supplied by user as SeisFlows has + no mechanism for automatically compiling from source code. + + :type cwd: str + :param cwd: optional scratch working directory to intialize. If None, + will set based on current running seisflows task (self.taskid) + """ + # Define a constant list of required SPECFEM dir structure, relative cwd + _required_structure = ["bin", "DATA", + "traces/obs", "traces/syn", "traces/adj", + self.model_databases, self.kernel_databases] + + # Allow this function to be called on system or in serial + if cwd is None: + cwd = self.cwd + _source_name = os.path.basename(cwd) + taskid = self.source_names.index(_source_name) + else: + cwd = self.cwd + taskid = self.taskid + + if taskid == 0: + logger.info(f"initializing {self.ntask} solver directories") - # Copy exectuables into the bin/ directory - src = glob(os.path.join(self.path_specfem_bin, "*")) - dst = os.path.join("bin", "") + # Starting from a fresh working directory + unix.rm(cwd) + unix.mkdir(cwd) + for dir_ in _required_structure: + unix.mkdir(os.path.join(cwd, dir_)) + + # Copy existing SPECFEM exectuables into the bin/ directory + src = glob(os.path.join(self.path.specfem_bin, "*")) + dst = os.path.join(cwd, "bin", "") unix.cp(src, dst) - # Copy all input files except source files - src = glob(os.path.join(self.path_specfem_data, "*")) + # Copy all input DATA/ files except the source files + src = glob(os.path.join(self.path.specfem_data, "*")) src = [_ for _ in src if self.source_prefix not in _] dst = os.path.join("DATA", "") unix.cp(src, dst) - # Symlink event source specifically, strip the source name as SPECFEM - # just expects `source_name` - src = os.path.join(self.path_specfem_data, + # Symlink event source specifically, only retain source prefix + src = os.path.join(self.path.specfem_data, f"{self.source_prefix}_{self.source_name}") dst = os.path.join("DATA", self.source_prefix) unix.ln(src, dst) - if self.taskid == 0: - # Symlink taskid_0 as mainsolver in solver directory for convenience - if not os.path.exists(self.path_mainsolver): - unix.ln(self.cwd, self.path_mainsolver) + # Symlink TaskID==0 as mainsolver in solver directory for convenience + if taskid == 0: + if not os.path.exists(self.path.mainsolver): logger.debug(f"symlink {self.source_name} as 'mainsolver'") - else: - # Copy the initial model from mainsolver into current directory - # Avoids the need to run multiple instances of xgenerate_databases - # TODO race condition if things havent been written? Sleep? - src = os.path.join( - self.path_mainsolver, "OUTPUT_FILES", "DATABASES_MPI" - ) - dst = os.path.join(self.cwd, "OUTPUT_FILES", "DATABASES_MPI") - unix.cp(src, dst) + unix.ln(cwd, self.path.mainsolver) def _initialize_adjoint_traces(self): """ @@ -806,20 +944,21 @@ def _initialize_adjoint_traces(self): def _check_source_names(self): """ - Determines names of sources by applying wildcard rule to - user-supplied input files + Determines names of sources by applying wildcard rule to user-supplied + input files. Source names are only provided up to PAR.NTASK and are + returned in alphabetical order. - .. note:: - Source list is sorted and collected from start up to PAR.NTASK + :rtype: list + :return: alphabetically ordered list of source names up to PAR.NTASK """ # Apply wildcard rule and check for available sources, exit if no # sources found because then we can't proceed wildcard = f"{self.source_prefix}_*" - fids = sorted(glob(os.path.join(self.path_specfem_data, wildcard))) + fids = sorted(glob(os.path.join(self.path.specfem_data, wildcard))) if not fids: print(msg.cli("No matching source files when searching PATH for " "the given WILDCARD", - items=[f"PATH: {self.path_specfem_data}", + items=[f"PATH: {self.path.specfem_data}", f"WILDCARD: {wildcard}"], header="error" ) ) @@ -827,4 +966,5 @@ def _check_source_names(self): # Create internal definition of sources names by stripping prefixes names = [os.path.basename(fid).split("_")[-1] for fid in fids] - self._source_names = names[:self.ntask] + + return names[:self.ntask] diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index c5f66abe..cd4871f1 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -6,11 +6,10 @@ Specfem2D. It inherits all attributes from seisflows.solver.Base, """ import os -import sys from glob import glob from seisflows.solver.specfem import Specfem -from seisflows.tools import unix, msg +from seisflows.tools import unix from seisflows.tools.specfem import getpar, setpar @@ -31,7 +30,7 @@ def __init__(self, source_prefix="SOURCE", multiples=False, **kwargs): self.source_prefix = source_prefix self.multiples = multiples - self._f0 = None + self.f0 = None # Define parameters based on material type if self.materials.upper() == "ACOUSTIC": @@ -42,76 +41,6 @@ def __init__(self, source_prefix="SOURCE", multiples=False, **kwargs): self._acceptable_source_prefix = ["SOURCE", "FORCE", "FORCESOLUTION"] - def data_wildcard(self, comp="?"): - """ - Returns a wildcard identifier for synthetic data based on SPECFEM2D - file naming schema. Allows formatting dcomponent e.g., - when called by solver.data_filenames - - :type comp: str - :param comp: component formatter, defaults to wildcard '?' - :rtype: str - :return: wildcard identifier for channels - """ - if self.data_format.upper() == "SU": - # return f"*.su" # too vague but maybe for a reason? -bryant - return f"U{comp}_file_single.su" - elif self.data_format.upper() == "ASCII": - return f"*.?X{comp}.sem?" - - def data_filenames(self, choice="obs"): - """ - Returns the filenames of all data, either by the requested components - or by all available files in the directory. - - .. note:: - If the glob returns an empty list, this function exits the - workflow because filenames should not be empty is they're being - queried - - :rtype: list - :return: list of data filenames - """ - assert(choice in ["obs", "syn", "adj"]), \ - f"choice must be: 'obs', 'syn' or 'adj'" - unix.cd(os.path.join(self.cwd, "traces", choice)) - - if self.components: - filenames = [] - if self.data_format.upper() == "SU": - for comp in self.components: - filenames += [self.data_wildcard(comp=comp.lower())] - elif self.data_format.upper() == "ASCII": - for comp in self.components: - filenames += glob(self.data_wildcard(comp=comp.upper())) - else: - filenames = glob(self.data_wildcard()) - - if not filenames: - print(msg.cli("The property solver.data_filenames, used to search " - "for traces in 'scratch/solver/*/traces' is empty " - "and should not be. Please check solver parameters: ", - items=[f"data_wildcard: {self.data_wildcard()}"], - header="data filenames error", border="=") - ) - sys.exit(-1) - - return filenames - - @property - def model_databases(self): - """ - The location of model inputs and outputs as defined by SPECFEM2D - """ - return os.path.join(self.cwd, "DATA") - - @property - def kernel_databases(self): - """ - The location of kernel inputs and outputs as defined by SPECFEM2D - """ - return os.path.join(self.cwd, "OUTPUT_FILES") - def setup(self): """ Additional SPECFEM2D setup steps @@ -126,67 +55,8 @@ def setup(self): else: setpar(key="absorbtop", val=".true.", file="DATA/Par_file") - def check(self): - """ - - """ - super().check() - assert(self.source_prefix in self._acceptable_source_prefix) - - def forward_simulation(self, output_seismograms=False): - """ - Calls SPECFEM2D forward solver, exports solver outputs to traces dir - - :type output_seismograms: str - :param output_seismograms: path to export traces to after completion of - simulation expected values are either 'traces/obs' for 'observation' - data (i.e., synthetics generated by the TRUE model), or - 'traces/syn', for synthetics generated during function evaluations. - If False, will leave seismograms in OUTPUT_FILES/ - """ - unix.cd(self.cwd) - - setpar(key="SIMULATION_TYPE", val="1", file="DATA/Par_file") - setpar(key="SAVE_FORWARD", val=".true.", file="DATA/Par_file") - - self._call_solver(executable="bin/xmeshfem2D", output="fwd_mesher.log") - self._call_solver(executable="bin/xspecfem2D", output="fwd_solver.log") - - if self.data_format.upper() == "SU": - # Work around SPECFEM2D's version dependent file names - for tag in ["d", "v", "a", "p"]: - unix.rename(old=f"single_{tag}.su", new="single.su", - names=glob(os.path.join("OUTPUT_FILES", "*.su"))) - - if output_seismograms: - unix.mv( - src=glob(os.path.join("OUTPUT_FILES", self.data_wildcard())), - dst=output_seismograms - ) - - def adjoint_simulation(self): - """ - Calls SPECFEM2D adjoint solver, creates the `SEM` folder with adjoint - traces which is required by the adjoint solver - """ - unix.cd(self.cwd) - - setpar(key="SIMULATION_TYPE", val="3", file="DATA/Par_file") - setpar(key="SAVE_FORWARD", val=".false.", file="DATA/Par_file") - - unix.rm("SEM") - unix.ln("traces/adj", "SEM") - - # Deal with different SPECFEM2D name conventions for regular traces and - # "adjoint" traces - if self.data_format.upper == "SU": - unix.rename(old=".su", new=".su.adj", - names=glob(os.path.join("traces", "adj", "*.su"))) - - self._call_solver(executable="bin/xspecfem2D", output="adj_solver.log") - def smooth(self, input_path, output_path, parameters=None, span_h=0., - span_v=0., output="smooth.log"): + span_v=0., use_gpu=False, output="smooth.log"): """ Specfem2D requires additional model parameters in directory to perform the xsmooth_sem task. This function will copy these files into the @@ -211,13 +81,15 @@ def smooth(self, input_path, output_path, parameters=None, span_h=0., :param span_v: vertical smoothing length in meters :type output: str :param output: file to output stdout to + :type use_gpu: bool + :param use_gpu: whether to use GPU acceleration for smoothing. Requires + GPU compiled binaries and GPU compute node. """ # Redundant to 'base' class but necessary if not os.path.exists(input_path): unix.mkdir(input_path) - unix.cd(self.cwd) - unix.cd("DATA") + unix.cd(os.path.join(self.cwd, "DATA")) # Copy over only the files that are required. Won't execute if no match files = [] diff --git a/seisflows/solver/specfem3d.py b/seisflows/solver/specfem3d.py index bad6f5f6..b9d13a98 100644 --- a/seisflows/solver/specfem3d.py +++ b/seisflows/solver/specfem3d.py @@ -30,7 +30,6 @@ def __init__(self, source_prefix="CMTSOLUTION", **kwargs): super().__init__(**kwargs) self.source_prefix = source_prefix - self._f0 = None # Define parameters based on material type if self.materials.upper() == "ACOUSTIC": @@ -39,14 +38,7 @@ def __init__(self, source_prefix="CMTSOLUTION", **kwargs): self._parameters.append("vp") self._parameters.append("vs") - self._acceptable_source_prefix = ["CMTSOLUTION", "FORCESOLUTION"] - - def check(self): - """ - Check parameter validitiy - """ - super().check() - assert(self.source_prefix in self._acceptable_source_prefix) + self._acceptable_source_prefixes = ["CMTSOLUTION", "FORCESOLUTION"] def data_wildcard(self, comp="?"): """ @@ -60,23 +52,6 @@ def data_wildcard(self, comp="?"): elif self.data_format.upper() == "ASCII": return f"*.?X{comp}.sem?" - @property - def data_filenames(self): - """ - Returns the filenames of all data, either by the requested components - or by all available files in the directory. - - :rtype: list - :return: list of data filenames - """ - unix.cd(os.path.join(self.cwd, "traces", "obs")) - - if self.components: - files = glob(self.data_wildcard(comp=self.components.lower())) - else: - files = glob(self.data_wildcard(comp="?")) - return sorted(files) - @property def model_databases(self): """ @@ -85,7 +60,7 @@ def model_databases(self): """ local_path = getpar(key="LOCAL_PATH", file=os.path.join(self.cwd, "DATA", "Par_file"))[1] - return os.path.join(self.cwd, local_path) + return local_path @property def kernel_databases(self): @@ -113,21 +88,23 @@ def eval_func(self, path, preprocess=None): # Work around SPECFEM3D conflicting name conventions of SU data self._rename_data() - def _forward(self, output_path): + def forward_simulation(self, output_seismograms=False): """ Calls SPECFEM3D forward solver, exports solver outputs to traces dir - :type output_path: str - :param output_path: path to export traces to after completion of + :type output_seismograms: str + :param output_seismograms: path to export traces to after completion of simulation expected values are either 'traces/obs' for 'observation' data (i.e., synthetics generated by the TRUE model), or - 'traces/syn', for synthetics generated during function evaluations + 'traces/syn', for synthetics generated during function evaluations. + If False, will leave seismograms in OUTPUT_FILES/s """ unix.cd(self.cwd) # Set parameters and run forward simulation setpar(key="SIMULATION_TYPE", val="1", file="DATA/Par_file") setpar(key="SAVE_FORWARD", val=".true.", file="DATA/Par_file") + if self.attenuation: setpar(key="ATTENUATION", val=".true.", file="DATA/Par_file") else: @@ -138,10 +115,13 @@ def _forward(self, output_path): self._call_solver(executable="bin/xmeshfem3D", output="fwd_solver.log") # Find and move output traces, by default to synthetic traces dir - unix.mv(src=glob(os.path.join("OUTPUT_FILES", self.data_wildcard())), - dst=output_path) + if output_seismograms: + unix.mv( + src=glob(os.path.join("OUTPUT_FILES", self.data_wildcard())), + dst=output_seismograms + ) - def _adjoint(self): + def adjoint_simulation(self): """ Calls SPECFEM3D adjoint solver, creates the `SEM` folder with adjoint traces which is required by the adjoint solver @@ -198,5 +178,3 @@ def _rename_data(self): if self.data_format.upper() == "SU": files = glob(os.path.join(self.cwd, "traces", "adj", "*SU")) unix.rename(old='_SU', new='_SU.adj', names=files) - - diff --git a/seisflows/workflow/forward_test.py b/seisflows/workflow/forward_test.py index d346cf07..a14d7e2a 100644 --- a/seisflows/workflow/forward_test.py +++ b/seisflows/workflow/forward_test.py @@ -15,14 +15,22 @@ def evaluate_objective_function(path_model): """ Performs forward simulation for a single given event. Also evaluates the objective function and writes residuals and adjoint sources for later tasks. + + .. note:: + if PAR.PREPROCESS == None, will not perform misfit quantification + + .. note:: + Must be run by system.run() so that solvers are assigned individual + task ids/ working directories. """ if system.taskid == 0: logger.info(msg.sub("EVALUATING OBJECTIVE FUNCTION")) # Run the forward simulation with the given input model - solver.import_model(path=path_model) + solver.import_model(path_model=path_model) solver.forward_simulation( - output_seismograms=os.path.join(solver.cwd, "traces", "syn") + save_traces=os.path.join(solver.cwd, "traces", "syn"), + export_traces=os.path.join(pars.path_output, solver.source_name, "syn") ) # Perform data-synthetic misfit quantification From de40cfc1c050527e6416829b285df74fc7e8e520 Mon Sep 17 00:00:00 2001 From: bch0w Date: Tue, 12 Jul 2022 19:31:40 -0800 Subject: [PATCH 064/195] starting solver test suite --- seisflows/solver/specfem.py | 17 ++++++++++---- seisflows/solver/specfem2d.py | 4 ++-- seisflows/solver/specfem3d.py | 4 ++-- seisflows/tests/test_solver.py | 42 ++++++++++++++++++++++++++++++++++ 4 files changed, 58 insertions(+), 9 deletions(-) diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 8bf71d4e..6b936495 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -38,8 +38,9 @@ class Specfem: """ def __init__(self, case="data", data_format="ascii", materials="elastic", density=False, nproc=1, ntask=1, attenuation=False, - components="ZNE", solver_io="fortran_binary", mpiexec=None, - path_solver=None, path_data=None, path_specfem_bin=None, + components="ZNE", solver_io="fortran_binary", + source_prefix=None,mpiexec=None, path_solver=None, + path_data=None, path_specfem_bin=None, path_specfem_data=None, path_model_init=None, path_model_true=None, path_output=None, **kwargs): """ @@ -124,7 +125,7 @@ def __init__(self, case="data", data_format="ascii", materials="elastic", "xsmooth_sem"] # These are parameters that need to be established by child classes - self.source_prefix = None + self.source_prefix = source_prefix self._acceptable_source_prefixes = [] @property @@ -193,7 +194,7 @@ def cwd(self): :rtype: str :return: current solver working directory """ - return os.path.join(self.path, self.source_name) + return os.path.join(self.path.scratch, self.source_name) def data_wildcard(self, comp="?"): """ @@ -230,6 +231,7 @@ def data_filenames(self, choice="obs"): :rtype: list :return: list of data filenames """ + assert(choice in ["obs", "syn", "adj"]), \ f"choice must be: 'obs', 'syn' or 'adj'" unix.cd(os.path.join(self.cwd, "traces", choice)) @@ -434,7 +436,7 @@ def generate_data(self, export_traces=False): export_traces=export_traces ) - def import_data(self, path_data): + def import_data(self): """ Import data from an existing directory into the current working directory, required if 'observed' waveform data will be provided by @@ -951,6 +953,11 @@ def _check_source_names(self): :rtype: list :return: alphabetically ordered list of source names up to PAR.NTASK """ + assert(self.path.specfem_data is not None), \ + f"solver source names requires 'solver.path.specefm_data' to exist" + assert(os.path.exists(self.path.specfem_data)), \ + f"solver source names requires 'solver.path.specfem_data' to exist" + # Apply wildcard rule and check for available sources, exit if no # sources found because then we can't proceed wildcard = f"{self.source_prefix}_*" diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index cd4871f1..d8f00bbe 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -17,7 +17,7 @@ class Specfem2D(Specfem): """ Python interface to Specfem2D. """ - def __init__(self, source_prefix="SOURCE", multiples=False, **kwargs): + def __init__(self, source_prefix=None, multiples=False, **kwargs): """ SPECFEM2D specific parameters @@ -28,7 +28,7 @@ def __init__(self, source_prefix="SOURCE", multiples=False, **kwargs): """ super().__init__(**kwargs) - self.source_prefix = source_prefix + self.source_prefix = source_prefix or "SOURCE" self.multiples = multiples self.f0 = None diff --git a/seisflows/solver/specfem3d.py b/seisflows/solver/specfem3d.py index b9d13a98..77cda030 100644 --- a/seisflows/solver/specfem3d.py +++ b/seisflows/solver/specfem3d.py @@ -18,7 +18,7 @@ class Specfem3D(Specfem): """ Python interface to Specfem3D Cartesian. """ - def __init__(self, source_prefix="CMTSOLUTION", **kwargs): + def __init__(self, source_prefix=None, **kwargs): """ SPECFEM2D specific parameters @@ -29,7 +29,7 @@ def __init__(self, source_prefix="CMTSOLUTION", **kwargs): """ super().__init__(**kwargs) - self.source_prefix = source_prefix + self.source_prefix = source_prefix or "CMTSOLUTION" # Define parameters based on material type if self.materials.upper() == "ACOUSTIC": diff --git a/seisflows/tests/test_solver.py b/seisflows/tests/test_solver.py index d054d47c..4b1aab33 100644 --- a/seisflows/tests/test_solver.py +++ b/seisflows/tests/test_solver.py @@ -8,4 +8,46 @@ from seisflows.tools.specfem import Model from seisflows.config import ROOT_DIR, NAMES, CFGPATHS +from seisflows.solver.specfem import Specfem +from seisflows.solver.specfem2d import Specfem2D +from seisflows.solver.specfem3d import Specfem3D +from seisflows.solver.specfem3d_globe import Specfem3DGlobe + +TEST_DATA = os.path.join(ROOT_DIR, "tests", "test_data") + + +def test_taskid(): + """ + Make sure that task id returns correctly + """ + solver = Specfem() + assert(solver.taskid == 0) + os.environ["SEISFLOWS_TASKID"] = "9" + assert(solver.taskid == 9) + + +def test_source_names(): + """ + Check that source names are established correctly + """ + # Establish solver that looks for CMTSOLUTIONS with NTASK==1 + sources = os.path.join(TEST_DATA, "test_solver", "sources") + solver = Specfem(path_specfem_data=sources, source_prefix="CMTSOLUTION") + assert(len(solver.source_names) == 1) + + # Set NTASK==2 to grab both source files + solver = Specfem(path_specfem_data=sources, source_prefix="CMTSOLUTION", + ntask=2) + source_names = glob(os.path.join(sources, "CMTSOLUTION*")) + source_names = [_.split("_")[-1] for _ in source_names] + + assert(source_names == solver.source_names) + +def test_data_filenames(): + """ + Test that data filenames are returned correctly + """ + sources = os.path.join(TEST_DATA, "test_solver", "sources") + solver = Specfem(path_specfem_data=sources, source_prefix="CMTSOLUTION") + pytest.set_trace() \ No newline at end of file From 20341508528aad92982afc6f95442cea6d2840ee Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Wed, 13 Jul 2022 11:12:09 -0800 Subject: [PATCH 065/195] renamed 'wrappers' to 'utils' and cleared out a number of unnused small wrapper/utility functions that were left over from the legacy code. Reasonining behind this being that these functions provided minor abstraction at the cost of less readable code. These are being replaced by explicit calls to the underlying functions for better code readability --- seisflows/config.py | 7 +- seisflows/plugins/solver_io/ascii.py | 2 +- seisflows/plugins/solver_io/fortran_binary.py | 2 +- seisflows/seisflows.py | 2 +- seisflows/solver/specfem.py | 16 +- seisflows/solver/specfem3d.py | 4 +- seisflows/system/workstation.py | 2 +- seisflows/tests/test_seisflows.py | 2 +- seisflows/tests/test_solver.py | 5 +- seisflows/tools/unix.py | 2 +- seisflows/tools/{wrappers.py => utils.py} | 181 ++---------------- 11 files changed, 46 insertions(+), 179 deletions(-) rename seisflows/tools/{wrappers.py => utils.py} (51%) diff --git a/seisflows/config.py b/seisflows/config.py index 702a26d2..1f35b7e2 100755 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -20,12 +20,14 @@ import logging import copyreg import traceback +from pkgutil import find_loader from importlib import import_module + from seisflows import logger from seisflows.core import Dict, Null from seisflows.tools import msg, unix -from seisflows.tools.wrappers import module_exists, load_yaml +from seisflows.tools.utils import load_yaml """ @@ -258,7 +260,8 @@ class 'Inversion'. # Check if modules exist, otherwise raise custom exception _exists = False full_dotted_name = ".".join(["seisflows", name, module]) - if not module_exists(full_dotted_name): + # find_loader() checks if the module exists or not + if not find_loader(full_dotted_name): print(msg.cli(f"The following module was not found within the package: " f"seisflows.{name}.{module}", header="custom import error", border="=") diff --git a/seisflows/plugins/solver_io/ascii.py b/seisflows/plugins/solver_io/ascii.py index 04d978be..2de06adf 100644 --- a/seisflows/plugins/solver_io/ascii.py +++ b/seisflows/plugins/solver_io/ascii.py @@ -5,7 +5,7 @@ import numpy as np from glob import glob from shutil import copyfile -from seisflows.tools.wrappers import iterable +from seisflows.tools.utils import iterable def read_slice(path, parameters, iproc): diff --git a/seisflows/plugins/solver_io/fortran_binary.py b/seisflows/plugins/solver_io/fortran_binary.py index 3428525b..21691fb6 100644 --- a/seisflows/plugins/solver_io/fortran_binary.py +++ b/seisflows/plugins/solver_io/fortran_binary.py @@ -4,7 +4,7 @@ import os import numpy as np from shutil import copyfile -from seisflows.tools.wrappers import iterable +from seisflows.tools.utils import iterable def read_slice(path, parameters, iproc): diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 861dc21b..7675c388 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -32,7 +32,7 @@ from seisflows.tools import unix, msg from seisflows.tools.specfem import (getpar, setpar, getpar_vel_model, setpar_vel_model) -from seisflows.tools.wrappers import load_yaml +from seisflows.tools.utils import load_yaml def sfparser(): diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 6b936495..0554203d 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -17,6 +17,7 @@ from seisflows.core import Dict from seisflows.plugins import solver_io as solver_io_dir from seisflows.tools import msg, unix +from seisflows.tools.utils import get_task_id from seisflows.tools.specfem import getpar, setpar @@ -143,12 +144,7 @@ def taskid(self): :rtype: int :return: task id for given solver """ - _taskid = os.getenv("SEISFLOWS_TASKID") - if _taskid is None: - _taskid = 0 - logger.warning("Environment variable 'SEISFLOWS_TASKID' not found. " - "Assigning Task ID == 0") - return int(_taskid) + return get_task_id() @property def source_names(self): @@ -231,7 +227,6 @@ def data_filenames(self, choice="obs"): :rtype: list :return: list of data filenames """ - assert(choice in ["obs", "syn", "adj"]), \ f"choice must be: 'obs', 'syn' or 'adj'" unix.cd(os.path.join(self.cwd, "traces", choice)) @@ -752,6 +747,13 @@ def import_model(self, path_model): dst = os.path.join(self.cwd, self.model_databases, "") unix.cp(src, dst) + def export(self, model=False, kernels=False, traces=False, residuals=False): + """ + Export scratch files to output path. Must be run by system as each + process is required to export its own traces and kernels. + """ + if self. + def _export_model(self): """ File transfer utility. Export the model to disk. Run from master solver. diff --git a/seisflows/solver/specfem3d.py b/seisflows/solver/specfem3d.py index 77cda030..6f5f5552 100644 --- a/seisflows/solver/specfem3d.py +++ b/seisflows/solver/specfem3d.py @@ -10,7 +10,7 @@ from seisflows.solver.specfem import Specfem from seisflows.tools import unix -from seisflows.tools.wrappers import exists +from seisflows.tools.utils import exists from seisflows.tools.specfem import setpar, getpar @@ -164,7 +164,7 @@ def _initialize_adjoint_traces(self): for iproc in range(self.nproc): for channel in ["x", "y", "z"]: dst = f"{iproc:d}_d{channel}_SU.adj" - if not exists(dst): + if not os.path.exists(dst): src = f"{iproc:d}_d{self.components[0]}_SU.adj" unix.cp(src, dst) diff --git a/seisflows/system/workstation.py b/seisflows/system/workstation.py index 8b8e93a4..1109a72a 100644 --- a/seisflows/system/workstation.py +++ b/seisflows/system/workstation.py @@ -11,7 +11,7 @@ from seisflows import logger from seisflows.config import CFGPATHS, save from seisflows.tools import msg, unix -from seisflows.tools.wrappers import number_fid +from seisflows.tools.utils import number_fid class Workstation: diff --git a/seisflows/tests/test_seisflows.py b/seisflows/tests/test_seisflows.py index cd69e5ca..859c3305 100644 --- a/seisflows/tests/test_seisflows.py +++ b/seisflows/tests/test_seisflows.py @@ -14,7 +14,7 @@ from seisflows.core import Dict from seisflows.seisflows import SeisFlows from seisflows.config import ROOT_DIR, NAMES, CFGPATHS -from seisflows.tools.wrappers import load_yaml +from seisflows.tools.utils import load_yaml TEST_DIR = os.path.join(ROOT_DIR, "tests") diff --git a/seisflows/tests/test_solver.py b/seisflows/tests/test_solver.py index 4b1aab33..17009b1e 100644 --- a/seisflows/tests/test_solver.py +++ b/seisflows/tests/test_solver.py @@ -14,7 +14,7 @@ from seisflows.solver.specfem3d_globe import Specfem3DGlobe -TEST_DATA = os.path.join(ROOT_DIR, "tests", "test_data") +TEST_DATA = os.path.join(ROOT_DIR, "tests", "test_data", "test_solver") def test_taskid(): @@ -32,7 +32,7 @@ def test_source_names(): Check that source names are established correctly """ # Establish solver that looks for CMTSOLUTIONS with NTASK==1 - sources = os.path.join(TEST_DATA, "test_solver", "sources") + sources = os.path.join(TEST_DATA, "sources") solver = Specfem(path_specfem_data=sources, source_prefix="CMTSOLUTION") assert(len(solver.source_names) == 1) @@ -44,6 +44,7 @@ def test_source_names(): assert(source_names == solver.source_names) + def test_data_filenames(): """ Test that data filenames are returned correctly diff --git a/seisflows/tools/unix.py b/seisflows/tools/unix.py index 19cdb404..abb82e51 100644 --- a/seisflows/tools/unix.py +++ b/seisflows/tools/unix.py @@ -9,7 +9,7 @@ import shutil import socket -from seisflows.tools.wrappers import iterable +from seisflows.tools.utils import iterable def cat(src, dst=None): diff --git a/seisflows/tools/wrappers.py b/seisflows/tools/utils.py similarity index 51% rename from seisflows/tools/wrappers.py rename to seisflows/tools/utils.py index e9bdd7e9..6797444d 100644 --- a/seisflows/tools/wrappers.py +++ b/seisflows/tools/utils.py @@ -1,7 +1,6 @@ """ -Wrappers of commonly used functions to reduce line count and provide an -aesthetically similar look in SeisFlows. Mostly concerend with file -manipulation, but also math and calling functions as well. +General utility functions that are mostly concerend with file manipulation, +but also math and calling functions as well. """ import os import re @@ -12,6 +11,25 @@ from importlib import import_module from pkgutil import find_loader from seisflows.core import Dict +from seisflows import logger + + +def get_task_id(): + """ + Task IDs are assigned to each child process spawned by the system module + during a SeisFlows workflow. SeisFlows modules use this Task ID to keep + track of embarassingly parallel process, e.g., solver uses the Task ID to + determine which source is being considered. + + :rtype: int + :return: task id for given solver + """ + _taskid = os.getenv("SEISFLOWS_TASKID") + if _taskid is None: + _taskid = 0 + logger.warning("Environment variable 'SEISFLOWS_TASKID' not found. " + "Assigning Task ID == 0") + return int(_taskid) def load_yaml(filename): @@ -52,74 +70,6 @@ def load_yaml(filename): return mydict -def diff(list1, list2): - """ - Difference between unique elements of lists - - :type list1: list - :param list1: first list - :type list2: list - :param list2: second list - """ - c = set(list1).union(set(list2)) - d = set(list1).intersection(set(list2)) - - return list(c - d) - - -def divides(i, j): - """ - Return True if `j` divides `i`. - - Bryant: I don't think this works in python3? - - :type i: int - :type j :int - """ - if j == 0: - return False - elif i % j: - return False - else: - return True - - -def exists(names): - """ - Wrapper for os.path.exists that also works on lists - - :type names: list or str - :param names: list of names to check existnce - """ - for name in iterable(names): - if not name: - return False - elif not isinstance(name, str): - raise TypeError - elif not os.path.exists(name): - return False - else: - return True - - -def findpath(name): - """ - Resolves absolute path of module - - :type name: str - :param name: absolute path of str - """ - path = import_module(name).__file__ - - # Adjust file extension - path = re.sub('.pyc$', '.py', path) - - # Strip trailing "__init__.py" - path = re.sub('__init__.py$', '', path) - - return path - - def iterable(arg): """ Make an argument iterable @@ -134,95 +84,6 @@ def iterable(arg): return arg -def module_exists(name): - """ - Determine if a module loader exists - - :type name: str - :param name: name of module - """ - return find_loader(name) - - -def package_exists(name): - """ - Determine if a package exists - - :type name: str - :param name: name of package - """ - return find_loader(name) - - -def pkgpath(name): - """ - Path to Seisflows package - - :type name: str - :param name: name of package - """ - for path in import_module('seisflows').__path__: - if os.path.join(name, 'seisflows') in path: - return path - - -def timestamp(): - """ - Return a timestamp for current time - """ - return time.strftime('%H:%M:%S') - - -def getset(arg): - """ - Return a set object - - :type arg: None, str or list - :param arg: argument to turn into a set - :rtype: set - :return: a set of the given argument - """ - if not arg: - return set() - elif isinstance(arg, str): - return set([arg]) - else: - return set(arg) - - -def parse_null(dictionary): - """ - Remove null, None or '' values from a dictionary - - :type dictionary: dict - :param dictionary: dict of parameters to parse - :rtype: dict - :return: dictionary that has been sanitized of all null values - """ - # Copy the dictionary to get around deleting keys while iterating - parsed_dict = dict(dictionary) - for key, item in dictionary.items(): - # Search for all None and "" items, ignore bools, 0's etc. - if not item and isinstance(item, (type(None), str)): - del parsed_dict[key] - - return parsed_dict - - -def loadtxt(filename): - """ - Load scalar from text file - """ - return float(np.loadtxt(filename)) - - -def savetxt(filename, v): - """ - Save scalar to text file - """ - np.savetxt(filename, [v], '%11.6e') - - def number_fid(fid, i=0): """ Number a filename. Used to store old log files without overwriting them. From ccc7cee4541d191265182cd117c1feed57566166 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Wed, 13 Jul 2022 17:43:29 -0800 Subject: [PATCH 066/195] completed solver tests and preprocess tests after refactoring both considerably to act more as standalone modules. Moving init docstrings into class docstrings to follow google styleguide and also make it easier to inherit docstrings from parent classes, which will be used during 'seisflows configure' --- seisflows/plugins/preprocess/readers.py | 54 - seisflows/plugins/preprocess/writers.py | 56 - seisflows/preprocess/default.py | 446 +- seisflows/seisflows.py | 111 +- seisflows/solver/specfem.py | 786 +-- seisflows/solver/specfem2d.py | 102 +- seisflows/solver/specfem3d.py | 178 +- seisflows/system/workstation.py | 2 +- .../test_preprocess/AA.S0001.BXY.adj | 5000 +++++++++++++++++ .../test_preprocess/AA.S0001.BXY.semd | 5000 +++++++++++++++++ .../test_preprocess/Uy_file_single_d.su | Bin 0 -> 20240 bytes .../test_preprocess/Uy_file_single_d.su.adj | Bin 0 -> 20240 bytes .../mainsolver/DATA/CMTSOLUTION_001 | 1 + .../mainsolver/DATA/CMTSOLUTION_002 | 1 + .../mainsolver/DATA/CMTSOLUTION_003 | 1 + .../mainsolver/DATA/CMTSOLUTION_004 | 1 + .../mainsolver/DATA/CMTSOLUTION_005 | 1 + .../mainsolver/DATA/CMTSOLUTION_006 | 1 + .../test_solver/mainsolver/DATA/Par_file | 1 + .../test_solver/mainsolver/DATA/STATIONS | 1 + .../test_solver/mainsolver/bin/xcombine_sem | 3 + .../test_solver/mainsolver/bin/xmeshfem2D | 2 + .../test_solver/mainsolver/bin/xsmooth_sem | 3 + .../test_solver/mainsolver/bin/xspecfem2D | 3 + .../proc000000_rho_vp_vs.dat | 0 .../test_file_formats/proc000000_vp.bin | Bin .../test_file_formats/proc000000_vs.bin | Bin seisflows/tests/test_preprocess.py | 95 + seisflows/tests/test_solver.py | 58 +- seisflows/tests/test_tools.py | 10 +- seisflows/tools/unix.py | 31 +- seisflows/tools/utils.py | 97 +- seisflows/workflow/forward_test.py | 33 + 33 files changed, 11079 insertions(+), 999 deletions(-) delete mode 100644 seisflows/plugins/preprocess/readers.py delete mode 100644 seisflows/plugins/preprocess/writers.py create mode 100644 seisflows/tests/test_data/test_preprocess/AA.S0001.BXY.adj create mode 100644 seisflows/tests/test_data/test_preprocess/AA.S0001.BXY.semd create mode 100644 seisflows/tests/test_data/test_preprocess/Uy_file_single_d.su create mode 100644 seisflows/tests/test_data/test_preprocess/Uy_file_single_d.su.adj create mode 100644 seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_001 create mode 100644 seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_002 create mode 100644 seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_003 create mode 100644 seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_004 create mode 100644 seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_005 create mode 100644 seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_006 create mode 100644 seisflows/tests/test_data/test_solver/mainsolver/DATA/Par_file create mode 100644 seisflows/tests/test_data/test_solver/mainsolver/DATA/STATIONS create mode 100755 seisflows/tests/test_data/test_solver/mainsolver/bin/xcombine_sem create mode 100755 seisflows/tests/test_data/test_solver/mainsolver/bin/xmeshfem2D create mode 100755 seisflows/tests/test_data/test_solver/mainsolver/bin/xsmooth_sem create mode 100755 seisflows/tests/test_data/test_solver/mainsolver/bin/xspecfem2D rename seisflows/tests/test_data/{ => test_tools}/test_file_formats/proc000000_rho_vp_vs.dat (100%) rename seisflows/tests/test_data/{ => test_tools}/test_file_formats/proc000000_vp.bin (100%) rename seisflows/tests/test_data/{ => test_tools}/test_file_formats/proc000000_vs.bin (100%) create mode 100644 seisflows/tests/test_preprocess.py diff --git a/seisflows/plugins/preprocess/readers.py b/seisflows/plugins/preprocess/readers.py deleted file mode 100644 index 1a54e5ea..00000000 --- a/seisflows/plugins/preprocess/readers.py +++ /dev/null @@ -1,54 +0,0 @@ -""" -SeisFlows uses obspy stream objects for holding and processing seismic data. -In some cases, obspy.read doesn't provide the desired behavior, so we -introduce an additonal level of indirection - -Used by the PREPROCESS class and specified by the READER parameter -""" -import os -from numpy import loadtxt -from obspy import read -from obspy.core import Stream, Stats, Trace - - -def su(filename): - """ - Reads seismic unix files outputted by Specfem, using Obspy - - :type filename: str - :param filename: full path to data file to read - """ - st = read(os.path.join(filename), format='SU', byteorder='<') - - return st - - -def ascii(filename): - """ - Reads SPECFEM3D-style ASCII data - - :type filename: str - :param filename: full path to data file to read - """ - st = Stream() - stats = Stats() - - time, data = loadtxt(filename).T - - stats.filename = os.path.basename(filename) - stats.starttime = time[0] - stats.delta = time[1] - time[0] - stats.npts = len(data) - - try: - parts = filename.split(".") - stats.network = parts[0] - stats.station = parts[1] - stats.channel = parts[2] - except: - pass - - st.append(Trace(data=data, header=stats)) - - return st - diff --git a/seisflows/plugins/preprocess/writers.py b/seisflows/plugins/preprocess/writers.py deleted file mode 100644 index 44cff58a..00000000 --- a/seisflows/plugins/preprocess/writers.py +++ /dev/null @@ -1,56 +0,0 @@ -""" -SeisFlows uses obspy stream objects for holding and processing seismic data. -In some cases, obspy.read doesn't provide the desired behavior, -so we introduce an additonal level of indirection - -Used by the PREPROCESS class and specified by the WRITER parameter -""" -import os -import numpy as np - - -def su(st, filename): - """ - Writes seismic unix files outputted by Specfem, using Obspy - - :type st: obspy.core.stream.Stream - :param st: stream to write - :type filename: str - :param filename: full path to filename to write data to - """ - for tr in st: - # Work around obspy data type conversion - tr.data = tr.data.astype(np.float32) - - max_delta = 0.065535 - dummy_delta = max_delta - - if st[0].stats.delta > max_delta: - for tr in st: - tr.stats.delta = dummy_delta - - # Write data to file - st.write(filename, format='SU') - - -def ascii(st, filename=None): - """ - Writes seismic traces as ascii files. Kwargs are left to keep structure of - inputs compatible with other input formats. - - :type st: obspy.core.stream.Stream - :param st: stream to write - :type filename: str - :param filename: full path to filename to write data to - """ - for tr in st: - if filename is None: - filename = tr.stats.filename - - # Float provides the time difference between starttime and default time - time_offset = float(tr.stats.starttime) - - data_out = np.vstack((tr.times() + time_offset, tr.data)).T - - np.savetxt(filename, data_out, ["%13.7f", "%17.7f"]) - diff --git a/seisflows/preprocess/default.py b/seisflows/preprocess/default.py index 35fc0f0b..96057a24 100644 --- a/seisflows/preprocess/default.py +++ b/seisflows/preprocess/default.py @@ -6,12 +6,16 @@ and write adjoint sources that are expected by the solver. """ import os -import obspy import numpy as np +from obspy import read as obspy_read +from obspy import Stream, Trace, UTCDateTime from seisflows import logger +from seisflows.core import Dict from seisflows.tools import signal, unix -from seisflows.plugins.preprocess import adjoint, misfit, readers, writers + +from seisflows.plugins.preprocess import misfit as misfit_functions +from seisflows.plugins.preprocess import adjoint as adjoint_sources class Default: @@ -21,10 +25,12 @@ class Default: Provides data processing functions for seismic traces, with options for data misfit, filtering, normalization and muting """ - def __init__(self, data_format="ascii", misfit="waveform", backproject=None, - normalize=None, filter=None, min_period=None, max_period=None, - min_freq=None, max_freq=None, mute=None, path_preprocess=None, - **kwargs): + def __init__(self, data_format="ascii", misfit="waveform", + adjoint="waveform", normalize=None, filter=None, + min_period=None, max_period=None, min_freq=None, max_freq=None, + mute=None, early_slope=None, early_const=None, late_slope=None, + late_const=None, short_dist=None, long_dist=None, + workdir=os.getcwd(), path_preprocess=None, **kwargs): """ Preprocessing module parameters @@ -78,10 +84,11 @@ def __init__(self, data_format="ascii", misfit="waveform", backproject=None, :param path_preprocess: scratch path for all preprocessing processes, including saving files """ - self.data_format = data_format.title() + self.data_format = data_format.upper() self.misfit = misfit - self.backproject = backproject, + self.adjoint = adjoint self.normalize = normalize + self.filter = filter self.min_period = min_period self.max_period = max_period @@ -89,20 +96,40 @@ def __init__(self, data_format="ascii", misfit="waveform", backproject=None, self.max_freq = max_freq self.mute = mute or [] self.normalize = normalize or [] - self.path = path_preprocess or \ - os.path.join(os.getcwd(), "scratch", "preprocess") - - # TODO: Add the mute parameters here, const, slope and dist - self._misfit = None - self._adjoint = None - self._reader = None - self._writer = None - - def check(self, validate=True): + # Mute arrivals sub-parameters + self.early_slope = early_slope + self.early_const = early_const + self.late_slope = late_slope + self.late_const = late_const + self.short_dist = short_dist + self.long_dist = long_dist + + self.path = Dict( + scratch=path_preprocess or os.path.join(workdir, "scratch", + "preprocess") + ) + + self._acceptable_data_formats = ["SU", "ASCII"] + + # Misfits and adjoint sources are defined by the available functions + # in each of these plugin files. Drop hidden variables from dir() + self._acceptable_misfits = [_ for _ in dir(misfit_functions) + if not _.startswith("_")] + self._acceptable_adjsrcs = [_ for _ in dir(adjoint_sources) + if not _.startswith("_")] + + def check(self): """ Checks parameters and paths """ + if self.misfit: + assert(self.misfit in self._acceptable_misfits), \ + f"preprocess.misfit must be in {self._acceptable_misfits}" + if self.adjoint: + assert(self.adjoint in self._acceptable_adjsrcs), \ + f"preprocess.misfit must be in {self._acceptable_adjsrcs}" + # Data normalization option if self.normalize: acceptable_norms = {"TNORML1", "TNORML2", "ENORML1", "ENORML2"} @@ -169,13 +196,8 @@ def check(self, validate=True): "PAR.MIN_FREQ < PAR.MAX_FREQ" ) - # Assert that readers and writers available - # TODO | This is a bit vague as dir contains imported modules and hidden - # TODO | variables (e.g., np, __name__) - assert(self.data_format.lower() in dir(readers)), ( - f"Reader {self.data_format} not found") - assert(self.data_format.lower() in dir(writers)), ( - f"Writer {self.data_format} not found") + assert(self.data_format.upper() in self._acceptable_data_formats), \ + f"data format must be in {self._acceptable_data_formats}" def setup(self): """ @@ -183,22 +205,7 @@ def setup(self): misfit, adjoint source type, and specifying the expected file type for input and output seismic data. """ - unix.mkdir(self.path) - - # Define misfit function and adjoint trace generator - if self.misfit: - logger.debug(f"misfit function is: '{self.misfit}'") - self._misfit = getattr(misfit, self.misfit.lower()) - self._adjoint = getattr(adjoint, self.misfit.lower()) - elif self.backproject: - logger.debug( - f"backproject function is: '{self.backproject}'" - ) - self._adjoint = getattr(adjoint, self.backproject.lower()) - - # Define seismic data reader and writer - self._reader = getattr(readers, self.data_format.lower()) - self._writer = getattr(writers, self.data_format.lower()) + unix.mkdir(self.path.scratch) def finalize(self): """ @@ -207,25 +214,114 @@ def finalize(self): """ pass - def initialize_adjoint_traces(self, filenames=None): + def read(self, fid): + """ + Waveform reading functionality. Imports waveforms as Obspy streams + + :type fid: str + :param fid: path to file to read data from + :rtype: obspy.core.stream.Stream + :return: ObsPy stream containing data stored in `fid` + """ + st = None + if self.data_format.upper() == "SU": + st = obspy_read(os.path.join(fid), format="SU", byteorder="<") + elif self.data_format.upper() == "ASCII": + st = self._read_ascii(fid) + return st + + def write(self, st, fid): + """ + Waveform writing functionality. Writes waveforms back to format that + SPECFEM recognizes + + :type st: obspy.core.stream.Stream + :param st: stream to write + :type fid: str + :param fid: path to file to write stream to + """ + if self.data_format.upper() == "SU": + for tr in st: + # Work around for ObsPy data type conversion + tr.data = tr.data.astype(np.float32) + max_delta = 0.065535 + dummy_delta = max_delta + + if st[0].stats.delta > max_delta: + for tr in st: + tr.stats.delta = dummy_delta + + # Write data to file + st.write(fid, format="SU") + + elif self.data_format.upper() == "ASCII": + for tr in st: + # Float provides time difference between starttime and default + time_offset = float(tr.stats.starttime) + data_out = np.vstack((tr.times() + time_offset, tr.data)).T + np.savetxt(fid, data_out, ["%13.7f", "%17.7f"]) + + def _calculate_misfit(self, **kwargs): + """Wrapper for plugins.preprocess.misfit misfit/objective function""" + if self.misfit is not None: + return getattr(misfit_functions, self.misfit)(**kwargs) + else: + return None + + def _generate_adjsrc(self, **kwargs): + """Wrapper for plugins.preprocess.adjoint source/backproject function""" + if self.adjoint is not None: + return getattr(adjoint_sources, self.adjoint)(**kwargs) + else: + return None + + def initialize_adjoint_traces(self, data_filenames, output): """ - TO DO + SPECFEM requires that adjoint traces be present for every matching + synthetic seismogram. If an adjoint source does not exist, it is + simply set as zeros. This function creates all adjoint traces as + zeros, to be filled out later + + Appends '.adj. to the solver filenames as expected by SPECFEM (if they + don't already have that extension) + + TODO there are some sem2d and 3d specific tasks that are not carried + TODO over here, were they required? + + :type data_filenames: list of str + :param data_filenames: existing solver waveforms to read from. + These will be copied, zerod out, and saved to path `save`. Should + come from solver.data_filenames + :type output: str + :param output: path to save the new adjoint traces to. """ - for filename in self.data_filenames: - st = self.preprocess.reader(path=os.path.join(self.cwd, "traces", "obs"), - filename=filename - ) - # Zero out data just so we have empty adjoint traces as SPECFEM - # will expect all adjoint sources to have all components - st *= 0 + for fid in data_filenames: + st = self.read(fid=fid).copy() + fid = os.path.basename(fid) # drop any path before filename + for tr in st: + tr.data *= 0 + + adj_fid = self._rename_as_adjoint_source(fid) # Write traces back to the adjoint trace directory - preprocess.writer(st=st, filename=filename, - path=os.path.join(self.cwd, "traces", "adj") - ) + self.write(st=st, fid=os.path.join(output, adj_fid)) + + def _rename_as_adjoint_source(self, fid): + """ + Rename synthetic waveforms into filenames consistent with how SPECFEM + expects adjoint sources to be named. Usually this just means adding + a '.adj' to the end of the filename + """ + if not fid.endswith(".adj"): + if self.data_format.upper() == "SU": + fid = f"{fid}.adj" + elif self.data_format.upper() == "ASCII": + og_extension = os.path.splitext(fid)[-1] # e.g., .semd + fid = fid.replace(og_extension, ".adj") + return fid def quantify_misfit(self, observed, synthetic, - write_residuals=False, write_adjsrcs=False, **kwargs): + write_residuals=None, write_adjsrcs=None, **kwargs): """ Prepares solver for gradient evaluation by writing residuals and adjoint traces. Meant to be called by solver.eval_func(). @@ -238,16 +334,18 @@ def quantify_misfit(self, observed, synthetic, Meant to be called by solver.eval_func(), may have unused arguments to keep functions general across subclasses. - :type cwd: str - :param cwd: current specfem working directory containing observed and - synthetic seismic data to be read and processed. Should be defined - by solver.cwd - :type filenames: list of str - :param filenames: list of filenames defining the files in traces + :type observed: list + :param observed: list of observed waveforms + :type synthetic: list + :param synthetic: list of synthetic waveforms + :type write_residuals: str + :param write_residuals: if not None, path to write misfit/residuls to + :type write_adjsrcs: str + :param write_adjsrcs: if not None, path to write adjoint sources to """ for obs_fid, syn_fid in zip(observed, synthetic): - obs = self._reader(filename=obs_fid) - syn = self._reader(filename=syn_fid) + obs = self.read(fid=obs_fid) + syn = self.read(fid=syn_fid) # Process observations and synthetics identically if self.filter: @@ -262,97 +360,27 @@ def quantify_misfit(self, observed, synthetic, # Write the residuals/misfit and adjoint sources for each component for tr_obs, tr_syn in zip(obs, syn): - if write_residuals: - residual = self._misfit(obs=tr_obs.data, syn=tr_syn.data, - nt=tr_syn.stats.npts, - dt=tr_syn.stats.delta) + # Simple check to make sure zip retains ordering + assert(tr_obs.stats.component == tr_syn.stats.component) + # Calculate the misfit value and write to file + if write_residuals and self._calculate_misfit: + residual = self._calculate_misfit( + obs=tr_obs.data, syn=tr_syn.data, + nt=tr_syn.stats.npts, dt=tr_syn.stats.delta + ) with open(write_residuals, "a") as f: f.write(f"{residual:.2E}\n") - if write_adjsrcs: - adjsrc = syn.copy() - adjsrc.data = self._adjoint(obs=tr_obs.data, syn=tr_syn.data, - nt=tr_syn.stats.npts, - dt=tr_syn.stats.delta) - if self.data_format.upper() == "ASCII": - # Change the extension to '.adj' from whatever it is - ext = os.path.splitext(os.path.basename(obs))[-1] - filename = os.path.basename(obs).replace(ext, ".adj") - elif self.data_format.upper() == "SU": - # TODO implement this - raise NotImplementedError - self._writer(st=adjsrc, - filename=os.path.join(write_adjsrcs, filename) - ) - - def prepare_eval_grad(self, cwd, taskid, filenames, **kwargs): - """ - Prepares solver for gradient evaluation by writing residuals and - adjoint traces. Meant to be called by solver.eval_func(). - - Reads in observed and synthetic waveforms, applies optional - preprocessing, assesses misfit, and writes out adjoint sources and - STATIONS_ADJOINT file. - - .. note:: - Meant to be called by solver.eval_func(), may have unused arguments - to keep functions general across subclasses. - - :type cwd: str - :param cwd: current specfem working directory containing observed and - synthetic seismic data to be read and processed. Should be defined - by solver.cwd - :type filenames: list of str - :param filenames: list of filenames defining the files in traces - """ - if taskid == 0: - logger.debug("preparing files for gradient evaluation") - for filename in filenames: - obs = self._reader(path=os.path.join(cwd, "traces", "obs"), - filename=filename) - syn = self._reader(path=os.path.join(cwd, "traces", "syn"), - filename=filename) - - # Process observations and synthetics identically - if self.filter: - if taskid == 0: - logger.debug(f"applying {self.filter} filter to data") - obs = self._apply_filter(obs) - syn = self._apply_filter(syn) - if self.mute: - if taskid == 0: - logger.debug(f"applying {self.mute} mutes to data") - obs = self._apply_mute(obs) - syn = self._apply_mute(syn) - if self.normalize: - if taskid == 0: - logger.debug( - f"normalizing data with: {self.normalize}" + # Generate an adjoint source trace, write to file + if write_adjsrcs and self._generate_adjsrc: + adjsrc = syn.copy() + adjsrc.data = self._generate_adjsrc( + obs=tr_obs.data, syn=tr_syn.data, + nt=tr_syn.stats.npts, dt=tr_syn.stats.delta ) - obs = self._apply_normalize(obs) - syn = self._apply_normalize(syn) - - if self.misfit is not None: - self._write_residuals(cwd, syn, obs) - - # Write the adjoint traces. Rename file extension for Specfem - if self.data_format.upper() == "ASCII": - # Change the extension to '.adj' from whatever it is - ext = os.path.splitext(filename)[-1] - filename_out = filename.replace(ext, ".adj") - elif self.data_format.upper() == "SU": - # TODO implement this - raise NotImplementedError - - self._write_adjoint_traces(path=os.path.join(cwd, "traces", "adj"), - syn=syn, obs=obs, filename=filename_out) - - # Copy over the STATIONS file to STATIONS_ADJOINT required by Specfem - # ASSUMING that all stations are used in adjoint simulation - # TODO !!! This is SPECFEM dependent? Belongs in solver.specfem? - src = os.path.join(cwd, "DATA", "STATIONS") - dst = os.path.join(cwd, "DATA", "STATIONS_ADJOINT") - unix.cp(src, dst) + fid = os.path.basename(syn_fid) + fid = self._rename_as_adjoint_source(fid) + self.write(st=adjsrc, fid=os.path.join(write_adjsrcs, fid)) def sum_residuals(self, files): """ @@ -369,61 +397,6 @@ def sum_residuals(self, files): return total_misfit - def _write_residuals(self, obs, syn, output): - """ - Computes residuals between observed and synthetic seismogram based on - the misfit function self.misfit. Saves the residuals for each - data-synthetic pair into a text file located at: - - ./scratch/solver/*/residuals - - The resulting file will be a single-column ASCII file that needs to be - summed before use by the solver - - :type path: str - :param path: location "adjoint traces" will be written - :type syn: obspy.core.stream.Stream - :param syn: synthetic data - :type obs: obspy.core.stream.Stream - :param syn: observed data - """ - residuals = [] - for tr_obs, tr_syn in zip(obs, syn): - residual = self._misfit(syn=tr_syn.data, obs=tr_obs.data, - nt=tr_syn.stats.npts, - dt=tr_syn.stats.delta - ) - residuals.append(residual) - - filename = os.path.join(output, "residuals") - if os.path.exists(filename): - residuals = np.append(residuals, np.loadtxt(filename)) - - np.savetxt(filename, residuals) - - def _write_adjoint_traces(self, path, syn, obs, filename): - """ - Writes "adjoint traces" required for gradient computation - - :type path: str - :param path: location "adjoint traces" will be written - :type syn: obspy.core.stream.Stream - :param syn: synthetic data - :type obs: obspy.core.stream.Stream - :param syn: observed data - :type filename: str - :param filename: filename to write adjoint traces to - """ - # Use the synthetics as a template for the adjoint sources - adj = syn.copy() - for tr_adj, tr_obs, tr_syn in zip(adj, obs, syn): - tr_adj.data = self._adjoint(syn=tr_syn.data, obs=tr_obs.data, - nt=tr_syn.stats.npts, - dt=tr_syn.stats.delta - ) - - self._writer(adj, path, filename) - def _apply_filter(self, st): """ Apply a filter to waveform data using ObsPy @@ -466,18 +439,14 @@ def _apply_mute(self, st): mute_choices = [_.upper() for _ in self.mute] if "EARLY" in mute_choices: st = signal.mute_arrivals(st, slope=self.early_slope, - const=self.early_const, - choice="EARLY") + const=self.early_const, choice="EARLY") if "LATE" in mute_choices: st = signal.mute_arrivals(st, slope=self.late_slope, - const=self.late_const, - choice="LATE") + const=self.late_const, choice="LATE") if "SHORT" in mute_choices: - st = signal.mute_offsets(st, dist=self.short_dist, - choice="SHORT") + st = signal.mute_offsets(st, dist=self.short_dist, choice="SHORT") if "LONG" in mute_choices: - st = signal.mute_arrivals(st, dist=self.long_dist, - choice="LONG") + st = signal.mute_offsets(st, dist=self.long_dist, choice="LONG") return st @@ -526,3 +495,56 @@ def _apply_normalize(self, st): tr.data /= w return st_out + + @staticmethod + def _read_ascii(fid, origintime=None): + """ + Read waveforms in two-column ASCII format. This is copied directly from + pyatoa.utils.read.read_sem() + """ + try: + times = np.loadtxt(fname=fid, usecols=0) + data = np.loadtxt(fname=fid, usecols=1) + + # At some point in 2018, the Specfem developers changed how the ascii files + # were formatted from two columns to comma separated values, and repeat + # values represented as 2*value_float where value_float represents the data + # value as a float + except ValueError: + times, data = [], [] + with open(fid, 'r') as f: + lines = f.readlines() + for line in lines: + try: + time_, data_ = line.strip().split(',') + except ValueError: + if "*" in line: + time_ = data_ = line.split('*')[-1] + else: + raise ValueError + times.append(float(time_)) + data.append(float(data_)) + + times = np.array(times) + data = np.array(data) + + if origintime is None: + print("No origintime given, setting to default 1970-01-01T00:00:00") + origintime = UTCDateTime("1970-01-01T00:00:00") + + # We assume that dt is constant after 'precision' decimal points + delta = round(times[1] - times[0], 4) + + # Honor that Specfem doesn't start exactly on 0 + origintime += times[0] + + # Write out the header information + net, sta, cha, fmt = os.path.basename(fid).split('.') + stats = {"network": net, "station": sta, "location": "", + "channel": cha, "starttime": origintime, "npts": len(data), + "delta": delta, "mseed": {"dataquality": 'D'}, + "time_offset": times[0], "format": fmt + } + st = Stream([Trace(data=data, header=stats)]) + + return st diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 7675c388..3a599339 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -28,7 +28,7 @@ from seisflows.core import Dict, SeisFlowsPathsParameters from seisflows.config import (custom_import, save, NAMES, ROOT_DIR, CFGPATHS, - config_logger) + config_logger, import_seisflows) from seisflows.tools import unix, msg from seisflows.tools.specfem import (getpar, setpar, getpar_vel_model, setpar_vel_model) @@ -581,22 +581,11 @@ def configure(self, absolute_paths=False, **kwargs): else if False, use path names relative to the working directory. Defaults to False, uses relative paths. """ - self._register_parameters() - - # Check if the User set turn off any modules (if None, dont instantiate) - names = copy(NAMES) - for name, choice in self._parameters.items(): - if choice is None: - names.remove(name.lower()) + # Load in a barebones parameter file and instantiate specific classes + parameters = load_yaml(os.path.join(self._args.workdir, + self._args.parameter_file)) + classes = [custom_import(name, parameters[name])() for name in NAMES] - # Need to attempt importing all modules before we access their par/paths - for NAME in NAMES: - sys.modules[f"seisflows_{NAME}"] = custom_import(NAME)() - - # System defines foundational directory structure required by other - # modules. Don't validate the parameters because they aren't yet set - sys.modules["seisflows_system"].required.validate(paths=True, - parameters=False) # If writing to parameter file fails for any reason, the file will be # mangled, create a temporary copy that can be re-instated upon failure @@ -604,29 +593,7 @@ def configure(self, absolute_paths=False, **kwargs): unix.cp(self._args.parameter_file, temp_par_file) try: - # Paths are collected for each but written at the end - seisflows_paths = {} - with open(self._args.parameter_file, "a") as f: - for name in names: - req = sys.modules[f"seisflows_{name}"].required - seisflows_paths.update(req.paths) - - # Write the docstring header and then the parameters in YAML - msg.write_par_file_header(f, req.parameters, name) - msg.write_par_file_paths_pars(f, req.parameters) - - # Write the paths in the same format as parameters - msg.write_par_file_header(f, seisflows_paths, name="PATHS") - f.write("PATHS:\n") - - # If requested, set the paths relative to the current dir - if not absolute_paths: - for key, attrs in seisflows_paths.items(): - if attrs["default"]: - seisflows_paths[key]["default"] = os.path.relpath( - attrs["default"]) - msg.write_par_file_paths_pars(f, seisflows_paths, indent=4) - # General error catch as anything can happen here + import pdb;pdb.set_trace() except Exception as e: unix.rm(self._args.parameter_file) unix.cp(temp_par_file, self._args.parameter_file) @@ -636,6 +603,72 @@ def configure(self, absolute_paths=False, **kwargs): else: unix.rm(temp_par_file) + # def configure(self, absolute_paths=False, **kwargs): + # """ + # Dynamically generate the parameter file by writing out docstrings and + # default values for each of the SeisFlows module parameters. + # This function writes files manually, consistent with the .yaml format. + # + # :type absolute_paths: bool + # :param absolute_paths: if True, expand pathnames to absolute paths, + # else if False, use path names relative to the working directory. + # Defaults to False, uses relative paths. + # """ + # self._register_parameters() + # + # # Check if the User set turn off any modules (if None, dont instantiate) + # names = copy(NAMES) + # for name, choice in self._parameters.items(): + # if choice is None: + # names.remove(name.lower()) + # + # # Need to attempt importing all modules before we access their par/paths + # for NAME in NAMES: + # sys.modules[f"seisflows_{NAME}"] = custom_import(NAME)() + # + # # System defines foundational directory structure required by other + # # modules. Don't validate the parameters because they aren't yet set + # sys.modules["seisflows_system"].required.validate(paths=True, + # parameters=False) + # + # # If writing to parameter file fails for any reason, the file will be + # # mangled, create a temporary copy that can be re-instated upon failure + # temp_par_file = f".{self._args.parameter_file}" + # unix.cp(self._args.parameter_file, temp_par_file) + # + # try: + # # Paths are collected for each but written at the end + # seisflows_paths = {} + # with open(self._args.parameter_file, "a") as f: + # for name in names: + # req = sys.modules[f"seisflows_{name}"].required + # seisflows_paths.update(req.paths) + # + # # Write the docstring header and then the parameters in YAML + # msg.write_par_file_header(f, req.parameters, name) + # msg.write_par_file_paths_pars(f, req.parameters) + # + # # Write the paths in the same format as parameters + # msg.write_par_file_header(f, seisflows_paths, name="PATHS") + # f.write("PATHS:\n") + # + # # If requested, set the paths relative to the current dir + # if not absolute_paths: + # for key, attrs in seisflows_paths.items(): + # if attrs["default"]: + # seisflows_paths[key]["default"] = os.path.relpath( + # attrs["default"]) + # msg.write_par_file_paths_pars(f, seisflows_paths, indent=4) + # # General error catch as anything can happen here + # except Exception as e: + # unix.rm(self._args.parameter_file) + # unix.cp(temp_par_file, self._args.parameter_file) + # print(msg.cli(text="seisflows configure traceback", header="error")) + # print(traceback.format_exc()) + # sys.exit(-1) + # else: + # unix.rm(temp_par_file) + def swap(self, module, classname, **kwargs): """ Swap the parameters of an existing parameter file with a new module. diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 0554203d..19a477b1 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -5,8 +5,17 @@ functions that work with all versions of SPECFEM. Subclasses will provide additional capabilities unique to each version of SPECFEM. -TODO add in `apply_hess` functionality that was partially written in legacy code -TODO move `_initialize_adjoint_traces` to workflow.migration +.. note:: + The Base class implementation is almost completely SPECFEM2D related. + However, SPECFEM2D requires a few unique parameters that 3D/3D_GLOBE + do not. Because of the inheritance architecture of SeisFlows, we do not + want the 3D and 3D_GLOBE versions to inherit 2D-specific parameters, so + we need this this more generalized SPECFEM base class. + +TODO + - add in `apply_hess` functionality that was partially written in legacy code + - move `_initialize_adjoint_traces` to workflow.migration + - Add density scaling based on Vp? """ import os import sys @@ -23,66 +32,69 @@ class Specfem: """ - This base class provides an interface through which solver simulations can - be set up and run. It should not be used by itself, but rather it is meant - to provide the foundation for: SPECFEM2D/3D/3D_GLOBE - - .. note:: - This Base class implementation is almost completely SPECFEM2D related. - However, SPECFEM2D requires a few unique parameters that 3D/3D_GLOBE - do not. Because of the inheritance architecture of SeisFlows, we do not - want the 3D and 3D_GLOBE versions to inherit 2D-specific parameters, so - we need this this more generalized SPECFEM base class. - - .. note::: - This class supports only acoustic and isotropic elastic inversions. + SPECFEM interface shared between 2D/3D/3D_GLOBE implementations. + + :type case: str + :param case: determine the type of workflow we will attempt + Available: ['DATA': data-synthetic comparisons. Data must be provided + by the user in `path_data`. 'SYNTHETIC': synthetic-synthetic + comparisons. `path_model_true` is required to generate target 'data'] + :type data_format: str + :param data_format: data format for reading traces into memory. + Available: ['SU' seismic unix format, 'ASCII' human-readable ascii] + :type materials: str + :param materials: Material parameters used to define model. Available: + ['ELASTIC': Vp, Vs, 'ACOUSTIC': Vp, 'ISOTROPIC', 'ANISOTROPIC'] + :type density: bool + :param density: How to treat density during inversion. If True, updates + density during inversion. If False, keeps it constant. + :type attenuation: bool + :param attenuation: How to treat attenuation during inversion. + if True, turns on attenuation during forward simulations only. If + False, attenuation is always set to False. Requires underlying + attenution (Q_mu, Q_kappa) model + :type components: str + :param components: components to consider and tag data with. Should be + string of letters such as 'RTZ' + :type solver_io: str + :param solver_io: format of model/kernel/gradient files expected by the + numerical solver. Available: ['fortran_binary': default .bin files]. + TODO: ['adios': ADIOS formatted files] + :type source_prefix: str + :param source_prefix: prefix of source/event/earthquake files. If None, + will attempt to guess based on the specific solver chosen. + :type mpiexec: str + :param mpiexec: MPI executable used to run parallel processes. Should also + be defined for the system module + :type workdir: str + :param workdir: working directory in which to look for data and store + results. Defaults to current working directory + :type path_solver: str + :param path_solver: scratch path for all solver related tasks + :type path_data: str + :param path_data: path to any externally stored data required by the solver + :type path_specfem_bin: str + :param path_specfem_bin: path to SPECFEM bin/ directory which + contains binary executables for running SPECFEM + :type path_specfem_data: str + :param path_specfem_data: path to SPECFEM DATA/ directory which must + contain the CMTSOLUTION, STATIONS and Par_file files used for + running SPECFEM + :type path_model_true: str + :param path_model_true: path to a target model if `case`=='synthetic' and + a set of synthetic 'observations' are required for workflow. + :type path_output: str + :param path_output: shared output directory on disk for more permanent + storage of solver related files such as traces, kernels, gradients. """ def __init__(self, case="data", data_format="ascii", materials="elastic", density=False, nproc=1, ntask=1, attenuation=False, components="ZNE", solver_io="fortran_binary", - source_prefix=None,mpiexec=None, path_solver=None, - path_data=None, path_specfem_bin=None, + source_prefix=None, mpiexec=None, workdir=os.getcwd(), + path_solver=None, path_data=None, path_specfem_bin=None, path_specfem_data=None, path_model_init=None, path_model_true=None, path_output=None, **kwargs): - """ - SPECFEM Solver parameters - - :type data_format: str - :param data_format: data format for reading traces into memory. - Availalble: ['SU' seismic unix format, 'ASCII' human-readable ascii] - :type materials: str - :param materials: Material parameters used to define model. Available: - ['ELASTIC': Vp, Vs, 'ACOUSTIC': Vp, 'ISOTROPIC', 'ANISOTROPIC'] - :type density: bool - :param density: How to treat density during inversion. If True, updates - density during inversion. If False, keeps it constant. - TODO Add density scaling based on Vp? - :type attenuation: bool - :param attenuation: How to treat attenuation during inversion. - if True, turns on attenuation during forward simulations only. If - False, attenuation is always set to False. Requires underlying - attenution (Q_mu, Q_kappa) model - :type components: str - :param components: components to consider and tag data with. Should be - string of letters such as 'RTZ' - :type path.specfem_bin: str - :param path.specfem_bin: path to SPECFEM bin/ directory which - contains binary executables for running SPECFEM - :type path.specfem_data: str - :param path.specfem_data: path to SPECFEM DATA/ directory which must - contain the CMTSOLUTION, STATIONS and Par_file files used for - running SPECFEM - - :type parameters: list of str - :param parameters: a list detailing the parameters to be used to - define the model, available: ['vp', 'vs', 'rho'] - :type _source_names: hidden attribute, - :param _source_names: the names of all the sources that are being used - by the solver - :type logger: Logger - :param logger: Class-specific logging module, log statements pushed - from this logger will be tagged by its specific module/classname - """ + """Set default SPECFEM interface parameters""" self.case = case self.data_format = data_format self.materials = materials @@ -93,19 +105,19 @@ def __init__(self, case="data", data_format="ascii", materials="elastic", self.components = components self.solver_io = solver_io self.mpiexec = mpiexec + self.source_prefix = source_prefix or "SOURCE" # Define internally used directory structure - _cwd = os.getcwd() self.path = Dict( - scratch=path_solver or os.path.join(_cwd, "scratch", "solver"), - data=path_data or os.path.join(_cwd, "SFDATA"), - output=path_output or os.path.join(_cwd, "output"), - mainsolver=os.path.join(_cwd, "scratch", "mainsolver"), + scratch=path_solver or os.path.join(workdir, "scratch", "solver"), + data=path_data or os.path.join(workdir, "SFDATA"), + output=path_output or os.path.join(workdir, "output"), specfem_bin=path_specfem_bin, specfem_data=path_specfem_data, model_init=path_model_init, model_true=path_model_true, ) + self.path.mainsolver = os.path.join(self.path.scratch, "mainsolver") # Establish internally defined parameter system self._parameters = [] @@ -115,19 +127,78 @@ def __init__(self, case="data", data_format="ascii", materials="elastic", self._source_names = None self._io = getattr(solver_io_dir, self.solver_io) - # Define available choices for check parameter + # Define available choices for check parameters self._available_model_types = ["gll"] self._available_materials = [ "ELASTIC", "ACOUSTIC", # specfem2d, specfem3d "ISOTROPIC", "ANISOTROPIC" # specfem3d_globe ] self._available_data_formats = ["ASCII", "SU"] - self._required_binaries = ["xspecfem2d", "xmeshfem2d", "xcombine_sem", + self._required_binaries = ["xspecfem2D", "xmeshfem2D", "xcombine_sem", "xsmooth_sem"] + self._acceptable_source_prefix = ["SOURCE", "FORCE", "FORCESOLUTION"] + + def check(self): + """ + Checks parameter validity for SPECFEM input files and model parameters + """ + assert(self.case.upper() in ["DATA", "SYNTHETIC"]), \ + f"solver.case must be 'DATA' or 'SYNTHETIC'" + + assert(self.materials.upper() in self._available_materials), \ + f"solver.materials must be in {self._available_materials}" + + if self.data_format.upper() not in self._available_data_formats: + raise NotImplementedError( + f"solver.data_format must be {self._available_data_formats}" + ) + + # Make sure we can read in the model/kernel/gradient files + assert hasattr(solver_io_dir, self.solver_io) + assert hasattr(self._io, "read_slice"), \ + "IO method has no attribute 'read'" + assert hasattr(self._io, "write_slice"), \ + "IO method has no attribute 'write'" + + # Check that User has provided appropriate bin/ and DATA/ directories + for name, dir_ in zip(["bin/", "DATA/"], + [self.path.specfem_bin, self.path.specfem_data]): + assert(dir_ is not None), f"SPECFEM path '{name}' cannot be None" + assert(os.path.exists(dir_)), f"SPECFEM path '{name}' must exist" + + # Check that the required SPECFEM files are available + for fid in ["STATIONS", "Par_file"]: + assert(os.path.exists(os.path.join(self.path.specfem_data, fid))), ( + f"DATA/{fid} does not exist but is required by SeisFlows solver" + ) + + # Make sure source files exist and are appropriately labeled + assert(self.source_prefix in self._acceptable_source_prefixes) + assert(glob(os.path.join(self.path.specfem_data, + f"{self.source_prefix}*"))), ( + f"No source files with prefix {self.source_prefix} found in DATA/") + + # Check that required binary files exist which are called upon by solver + for fid in self._required_binaries: + assert(os.path.exists(os.path.join(self.path.specfem_bin, fid))), ( + f"bin/{fid} does not exist but is required by SeisFlows solver" + ) + + # Check that the 'case' variable matches required models + if self.case.upper() == "SYNTHETIC": + assert(os.path.exists(self.path.model_true)), ( + f"solver.case == 'synthetic' requires `path.model_true`" + ) + + # Check that model type is set correctly in the Par_file + model_type = getpar(key="MODEL", + file=os.path.join(self.path.specfem_data, + "Par_file"))[1] + assert(model_type in self._available_model_types), \ + f"{model_type} not in available types {self._available_model_types}" - # These are parameters that need to be established by child classes - self.source_prefix = source_prefix - self._acceptable_source_prefixes = [] + # Check that the number of tasks/events matches the number of events + self._source_names = self._check_source_names() @property def taskid(self): @@ -279,65 +350,6 @@ def kernel_databases(self): """ return "OUTPUT_FILES" - def check(self): - """ - Checks parameter validity for SPECFEM input files and model parameters - """ - assert(self.case.upper() in ["DATA", "SYNTHETIC"]), \ - f"solver.case must be 'DATA' or 'SYNTHETIC'" - - assert(self.materials.upper() in self._available_materials), \ - f"solver.materials must be in {self._available_materials}" - - if self.data_format.upper() not in self._available_data_formats: - raise NotImplementedError( - f"solver.data_format must be {self._available_data_formats}" - ) - - # Make sure we can read in the model/kernel/gradient files - assert hasattr(solver_io_dir, self.solver_io) - assert hasattr(self._io, "read_slice"), \ - "IO method has no attribute 'read'" - assert hasattr(self._io, "write_slice"), \ - "IO method has no attribute 'write'" - - # Check that User has provided appropriate bin/ and DATA/ directories - for name, dir_ in zip(["bin/", "DATA/"], - [self.path.specfem_bin, self.path.specfem_data]): - assert(dir_ is not None), f"SPECFEM path '{name}' cannot be None" - assert(os.path.exists(dir_)), f"SPECFEM path '{name}' must exist" - - # Check that the required SPECFEM files are available - for fid in [self.source_prefix, "STATIONS", "Par_file"]: - assert(os.path.exists(os.path.join(self.path.specfem_data, fid))), ( - f"DATA/{fid} does not exist but is required by SeisFlows solver" - ) - - # Check that required binary files exist which are called upon by solver - for fid in self._required_binaries: - assert(os.path.exists(os.path.join(self.path.specfem_bin, fid))), ( - f"bin/{fid} does not exist but is required by SeisFlows solver" - ) - - # Check that the 'case' variable matches required models - if self.case.upper() == "SYNTHETIC": - assert(os.path.exists(self.path.model_true)), ( - f"solver.case == 'synthetic' requires `path.model_true`" - ) - - # Make sure source files exist and are appropriately labeled - assert(self.source_prefix in self._acceptable_source_prefixes) - assert(glob(os.path.join(self.path.specfem_data, - f"{self.source_prefix}*"))), ( - f"No source files with prefix {self.source_prefix} found in DATA/") - - # Check that model type is set correctly in the Par_file - model_type = getpar(key="MODEL", - file=os.path.join(self.path.specfem_data, - "Par_file"))[1] - assert(model_type in self._available_model_types), \ - f"{model_type} not in available types {self._available_model_types}" - def setup(self): """ Prepares solver scratch directories for an impending workflow. @@ -448,88 +460,98 @@ def import_data(self): dst = os.path.join(self.cwd, "traces", "obs") unix.cp(src, dst) + # + # def eval_func(self, path, preprocess=None): + # """ + # Performs forward simulations and evaluates the misfit function using + # the preprocess module. solver.eval_func is bundled with + # preprocess.prepare_eval_grad because they are meant to be run serially + # so it is better to lump them together into a single allocation. + # + # .. note:: + # This task should be run in parallel by system.run() + # + # :type path: str + # :param path: directory from which model is imported and where residuals + # will be exported + # :type preprocess: instance + # :param preprocess: SeisFlows preprocess module which can be used to + # prepare gradient evaluation by comparing misfit and creating + # adjoint sources. If None, only forward simulations will be + # performed + # """ + # unix.cd(self.cwd) + # + # if self.taskid == 0: + # logger.info("running forward simulations") + # + # self._import_model(path) + # self._forward(output_path=os.path.join("traces", "syn")) + # + # if preprocess: + # if self.taskid == 0: + # logger.debug("call preprocess to prepare gradient evaluation") + # preprocess.prepare_eval_grad(cwd=self.cwd, taskid=self.taskid, + # source_name=self.source_name, + # filenames=self.data_filenames + # ) + # self._export_residuals(path) + + # def eval_grad(self, path, export_traces=False): + # """ + # Evaluates gradient by carrying out adjoint simulations. + # + # .. note:: + # It is expected that eval_func() has already been run as this + # function looks for adjoint sources in 'cwd/traces/adj' + # + # :type path: str + # :param path: directory from which model is imported + # :type export_traces: bool + # :param export_traces: if True, save traces to OUTPUT. + # if False, discard traces + # """ + # unix.cd(self.cwd) + # + # if self.taskid == 0: + # logger.debug("running adjoint simulations") + # + # # Check to make sure that preprocessing module created adjoint traces + # adjoint_traces_wildcard = os.path.join("traces", "adj", "*") + # if not glob(adjoint_traces_wildcard): + # print(msg.cli(f"Event {self.source_name} has no adjoint traces, " + # f"which will lead to an external solver error. " + # f"Please check that solver.eval_func() executed " + # f"properly", border="=", header="solver error") + # ) + # sys.exit(-1) + # + # self._adjoint() + # self._export_kernels(path) + # + # if export_traces: + # self._export_traces(path=os.path.join(path, "traces", "syn"), + # prefix="traces/syn") + # self._export_traces(path=os.path.join(path, "traces", "adj"), + # prefix="traces/adj") - def eval_func(self, path, preprocess=None): - """ - Performs forward simulations and evaluates the misfit function using - the preprocess module. solver.eval_func is bundled with - preprocess.prepare_eval_grad because they are meant to be run serially - so it is better to lump them together into a single allocation. - - .. note:: - This task should be run in parallel by system.run() - - :type path: str - :param path: directory from which model is imported and where residuals - will be exported - :type preprocess: instance - :param preprocess: SeisFlows preprocess module which can be used to - prepare gradient evaluation by comparing misfit and creating - adjoint sources. If None, only forward simulations will be - performed - """ - unix.cd(self.cwd) - - if self.taskid == 0: - logger.info("running forward simulations") - - self._import_model(path) - self._forward(output_path=os.path.join("traces", "syn")) - - if preprocess: - if self.taskid == 0: - logger.debug("call preprocess to prepare gradient evaluation") - preprocess.prepare_eval_grad(cwd=self.cwd, taskid=self.taskid, - source_name=self.source_name, - filenames=self.data_filenames - ) - self._export_residuals(path) - - def eval_grad(self, path, export_traces=False): - """ - Evaluates gradient by carrying out adjoint simulations. - - .. note:: - It is expected that eval_func() has already been run as this - function looks for adjoint sources in 'cwd/traces/adj' - - :type path: str - :param path: directory from which model is imported - :type export_traces: bool - :param export_traces: if True, save traces to OUTPUT. - if False, discard traces + def forward_simulation(self, executables=None, save_traces=False, + export_traces=False): """ - unix.cd(self.cwd) - - if self.taskid == 0: - logger.debug("running adjoint simulations") - - # Check to make sure that preprocessing module created adjoint traces - adjoint_traces_wildcard = os.path.join("traces", "adj", "*") - if not glob(adjoint_traces_wildcard): - print(msg.cli(f"Event {self.source_name} has no adjoint traces, " - f"which will lead to an external solver error. " - f"Please check that solver.eval_func() executed " - f"properly", border="=", header="solver error") - ) - sys.exit(-1) - - self._adjoint() - self._export_kernels(path) + Wrapper for SPECFEM binaries: 'xmeshfem?D' 'xgenerate_databases', + 'xspecfem?D' - if export_traces: - self._export_traces(path=os.path.join(path, "traces", "syn"), - prefix="traces/syn") - self._export_traces(path=os.path.join(path, "traces", "adj"), - prefix="traces/adj") - - def forward_simulation(self, save_traces=False, export_traces=False): - """ Calls SPECFEM2D forward solver, exports solver outputs to traces dir .. note:: SPECFEM3D/3D_GLOBE versions must overwrite this function + :type executables: list or None + :param executables: list of SPECFEM executables to run, in order, to + complete a forward simulation. This can be left None in most cases, + which will select default values based on the specific solver + being called (2D/3D/3D_GLOBE). It is made an optional parameter + to keep the function more general for inheritance purposes. :type save_traces: str :param save_traces: move files from their native SPECFEM output location to another directory. This is used to move output waveforms to @@ -541,15 +563,20 @@ def forward_simulation(self, save_traces=False, export_traces=False): permanent storage location. i.e., copy files from their original location """ + if executables is None: + executables = ["bin/xmeshfem2D", "bin/xspecfem2D"] unix.cd(self.cwd) setpar(key="SIMULATION_TYPE", val="1", file="DATA/Par_file") setpar(key="SAVE_FORWARD", val=".true.", file="DATA/Par_file") - self._call_solver(executable="bin/xmeshfem2D", output="fwd_mesher.log") - self._call_solver(executable="bin/xspecfem2D", output="fwd_solver.log") + # Calling subprocess.run() for each of the binary executables listed + for exc in executables: + # e.g., fwd_mesher.log + stdout = f"fwd_{self._exc2log(exc)}.log" + self._run_binary(executable=exc, stdout=stdout) - # Work around SPECFEM2D's version dependent file names + # Work around SPECFEM's version dependent file names if self.data_format.upper() == "SU": for tag in ["d", "v", "a", "p"]: unix.rename(old=f"single_{tag}.su", new="single.su", @@ -567,14 +594,40 @@ def forward_simulation(self, save_traces=False, export_traces=False): dst=save_traces ) - def adjoint_simulation(self): + def adjoint_simulation(self, executables=None, save_kernels=False, + export_kernels=False): """ + Wrapper for SPECFEM binary 'xspecfem?D' + Calls SPECFEM2D adjoint solver, creates the `SEM` folder with adjoint - traces which is required by the adjoint solver. + traces which is required by the adjoint solver. Renames kernels + after they have been created from 'alpha' and 'beta' to 'vp' and 'vs', + respectively. .. note:: SPECFEM3D/3D_GLOBE versions must overwrite this function - """ + + :type executables: list or None + :param executables: list of SPECFEM executables to run, in order, to + complete an adjoint simulation. This can be left None in most cases, + which will select default values based on the specific solver + being called (2D/3D/3D_GLOBE). It is made an optional parameter + to keep the function more general for inheritance purposes. + :type save_kernels: str + :param save_kernels: move the kernels from their native SPECFEM output + location to another path. This is used to move kernels to another + SeisFlows scratch directory so that they are discoverable by + other modules. The typical location they are moved to is + path_eval_grad + :type export_kernels: str + :param export_kernels: export/copy/save kernels from the scratch + directory to a more permanent storage location. i.e., copy files + from their original location. Note that kernel file sizes are LARGE, + so exporting kernels can lead to massive storage requirements. + """ + if executables is None: + executables = ["bin/xspecfem2D"] + unix.cd(self.cwd) setpar(key="SIMULATION_TYPE", val="3", file="DATA/Par_file") @@ -583,63 +636,36 @@ def adjoint_simulation(self): unix.rm("SEM") unix.ln("traces/adj", "SEM") - # Deal with different SPECFEM2D name conventions for regular traces and - # "adjoint" traces - if self.data_format.upper == "SU": - unix.rename(old=".su", new=".su.adj", - names=glob(os.path.join("traces", "adj", "*.su"))) + # Calling subprocess.run() for each of the binary executables listed + for exc in executables: + # e.g., adj_solver.log + stdout = f"adj_{self._exc2log(exc)}.log" + logger.info(f"running SPECFEM executable {exc}, log to '{stdout}'") + self._run_binary(executable=exc, stdout=stdout) - self._call_solver(executable="bin/xspecfem2D", output="adj_solver.log") - - def _call_solver(self, executable, output="solver.log"): - """ - Calls MPI solver executable to run solver binaries, used by individual - processes to run the solver on system. If the external solver returns a - non-zero exit code (failure), this function will return a negative - boolean. + # Rename kernels to work w/ conflicting name conventions + unix.cd(self.kernel_databases) + logger.info(f"renaming event kernels for {self.source_name}") + for tag in ["alpha", "alpha[hv]", "reg1_alpha", "reg1_alpha[hv]"]: + names = glob(f"*proc??????_{tag}_kernel.bin") + unix.rename(old="alpha", new="vp", names=names) - :type executable: str - :param executable: executable function to call - :type output: str - :param output: where to redirect stdout - """ - # Executable may come with additional sub arguments, we only need to - # check that the actually executable exists - if not os.path.exists(executable.split(" ")[0]): - print(msg.cli(f"solver executable {executable} does not exist", - header="external solver error", border="=")) - sys.exit(-1) + for tag in ["beta", "beta[hv]", "reg1_beta", "reg1_beta[hv]"]: + names = glob(f"*proc??????_{tag}_kernel.bin") + unix.rename(old="beta", new="vs", names=names) - # mpiexec is None when running in serial mode, so e.g., ./xmeshfem2D - if not self.mpiexec: - exc_cmd = f"./{executable}" - # Otherwise mpiexec is system dependent (e.g., srun, mpirun) - else: - exc_cmd = f"{self.mpiexec} {executable}" + # Save and export the kernels to user-defined locations + if export_kernels: + unix.cp(src=glob("*_kernel.bin"), dst=export_kernels) - # Run solver. Write solver stdout (log files) to text file - try: - with open(output, "w") as f: - subprocess.run(exc_cmd, shell=True, check=True, stdout=f) - except (subprocess.CalledProcessError, OSError) as e: - print(msg.cli("The external numerical solver has returned a nonzero " - "exit code (failure). Consider stopping any currently " - "running jobs to avoid wasted computational resources. " - f"Check 'scratch/solver/mainsolver/{output}' for the " - f"solvers stdout log message. " - f"The failing command and error message are: ", - items=[f"exc: {exc_cmd}", f"err: {e}"], - header="external solver error", - border="=") - ) - sys.exit(-1) + if save_kernels: + unix.mv(src=glob("*_kernel.bin"), dst=save_kernels) def combine(self, input_path, output_path, parameters=None): """ - Postprocessing wrapper: xcombine_sem + Wrapper for 'xcombine_sem'. Sums kernels from individual source contributions to create gradient. - .. note:: The binary xcombine_sem simply sums matching databases (.bin) @@ -675,13 +701,15 @@ def combine(self, input_path, output_path, parameters=None): for name in parameters: # e.g.: mpiexec bin/xcombine_sem alpha_kernel kernel_paths output/ exc = f"bin/xcombine_sem {name}_kernel kernel_paths {output_path}" - self._call_solver(executable=exc) + # e.g., smooth_vp.log + stdout = f"{self._exc2log(exc)}_{name}.log" + self._run_binary(executable=exc, stdout=stdout) def smooth(self, input_path, output_path, parameters=None, span_h=0., - span_v=0., use_gpu=False, output="smooth.log"): + span_v=0., use_gpu=False): """ - Postprocessing wrapper: xsmooth_sem - Smooths kernels by convolving them with a Gaussian. + Wrapper for SPECFEM binary: xsmooth_sem + Smooths kernels by convolving them with a 3D Gaussian .. note:: It is ASSUMED that this function is being called by @@ -699,8 +727,6 @@ def smooth(self, input_path, output_path, parameters=None, span_h=0., :param span_h: horizontal smoothing length in meters :type span_v: float :param span_v: vertical smoothing length in meters - :type output: str - :param output: file to output stdout to :type use_gpu: bool :param use_gpu: whether to use GPU acceleration for smoothing. Requires GPU compiled binaries and GPU compute node. @@ -724,119 +750,104 @@ def smooth(self, input_path, output_path, parameters=None, span_h=0., for name in parameters: exc = (f"bin/xsmooth_sem {str(span_h)} {str(span_v)} {name}_kernel " f"{input_path} {output_path} {use_gpu}") - self._call_solver(executable=exc, output=output) + # e.g., combine_vs.log + stdout = f"{self._exc2log(exc)}_{name}.log" + self._run_binary(executable=exc, stdout=stdout) # Rename output files to remove the '_smooth' suffix which SeisFlows # will not recognize files = glob(os.path.join(output_path, "*")) unix.rename(old="_smooth", new="", names=files) - def import_model(self, path_model): - """ - Copy files from given `path_model` into the current working directory - model database - - :type path_model: str - :param path_model: path to an existing starting model + def _run_binary(self, executable, stdout="solver.log"): """ - assert(os.path.exists(path_model)), f"model {path_model} does not exist" - unix.cd(self.cwd) - - # Copy the model files (ex: proc000023_vp.bin ...) into database dir - src = glob(os.path.join(path_model, "*")) - dst = os.path.join(self.cwd, self.model_databases, "") - unix.cp(src, dst) + Calls MPI solver executable to run solver binaries, used by individual + processes to run the solver on system. If the external solver returns a + non-zero exit code (failure), this function will return a negative + boolean. - def export(self, model=False, kernels=False, traces=False, residuals=False): - """ - Export scratch files to output path. Must be run by system as each - process is required to export its own traces and kernels. - """ - if self. + .. note:: + This function ASSUMES it is being run from a SPECFEM working + directory, i.e., that the executables are located in ./bin/ - def _export_model(self): - """ - File transfer utility. Export the model to disk. Run from master solver. - """ - unix.mkdir(self.path.output) - for key in self._parameters: - files = glob(os.path.join(self.model_databases, f"*{key}.bin")) - unix.cp(files, self.path.output) + .. note:: + This is essentially an error-catching wrapper of subprocess.run() - def _export_kernels(self): - """ - File transfer utility. Export kernels to disk + :type executable: str + :param executable: executable function to call. May or may not start + E.g., acceptable calls for the solver would './bin/xspecfem2D'. + Also accepts additional command line arguments such as: + 'xcombine_sem alpha_kernel kernel_paths...' + :type stdout: str + :param stdout: where to redirect stdout + :raises SystemExit: If external numerical solver return any failure + code while running """ - unix.cd(self.kernel_databases) - - # Work around conflicting name conventions - self._rename_kernels() + # Executable may come with additional sub arguments, we only need to + # check that the actually executable exists + if not unix.which(executable.split(" ")[0]): + print(msg.cli(f"executable '{executable}' does not exist", + header="external solver error", border="=")) + sys.exit(-1) - src = glob("*_kernel.bin") - dst = os.path.join(self.path.output, "kernels", self.source_name) - unix.mkdir(dst) - unix.mv(src, dst) + # Append with mpiexec if we are running with MPI + if self.mpiexec: + executable = f"{self.mpiexec} {executable}" - def _export_residuals(self, path): - """ - File transfer utility. Export residuals to disk. + try: + with open(stdout, "w") as f: + subprocess.run(executable, shell=True, check=True, stdout=f) + except (subprocess.CalledProcessError, OSError) as e: + print(msg.cli("The external numerical solver has returned a " + "nonzero exit code (failure). Consider stopping any " + "currently running jobs to avoid wasted " + "computational resources. Check 'scratch/solver/" + "mainsolver/{stdout}' for the solvers stdout log " + "message. The failing command and error message are:", + items=[f"exc: {executable}", f"err: {e}"], + header="external solver error", + border="=") + ) + sys.exit(-1) - :type path: str - :param path: path to save residuals + @staticmethod + def _exc2log(exc): """ - if self.taskid == 0: - logger.debug(f"exporting residuals to:\n{path}") - - unix.mkdir(os.path.join(path, "residuals")) - src = os.path.join(self.cwd, "residuals") - - # If this residuals directory has not been created, something - # has gone wrong with the preprocessing and workflow cannot proceed - if not os.path.exists(src): - print(msg.cli("The Solver function 'export_residuals' expected " - "'residuals' directories to be created but could not " - "find them and cannot continue the workflow. Please " - "check the preprocess.prepare_eval_grad() function", - header="preprocess error", border="=")) - sys.exit(-1) + Very simple conversion utility to get log file names based on binaries. + e.g., binary 'xspecfem2D' will return 'solver'. Helps keep log file + naming consistent and generalizable - dst = os.path.join(path, "residuals", self.source_name) - unix.mv(src, dst) + :type exc: str + :param exc: specfem executable, e.g., xspecfem2D, xgenerate_databases + :rtype: str + :return: logfile name that matches executable name + """ + convert_dict = {"specfem": "solver", "meshfem": "mesher", + "generate_databases": "mesher", "smooth": "smooth", + "combine": "combine"} + for key, val in convert_dict.items(): + if key in exc: + return val + else: + return "logger" - def _export_traces(self, path, prefix="traces/obs"): + def import_model(self, path_model): """ - File transfer utility. Export traces to disk. + Copy files from given `path_model` into the current working directory + model database. Used for grabbing starting models (e.g., MODEL_INIT) + and models that have been perturbed by the optimization library. - :type path: str - :param path: path to save traces - :type prefix: str - :param prefix: location of traces w.r.t self.cwd + :type path_model: str + :param path_model: path to an existing starting model """ - if self.taskid == 0: - logger.debug("exporting traces to {path} {prefix}") - - unix.mkdir(os.path.join(path)) + assert(os.path.exists(path_model)), f"model {path_model} does not exist" + unix.cd(self.cwd) - src = os.path.join(self.cwd, prefix) - dst = os.path.join(path, self.source_name) + # Copy the model files (ex: proc000023_vp.bin ...) into database dir + src = glob(os.path.join(path_model, "*")) + dst = os.path.join(self.cwd, self.model_databases, "") unix.cp(src, dst) - @staticmethod - def _rename_kernels(): - """ - Works around conflicting kernel filename conventions by renaming - `alpha` to `vp` and `beta` to `vs` - """ - # Rename 'alpha' to 'vp' - for tag in ["alpha", "alpha[hv]", "reg1_alpha", "reg1_alpha[hv]"]: - names = glob(f"*proc??????_{tag}_kernel.bin") - unix.rename(old="alpha", new="vp", names=names) - - # Rename 'beta' to 'vs' - for tag in ["beta", "beta[hv]", "reg1_beta", "reg1_beta[hv]"]: - names = glob(f"*proc??????_{tag}_kernel.bin") - unix.rename(old="beta", new="vs", names=names) - def _initialize_working_directories(self): """ Serial task used to initialize working directories for each of the a @@ -846,7 +857,7 @@ def _initialize_working_directories(self): """ logger.info(f"initializing {self.ntask} solver directories") for source_name in self.source_names: - cwd = os.path.join(self.path, source_name) + cwd = os.path.join(self.path.scratch, source_name) self._initialize_working_directory(cwd=cwd) def _initialize_working_directory(self, cwd=None): @@ -898,13 +909,13 @@ def _initialize_working_directory(self, cwd=None): # Copy all input DATA/ files except the source files src = glob(os.path.join(self.path.specfem_data, "*")) src = [_ for _ in src if self.source_prefix not in _] - dst = os.path.join("DATA", "") + dst = os.path.join(cwd, "DATA", "") unix.cp(src, dst) # Symlink event source specifically, only retain source prefix src = os.path.join(self.path.specfem_data, f"{self.source_prefix}_{self.source_name}") - dst = os.path.join("DATA", self.source_prefix) + dst = os.path.join(cwd, "DATA", self.source_prefix) unix.ln(src, dst) # Symlink TaskID==0 as mainsolver in solver directory for convenience @@ -913,38 +924,38 @@ def _initialize_working_directory(self, cwd=None): logger.debug(f"symlink {self.source_name} as 'mainsolver'") unix.ln(cwd, self.path.mainsolver) - def _initialize_adjoint_traces(self): - """ - Setup utility: Creates the "adjoint traces" expected by SPECFEM. - This is only done for the 'base' the Preprocess class. - - TODO move this into workflow setup - - .. note:: - Adjoint traces are initialized by writing zeros for all channels. - Channels actually in use during an inversion or migration will be - overwritten with nonzero values later on. - """ - preprocess = self.module("preprocess") - - if self.par.PREPROCESS.upper() == "DEFAULT": - if self.taskid == 0: - logger.debug(f"intializing {len(self.data_filenames)} " - f"empty adjoint traces per event") - - for filename in self.data_filenames: - st = preprocess.reader( - path=os.path.join(self.cwd, "traces", "obs"), - filename=filename - ) - # Zero out data just so we have empty adjoint traces as SPECFEM - # will expect all adjoint sources to have all components - st *= 0 - - # Write traces back to the adjoint trace directory - preprocess.writer(st=st, filename=filename, - path=os.path.join(self.cwd, "traces", "adj") - ) + # def _initialize_adjoint_traces(self): + # """ + # Setup utility: Creates the "adjoint traces" expected by SPECFEM. + # This is only done for the 'base' the Preprocess class. + # + # TODO move this into workflow setup + # + # .. note:: + # Adjoint traces are initialized by writing zeros for all channels. + # Channels actually in use during an inversion or migration will be + # overwritten with nonzero values later on. + # """ + # preprocess = self.module("preprocess") + # + # if self.par.PREPROCESS.upper() == "DEFAULT": + # if self.taskid == 0: + # logger.debug(f"intializing {len(self.data_filenames)} " + # f"empty adjoint traces per event") + # + # for filename in self.data_filenames: + # st = preprocess.reader( + # path=os.path.join(self.cwd, "traces", "obs"), + # filename=filename + # ) + # # Zero out data just so we have empty adjoint traces as SPECFEM + # # will expect all adjoint sources to have all components + # st *= 0 + # + # # Write traces back to the adjoint trace directory + # preprocess.writer(st=st, filename=filename, + # path=os.path.join(self.cwd, "traces", "adj") + # ) def _check_source_names(self): """ @@ -956,7 +967,7 @@ def _check_source_names(self): :return: alphabetically ordered list of source names up to PAR.NTASK """ assert(self.path.specfem_data is not None), \ - f"solver source names requires 'solver.path.specefm_data' to exist" + f"solver source names requires 'solver.path.specfem_data' to exist" assert(os.path.exists(self.path.specfem_data)), \ f"solver source names requires 'solver.path.specfem_data' to exist" @@ -972,6 +983,11 @@ def _check_source_names(self): ) ) sys.exit(-1) + else: + assert(len(fids) >= self.ntask), ( + f"Number of requested tasks/events {self.ntask} exceeds number " + f"of available sources {len(fids)}" + ) # Create internal definition of sources names by stripping prefixes names = [os.path.basename(fid).split("_")[-1] for fid in fids] diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index d8f00bbe..e536b1be 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -15,20 +15,20 @@ class Specfem2D(Specfem): """ - Python interface to Specfem2D. + SPECFEM2D-specific parameters + + :type source_prefix: str + :param source_prefix: Prefix of source files in path SPECFEM_DATA. Defaults + to 'SOURCE' + :type multiples: bool + :param multiples: set an absorbing top-boundary condition """ - def __init__(self, source_prefix=None, multiples=False, **kwargs): - """ - SPECFEM2D specific parameters + __doc__ = Specfem.__doc__ + __doc__ - :type source_prefix: str - :param source_prefix: Prefix of SOURCE files in path SPECFEM_DATA. - :type multiples: bool - :param multiples: set an absorbing top-boundary condition - """ - super().__init__(**kwargs) + def __init__(self, source_prefix="SOURCE", multiples=False, **kwargs): + """Instantiate a Specfem2D solver interface""" + super().__init__(source_prefix=source_prefix, **kwargs) - self.source_prefix = source_prefix or "SOURCE" self.multiples = multiples self.f0 = None @@ -39,24 +39,21 @@ def __init__(self, source_prefix=None, multiples=False, **kwargs): self._parameters.append("vp") self._parameters.append("vs") - self._acceptable_source_prefix = ["SOURCE", "FORCE", "FORCESOLUTION"] - def setup(self): - """ - Additional SPECFEM2D setup steps - """ - super().setup() - - self.f0 = getpar(key="f0", - file=os.path.join(self.cwd, "DATA/SOURCE"))[1] + """Setup the SPECFEM2D solver interface in a SeisFlows workflow""" + source_file = os.path.join(self.path.specfem_data, self.source_prefix) + self.f0 = getpar(key="f0", file=source_file)[1] + par_file = os.path.join(self.path.specfem_data, "Par_file") if self.multiples: - setpar(key="absorbtop", val=".false.", file="DATA/Par_file") + setpar(key="absorbtop", val=".false.", file=par_file) else: - setpar(key="absorbtop", val=".true.", file="DATA/Par_file") + setpar(key="absorbtop", val=".true.", file=par_file) + + super().setup() def smooth(self, input_path, output_path, parameters=None, span_h=0., - span_v=0., use_gpu=False, output="smooth.log"): + span_v=0., use_gpu=False): """ Specfem2D requires additional model parameters in directory to perform the xsmooth_sem task. This function will copy these files into the @@ -79,19 +76,13 @@ def smooth(self, input_path, output_path, parameters=None, span_h=0., :param span_h: horizontal smoothing length in meters :type span_v: float :param span_v: vertical smoothing length in meters - :type output: str - :param output: file to output stdout to :type use_gpu: bool :param use_gpu: whether to use GPU acceleration for smoothing. Requires GPU compiled binaries and GPU compute node. """ - # Redundant to 'base' class but necessary - if not os.path.exists(input_path): - unix.mkdir(input_path) + unix.cd(os.path.join(self.cwd, self.model_databases)) - unix.cd(os.path.join(self.cwd, "DATA")) - - # Copy over only the files that are required. Won't execute if no match + # SPECFEM2D requires these files to run the smoother files = [] for tag in ["jacobian", "NSPEC_ibool", "x", "y", "z"]: files += glob(f"*_{tag}.bin") @@ -99,51 +90,4 @@ def smooth(self, input_path, output_path, parameters=None, span_h=0., unix.cp(src=src, dst=input_path) super().smooth(input_path=input_path, output_path=output_path, - parameters=parameters, span_h=span_h, span_v=span_v, - output=output) - - def _initialize_adjoint_traces(self): - """ - Setup utility: Creates the "adjoint traces" expected by SPECFEM. - This is only done for the 'base' the Preprocess class. - - .. note:: - Adjoint traces are initialized by writing zeros for all channels. - Channels actually in use during an inversion or migration will be - overwritten with nonzero values later on. - """ - super()._initialize_adjoint_traces() - - unix.cd(os.path.join(self.cwd, "traces", "adj")) - - # work around SPECFEM2D's use of different name conventions for - # regular traces and 'adjoint' traces - if self.data_format.upper() == "SU": - files = glob("*SU") - unix.rename(old="_SU", new="_SU.adj", names=files) - elif self.data_format.upper() == "ASCII": - files = glob("*sem?") - - # Get the available extensions, which are named based on unit - extensions = set([os.path.splitext(_)[-1] for _ in files]) - for extension in extensions: - unix.rename(old=extension, new=".adj", names=files) - - # SPECFEM2D requires that all components exist even if ununsed - components = ["x", "y", "z", "p"] - - if self.data_format.upper() == "SU": - for comp in components: - src = f"U{self.components[0]}_file_single.su.adj" - dst = f"U{comp.lower()}s_file_single.su.adj" - if not os.path.exists(dst): - unix.cp(src, dst) - elif self.data_format.upper() == "ASCII": - for fid in glob("*.adj"): - net, sta, cha, ext = fid.split(".") - for comp in components: - # Replace the last value in the channel with new component - cha_check = cha[:-1] + comp.upper() - fid_check = ".".join([net, sta, cha_check, ext]) - if not os.path.exists(fid_check): - unix.cp(fid, fid_check) + parameters=parameters, span_h=span_h, span_v=span_v) diff --git a/seisflows/solver/specfem3d.py b/seisflows/solver/specfem3d.py index 6f5f5552..c9a1274c 100644 --- a/seisflows/solver/specfem3d.py +++ b/seisflows/solver/specfem3d.py @@ -6,30 +6,28 @@ and overwrites these functions to provide specified interaction with Specfem3D """ import os -from glob import glob -from seisflows.solver.specfem import Specfem from seisflows.tools import unix -from seisflows.tools.utils import exists from seisflows.tools.specfem import setpar, getpar +from seisflows.solver.specfem import Specfem class Specfem3D(Specfem): """ - Python interface to Specfem3D Cartesian. + SPECFEM3D_Cartesian specific parameters + + :type source_prefix: str + :param source_prefix: Prefix of source files in path SPECFEM_DATA. Defaults + to 'CMTSOLUTION' + :type multiples: bool + :param multiples: set an absorbing top-boundary condition """ - def __init__(self, source_prefix=None, **kwargs): - """ - SPECFEM2D specific parameters + __doc__ = Specfem.__doc__ + __doc__ - :type source_prefix: str - :param source_prefix: Prefix of SOURCE files in path SPECFEM_DATA. - :type multiples: bool - :param multiples: set an absorbing top-boundary condition - """ - super().__init__(**kwargs) + def __init__(self, source_prefix="CMTSOLUTION", **kwargs): + """Instantiate a Specfem3D_Cartesian solver interface""" - self.source_prefix = source_prefix or "CMTSOLUTION" + super().__init__(source_prefix=source_prefix, **kwargs) # Define parameters based on material type if self.materials.upper() == "ACOUSTIC": @@ -38,12 +36,18 @@ def __init__(self, source_prefix=None, **kwargs): self._parameters.append("vp") self._parameters.append("vs") + # Overwriting the base class parameters self._acceptable_source_prefixes = ["CMTSOLUTION", "FORCESOLUTION"] + self._required_binaries = ["xspecfem3D", "xmeshfem3D", + "xgenerate_databases" "xcombine_sem", + "xsmooth_sem"] def data_wildcard(self, comp="?"): """ Returns a wildcard identifier for synthetic data + TODO where does SU put its component? + :rtype: str :return: wildcard identifier for channels """ @@ -70,111 +74,75 @@ def kernel_databases(self): """ return self.model_databases - def eval_func(self, path, preprocess=None): - """ - Performs forward simulations and evaluates the misfit function using - the preprocess module. Overrides to add a data renaming call - - .. note:: - This task should be run in parallel by system.run() - - :type path: str - :param path: directory from which model is imported and where residuals - will be exported - :type write_residuals: bool - :param write_residuals: calculate and export residuals """ - super().eval_func(path=path, preprocess=preprocess) - - # Work around SPECFEM3D conflicting name conventions of SU data - self._rename_data() - - def forward_simulation(self, output_seismograms=False): + def forward_simulation(self, executables=None, save_traces=False, + export_traces=False): """ Calls SPECFEM3D forward solver, exports solver outputs to traces dir - :type output_seismograms: str - :param output_seismograms: path to export traces to after completion of - simulation expected values are either 'traces/obs' for 'observation' - data (i.e., synthetics generated by the TRUE model), or - 'traces/syn', for synthetics generated during function evaluations. - If False, will leave seismograms in OUTPUT_FILES/s - """ - unix.cd(self.cwd) + :type executables: list or None + :param executables: list of SPECFEM executables to run, in order, to + complete a forward simulation. This can be left None in most cases, + which will select default values based on the specific solver + being called (2D/3D/3D_GLOBE). It is made an optional parameter + to keep the function more general for inheritance purposes. + :type save_traces: str + :param save_traces: move files from their native SPECFEM output location + to another directory. This is used to move output waveforms to + 'traces/obs' or 'traces/syn' so that SeisFlows knows where to look + for them, and so that SPECFEM doesn't overwrite existing files + during subsequent forward simulations + :type export_traces: str + :param export_traces: export traces from the scratch directory to a more + permanent storage location. i.e., copy files from their original + location + """ + if executables is None: + executables = ["bin/xgenerate_databases", "bin/xspecfem3D"] - # Set parameters and run forward simulation - setpar(key="SIMULATION_TYPE", val="1", file="DATA/Par_file") - setpar(key="SAVE_FORWARD", val=".true.", file="DATA/Par_file") + unix.cd(self.cwd) + # SPECFEM3D has to deal with attenuation if self.attenuation: setpar(key="ATTENUATION", val=".true.", file="DATA/Par_file") else: setpar(key="ATTENUATION", val=".false`.", file="DATA/Par_file") - self._call_solver(executable="bin/xgenerate_databases", - output="fwd_mesher.log") - self._call_solver(executable="bin/xmeshfem3D", output="fwd_solver.log") - - # Find and move output traces, by default to synthetic traces dir - if output_seismograms: - unix.mv( - src=glob(os.path.join("OUTPUT_FILES", self.data_wildcard())), - dst=output_seismograms - ) + super().forward_simulation(executables=executables, + save_traces=save_traces, + export_traces=export_traces + ) - def adjoint_simulation(self): + def adjoint_simulation(self, executables=None, save_kernels=False, + export_kernels=False): """ Calls SPECFEM3D adjoint solver, creates the `SEM` folder with adjoint traces which is required by the adjoint solver - """ - unix.cd(self.cwd) - setpar(key="SIMULATION_TYPE", val="3", file="DATA/Par_file") - setpar(key="SAVE_FORWARD", val=".false.", file="DATA/Par_file") - - # Attenuation should always be OFF during adjoint simulations, else - # you will get a floating point error + :type executables: list or None + :param executables: list of SPECFEM executables to run, in order, to + complete an adjoint simulation. This can be left None in most cases, + which will select default values based on the specific solver + being called (2D/3D/3D_GLOBE). It is made an optional parameter + to keep the function more general for inheritance purposes. + :type save_kernels: str + :param save_kernels: move the kernels from their native SPECFEM output + location to another path. This is used to move kernels to another + SeisFlows scratch directory so that they are discoverable by + other modules. The typical location they are moved to is + path_eval_grad + :type export_kernels: str + :param export_kernels: export/copy/save kernels from the scratch + directory to a more permanent storage location. i.e., copy files + from their original location. Note that kernel file sizes are LARGE, + so exporting kernels can lead to massive storage requirements. + """ + if executables is None: + executables = ["bin/xspecfem3D"] + + # Make sure attenuation is OFF, if ON you'll get a floating point error + unix.cd(self.cwd) setpar(key="ATTENUATION", val=".false.", file="DATA/Par_file") - unix.rm("SEM") - unix.ln("traces/adj", "SEM") - - self._call_solver(executable="bin/xspecfem3D", output="adj_solver.log") - - def _initialize_adjoint_traces(self): - """ - Setup utility: Creates the "adjoint traces" expected by SPECFEM - - .. note:: - Adjoint traces are initialized by writing zeros for all channels. - Channels actually in use during an inversion or migration will be - overwritten with nonzero values later on. - """ - # Initialize adjoint traces as zeroes for all data_filenames - # write to `traces/adj` - super()._initialize_adjoint_traces() - - # Rename data to work around Specfem naming convetions - self._rename_data() - - # Workaround for Specfem3D's requirement that all components exist, - # even ones not in use as adjoint traces - if self.data_format.upper() == "SU": - unix.cd(os.path.join(self.cwd, "traces", "adj")) - - for iproc in range(self.nproc): - for channel in ["x", "y", "z"]: - dst = f"{iproc:d}_d{channel}_SU.adj" - if not os.path.exists(dst): - src = f"{iproc:d}_d{self.components[0]}_SU.adj" - unix.cp(src, dst) - - def _rename_data(self): - """ - Works around conflicting data filename conventions - - Specfem3D's uses different name conventions for regular traces - and 'adjoint' traces - """ - if self.data_format.upper() == "SU": - files = glob(os.path.join(self.cwd, "traces", "adj", "*SU")) - unix.rename(old='_SU', new='_SU.adj', names=files) + super().adjoint_simulation(executables=executables, + save_kernels=save_kernels, + export_kernels=export_kernels) diff --git a/seisflows/system/workstation.py b/seisflows/system/workstation.py index 1109a72a..9e1b301f 100644 --- a/seisflows/system/workstation.py +++ b/seisflows/system/workstation.py @@ -24,7 +24,7 @@ def __init__(self, title=None, mpiexec=None, ntask=1, nproc=1, path_system=None, path_output_log=None, path_error_log=None, path_log_files=None, path_par_file=None, **kwargs): """ - Instantiate the Workstation base class + Workstation System Class Parameters :type title: str :param title: The name used to submit jobs to the system, defaults diff --git a/seisflows/tests/test_data/test_preprocess/AA.S0001.BXY.adj b/seisflows/tests/test_data/test_preprocess/AA.S0001.BXY.adj new file mode 100644 index 00000000..a0de276c --- /dev/null +++ b/seisflows/tests/test_data/test_preprocess/AA.S0001.BXY.adj @@ -0,0 +1,5000 @@ + -48.0000000 0.0000000 + -47.9400000 0.0000000 + -47.8800000 0.0000000 + -47.8200000 0.0000000 + -47.7600000 0.0000000 + -47.7000000 0.0000000 + -47.6400000 0.0000000 + -47.5800000 0.0000000 + -47.5200000 0.0000000 + -47.4600000 0.0000000 + -47.4000000 0.0000000 + -47.3400000 0.0000000 + -47.2800000 0.0000000 + -47.2200000 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/dev/null +++ b/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_003 @@ -0,0 +1 @@ +EVENT 3 diff --git a/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_004 b/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_004 new file mode 100644 index 00000000..ccb69325 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_004 @@ -0,0 +1 @@ +EVENT 4 diff --git a/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_005 b/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_005 new file mode 100644 index 00000000..0cea6274 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_005 @@ -0,0 +1 @@ +EVENT 5 diff --git a/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_006 b/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_006 new file mode 100644 index 00000000..52c713f0 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_006 @@ -0,0 +1 @@ +EVENT 6 diff --git a/seisflows/tests/test_data/test_solver/mainsolver/DATA/Par_file b/seisflows/tests/test_data/test_solver/mainsolver/DATA/Par_file new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/mainsolver/DATA/Par_file @@ -0,0 +1 @@ + diff --git a/seisflows/tests/test_data/test_solver/mainsolver/DATA/STATIONS b/seisflows/tests/test_data/test_solver/mainsolver/DATA/STATIONS new file mode 100644 index 00000000..7cffad4d --- /dev/null +++ b/seisflows/tests/test_data/test_solver/mainsolver/DATA/STATIONS @@ -0,0 +1 @@ + ABC XX -99.999 -66.666 0.0 0.0 diff --git a/seisflows/tests/test_data/test_solver/mainsolver/bin/xcombine_sem b/seisflows/tests/test_data/test_solver/mainsolver/bin/xcombine_sem new file mode 100755 index 00000000..969a4d93 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/mainsolver/bin/xcombine_sem @@ -0,0 +1,3 @@ +#!/bin/bash -e +echo "xcombine_sem" + diff --git a/seisflows/tests/test_data/test_solver/mainsolver/bin/xmeshfem2D b/seisflows/tests/test_data/test_solver/mainsolver/bin/xmeshfem2D new file mode 100755 index 00000000..149ca704 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/mainsolver/bin/xmeshfem2D @@ -0,0 +1,2 @@ +#!/bin/bash -e +echo "xmeshfem2D" diff --git a/seisflows/tests/test_data/test_solver/mainsolver/bin/xsmooth_sem b/seisflows/tests/test_data/test_solver/mainsolver/bin/xsmooth_sem new file mode 100755 index 00000000..376b4aa5 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/mainsolver/bin/xsmooth_sem @@ -0,0 +1,3 @@ +#!/bin/bash -e +echo "xsmooth_sem" + diff --git a/seisflows/tests/test_data/test_solver/mainsolver/bin/xspecfem2D b/seisflows/tests/test_data/test_solver/mainsolver/bin/xspecfem2D new file mode 100755 index 00000000..e50c2b0b --- /dev/null +++ b/seisflows/tests/test_data/test_solver/mainsolver/bin/xspecfem2D @@ -0,0 +1,3 @@ +#!/bin/bash -e +echo "xspecfem2D" + diff --git a/seisflows/tests/test_data/test_file_formats/proc000000_rho_vp_vs.dat b/seisflows/tests/test_data/test_tools/test_file_formats/proc000000_rho_vp_vs.dat similarity index 100% rename from seisflows/tests/test_data/test_file_formats/proc000000_rho_vp_vs.dat rename to seisflows/tests/test_data/test_tools/test_file_formats/proc000000_rho_vp_vs.dat diff --git a/seisflows/tests/test_data/test_file_formats/proc000000_vp.bin b/seisflows/tests/test_data/test_tools/test_file_formats/proc000000_vp.bin similarity index 100% rename from seisflows/tests/test_data/test_file_formats/proc000000_vp.bin rename to seisflows/tests/test_data/test_tools/test_file_formats/proc000000_vp.bin diff --git a/seisflows/tests/test_data/test_file_formats/proc000000_vs.bin b/seisflows/tests/test_data/test_tools/test_file_formats/proc000000_vs.bin similarity index 100% rename from seisflows/tests/test_data/test_file_formats/proc000000_vs.bin rename to seisflows/tests/test_data/test_tools/test_file_formats/proc000000_vs.bin diff --git a/seisflows/tests/test_preprocess.py b/seisflows/tests/test_preprocess.py new file mode 100644 index 00000000..aace73ae --- /dev/null +++ b/seisflows/tests/test_preprocess.py @@ -0,0 +1,95 @@ +""" +Test the ability of the Solver module to interact with various versions of +SPECFEM +""" +import os +import pytest +from glob import glob +from seisflows.config import ROOT_DIR +from seisflows.preprocess.default import Default + + +TEST_DATA = os.path.join(ROOT_DIR, "tests", "test_data", "test_preprocess") + + +def test_read(): + """ + Test that we can read SPECFEM generated synthetics with the preprocess mod. + """ + # If new data formats are added to preprocess, they need to be tested + tested_data_formats = ["ASCII", "SU"] + preprocess = Default() + assert(set(tested_data_formats) == set(preprocess._acceptable_data_formats)) + + preprocess = Default(data_format="ascii") + st1 = preprocess.read(os.path.join(TEST_DATA, "AA.S0001.BXY.semd")) + + preprocess = Default(data_format="su") + st2 = preprocess.read(os.path.join(TEST_DATA, "Uy_file_single_d.su")) + + assert(st1[0].stats.npts == st2[0].stats.npts) + + +def test_write(tmpdir): + """ + Make sure we can write both data formats + """ + # If new data formats are added to preprocess, they need to be tested + tested_data_formats = ["ASCII", "SU"] + preprocess = Default() + assert(set(tested_data_formats) == set(preprocess._acceptable_data_formats)) + + preprocess = Default(data_format="ascii") + st1 = preprocess.read(os.path.join(TEST_DATA, "AA.S0001.BXY.semd")) + preprocess.write(st1, fid=os.path.join(tmpdir, "test_stream_ascii")) + + preprocess.data_format = "SU" + preprocess.write(st1, fid=os.path.join(tmpdir, "test_stream_su")) + + +def test_initialize_adjoint_traces(tmpdir): + """ + Make sure we can write empty adjoint sources expected by SPECFEM + """ + preprocess = Default(data_format="ascii") + data_filenames = glob(os.path.join(TEST_DATA, "*semd")) + preprocess.initialize_adjoint_traces(data_filenames=data_filenames, + output=tmpdir) + + preprocess.data_format = "SU" + data_filenames = glob(os.path.join(TEST_DATA, "*su")) + preprocess.initialize_adjoint_traces(data_filenames=data_filenames, + output=tmpdir) + + assert(len(glob(os.path.join(tmpdir, "*"))) == 2) + for fid in glob(os.path.join(tmpdir, "*")): + assert(fid.endswith(".adj")) + + +def test_quantify_misfit(tmpdir): + """ + Quantify misfit with some example data + """ + preprocess = Default(data_format="ascii", misfit="waveform", + adjoint="waveform", path_preprocess=tmpdir) + preprocess.setup() + + data_filenames = glob(os.path.join(TEST_DATA, "*semd")) + preprocess.quantify_misfit( + observed=data_filenames, synthetic=data_filenames, + write_residuals=os.path.join(tmpdir, "residuals_ascii"), + write_adjsrcs=tmpdir + ) + + preprocess.data_format = "SU" + data_filenames = glob(os.path.join(TEST_DATA, "*su")) + preprocess.quantify_misfit( + observed=data_filenames, synthetic=data_filenames, + write_residuals=os.path.join(tmpdir, "residuals_su"), + write_adjsrcs=tmpdir + ) + + assert(len(glob(os.path.join(tmpdir, "*"))) == 4) + residuals = open(os.path.join(tmpdir, "residuals_ascii")).readlines() + assert(len(residuals) == 1) + assert(float(residuals[0]) == 0) diff --git a/seisflows/tests/test_solver.py b/seisflows/tests/test_solver.py index 17009b1e..7fefb361 100644 --- a/seisflows/tests/test_solver.py +++ b/seisflows/tests/test_solver.py @@ -5,13 +5,9 @@ import os import pytest from glob import glob -from seisflows.tools.specfem import Model -from seisflows.config import ROOT_DIR, NAMES, CFGPATHS - +from seisflows.config import ROOT_DIR +from seisflows.tools.utils import set_task_id from seisflows.solver.specfem import Specfem -from seisflows.solver.specfem2d import Specfem2D -from seisflows.solver.specfem3d import Specfem3D -from seisflows.solver.specfem3d_globe import Specfem3DGlobe TEST_DATA = os.path.join(ROOT_DIR, "tests", "test_data", "test_solver") @@ -20,6 +16,8 @@ def test_taskid(): """ Make sure that task id returns correctly + + TODO move this into test_utils """ solver = Specfem() assert(solver.taskid == 0) @@ -45,10 +43,50 @@ def test_source_names(): assert(source_names == solver.source_names) -def test_data_filenames(): +def test_initialize_working_directory(tmpdir): """ Test that data filenames are returned correctly """ - sources = os.path.join(TEST_DATA, "test_solver", "sources") - solver = Specfem(path_specfem_data=sources, source_prefix="CMTSOLUTION") - pytest.set_trace() \ No newline at end of file + specfem_data = os.path.join(TEST_DATA, "mainsolver", "DATA") + specfem_bin = os.path.join(TEST_DATA, "mainsolver", "bin") + + solver = Specfem(path_specfem_data=specfem_data, + path_specfem_bin=specfem_bin, + source_prefix="CMTSOLUTION", workdir=tmpdir + ) + + assert(not os.path.exists(solver.path.mainsolver)) + + # Set the environment task id so that the logger doesn't throw warnings + # about not finding the task id + set_task_id(0) + + # Generate the required directory structure + solver._initialize_working_directory(cwd=tmpdir) + + # Simple checks to make sure the directory structure was set up properly + assert(os.path.islink(solver.path.mainsolver)) + assert(os.path.exists(solver.cwd)) + assert(glob(os.path.join(solver.cwd, "*"))) + event_fid = os.path.join(solver.cwd, "DATA", "CMTSOLUTION") + assert(os.path.islink(event_fid)) + event_line = open(event_fid).readlines()[0].strip() + assert(event_line == "EVENT 1") + + +def test_run_binary(): + """ + Just run a known and intentially incorrect binary with the run binary + function to check that we can, and that error catching is working + """ + solver = Specfem() + solver._run_binary(executable="echo hello world") + + # Executables that don't exist will not run + with pytest.raises(SystemExit): + solver._run_binary(executable="gobbledigook") + + # Executables that do exist but error out (e.g., with invalid options) will + # also throw an error + with pytest.raises(SystemExit): + solver._run_binary(executable="ls -//daflkjeaf") diff --git a/seisflows/tests/test_tools.py b/seisflows/tests/test_tools.py index a5188056..cccd1fc9 100644 --- a/seisflows/tests/test_tools.py +++ b/seisflows/tests/test_tools.py @@ -18,13 +18,15 @@ def test_specfem_model(tmpdir): TODO eventually we want to split this into multiple tests that test each TODO of the IO formats (binary, adios etc.). Currently only testing binary. """ + model_data = os.path.join(TEST_DIR, "test_data", "test_tools", + "test_file_formats") + # Make sure that multiple acceptable file extensions throw error with pytest.raises(AssertionError): - m = Model(path=os.path.join(TEST_DIR, "test_data", "test_file_formats"), - read=True) + Model(path=model_data) + # Check that model values are read in correctly - m = Model(path=os.path.join(TEST_DIR, "test_data", "test_file_formats"), - read=True, fmt=".bin") + m = Model(path=model_data, fmt=".bin") assert(m.ngll[0] == 40000) assert(m.nproc == 1) assert("vp" in m.model.keys()) diff --git a/seisflows/tools/unix.py b/seisflows/tools/unix.py index abb82e51..5575c863 100644 --- a/seisflows/tools/unix.py +++ b/seisflows/tools/unix.py @@ -8,7 +8,7 @@ import random import shutil import socket - +import subprocess from seisflows.tools.utils import iterable @@ -224,7 +224,7 @@ def touch(filename, times=None): def which(name): """ - Shows the full path of shell commands + Shows the full path of shell commands and executables :type name: str :param name: name of shell command to check @@ -245,3 +245,30 @@ def isexe(file): else: return None + +def nproc(): + """ + Get the number of processors available. Same as calling 'nproc' from + Linux command line. + + :rtype: int + :return: number of processors + :raises EnvironmentError: if nproc cannot be determined + """ + # Method 1 calls 'nproc'. May fail and return '' if 'nproc' not avail. + _nproc = subprocess.run("nproc", shell=True, text=True, + stdout=subprocess.PIPE).stdout.strip() + # Method 2 checks /proc/cpuinfo + if not _nproc: + if os.path.exists("/proc/cpuinfo"): + processors = 0 + lines = open("/proc/cpuinfo", "r").readlines() + for line in lines: + if line.startswith("processor"): + processors += 1 + if processors: + _nproc = processors + if not _nproc: + raise EnvironmentError("Could not access 'nproc' information on system") + + return int(_nproc) diff --git a/seisflows/tools/utils.py b/seisflows/tools/utils.py index 6797444d..fe602433 100644 --- a/seisflows/tools/utils.py +++ b/seisflows/tools/utils.py @@ -4,16 +4,57 @@ """ import os import re -import time import yaml -import subprocess import numpy as np -from importlib import import_module -from pkgutil import find_loader from seisflows.core import Dict from seisflows import logger +def log_status(func): + """ + Decorator function that logs the completion status of a function to a + state file. This is used for checkpointing a workflow and resuming + failed workflows without repeating computational intense tasks + """ + STATE_FILE = os.path.join(os.getcwd(), "sfstatefile") + + def logged_func(): + """Log the completion status of the function""" + try: + func() + with open(STATE_FILE, "a") as f: + f.write(f"{func.__name__}\tCOMPLETED") + except Exception as e: + f.write(f"{func.__name__}\tFAILED") + logger.error(e) + raise + + lines = open(STATE_FILE, "r").readlines() + for line in lines: + function, status = line.split(" ") + if func.__name__ == function: + if status == "COMPLETE": + return + elif status == "FAILED": + return logged_func() + else: + return logged_func() + + +def set_task_id(task_id): + """ + Set the SEISFLOWS_TASKID in os environs + + .. note:: + Mostly used for debugging/testing purposes as a way of mimicing + system.run() assigning task ids to child processes + + :type task_id: int + :param task_id: integer task id to assign to the current working environment + """ + os.environ["SEISFLOWS_TASKID"] = str(task_id) + + def get_task_id(): """ Task IDs are assigned to each child process spawned by the system module @@ -109,51 +150,3 @@ def number_fid(fid, i=0): new_fid = fid_only.replace(ext, new_ext) return new_fid - -def nproc(): - """ - Get the number of processors available - - :rtype: int - :return: number of processors - """ - try: - return _nproc_method1() - except EnvironmentError: - return _nproc_method2() - - -def _nproc_method1(): - """ - Used subprocess to determine the number of processeors available - - :rtype: int - :return: number of processors - """ - # Check if the command `nproc` works - if not subprocess.getstatusoutput('nproc')[0] == 0: - raise EnvironmentError - - num_proc = int(subprocess.getstatusoutput('nproc')[1]) - - return num_proc - - -def _nproc_method2(): - """ - Get number of processors using /proc/cpuinfo - - Bryant: This doesnt work? - - :rtype: int - :return: number of processors - """ - if not os.path.exists('/proc/cpuinfo'): - raise EnvironmentError - - stdout = subprocess.check_output( - "cat /proc/cpuinfo | awk '/^processor/{print $3}'", shell=True) - num_proc = len(stdout.split('\n')) - - return num_proc - diff --git a/seisflows/workflow/forward_test.py b/seisflows/workflow/forward_test.py index a14d7e2a..bfdf17d7 100644 --- a/seisflows/workflow/forward_test.py +++ b/seisflows/workflow/forward_test.py @@ -2,12 +2,16 @@ Test workflow to see if a new form of seisflows workflow can be used """ import os +from glob import glob from seisflows import logger from seisflows.config import import_seisflows from seisflows.tools import msg +from seisflows.tools.utils import log_status +from seisflows.config import config_logger # Standard SeisFlows setup, makes modules global variables to the workflow pars, modules = import_seisflows() +# config_logger(level=pars.log_level, filename=pars.path_log_ system, preprocess, solver, postprocess, optimize = modules @@ -42,6 +46,35 @@ def evaluate_objective_function(path_model): ) +@log_status +def run_forward_simulation(path_model): + """ + + """ + # Run the forward simulation with the given input model + solver.import_model(path_model=path_model) + solver.forward_simulation( + save_traces=os.path.join(solver.cwd, "traces", "syn"), + export_traces=os.path.join(pars.path_output, solver.source_name, "syn") + ) + + +@log_status +def quantify_misfit(): + """ + Quantify the data-synthetic misfit, write residuals to scratch for use in + potential optimization, generate adjoint sources required for adjoint + simulations + """ + preprocess.quantify_misfit( + observed=solver.data_filenames(choice="obs"), + synthetics=solver.data_filenames(choice="syn"), + write_adjsrcs=os.path.join(solver.cwd, "traces", "adj"), + write_residuals=os.path.join(pars.path_eval_grad, solver.source_name) + ) + preprocess.sum_residuals(files=glob(os.path.join(pars.path_eval_grad, "*"))) + + if __name__ == "__main__": # Begin the forward simulation workflow logger.info(msg.mjr("Starting forward simulation workflow")) From 64d87984090a33a1568cf47463e551ce8d944279 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Wed, 13 Jul 2022 19:25:43 -0800 Subject: [PATCH 067/195] workin g on new forward workflow which is given in script form rather than class definition. --- seisflows/preprocess/default.py | 110 +++---- seisflows/solver/specfem.py | 119 ++------ seisflows/solver/specfem2d.py | 7 +- seisflows/solver/specfem3d.py | 7 +- seisflows/solver/specfem3d_globe.py | 305 +++++++++----------- seisflows/system/runscripts/run_function.py | 1 - seisflows/system/slurm.py | 2 +- seisflows/system/workstation.py | 89 +++--- seisflows/tools/utils.py | 27 +- seisflows/workflow/forward_test.py | 42 +-- 10 files changed, 294 insertions(+), 415 deletions(-) diff --git a/seisflows/preprocess/default.py b/seisflows/preprocess/default.py index 96057a24..e103f9e8 100644 --- a/seisflows/preprocess/default.py +++ b/seisflows/preprocess/default.py @@ -20,10 +20,59 @@ class Default: """ - Default SeisFlows preprocessing class - - Provides data processing functions for seismic traces, with options for - data misfit, filtering, normalization and muting + [preprocess.default] SeisFlows preprocessing module provides data processing + functions for seismic traces, with options for data misfit, filtering, + normalization and muting. + + :type data_format: str + :param data_format: data format for reading traces into memory. For + available see: seisflows.plugins.preprocess.readers + :type misfit: str + :param misfit: misfit function for waveform comparisons. For available + see seisflows.plugins.preprocess.misfit + :type backproject: str + :param backproject: backprojection function for migration, or the + objective function in FWI. For available see + seisflows.plugins.preprocess.adjoint + :type normalize: str + :param normalize: Data normalization parameters used to normalize the + amplitudes of waveforms. Choose from two sets: + ENORML1: normalize per event by L1 of traces; OR + ENORML2: normalize per event by L2 of traces; + & + TNORML1: normalize per trace by L1 of itself; OR + TNORML2: normalize per trace by L2 of itself + :type filter: str + :param filter: Data filtering type, available options are: + BANDPASS (req. MIN/MAX PERIOD/FREQ); + LOWPASS (req. MAX_FREQ or MIN_PERIOD); + HIGHPASS (req. MIN_FREQ or MAX_PERIOD) + :type min_period: float + :param min_period: Minimum filter period applied to time series. + See also MIN_FREQ, MAX_FREQ, if User defines FREQ parameters, they + will overwrite PERIOD parameters. + :type max_period: float + :param max_period: Maximum filter period applied to time series. See + also MIN_FREQ, MAX_FREQ, if User defines FREQ parameters, they will + overwrite PERIOD parameters. + :type min_freq: float + :param min_freq: Maximum filter frequency applied to time series, + See also MIN_PERIOD, MAX_PERIOD, if User defines FREQ parameters, + they will overwrite PERIOD parameters. + :type max_freq: float + :param max_freq: Maximum filter frequency applied to time series, + See also MIN_PERIOD, MAX_PERIOD, if User defines FREQ parameters, + they will overwrite PERIOD parameters. + :type mute: list + :param mute: Data mute parameters used to zero out early / late + arrivals or offsets. Choose any number of: + EARLY: mute early arrivals; + LATE: mute late arrivals; + SHORT: mute short source-receiver distances; + LONG: mute long source-receiver distances + :type path_preprocess: str + :param path_preprocess: scratch path for all preprocessing processes, + including saving files """ def __init__(self, data_format="ascii", misfit="waveform", adjoint="waveform", normalize=None, filter=None, @@ -34,55 +83,7 @@ def __init__(self, data_format="ascii", misfit="waveform", """ Preprocessing module parameters - :type data_format: str - :param data_format: data format for reading traces into memory. For - available see: seisflows.plugins.preprocess.readers - :type misfit: str - :param misfit: misfit function for waveform comparisons. For available - see seisflows.plugins.preprocess.misfit - :type backproject: str - :param backproject: backprojection function for migration, or the - objective function in FWI. For available see - seisflows.plugins.preprocess.adjoint - :type normalize: str - :param normalize: Data normalization parameters used to normalize the - amplitudes of waveforms. Choose from two sets: - ENORML1: normalize per event by L1 of traces; OR - ENORML2: normalize per event by L2 of traces; - & - TNORML1: normalize per trace by L1 of itself; OR - TNORML2: normalize per trace by L2 of itself - :type filter: str - :param filter: Data filtering type, available options are: - BANDPASS (req. MIN/MAX PERIOD/FREQ); - LOWPASS (req. MAX_FREQ or MIN_PERIOD); - HIGHPASS (req. MIN_FREQ or MAX_PERIOD) - :type min_period: float - :param min_period: Minimum filter period applied to time series. - See also MIN_FREQ, MAX_FREQ, if User defines FREQ parameters, they - will overwrite PERIOD parameters. - :type max_period: float - :param max_period: Maximum filter period applied to time series. See - also MIN_FREQ, MAX_FREQ, if User defines FREQ parameters, they will - overwrite PERIOD parameters. - :type min_freq: float - :param min_freq: Maximum filter frequency applied to time series, - See also MIN_PERIOD, MAX_PERIOD, if User defines FREQ parameters, - they will overwrite PERIOD parameters. - :type max_freq: float - :param max_freq: Maximum filter frequency applied to time series, - See also MIN_PERIOD, MAX_PERIOD, if User defines FREQ parameters, - they will overwrite PERIOD parameters. - :type mute: list - :param mute: Data mute parameters used to zero out early / late - arrivals or offsets. Choose any number of: - EARLY: mute early arrivals; - LATE: mute late arrivals; - SHORT: mute short source-receiver distances; - LONG: mute long source-receiver distances - :type path_preprocess: str - :param path_preprocess: scratch path for all preprocessing processes, - including saving files + """ self.data_format = data_format.upper() self.misfit = misfit @@ -311,6 +312,9 @@ def _rename_as_adjoint_source(self, fid): Rename synthetic waveforms into filenames consistent with how SPECFEM expects adjoint sources to be named. Usually this just means adding a '.adj' to the end of the filename + + TODO how does SPECFEM3D_GLOBE expect this? filenames end with .sem.ascii + so the .ascii will get replaced. Is that okay? """ if not fid.endswith(".adj"): if self.data_format.upper() == "SU": diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 19a477b1..134f57f4 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -32,7 +32,10 @@ class Specfem: """ - SPECFEM interface shared between 2D/3D/3D_GLOBE implementations. + [solver.specfem] SPECFEM interface shared between 2D/3D/3D_GLOBE + implementations providing generalized interface to establish SPECFEM + working directories, call SPECFEM binaries, and keep track of a number of + parallel processes. :type case: str :param case: determine the type of workflow we will attempt @@ -87,13 +90,14 @@ class Specfem: :param path_output: shared output directory on disk for more permanent storage of solver related files such as traces, kernels, gradients. """ - def __init__(self, case="data", data_format="ascii", materials="elastic", + def __init__(self, case="data", data_format="ascii", materials="acoustic", density=False, nproc=1, ntask=1, attenuation=False, components="ZNE", solver_io="fortran_binary", source_prefix=None, mpiexec=None, workdir=os.getcwd(), - path_solver=None, path_data=None, path_specfem_bin=None, - path_specfem_data=None, path_model_init=None, - path_model_true=None, path_output=None, **kwargs): + path_solver=None, path_eval_grad=None, path_data=None, + path_specfem_bin=None, path_specfem_data=None, + path_model_init=None, path_model_true=None, path_output=None, + **kwargs): """Set default SPECFEM interface parameters""" self.case = case self.data_format = data_format @@ -110,6 +114,8 @@ def __init__(self, case="data", data_format="ascii", materials="elastic", # Define internally used directory structure self.path = Dict( scratch=path_solver or os.path.join(workdir, "scratch", "solver"), + eval_grad=path_eval_grad or + os.path.join(workdir, "scratch", "evalgrad"), data=path_data or os.path.join(workdir, "SFDATA"), output=path_output or os.path.join(workdir, "output"), specfem_bin=path_specfem_bin, @@ -136,7 +142,7 @@ def __init__(self, case="data", data_format="ascii", materials="elastic", self._available_data_formats = ["ASCII", "SU"] self._required_binaries = ["xspecfem2D", "xmeshfem2D", "xcombine_sem", "xsmooth_sem"] - self._acceptable_source_prefix = ["SOURCE", "FORCE", "FORCESOLUTION"] + self._acceptable_source_prefixes = ["SOURCE", "FORCE", "FORCESOLUTION"] def check(self): """ @@ -184,12 +190,6 @@ def check(self): f"bin/{fid} does not exist but is required by SeisFlows solver" ) - # Check that the 'case' variable matches required models - if self.case.upper() == "SYNTHETIC": - assert(os.path.exists(self.path.model_true)), ( - f"solver.case == 'synthetic' requires `path.model_true`" - ) - # Check that model type is set correctly in the Par_file model_type = getpar(key="MODEL", file=os.path.join(self.path.specfem_data, @@ -197,6 +197,16 @@ def check(self): assert(model_type in self._available_model_types), \ f"{model_type} not in available types {self._available_model_types}" + # Check that the 'case' variable matches required models + if self.case.upper() == "SYNTHETIC": + assert(os.path.exists(self.path.model_true)), ( + f"solver.case == 'synthetic' requires `path_model_true`" + ) + + assert(self.path.model_init is not None and + os.path.exists(self.path.model_init)), \ + f"`path_model_init` is required for the solver, but does not exist" + # Check that the number of tasks/events matches the number of events self._source_names = self._check_source_names() @@ -365,12 +375,9 @@ def setup(self): unix.mkdir(self.path.output) for key in self._parameters: src = glob(os.path.join(self.path.model_init, f"*{key}.bin")) - dst = os.path.join(self.path.output, "MODEL_INIT", "") + dst = os.path.join(self.path.output, "MODEL_INIT") unix.cp(src, dst) - # TODO move this into workflow.migration - # self._initialize_adjoint_traces() - # def generate_data(self, save_traces=False): # """ # Generates observation data to be compared to synthetics. This must @@ -460,80 +467,6 @@ def import_data(self): dst = os.path.join(self.cwd, "traces", "obs") unix.cp(src, dst) - # - # def eval_func(self, path, preprocess=None): - # """ - # Performs forward simulations and evaluates the misfit function using - # the preprocess module. solver.eval_func is bundled with - # preprocess.prepare_eval_grad because they are meant to be run serially - # so it is better to lump them together into a single allocation. - # - # .. note:: - # This task should be run in parallel by system.run() - # - # :type path: str - # :param path: directory from which model is imported and where residuals - # will be exported - # :type preprocess: instance - # :param preprocess: SeisFlows preprocess module which can be used to - # prepare gradient evaluation by comparing misfit and creating - # adjoint sources. If None, only forward simulations will be - # performed - # """ - # unix.cd(self.cwd) - # - # if self.taskid == 0: - # logger.info("running forward simulations") - # - # self._import_model(path) - # self._forward(output_path=os.path.join("traces", "syn")) - # - # if preprocess: - # if self.taskid == 0: - # logger.debug("call preprocess to prepare gradient evaluation") - # preprocess.prepare_eval_grad(cwd=self.cwd, taskid=self.taskid, - # source_name=self.source_name, - # filenames=self.data_filenames - # ) - # self._export_residuals(path) - - # def eval_grad(self, path, export_traces=False): - # """ - # Evaluates gradient by carrying out adjoint simulations. - # - # .. note:: - # It is expected that eval_func() has already been run as this - # function looks for adjoint sources in 'cwd/traces/adj' - # - # :type path: str - # :param path: directory from which model is imported - # :type export_traces: bool - # :param export_traces: if True, save traces to OUTPUT. - # if False, discard traces - # """ - # unix.cd(self.cwd) - # - # if self.taskid == 0: - # logger.debug("running adjoint simulations") - # - # # Check to make sure that preprocessing module created adjoint traces - # adjoint_traces_wildcard = os.path.join("traces", "adj", "*") - # if not glob(adjoint_traces_wildcard): - # print(msg.cli(f"Event {self.source_name} has no adjoint traces, " - # f"which will lead to an external solver error. " - # f"Please check that solver.eval_func() executed " - # f"properly", border="=", header="solver error") - # ) - # sys.exit(-1) - # - # self._adjoint() - # self._export_kernels(path) - # - # if export_traces: - # self._export_traces(path=os.path.join(path, "traces", "syn"), - # prefix="traces/syn") - # self._export_traces(path=os.path.join(path, "traces", "adj"), - # prefix="traces/adj") def forward_simulation(self, executables=None, save_traces=False, export_traces=False): @@ -886,11 +819,11 @@ def _initialize_working_directory(self, cwd=None): # Allow this function to be called on system or in serial if cwd is None: cwd = self.cwd - _source_name = os.path.basename(cwd) - taskid = self.source_names.index(_source_name) + taskid = self.taskid else: cwd = self.cwd - taskid = self.taskid + _source_name = os.path.basename(cwd) + taskid = self.source_names.index(_source_name) if taskid == 0: logger.info(f"initializing {self.ntask} solver directories") diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index e536b1be..18176407 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -15,7 +15,7 @@ class Specfem2D(Specfem): """ - SPECFEM2D-specific parameters + [solver.specfem2d] SPECFEM2D-specific alterations to the base SPECFEM module :type source_prefix: str :param source_prefix: Prefix of source files in path SPECFEM_DATA. Defaults @@ -34,10 +34,9 @@ def __init__(self, source_prefix="SOURCE", multiples=False, **kwargs): # Define parameters based on material type if self.materials.upper() == "ACOUSTIC": - self._parameters.append("vp") + self._parameters += ["vp"] elif self.materials.upper() == "ELASTIC": - self._parameters.append("vp") - self._parameters.append("vs") + self._parameters += ["vp", "vs"] def setup(self): """Setup the SPECFEM2D solver interface in a SeisFlows workflow""" diff --git a/seisflows/solver/specfem3d.py b/seisflows/solver/specfem3d.py index c9a1274c..93dcc500 100644 --- a/seisflows/solver/specfem3d.py +++ b/seisflows/solver/specfem3d.py @@ -14,7 +14,7 @@ class Specfem3D(Specfem): """ - SPECFEM3D_Cartesian specific parameters + [solver.specfem3d] SPECFEM3D-specific alterations to the base SPECFEM module :type source_prefix: str :param source_prefix: Prefix of source files in path SPECFEM_DATA. Defaults @@ -31,10 +31,9 @@ def __init__(self, source_prefix="CMTSOLUTION", **kwargs): # Define parameters based on material type if self.materials.upper() == "ACOUSTIC": - self._parameters.append("vp") + self._parameters += ["vp"] elif self.materials.upper() == "ELASTIC": - self._parameters.append("vp") - self._parameters.append("vs") + self._parameters += ["vp", "vs"] # Overwriting the base class parameters self._acceptable_source_prefixes = ["CMTSOLUTION", "FORCESOLUTION"] diff --git a/seisflows/solver/specfem3d_globe.py b/seisflows/solver/specfem3d_globe.py index 37897fcc..8fcbf027 100644 --- a/seisflows/solver/specfem3d_globe.py +++ b/seisflows/solver/specfem3d_globe.py @@ -8,39 +8,28 @@ SPECFEM3D_Globe specfic notes: - does not allow SU seismogram outputs, only ASCII, SAC, ASDF, 3D_Array """ -import os -from glob import glob - from seisflows.solver.specfem3d import Specfem3D -from seisflows.tools import unix class Specfem3DGlobe(Specfem3D): """ - Python interface to Specfem3D Globe. A very simple overload of Specfem3D - - See class `seisflows.solver.specfem3d.Specfem3D` for a more detailed - explanation of methods and attributes of this class + [solver.specfem3d_globe] SPECFEM3D_Globe-specific alterations to the + solver.specfem3d (cartesian) module """ - def __init__(self): - """ - These parameters should not be set by the user. - Attributes are initialized as NoneTypes for clarity and docstrings. - """ - super().__init__() - if self.materials.upper() == "ELASTIC": - assert(self.par.SOLVER.lower() in ["specfem2d", "specfem3d"]) - self.parameters += ["vp", "vs"] - elif self.materials.upper() == "ACOUSTIC": - assert(self.par.SOLVER.lower() in ["specfem2d", "specfem3d"]) - self.parameters += ["vp"] + __doc__ = Specfem3D.__doc__ + __doc__ + + def __init__(self, **kwargs): + """Instantiate a Specfem3D_Globe solver interface""" + super().__init__(**kwargs) + + if self.materials.upper() == "ACOUSTIC": + self._parameters += ["vp"] + elif self.materials.upper() == "ELASTIC": + self._parameters += ["vp", "vs"] elif self.materials.upper() == "ISOTROPIC": - assert(self.par.SOLVER.lower() in ["specfem3d_globe"]) - self.parameters += ["vp", "vs"] + self._parameters += ["vp", "vs"] elif self.materials.upper() == "ANISOTROPIC": - assert(self.par.SOLVER.lower() in ["specfem3d_globe"]) - self.parameters += ["vpv", "vph", "vsv", "vsh", "eta"] - + self._parameters += ["vpv", "vph", "vsv", "vsh", "eta"] def data_wildcard(self, comp="?"): """ @@ -49,147 +38,137 @@ def data_wildcard(self, comp="?"): :rtype: str :return: wildcard identifier for channels """ - if self.par.FORMAT.upper() == "SU": + if self.data_format.upper() == "SU": raise NotImplementedError("SU file access is still a WIP") - elif self.par.FORMAT.upper() == "ASCII": + elif self.data_format.upper() == "ASCII": return f"*.?X{comp}.sem.ascii" - def load(self, path, prefix="reg1_", suffix="", parameters=None): - """ - Reads SPECFEM model or kernel - - .. note:: - SPECFEM3D_Globe meshes are broken into 3 regions. - Region 1 == Crust + Mantle - Region 2 == Outer core - Region 3 == Inner core - - .. warning:: - Currently SeisFlows only considers the crust + mantle in Globe - simulations - - - :type path: str - :param path: directory from which model is read - :type prefix: str - :param prefix: optional filename prefix - :type suffix: str - :param suffix: optional filename suffix, eg '_kernel' - :type parameters: list - :param parameters: material parameters to be read - (if empty, defaults to self.parameters) - :rtype: dict - :return: model or kernels indexed by material parameter and - processor rank, ie dict[parameter][iproc] - """ - parameters = parameters or self.parameters - - model = Model(parameters) - minmax = Minmax(parameters) - - for iproc in range(self.mesh_properties.nproc): - # read database files based on parameters - keys, vals = loadbypar(path, self.parameters, iproc, prefix, - suffix) - for key, val in zip(keys, vals): - model[key] += [val] - - minmax.update(keys, vals) - - return model - - def save(self, path, model, prefix="reg1_", suffix=""): - """ - Writes SPECFEM3D_GLOBE transerverly isotropic model - - :type path: str - :param path: - :type model - :param model: - :type prefix: str - :param prefix: prefix that begins the name of the model parameters - :type suffix: str - :param suffix: that follow the name of model parameters - """ - unix.mkdir(path) - - for iproc in range(self.mesh_properties.nproc): - for check_key in ["vpv", "vph", "vsv", "vsh", "eta"]: - if check_key in self.parameters: - savebin(model[key][iproc], path, iproc, prefix+key+suffix) - elif 'kernel' in suffix: - pass - else: - src = self.path.OUTPUT + '/' + 'model_init' - dst = path - copybin(src, dst, iproc, prefix+key+suffix) - - if 'rho' in self.parameters: - savebin(model['rho'][iproc], path, iproc, prefix+'rho'+suffix) - elif 'kernel' in suffix: - pass - else: - src = self.path.OUTPUT + '/' + 'model_init' - dst = path - copybin(src, dst, iproc, prefix+'rho'+suffix) - - def check_mesh_properties(self, path=None, parameters=None): - """ - Determine if Mesh properties are okay for workflow - - :type path: str - :param path: path to the mesh file - """ - if not hasattr(self, '_mesh_properties'): - if path is None: - path = self.path.MODEL_INIT - - if parameters is None: - parameters = self.parameters - - nproc = 0 - ngll = [] - while True: - dummy = loadbin(path, nproc, 'reg1_' + parameters[0]) - ngll += [len(dummy)] - nproc += 1 - if not os.path.exists( - os.path.join(path, - f"proc{nrpoc}_reg1_{parameters[0]}.bin")): - break - - self._mesh_properties = Struct([['nproc', nproc], - ['ngll', ngll]] - ) - - def rename_data(self): - """ - Works around conflicting data filename conventions - - Specfem3D's uses different name conventions for regular traces - and 'adjoint' traces - """ - files = glob(os.path.join(self.cwd, "traces", "adj", "*sem.ascii")) - unix.rename("sem.ascii", "sem.ascii.adj", files) - - def initialize_adjoint_traces(self): - """ - Setup utility: Creates the "adjoint traces" expected by SPECFEM - - !!! This probably doesnt work - - .. note:: - Adjoint traces are initialized by writing zeros for all channels. - Channels actually in use during an inversion or migration will be - overwritten with nonzero values later on. - """ - super().initialize_adjoint_traces() - - # workaround for SPECFEM's use of different name conventions for - # regular traces and 'adjoint' traces - if self.par.FORMAT.upper() in ['ASCII', 'ascii']: - files = glob(os.path.join(self.cwd, "traces", "adj", "*sem.ascii")) - unix.rename("sem.ascii", "adj", files) + # def load(self, path, prefix="reg1_", suffix="", parameters=None): + # """ + # Reads SPECFEM model or kernel + # + # .. note:: + # SPECFEM3D_Globe meshes are broken into 3 regions. + # Region 1 == Crust + Mantle + # Region 2 == Outer core + # Region 3 == Inner core + # + # .. warning:: + # Currently SeisFlows only considers the crust + mantle in Globe + # simulations + # + # + # :type path: str + # :param path: directory from which model is read + # :type prefix: str + # :param prefix: optional filename prefix + # :type suffix: str + # :param suffix: optional filename suffix, eg '_kernel' + # :type parameters: list + # :param parameters: material parameters to be read + # (if empty, defaults to self.parameters) + # :rtype: dict + # :return: model or kernels indexed by material parameter and + # processor rank, ie dict[parameter][iproc] + # """ + # parameters = parameters or self.parameters + # + # model = Model(parameters) + # minmax = Minmax(parameters) + # + # for iproc in range(self.mesh_properties.nproc): + # # read database files based on parameters + # keys, vals = loadbypar(path, self.parameters, iproc, prefix, + # suffix) + # for key, val in zip(keys, vals): + # model[key] += [val] + # + # minmax.update(keys, vals) + # + # return model + # + # def save(self, path, model, prefix="reg1_", suffix=""): + # """ + # Writes SPECFEM3D_GLOBE transerverly isotropic model + # + # :type path: str + # :param path: + # :type model + # :param model: + # :type prefix: str + # :param prefix: prefix that begins the name of the model parameters + # :type suffix: str + # :param suffix: that follow the name of model parameters + # """ + # unix.mkdir(path) + # + # for iproc in range(self.mesh_properties.nproc): + # for check_key in ["vpv", "vph", "vsv", "vsh", "eta"]: + # if check_key in self.parameters: + # savebin(model[key][iproc], path, iproc, prefix+key+suffix) + # elif 'kernel' in suffix: + # pass + # else: + # src = self.path.OUTPUT + '/' + 'model_init' + # dst = path + # copybin(src, dst, iproc, prefix+key+suffix) + # + # if 'rho' in self.parameters: + # savebin(model['rho'][iproc], path, iproc, prefix+'rho'+suffix) + # elif 'kernel' in suffix: + # pass + # else: + # src = self.path.OUTPUT + '/' + 'model_init' + # dst = path + # copybin(src, dst, iproc, prefix+'rho'+suffix) + # + # def check_mesh_properties(self, path=None, parameters=None): + # """ + # Determine if Mesh properties are okay for workflow + # + # :type path: str + # :param path: path to the mesh file + # """ + # if not hasattr(self, '_mesh_properties'): + # if path is None: + # path = self.path.MODEL_INIT + # + # if parameters is None: + # parameters = self.parameters + # + # nproc = 0 + # ngll = [] + # while True: + # dummy = loadbin(path, nproc, 'reg1_' + parameters[0]) + # ngll += [len(dummy)] + # nproc += 1 + # if not os.path.exists( + # os.path.join(path, + # f"proc{nrpoc}_reg1_{parameters[0]}.bin")): + # break + # + # self._mesh_properties = Struct([['nproc', nproc], + # ['ngll', ngll]] + # ) + # + # def initialize_adjoint_traces(self): + # """ + # Setup utility: Creates the "adjoint traces" expected by SPECFEM + # + # !!! This probably doesnt work + # + # .. note:: + # Adjoint traces are initialized by writing zeros for all channels. + # Channels actually in use during an inversion or migration will be + # overwritten with nonzero values later on. + # """ + # super().initialize_adjoint_traces() + # + # # workaround for SPECFEM's use of different name conventions for + # # regular traces and 'adjoint' traces + # if self.par.FORMAT.upper() in ['ASCII', 'ascii']: + # files = glob(os.path.join(self.cwd, "traces", "adj", "*sem.ascii")) + # unix.rename("sem.ascii", "adj", files) diff --git a/seisflows/system/runscripts/run_function.py b/seisflows/system/runscripts/run_function.py index 064d673e..164dc289 100644 --- a/seisflows/system/runscripts/run_function.py +++ b/seisflows/system/runscripts/run_function.py @@ -23,7 +23,6 @@ from seisflows.config import load, config_logger - def parse_args(): """ Get command line arguments diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index 0d274faa..6ebe6574 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -140,7 +140,7 @@ def run(self, classname, method, single=False, run_call=None, **kwargs): # custom run calls can still benefit from this if single: logger.info("replacing parts of sbatch run call for single " - "process job") + "process job") run_call = _modify_run_call_single_proc(run_call) # The standard response from SLURM when submitting jobs diff --git a/seisflows/system/workstation.py b/seisflows/system/workstation.py index 9e1b301f..5b86302c 100644 --- a/seisflows/system/workstation.py +++ b/seisflows/system/workstation.py @@ -9,9 +9,10 @@ from contextlib import redirect_stdout from seisflows import logger +from seisflows.core import Dict from seisflows.config import CFGPATHS, save from seisflows.tools import msg, unix -from seisflows.tools.utils import number_fid +from seisflows.tools.utils import number_fid, get_task_id class Workstation: @@ -20,7 +21,8 @@ class Workstation: Base System module, upon which all other System classes should be built. """ def __init__(self, title=None, mpiexec=None, ntask=1, nproc=1, - log_level="DEBUG", verbose=False, path_output=None, + log_level="DEBUG", verbose=False, workdir=os.getcwd(), + path_output=None, path_system=None, path_output_log=None, path_error_log=None, path_log_files=None, path_par_file=None, **kwargs): """ @@ -57,26 +59,34 @@ def __init__(self, title=None, mpiexec=None, ntask=1, nproc=1, self.verbose = verbose # Define internal path system - self.path = path_system or \ - os.path.join(os.getcwd(), "scratch", "system") - self.path_par_file = path_par_file or \ - os.path.join(os.getcwd(), "parameters.yaml") - self.path_output = path_output - - # Define where to write logs - self.path_log_files = path_log_files or \ - os.path.join(os.getcwd(), "logs") - self.path_output_log = path_output_log or \ - os.path.join(os.getcwd(), "sfoutput.log") - self.path_error_log = path_error_log or \ - os.path.join(os.getcwd(), "sferror.log") + self.path = Dict( + scratch=path_system or os.path.join(workdir, "scratch", "system"), + par_file=path_par_file or os.path.join(workdir, "parameters.yaml"), + output=path_output or os.path.join(workdir, "output"), + log_files=path_log_files or os.path.join(workdir, "logs"), + output_log=path_output_log or os.path.join(workdir, "sfoutput.log"), + error_log=path_error_log or os.path.join(workdir, "sferror.log"), + ) def check(self): """ Checks parameters and paths """ - pass + assert(os.path.exists(self.path.par_file)), \ + f"parameter file does not exist but should" + + def taskid(self): + """ + Provides a unique identifier for each running task, which should be set + by the 'run'' command. + + :rtype: int + :return: returns the os environment variable SEISFLOWS_TASKID which is + set by run() to label each of the currently + running processes on the SYSTEM. + """ + return get_task_id() def setup(self): """ @@ -96,19 +106,19 @@ def setup(self): :rtype: tuple of str :return: (path to output log, path to error log) """ - for path in [self.path, self.path_output, self.path_log_files]: + for path in [self.path.scratch, self.path.output, self.path.log_files]: unix.mkdir(path) # If resuming, move old log files to keep them out of the way. Number # in ascending order, so we don't end up overwriting things - for src in [self.path_output_log, self.path_error_log, - self.path_par_file]: + for src in [self.path.output_log, self.path.error_log, + self.path.par_file]: i = 1 if os.path.exists(src): - dst = os.path.join(self.path_log_files, number_fid(src, i)) + dst = os.path.join(self.path.log_files, number_fid(src, i)) while os.path.exists(dst): i += 1 - dst = os.path.join(self.path_log_files, number_fid(src, i)) + dst = os.path.join(self.path.log_files, number_fid(src, i)) logger.debug(f"copying par/log file to: {dst}") unix.cp(src=src, dst=dst) @@ -129,7 +139,7 @@ def submit(self, workflow, submit_call=None): workflow.checkpoint() workflow.main() - def run(self, classname, method, single=False, **kwargs): + def run(self, func, single=False, **kwargs): """ Executes task multiple times in serial. @@ -147,13 +157,6 @@ def run(self, classname, method, single=False, **kwargs): defined, such that the job is submitted as a single-core job to the system. """ - self.save_kwargs_to_disk(self.path_output, classname, method, kwargs) - - # Allows dynamic retrieval of any function from within package, e.g., - # 4}_{taskid:0>2}.log") if os.path.exists(log_file): idx += 1 @@ -175,33 +178,13 @@ def run(self, classname, method, single=False, **kwargs): break if taskid == 0: - logger.info(f"running task {classname}.{method} " - f"{self.ntask} times") + logger.info(f"running task {func.__name__} {self.ntask} times") # Redirect output to a log file to mimic cluster runs where 'run' # task output logs are sent to different files with open(log_file, "w") as f: with redirect_stdout(f): - function(**kwargs) - - def taskid(self): - """ - Provides a unique identifier for each running task, which should be set - by the 'run'' command. - - :rtype: int - :return: returns the os environment variable SEISFLOWS_TASKID which is - set by run() to label each of the currently - running processes on the SYSTEM. - """ - sftaskid = os.getenv("SEISFLOWS_TASKID") - if sftaskid is None: - print(msg.cli("system.taskid() environment variable not found. " - "Assuming DEBUG mode and returning taskid==0. " - "If not DEBUG mode, please check SYSTEM.run()", - header="warning", border="=")) - sftaskid = 0 - return int(sftaskid) + func(**kwargs) def save_kwargs_to_disk(self, path, classname, method, kwargs): """ @@ -223,4 +206,4 @@ def save_kwargs_to_disk(self, path, classname, method, kwargs): with open(argsfile, "wb") as f: pickle.dump(kwargs, f) - save(path=self.path_output) \ No newline at end of file + save(path=self.path.output) \ No newline at end of file diff --git a/seisflows/tools/utils.py b/seisflows/tools/utils.py index fe602433..dbbc8625 100644 --- a/seisflows/tools/utils.py +++ b/seisflows/tools/utils.py @@ -4,6 +4,7 @@ """ import os import re +import time import yaml import numpy as np from seisflows.core import Dict @@ -15,30 +16,46 @@ def log_status(func): Decorator function that logs the completion status of a function to a state file. This is used for checkpointing a workflow and resuming failed workflows without repeating computational intense tasks + + :type func: function """ + raise NotImplementedError("This is not working as expected") + STATE_FILE = os.path.join(os.getcwd(), "sfstatefile") + if not os.path.exists(STATE_FILE): + with open(STATE_FILE, "w") as f: + f.write(f"# SeisFlows State File\n") + f.write(f"# {time.asctime()}\n") + f.write(f"# =========================\n") + def logged_func(): """Log the completion status of the function""" try: - func() + output = func() with open(STATE_FILE, "a") as f: - f.write(f"{func.__name__}\tCOMPLETED") + f.write(f"{func.__name__}\tCOMPLETED\n") except Exception as e: - f.write(f"{func.__name__}\tFAILED") + with open(STATE_FILE, "a") as f: + f.write(f"{func.__name__}\tFAILED\n") logger.error(e) raise + return output lines = open(STATE_FILE, "r").readlines() for line in lines: - function, status = line.split(" ") + if line.startswith("#"): + continue + function, status = line.strip().split("\t") if func.__name__ == function: - if status == "COMPLETE": + if status == "COMPLETED": return elif status == "FAILED": return logged_func() else: return logged_func() + else: + return logged_func() def set_task_id(task_id): diff --git a/seisflows/workflow/forward_test.py b/seisflows/workflow/forward_test.py index bfdf17d7..aadbf92c 100644 --- a/seisflows/workflow/forward_test.py +++ b/seisflows/workflow/forward_test.py @@ -6,12 +6,9 @@ from seisflows import logger from seisflows.config import import_seisflows from seisflows.tools import msg -from seisflows.tools.utils import log_status -from seisflows.config import config_logger # Standard SeisFlows setup, makes modules global variables to the workflow pars, modules = import_seisflows() -# config_logger(level=pars.log_level, filename=pars.path_log_ system, preprocess, solver, postprocess, optimize = modules @@ -27,14 +24,12 @@ def evaluate_objective_function(path_model): Must be run by system.run() so that solvers are assigned individual task ids/ working directories. """ - if system.taskid == 0: - logger.info(msg.sub("EVALUATING OBJECTIVE FUNCTION")) - # Run the forward simulation with the given input model solver.import_model(path_model=path_model) solver.forward_simulation( save_traces=os.path.join(solver.cwd, "traces", "syn"), - export_traces=os.path.join(pars.path_output, solver.source_name, "syn") + export_traces=os.path.join(solver.path.output, + solver.source_name, "syn") ) # Perform data-synthetic misfit quantification @@ -46,35 +41,6 @@ def evaluate_objective_function(path_model): ) -@log_status -def run_forward_simulation(path_model): - """ - - """ - # Run the forward simulation with the given input model - solver.import_model(path_model=path_model) - solver.forward_simulation( - save_traces=os.path.join(solver.cwd, "traces", "syn"), - export_traces=os.path.join(pars.path_output, solver.source_name, "syn") - ) - - -@log_status -def quantify_misfit(): - """ - Quantify the data-synthetic misfit, write residuals to scratch for use in - potential optimization, generate adjoint sources required for adjoint - simulations - """ - preprocess.quantify_misfit( - observed=solver.data_filenames(choice="obs"), - synthetics=solver.data_filenames(choice="syn"), - write_adjsrcs=os.path.join(solver.cwd, "traces", "adj"), - write_residuals=os.path.join(pars.path_eval_grad, solver.source_name) - ) - preprocess.sum_residuals(files=glob(os.path.join(pars.path_eval_grad, "*"))) - - if __name__ == "__main__": # Begin the forward simulation workflow logger.info(msg.mjr("Starting forward simulation workflow")) @@ -83,8 +49,8 @@ def quantify_misfit(): module.setup() # Run objective function evaluation NTASK times - system.run(evaluate_objective_function, path_model=pars.path_model_init, - suffix="new", system=system) + logger.info(msg.sub("EVALUATING OBJECTIVE FUNCTION")) + system.run(evaluate_objective_function, path_model=pars.path_model_init) logger.info(msg.mjr("Finished forward simulation workflow")) From 0800eeb05278cd1853911eac3911d6a23d897a8d Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Fri, 15 Jul 2022 16:27:07 -0800 Subject: [PATCH 068/195] reworking workflows as classes but with a bookkeeping mechanic to more easily resume failed workflows. removed agency from the other modules and have moved most of the logic and path choices into the workflows --- seisflows/config.py | 23 +- seisflows/core.py | 3 + seisflows/preprocess/default.py | 18 +- seisflows/solver/specfem.py | 160 ++------ seisflows/system/workstation.py | 15 +- seisflows/tools/unix.py | 9 +- seisflows/tools/utils.py | 82 ++-- seisflows/workflow/forward.py | 584 +++++++++++++++++------------ seisflows/workflow/forward_test.py | 56 --- seisflows/workflow/migration.py | 222 +++++------ 10 files changed, 565 insertions(+), 607 deletions(-) delete mode 100644 seisflows/workflow/forward_test.py diff --git a/seisflows/config.py b/seisflows/config.py index 1f35b7e2..c3398945 100755 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -40,7 +40,8 @@ """ # List of module names required by SeisFlows for imports. Order-sensitive # In sys.modules these will be prepended by 'seisflows_', e.g., seisflows_system -NAMES = ["system", "preprocess", "solver", "postprocess", "optimize"] #, "workflow"] +NAMES = ["system", "preprocess", "solver", + "postprocess", "optimize", "workflow"] # The location of this config file, which is the main repository ROOT_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__))) @@ -49,8 +50,6 @@ # be returned as a Dict() object, defined below. All of these files and # directories will be created relative to the user-defined working directory CFGPATHS = Dict( - SCRATCHDIR="scratch", - OUTPUTDIR="output", PAR_FILE="parameters.yaml", # Default SeisFlows parameter file LOGFILE="sfoutput.txt", # Log files for all system log ERRLOGFILE="sferror.txt", # StdErr dump site for crash messages @@ -135,11 +134,16 @@ class names. config_logger(level=parameters.log_level, filename=parameters.path_log_file, verbose=parameters.verbose) - classes = [custom_import(name, parameters[name]) for name in NAMES] - modules = [cls(**parameters) for cls in classes] - # Check that parameters have been set correctly by running their check funcs - for module in modules: - module.check() + # Instantiate SeisFlows modules dynamically based on choices and parameters + # provided in the input parameter file + modules = {name: custom_import(name, parameters[name])(**parameters) for + name in NAMES} + modules = Dict(modules) + + # Drop NAMES from parameters, we don't need them anymore and they get + # muddled with the actual modules upon instantiation + for name in NAMES: + parameters.pop(name) return parameters, modules @@ -181,7 +185,7 @@ def config_logger(level="DEBUG", filename=None, filemode="a", verbose=True): ) else: # Clean logging statement with only time and message - fmt_str = "%(asctime)s | %(message)s" + fmt_str = "%(asctime)s (%(levelname).1s) | %(message)s" # Instantiate logger during _register() as we now have user-defined pars logger.setLevel(level) @@ -238,6 +242,7 @@ class 'Inversion'. "is implemented correctly, where name must be in the following:", items=NAMES, header="custom import error", border="=")) sys.exit(-1) + sys.exit(-1) # Attempt to retrieve currently assigned classname from parameters if module is None: try: diff --git a/seisflows/core.py b/seisflows/core.py index cff4917b..b0667248 100755 --- a/seisflows/core.py +++ b/seisflows/core.py @@ -131,6 +131,9 @@ def __init__(self, *args, **kwargs): def __call__(self, *args, **kwargs): return self + def __bool__(self): + return False + def __nonzero__(self): return False diff --git a/seisflows/preprocess/default.py b/seisflows/preprocess/default.py index e103f9e8..c9a46843 100644 --- a/seisflows/preprocess/default.py +++ b/seisflows/preprocess/default.py @@ -325,7 +325,7 @@ def _rename_as_adjoint_source(self, fid): return fid def quantify_misfit(self, observed, synthetic, - write_residuals=None, write_adjsrcs=None, **kwargs): + save_residuals=None, save_adjsrcs=None, **kwargs): """ Prepares solver for gradient evaluation by writing residuals and adjoint traces. Meant to be called by solver.eval_func(). @@ -342,10 +342,10 @@ def quantify_misfit(self, observed, synthetic, :param observed: list of observed waveforms :type synthetic: list :param synthetic: list of synthetic waveforms - :type write_residuals: str - :param write_residuals: if not None, path to write misfit/residuls to - :type write_adjsrcs: str - :param write_adjsrcs: if not None, path to write adjoint sources to + :type save_residuals: str + :param save_residuals: if not None, path to write misfit/residuls to + :type save_adjsrcs: str + :param save_adjsrcs: if not None, path to write adjoint sources to """ for obs_fid, syn_fid in zip(observed, synthetic): obs = self.read(fid=obs_fid) @@ -367,16 +367,16 @@ def quantify_misfit(self, observed, synthetic, # Simple check to make sure zip retains ordering assert(tr_obs.stats.component == tr_syn.stats.component) # Calculate the misfit value and write to file - if write_residuals and self._calculate_misfit: + if save_residuals and self._calculate_misfit: residual = self._calculate_misfit( obs=tr_obs.data, syn=tr_syn.data, nt=tr_syn.stats.npts, dt=tr_syn.stats.delta ) - with open(write_residuals, "a") as f: + with open(save_residuals, "a") as f: f.write(f"{residual:.2E}\n") # Generate an adjoint source trace, write to file - if write_adjsrcs and self._generate_adjsrc: + if save_adjsrcs and self._generate_adjsrc: adjsrc = syn.copy() adjsrc.data = self._generate_adjsrc( obs=tr_obs.data, syn=tr_syn.data, @@ -384,7 +384,7 @@ def quantify_misfit(self, observed, synthetic, ) fid = os.path.basename(syn_fid) fid = self._rename_as_adjoint_source(fid) - self.write(st=adjsrc, fid=os.path.join(write_adjsrcs, fid)) + self.write(st=adjsrc, fid=os.path.join(save_adjsrcs, fid)) def sum_residuals(self, files): """ diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 134f57f4..46380695 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -37,11 +37,6 @@ class Specfem: working directories, call SPECFEM binaries, and keep track of a number of parallel processes. - :type case: str - :param case: determine the type of workflow we will attempt - Available: ['DATA': data-synthetic comparisons. Data must be provided - by the user in `path_data`. 'SYNTHETIC': synthetic-synthetic - comparisons. `path_model_true` is required to generate target 'data'] :type data_format: str :param data_format: data format for reading traces into memory. Available: ['SU' seismic unix format, 'ASCII' human-readable ascii] @@ -56,6 +51,12 @@ class Specfem: if True, turns on attenuation during forward simulations only. If False, attenuation is always set to False. Requires underlying attenution (Q_mu, Q_kappa) model + :type smooth_h: float + :param smooth_h: Gaussian half-width for horizontal smoothing in units + of meters. If 0., no smoothing applied + :type smooth_h: float + :param smooth_v: Gaussian half-width for vertical smoothing in units + of meters. :type components: str :param components: components to consider and tag data with. Should be string of letters such as 'RTZ' @@ -90,22 +91,23 @@ class Specfem: :param path_output: shared output directory on disk for more permanent storage of solver related files such as traces, kernels, gradients. """ - def __init__(self, case="data", data_format="ascii", materials="acoustic", + def __init__(self, data_format="ascii", materials="acoustic", density=False, nproc=1, ntask=1, attenuation=False, - components="ZNE", solver_io="fortran_binary", - source_prefix=None, mpiexec=None, workdir=os.getcwd(), - path_solver=None, path_eval_grad=None, path_data=None, - path_specfem_bin=None, path_specfem_data=None, + smooth_h=0., smooth_v=0., components="ZNE", + solver_io="fortran_binary", source_prefix=None, mpiexec=None, + workdir=os.getcwd(), path_solver=None, path_eval_grad=None, + path_data=None, path_specfem_bin=None, path_specfem_data=None, path_model_init=None, path_model_true=None, path_output=None, **kwargs): """Set default SPECFEM interface parameters""" - self.case = case self.data_format = data_format self.materials = materials self.nproc = nproc self.ntask = ntask self.density = density self.attenuation = attenuation + self.smooth_h = smooth_h + self.smooth_v = smooth_v self.components = components self.solver_io = solver_io self.mpiexec = mpiexec @@ -148,9 +150,6 @@ def check(self): """ Checks parameter validity for SPECFEM input files and model parameters """ - assert(self.case.upper() in ["DATA", "SYNTHETIC"]), \ - f"solver.case must be 'DATA' or 'SYNTHETIC'" - assert(self.materials.upper() in self._available_materials), \ f"solver.materials must be in {self._available_materials}" @@ -197,12 +196,6 @@ def check(self): assert(model_type in self._available_model_types), \ f"{model_type} not in available types {self._available_model_types}" - # Check that the 'case' variable matches required models - if self.case.upper() == "SYNTHETIC": - assert(os.path.exists(self.path.model_true)), ( - f"solver.case == 'synthetic' requires `path_model_true`" - ) - assert(self.path.model_init is not None and os.path.exists(self.path.model_init)), \ f"`path_model_init` is required for the solver, but does not exist" @@ -419,55 +412,6 @@ def setup(self): # path=os.path.join(self.path.output, "traces", "obs") # ) - def generate_data(self, export_traces=False): - """ - Generates observation data to be compared to synthetics. This must - only be run once. If `PAR.CASE`=='data', then real data will be copied - over. - - If `PAR.CASE`=='synthetic' then the external solver will use the - True model to generate 'observed' synthetics. Finally exports traces to - 'cwd/traces/obs' - - Elif `PAR.CASE`=='DATA', will look in PATH.DATA for directories matching - the given source name and copy ANY files that exist there. e.g., if - source name is '001', you must store waveform data in PATH.DATA/001/* - - :type export_traces: str - :param export_traces: path to copy and save traces to a more permament - storage location as waveform stored in scratch/ are liable to be - deleted or overwritten - """ - # Basic checks to make sure there are True model files to copy - assert(self.case.upper() == "SYNTHETIC") - assert(os.path.exists(self.path.model_true)) - assert(glob(os.path.join(self.path.model_true, "*"))) - - # Generate synthetic data on the fly using the true model - self.import_model(path_model=self.path.model_true) - self.forward_simulation( - save_traces=os.path.join(self.cwd, "traces", "obs"), - export_traces=export_traces - ) - - def import_data(self): - """ - Import data from an existing directory into the current working - directory, required if 'observed' waveform data will be provided by - the User rather than automatically collected (with Pyatoa) or generated - synthetically (with external solver) - """ - # Simple checks to make sure we can actually import data - assert(self.case.upper() == "DATA") - assert(self.path.data is not None) - assert(os.path.exists(os.path.join(self.path.data, self.source_name))) - assert(glob(os.path.join(self.path.data, self.source_name, "*"))) - - src = os.path.join(self.path.data, self.source_name, "*") - dst = os.path.join(self.cwd, "traces", "obs") - - unix.cp(src, dst) - def forward_simulation(self, executables=None, save_traces=False, export_traces=False): """ @@ -638,8 +582,8 @@ def combine(self, input_path, output_path, parameters=None): stdout = f"{self._exc2log(exc)}_{name}.log" self._run_binary(executable=exc, stdout=stdout) - def smooth(self, input_path, output_path, parameters=None, span_h=0., - span_v=0., use_gpu=False): + def smooth(self, input_path, output_path, parameters=None, span_h=None, + span_v=None, use_gpu=False): """ Wrapper for SPECFEM binary: xsmooth_sem Smooths kernels by convolving them with a 3D Gaussian @@ -666,8 +610,16 @@ def smooth(self, input_path, output_path, parameters=None, span_h=0., """ unix.cd(self.cwd) + # Assign some default parameters from class attributes if not given if parameters is None: parameters = self._parameters + if span_h is None: + span_h = self.smooth_h + if span_v is None: + span_v = self.smooth_v + + logger.info(f"smoothing {parameters} with horizontal Gaussian " + f"{span_h}m and vertical Gaussian {span_v}m") if not os.path.exists(output_path): unix.mkdir(output_path) @@ -785,26 +737,28 @@ def _initialize_working_directories(self): """ Serial task used to initialize working directories for each of the a available sources - - TODO run this with concurrent futures for speedup? """ logger.info(f"initializing {self.ntask} solver directories") for source_name in self.source_names: cwd = os.path.join(self.path.scratch, source_name) + if os.path.exists(cwd): + logger.warning(f"solver scratch path for source {source_name} " + f"already exists") + continue self._initialize_working_directory(cwd=cwd) def _initialize_working_directory(self, cwd=None): """ - Creates directory structure expected by SPECFEM - (i.e., bin/, DATA/, OUTPUT_FILES/). Copies executables and prepares - input files. + Creates scratch directory structure expected by SPECFEM + (i.e., bin, DATA, OUTPUT_FILES). Copies executables (bin) and + input data (DATA) directories, prepares simulation input files. Each directory will act as completely independent Specfem working dir. This allows for embarrassing parallelization while avoiding the need for intra-directory communications, at the cost of temporary disk space. .. note:: - Path to binary executables must be supplied by user as SeisFlows has + path to binary executables must be supplied by user as SeisFlows has no mechanism for automatically compiling from source code. :type cwd: str @@ -812,21 +766,20 @@ def _initialize_working_directory(self, cwd=None): will set based on current running seisflows task (self.taskid) """ # Define a constant list of required SPECFEM dir structure, relative cwd - _required_structure = ["bin", "DATA", - "traces/obs", "traces/syn", "traces/adj", - self.model_databases, self.kernel_databases] + _required_structure = {"bin", "DATA", "traces/obs", "traces/syn", + "traces/adj", self.model_databases, + self.kernel_databases} # Allow this function to be called on system or in serial if cwd is None: cwd = self.cwd + source_name = self.source_name taskid = self.taskid else: - cwd = self.cwd - _source_name = os.path.basename(cwd) - taskid = self.source_names.index(_source_name) + source_name = os.path.basename(cwd) + taskid = self.source_names.index(source_name) - if taskid == 0: - logger.info(f"initializing {self.ntask} solver directories") + logger.debug(f"initializing solver directory source: {source_name}") # Starting from a fresh working directory unix.rm(cwd) @@ -847,49 +800,16 @@ def _initialize_working_directory(self, cwd=None): # Symlink event source specifically, only retain source prefix src = os.path.join(self.path.specfem_data, - f"{self.source_prefix}_{self.source_name}") + f"{self.source_prefix}_{source_name}") dst = os.path.join(cwd, "DATA", self.source_prefix) unix.ln(src, dst) # Symlink TaskID==0 as mainsolver in solver directory for convenience if taskid == 0: if not os.path.exists(self.path.mainsolver): - logger.debug(f"symlink {self.source_name} as 'mainsolver'") + logger.debug(f"linking source '{source_name}' as 'mainsolver'") unix.ln(cwd, self.path.mainsolver) - # def _initialize_adjoint_traces(self): - # """ - # Setup utility: Creates the "adjoint traces" expected by SPECFEM. - # This is only done for the 'base' the Preprocess class. - # - # TODO move this into workflow setup - # - # .. note:: - # Adjoint traces are initialized by writing zeros for all channels. - # Channels actually in use during an inversion or migration will be - # overwritten with nonzero values later on. - # """ - # preprocess = self.module("preprocess") - # - # if self.par.PREPROCESS.upper() == "DEFAULT": - # if self.taskid == 0: - # logger.debug(f"intializing {len(self.data_filenames)} " - # f"empty adjoint traces per event") - # - # for filename in self.data_filenames: - # st = preprocess.reader( - # path=os.path.join(self.cwd, "traces", "obs"), - # filename=filename - # ) - # # Zero out data just so we have empty adjoint traces as SPECFEM - # # will expect all adjoint sources to have all components - # st *= 0 - # - # # Write traces back to the adjoint trace directory - # preprocess.writer(st=st, filename=filename, - # path=os.path.join(self.cwd, "traces", "adj") - # ) - def _check_source_names(self): """ Determines names of sources by applying wildcard rule to user-supplied diff --git a/seisflows/system/workstation.py b/seisflows/system/workstation.py index 5b86302c..2b32bc29 100644 --- a/seisflows/system/workstation.py +++ b/seisflows/system/workstation.py @@ -12,7 +12,7 @@ from seisflows.core import Dict from seisflows.config import CFGPATHS, save from seisflows.tools import msg, unix -from seisflows.tools.utils import number_fid, get_task_id +from seisflows.tools.utils import number_fid, get_task_id, set_task_id, iterable class Workstation: @@ -139,7 +139,7 @@ def submit(self, workflow, submit_call=None): workflow.checkpoint() workflow.main() - def run(self, func, single=False, **kwargs): + def run(self, funcs, single=False, **kwargs): """ Executes task multiple times in serial. @@ -163,9 +163,8 @@ def run(self, func, single=False, **kwargs): ntasks = self.ntask for taskid in range(ntasks): - # os environment variables can only be strings, these need to be - # converted back to integers by system.taskid() - os.environ["SEISFLOWS_TASKID"] = str(taskid) + # Set Task ID for currently running process + set_task_id(taskid) # Make sure that we're creating new log files EACH time we run() idx = 0 @@ -177,14 +176,12 @@ def run(self, func, single=False, **kwargs): else: break - if taskid == 0: - logger.info(f"running task {func.__name__} {self.ntask} times") - # Redirect output to a log file to mimic cluster runs where 'run' # task output logs are sent to different files with open(log_file, "w") as f: with redirect_stdout(f): - func(**kwargs) + for func in funcs: + func(**kwargs) def save_kwargs_to_disk(self, path, classname, method, kwargs): """ diff --git a/seisflows/tools/unix.py b/seisflows/tools/unix.py index 5575c863..1c039d89 100644 --- a/seisflows/tools/unix.py +++ b/seisflows/tools/unix.py @@ -41,7 +41,7 @@ def cd(path): os.chdir(path) -def cp(src='', dst=''): +def cp(src, dst): """ Copy files @@ -52,7 +52,7 @@ def cp(src='', dst=''): """ if isinstance(src, (list, tuple)): if len(src) > 1: - assert os.path.isdir(dst), "unexpected type for unix.cp 'dst'" + assert os.path.isdir(dst), f"unix.cp 'dst' must be directory: {dst}" for sub in src: cp(sub, dst) return @@ -89,7 +89,7 @@ def ln(src, dst): >>> from seisflows.tools.unix import ln >>> ln("example_file", "path/to/sylink/new_filename") >>> # OR - >>> sln("example_file", "path/to/sylink/") + >>> ln("example_file", "path/to/sylink/") :type src: str :param src: path to file or directory to symlink @@ -251,10 +251,13 @@ def nproc(): Get the number of processors available. Same as calling 'nproc' from Linux command line. + TODO probably replace this with multiprocessing.cpu_count() + :rtype: int :return: number of processors :raises EnvironmentError: if nproc cannot be determined """ + # Method 1 calls 'nproc'. May fail and return '' if 'nproc' not avail. _nproc = subprocess.run("nproc", shell=True, text=True, stdout=subprocess.PIPE).stdout.strip() diff --git a/seisflows/tools/utils.py b/seisflows/tools/utils.py index dbbc8625..a1a9a794 100644 --- a/seisflows/tools/utils.py +++ b/seisflows/tools/utils.py @@ -11,6 +11,56 @@ from seisflows import logger +class TaskIDError(Exception): + """ + A specific error that gets called when tasks are not run on system, + i.e., when we can't find 'SEISFLOWS_TASKID' in the environment variables. + This means we are attempting to access child process variables inside + the parent process. + """ + pass + + +def set_task_id(task_id): + """ + Set the SEISFLOWS_TASKID in os environs + + .. note:: + Mostly used for debugging/testing purposes as a way of mimicing + system.run() assigning task ids to child processes + + :type task_id: int + :param task_id: integer task id to assign to the current working environment + """ + os.environ["SEISFLOWS_TASKID"] = str(task_id) + + +def get_task_id(force=False): + """ + Task IDs are assigned to each child process spawned by the system module + during a SeisFlows workflow. SeisFlows modules use this Task ID to keep + track of embarassingly parallel process, e.g., solver uses the Task ID to + determine which source is being considered. + + :type force: bool + :param force: If no task id is found, force set it to 0 + :rtype: int + :return: task id for given solver + :raises TaskIDError: if no environment variable is found + """ + _taskid = os.getenv("SEISFLOWS_TASKID") + if _taskid is None: + if force: + _taskid = 0 + logger.warning("Environment variable 'SEISFLOWS_TASKID' not found. " + "Assigning Task ID == 0") + else: + raise TaskIDError("Environment variable 'SEISFLOWS_TASKID' not " + "found. Please make sure the process asking " + "for task id is called by system.") + return int(_taskid) + + def log_status(func): """ Decorator function that logs the completion status of a function to a @@ -58,38 +108,6 @@ def logged_func(): return logged_func() -def set_task_id(task_id): - """ - Set the SEISFLOWS_TASKID in os environs - - .. note:: - Mostly used for debugging/testing purposes as a way of mimicing - system.run() assigning task ids to child processes - - :type task_id: int - :param task_id: integer task id to assign to the current working environment - """ - os.environ["SEISFLOWS_TASKID"] = str(task_id) - - -def get_task_id(): - """ - Task IDs are assigned to each child process spawned by the system module - during a SeisFlows workflow. SeisFlows modules use this Task ID to keep - track of embarassingly parallel process, e.g., solver uses the Task ID to - determine which source is being considered. - - :rtype: int - :return: task id for given solver - """ - _taskid = os.getenv("SEISFLOWS_TASKID") - if _taskid is None: - _taskid = 0 - logger.warning("Environment variable 'SEISFLOWS_TASKID' not found. " - "Assigning Task ID == 0") - return int(_taskid) - - def load_yaml(filename): """ Define how the PyYaml yaml loading function behaves. diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index 373d2c11..40d7718b 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -6,99 +6,200 @@ of the scaffolding defined by the Forward class. """ import os -import sys -from glob import glob +from time import asctime -from seisflows.tools import msg -from seisflows.config import save, import_seisflows -from seisflows.tools.specfem import Model +from seisflows import logger +from seisflows.tools import msg, unix +from seisflows.core import Dict +from seisflows.config import import_seisflows class Forward: """ - Workflow abstract base class representing an en-masse forward solver and - misfit calculator. + [workflow.forward] Run forward solver in parallel and optionally calculate + data-synthetic misfit and adjoint sources. + + :type modules: list of module + :param modules: instantiated SeisFlows modules which should have been + generated by the function `seisflows.config.import_seisflows` with a + parameter file generated by seisflows.configure + :type data_case: str + :param data_case: How to address 'data' in the workflow, available options: + 'data': real data will be provided by the user in + `path_data/{source_name}` in the same format that the solver will + produce synthetics (controlled by `solver.format`) OR + synthetic': 'data' will be generated as synthetic seismograms using + a target model provided in `path_model_true`. If None, workflow will + not attempt to generate data. + :type export_traces: bool + :param export_traces: export all waveforms that are generated by the + external solver to `path_output`. If False, solver traces stored in + scratch may be discarded at any time in the workflow + :type export_residuals: bool + :param export_residuals: export all residuals (data-synthetic misfit) that + are generated by the external solver to `path_output`. If False, + residuals stored in scratch may be discarded at any time in the workflow + :type workdir: str + :param workdir: working directory in which to look for data and store + results. Defaults to current working directory + :type path_eval_grad: str + :param path_eval_grad: scratch path to store files for gradient evaluation, + including models, kernels, gradient and residuals. """ - def __init__(self, save_traces=False, save_residuals=False, - path_eval_grad=None): - """ - En-masse forward simulation parameters - """ - self.save_traces = save_traces - self.save_residuals = save_residuals - self.path_eval_grad = self.path_eval_grad - - self.required.par( - "SAVETRACES", required=False, default=False, par_type=bool, - docstr="Save waveform traces to disk after they have been " - "generated by the external solver" - ) - self.required.par( - "SAVERESIDUALS", required=False, default=False, par_type=bool, - docstr="Save data-synthetic residuals each time they are " - "caluclated" - ) - self.required.path( - "DATA", required=False, default=None, - docstr="path to observed waveform data available to workflow" - ) - self.required.path( - "MODEL_INIT", required=False, - default=os.path.join(self.path.WORKDIR, "specfem", "MODEL_INIT"), - docstr="Path location of the initial model to be used to generate " - "the the first evaluation of synthetic seismograms." + def __init__(self, modules=None, data_case=None, export_traces=False, + export_residuals=False, workdir=os.getcwd(), + path_eval_grad=None, path_output=None, path_data=None, + path_state_file=None, path_model_init=None, + path_model_true=None, **kwargs): + """Set default forward workflow parameters""" + # Keep modules hidden so that seisflows configure doesnt count them + # as 'parameters' + self._modules = modules + + self.data_case = data_case + self.export_traces = export_traces + self.export_residuals = export_residuals + + self.path = Dict( + workdir=workdir, + scratch=os.path.join(workdir, "scratch"), + eval_grad=path_eval_grad or + os.path.join(workdir, "scratch", "evalgrad"), + output=path_output or os.path.join(workdir, "output"), + state_file=path_state_file or + os.path.join(workdir, "statefile.txt"), + data=path_data, + model_init=path_model_init, + model_true=path_model_true ) - self.required.path( - "GRAD", required=False, - default=os.path.join(self.path.WORKDIR, "scratch", "evalgrad"), - docstr="scratch path to store data related to gradient evaluations" - ) - - # For keeping track of what functions to start and stop a workflow with - self.start = None - self.stop = None - def check(self, validate=True): + self._required_modules = ["system", "solver"] + self._acceptable_data_cases = ["data", "synthetic"] + self._optional_modules = ["preprocess"] + + # Empty module variables that should be filled in by setup + self.system = None + self.solver = None + self.preprocess = None + + # Read in any existing state file which keeps track of workflow tasks + self._states = {} + if os.path.exists(self.path.state_file): + for line in open(self.path.state_file, "r").readlines(): + if line.startswith("#"): + continue + key, val = line.strip().split(":") + self._states[key] = val.strip() + + @property + def task_list(self): """ - Checks parameters and paths. Must be implemented by sub-class + USER-DEFINED TASK LIST. This property defines a list of class methods + that take NO INPUT and have NO RETURN STATEMENTS. This defines your + linear workflow, i.e., these tasks are to be run in order from start to + finish to complete a workflow. + + This excludes 'check' (which is run during 'import_seisflows') and + 'setup' which should be run separately + + .. note:: + For workflows that require an iterative approach (e.g. inversion), + this task list will be looped over, so ensure that any setup and + teardown tasks (run once per workflow, not once per iteration) are + not included. + + :rtype: list + :return: list of methods to call in order during a workflow """ - super().check(validate=validate) + return [self.evaluate_initial_misfit] - def setup(self, flow=None, return_flow=False): + def check(self): """ - Setup workflow by intaking functions to be run and checking start and - stop criteria + Check that workflow has required modules. Run their respective checks """ - # REQUIRED: CLI command `seisflows print flow` needs this for output - if return_flow: - return flow - - # Allow User to start the workflow mid-FLOW, in the event that a - # previous workflow errored, or if the User had previously stopped - # a workflow to look at results and they want to pick up where - # they left off - self.start, self.stop = self._check_stop_resume_cond(flow) - - self.logger.info( - msg.mjr(f"BEGINNING {self.__class__.__name__.upper()} WORKFLOW") - ) - - # Required modules that need to be set up - self.logger.info(msg.mnr("PERFORMING MODULE SETUP")) - self.module("system").setup() - self.module("preprocess").setup() - self.logger.info("setting up solver on system...") - self.module("system").run("solver", "setup") - - def finalize(self): + # Check that required modules have been instantiated + for req_mod in self._required_modules: + assert(self._modules[req_mod]), ( + f"'{req_mod}' is a required module for workflow " + f"'{self.__class__.__name__}'" + ) + # Make sure that the modules are actually instances (not e.g., str) + assert(hasattr(self._modules[req_mod], "__class__")), \ + f"workflow attribute {req_mod} must be an instance" + + # Run check function of these modules + self._modules[req_mod].check() + + # Tell the user whether optional modules are instantiated + for opt_mod in self._optional_modules: + if self._modules[opt_mod]: + self._modules[opt_mod].check() + else: + logger.warning(f"optional module '{opt_mod}' has not been " + f"instantiated, some functionality of the " + f"'{self.__class__.__name__}' workflow may be " + f"skipped") + + if self.data_case is not None: + assert(self.data_case.lower() in self._acceptable_data_cases), \ + f"`data_case` must be in {self._acceptable_data_cases}" + if self.data_case.lower() == "data": + assert(self.path.data is not None and + os.path.exists(self.path.data)), \ + f"importing data with `data_case`=='import' requires " \ + f"'path_data' to exist" + elif self.data_case.lower() == "synthetic": + assert(self.path.model_true is not None and + os.path.exists(self.path.model_true)), \ + f"creating data with `data_case`=='create' requires " \ + f"'path_model_true' to exist and point to a target model" + else: + logger.warning(f"`workflow.data_case` is None, SeisFlows will not " + f"be able to find data for data-synthetic comparison" + ) + + def setup(self): """ - Tasks related to tearing down a workflow + Makes required path structure for the workflow, runs setup functions + for all the required modules of this workflow. """ - super().finalize() - - self.logger.info( - msg.mjr(f"FINISHED {self.__class__.__name__} WORKFLOW") - ) + # Create the desired directory structure + for path in self.path.values(): + if path is not None and not os.path.splitext(path)[-1]: + unix.mkdir(path) + + # Run setup() for each of the required modules + for req_mod in self._required_modules: + logger.info( + f"setup for module " + f"'{req_mod}.{self._modules[req_mod].__class__.__name__}'" + ) + self._modules[req_mod].setup() + + # Run setup() for each of the instantiated modules + for opt_mod in self._optional_modules: + if self._modules[opt_mod]: + logger.info( + f"setup for module " + f"'{opt_mod}.{self._modules[opt_mod].__class__.__name__}'" + ) + self._modules[opt_mod].setup() + + # Generate the state file to keep track of task completion + if not os.path.exists(self.path.state_file): + logger.info(f"generating SeisFlows state file") + logger.debug(self.path.state_file) + with open(self.path.state_file, "w") as f: + f.write(f"# SeisFlows State File\n") + f.write(f"# {asctime()}\n") + f.write(f"# Acceptable states: 'completed', 'failed'\n") + f.write(f"# =======================================\n") + + # Distribute modules to the class namespace. We don't do this at init + # incase _modules was set as NoneType + self.solver = self._modules.solver + self.system = self._modules.system + self.preprocess = self._modules.preprocess def checkpoint(self): """ @@ -106,183 +207,182 @@ def checkpoint(self): the workflow can be resumed following a crash, pause or termination of workflow. """ - save(path=self.path.OUTPUT) - - def main(self, flow=None, return_flow=False): + # Grab State file header values + with open(self.path.state_file, "r") as f: + lines = f.readlines() + + with open(self.path.state_file, "w") as f: + # Rewrite header values + for line in lines: + if line.startswith("#"): + f.write(line) + for key, val in self._states.items(): + f.write(f"{key}: {val}\n") + + def run(self): """ - Execution of a workflow is equal to stsepping through workflow.main() - - An example main() script is provided below which details the requisite - parts. This function will NOT execute as it is written in pseudocode. - - :type flow: list or tuple - :param flow: list of Class methods that will be run in the order they - are provided. If None, defaults to the evaluate_function() as - defined by Forward class - :type return_flow: bool - :param return_flow: for CLI tool, simply returns the flow function - rather than running the workflow. Used for print statements etc. + Call the Task List in order to 'run' the workflow. Contains logic for + to keep track of completed tasks and avoids re-running tasks that have + previously been completed (e.g., if you are restarting your workflow) """ - # The FLOW function defines a list of functions to execute IN ORDER - if flow is None: - flow = (self.evaluate_initial_misfit) - - self.setup(flow, return_flow) - # Iterate through the `FLOW` to step through workflow.main() - for func in flow[self.start: self.stop]: - func() - self.finalize() + for func in self.task_list: + # Skip over functions which have already been completed + if (func.__name__ in self._states.keys()) and ( + self._states[func.__name__] == "completed"): + logger.info(f"'{func.__name__}' has already been run, skipping") + continue + # Otherwise attempt to run functions that have failed or are + # encountered for the first time + else: + try: + func() + self._states[func.__name__] = "completed" + except Exception as e: + self._states[func.__name__] = "failed" + self.checkpoint() + raise + + self.checkpoint() def evaluate_initial_misfit(self): """ - Wrapper for evaluate_function that generates synthetics via forward - simulations, calculates misfits and sends residuals to PATH.GRAD and - sets up the 'm_new' model for future evaluations - """ - self.logger.info(msg.mjr("EVALUATING INITIAL MISFIT")) - self._evaluate_function(path=self.path.GRAD, suffix="new") - - def _evaluate_function(self, path, suffix): - """ - Performs forward simulation, and evaluates the objective function - - :type path: str - :param path: path in the scratch directory to use for I/O - :type suffix: str - :param suffix: suffix to use for I/O + System wrapper for 'evaluate_objective function' that passes in """ - system = self.module("system") - - self.logger.info(msg.sub("EVALUATING OBJECTIVE FUNCTION")) - - model_tag = f"m_{suffix}" - misfit_tag = f"f_{suffix}" - - self._write_model(path=path, model_tag=model_tag) + logger.info(msg.mnr("EVALUATING MISFIT FOR INITIAL MODEL")) + self.system.run([self.prepare_data_for_solver, + self.evaluate_objective_function], + path_model=self.path.model_init + ) - self.logger.debug(f"evaluating objective function {self.par.NTASK} " - f"times on system...") - system.run("solver", "eval_func", path=path) - - self._write_misfit(path=path, misfit_tag=misfit_tag) - - def _write_model(self, path, model_tag): + def prepare_data_for_solver(self, **kwargs): """ - Writes model in format expected by solver - - :type path: str - :param path: path to write the model to - :type model_tag: str - :param model_tag: name of the model to be saved, usually tagged as 'm' with - a suffix depending on where in the inversion we are. e.g., 'm_try'. - Expected that these tags are defined in OPTIMIZE module - """ - m = Model(path=os.path.join(self.path.OPTIMIZE, f"{model_tag}.npz")) - m.write(path=os.path.join(path, "model")) - - self.logger.debug(f"saving model '{model_tag}'") + Determines how to provide data to each of the solvers. Either by copying + data in from a user-provided path, or generating synthetic 'data' using + a target model. - def _write_misfit(self, path, misfit_tag): + .. note :: + Must be run by system.run() so that solvers are assigned individual + task ids and working directories """ - Writes misfit in format expected by nonlinear optimization library. - Collects all misfit values within the given residuals directory and sums - them in a manner chosen by the preprocess class. - - :type path: str - :param path: path to write the misfit to - :type misfit_tag: str - :param misfit_tag: name of the model to be saved, usually tagged as - 'f' with a suffix depending on where in the inversion we are. - e.g., 'f_try'. Expected that these tags are defined in OPTIMIZE - module + logger.info(msg.sub("preparing data for solver")) + + if self.data_case == "data": + logger.info(f"copying data from `path_data`") + src = os.path.join(self.path.data, self.solver.source_name, "*") + dst = os.path.join(self.solver.cwd, "traces", "obs", "") + unix.cp(src, dst) + elif self.data_case == "synthetic": + # Figure out where to export waveform files to, if requested + if self.export_traces: + export_traces = os.path.join(self.path.output, + self.solver.source_name, "obs") + else: + export_traces = False + + # Run the forward solver with target model and save traces the 'obs' + logger.info(f"running forward simulation for " + f"{self.solver.source_name}") + self.solver.import_model(path_model=self.path.model_true) + self.solver.forward_simulation( + save_traces=os.path.join(self.solver.cwd, "traces", "obs"), + export_traces=export_traces + ) + + def evaluate_objective_function(self, path_model, **kwargs): """ - preprocess = self.module("preprocess") - optimize = self.module("optimize") + Performs forward simulation for a single given event. Also evaluates the + objective function and writes residuals and adjoint sources for later + tasks. - self.logger.info("summing residuals with preprocess module") - src = glob(os.path.join(path, "residuals", "*")) - total_misfit = preprocess.sum_residuals(src) + .. note:: + if PAR.PREPROCESS == None, will not perform misfit quantification - self.logger.debug(f"saving misfit {total_misfit:.3E} to '{misfit_tag}'") - optimize.save(misfit_tag, total_misfit) - - def _check_stop_resume_cond(self, flow): - """ - Chek the stop after and resume from conditions - - Allow the main() function to resume a workflow from a given flow - argument, or stop the workflow after a given argument. In the event - that a previous workflow errored, or if the User had previously - stopped a workflow to look at results and they want to pick up where - they left off. - - Late check: Exits the workflow if RESUME_FROM or STOP_AFTER arguments - do not match any of the given flow arguments. - - :type flow: tuple of functions - :param flow: an ordered list of functions that will be - :rtype: tuple of int - :return: (start, stop) indices of the `flow` input dictating where the - list should be begun and ended. If RESUME_FROM and STOP_AFTER - conditions are NOT given by the user, start and stop will be 0 and - -1 respectively, meaning we should execute the ENTIRE list + .. note:: + Must be run by system.run() so that solvers are assigned individual + task ids/ working directories. """ - fxnames = [func.__name__ for func in flow] - - # Default values which dictate that flow will execute in its entirety - start_idx = None - stop_idx = None - - # Overwrite start_idx if RESUME_FROM given, exit condition if no match - if self.par.RESUME_FROM: - try: - start_idx = fxnames.index(self.par.RESUME_FROM) - fx_name = flow[start_idx].__name__ - self.logger.info( - msg.mnr(f"WORKFLOW WILL RESUME FROM FUNC: '{fx_name}'") - ) - except ValueError: - self.logger.info( - msg.cli(f"{self.par.RESUME_FROM} does not correspond to any FLOW " - f"functions. Please check that self.par.RESUME_FROM " - f"matches one of the functions listed out in " - f"`seisflows print flow`.", header="error", - border="=") - ) - sys.exit(-1) - - # Overwrite stop_idx if STOP_AFTER provided, exit condition if no match - if self.par.STOP_AFTER: - try: - stop_idx = fxnames.index(self.par.STOP_AFTER) - fx_name = flow[stop_idx].__name__ - stop_idx += 1 # increment to stop AFTER, due to python indexing - self.logger.info( - msg.mnr(f"WORKFLOW WILL STOP AFTER FUNC: '{fx_name}'") - ) - except ValueError: - self.logger.info( - msg.cli( - f"{self.par.STOP_AFTER} does not correspond to any " - f"FLOW functions. Please check that PAR.STOP_AFTER " - f"matches one of the functions listed out in " - f"`seisflows print flow`.", header="error", - border="=") - ) - sys.exit(-1) - - # Make sure stop after doesn't come before resume_from, otherwise none - # of the flow will execute - if self.par.STOP_AFTER and self.par.RESUME_FROM: - if stop_idx <= start_idx: - self.logger.info( - msg.cli( - f"PAR.STOP_AFTER=='{self.par.STOP_AFTER}' is called " - f"before PAR.RESUME_FROM=='{self.par.RESUME_FROM}' in " - f"the FLOW functions. Please adjust accordingly " - f"and rerun.", header="error", border="=") - ) - sys.exit(-1) + logger.info(f"running forward simulation with " + f"'{self.solver.__class__.__name__}'") + + # Figure out where to export waveform files to, if requested + if self.export_traces: + export_traces = os.path.join(self.path.output, + self.solver.source_name, "syn") + else: + export_traces = False + + # Run the forward simulation with the given input model + self.solver.import_model(path_model=path_model) + self.solver.forward_simulation( + save_traces=os.path.join(self.solver.cwd, "traces", "syn"), + export_traces=export_traces + ) - return start_idx, stop_idx + # (optional) Perform data-synthetic misfit quantification + if self.preprocess: + logger.info(f"quantifying misfit with " + f"'{self.preprocess.__class__.__name__}'") + self.preprocess.quantify_misfit( + observed=self.solver.data_filenames(choice="obs"), + synthetics=self.solver.data_filenames(choice="syn"), + save_adjsrcs=os.path.join(self.solver.cwd, "traces", "adj"), + save_residuals=os.path.join(self.path.evalgrad, "residuals") + ) + + # def alsdkjfla: + # self._write_misfit(path=path, misfit_tag=misfit_tag) + # + # def _write_model(self, path, model_tag): + # """ + # Writes model in format expected by solver + # + # :type path: str + # :param path: path to write the model to + # :type model_tag: str + # :param model_tag: name of the model to be saved, usually tagged as 'm' with + # a suffix depending on where in the inversion we are. e.g., 'm_try'. + # Expected that these tags are defined in OPTIMIZE module + # """ + # m = Model(path=os.path.join(self.path.OPTIMIZE, f"{model_tag}.npz")) + # m.write(path=os.path.join(path, "model")) + # + # self.logger.debug(f"saving model '{model_tag}'") + # + # def _write_misfit(self, path, misfit_tag): + # """ + # Writes misfit in format expected by nonlinear optimization library. + # Collects all misfit values within the given residuals directory and sums + # them in a manner chosen by the preprocess class. + # + # :type path: str + # :param path: path to write the misfit to + # :type misfit_tag: str + # :param misfit_tag: name of the model to be saved, usually tagged as + # 'f' with a suffix depending on where in the inversion we are. + # e.g., 'f_try'. Expected that these tags are defined in OPTIMIZE + # module + # """ + # preprocess = self.module("preprocess") + # optimize = self.module("optimize") + # + # self.logger.info("summing residuals with preprocess module") + # src = glob(os.path.join(path, "residuals", "*")) + # total_misfit = preprocess.sum_residuals(src) + # + # self.logger.debug(f"saving misfit {total_misfit:.3E} to '{misfit_tag}'") + # optimize.save(misfit_tag, total_misfit) + + +if __name__ == "__main__": + # Standard SeisFlows setup, makes modules global variables to the workflow + pars, modules = import_seisflows() + + logger.info(msg.mjr("Starting forward simulation workflow")) + + workflow = Forward(modules, **pars) + workflow.check() + workflow.setup() + workflow.run() + + logger.info(msg.mjr("Finished forward simulation workflow")) diff --git a/seisflows/workflow/forward_test.py b/seisflows/workflow/forward_test.py deleted file mode 100644 index aadbf92c..00000000 --- a/seisflows/workflow/forward_test.py +++ /dev/null @@ -1,56 +0,0 @@ -""" -Test workflow to see if a new form of seisflows workflow can be used -""" -import os -from glob import glob -from seisflows import logger -from seisflows.config import import_seisflows -from seisflows.tools import msg - -# Standard SeisFlows setup, makes modules global variables to the workflow -pars, modules = import_seisflows() -system, preprocess, solver, postprocess, optimize = modules - - -def evaluate_objective_function(path_model): - """ - Performs forward simulation for a single given event. Also evaluates the - objective function and writes residuals and adjoint sources for later tasks. - - .. note:: - if PAR.PREPROCESS == None, will not perform misfit quantification - - .. note:: - Must be run by system.run() so that solvers are assigned individual - task ids/ working directories. - """ - # Run the forward simulation with the given input model - solver.import_model(path_model=path_model) - solver.forward_simulation( - save_traces=os.path.join(solver.cwd, "traces", "syn"), - export_traces=os.path.join(solver.path.output, - solver.source_name, "syn") - ) - - # Perform data-synthetic misfit quantification - if preprocess is not None: - preprocess.quantify_misfit( - observed=solver.data_filenames(choice="obs"), - synthetics=solver.data_filenames(choice="syn"), - output=os.path.join(solver.cwd, "traces", "adj") - ) - - -if __name__ == "__main__": - # Begin the forward simulation workflow - logger.info(msg.mjr("Starting forward simulation workflow")) - - for module in modules: - module.setup() - - # Run objective function evaluation NTASK times - logger.info(msg.sub("EVALUATING OBJECTIVE FUNCTION")) - system.run(evaluate_objective_function, path_model=pars.path_model_init) - - logger.info(msg.mjr("Finished forward simulation workflow")) - diff --git a/seisflows/workflow/migration.py b/seisflows/workflow/migration.py index 54e23bce..f2201d6b 100644 --- a/seisflows/workflow/migration.py +++ b/seisflows/workflow/migration.py @@ -8,162 +8,130 @@ """ import os +from seisflows import logger from seisflows.workflow.forward import Forward -from seisflows.tools import msg +from seisflows.tools import msg, unix from seisflows.tools.specfem import Model class Migration(Forward): """ - Migration base class. - - Performs the workflow of an inversion up to the postprocessing. In the - terminology of seismic exploration, implements a 'reverse time migration'. + [workflow.migration] Run forward and adjoint solver to produce + event-dependent misfit kernels. Sum and postprocess kernels to produce + gradient. In seismic exploration this is 'reverse time migration'. + + .. warning:: + Misfit kernels require large amounts of disk space for storage. + Setting `export_kernel`==True when PAR.NTASK is large and model files + are large may lead to large file overhead. + + :type export_gradient: bool + :param export_gradient: export the gradient after it has been generated + in the scratch directory. If False, gradient can be discarded from + scratch at any time in the workflow + :type export_kernels: bool + :param export_kernels: export each sources event kernels after they have + been generated in the scratch directory. If False, gradient can be + discarded from scratch at any time in the workflow """ - def __init__(self): + __doc__ = Forward.__doc__ + __doc__ + + def __init__(self, _modules=None, export_gradient=False, + export_kernels=False, **kwargs): """ Init is used to instantiate global parameters defined by the input parameter file. """ - super().__init__() - - self.required.par( - "CASE", required=False, default="data", par_type=str, - docstr="How to address 'data' in your workflow, available options: " - "1) 'data': Real data inversion. Observed waveforms must be " - "provided by the user in PATH.DATA/{SOURCE_NAME}. OR if " - "PAR.PREPROCESS=='pyatoa' data should be discoverable " - "via IRIS webservices based on event ID and station codes" - "2) 'synthetic': A synthetic-synthetic workflow. 'Data' " - "will be generated as synthetics using PATH.MODEL_TRUE. " - ) - self.required.par( - "SAVEGRADIENT", required=False, default=True, par_type=bool, - docstr="Save gradient files each time the gradient is evaluated" - ) - self.required.par( - "SAVEKERNELS", required=False, default=False, par_type=bool, - docstr="Save event kernel files each time they are evaluated" - ) - self.required.par( - "SAVEAS", required=False, default="binary", par_type=str, - docstr="Format to save models, gradients, kernels. Available: " - "['binary': save files in native SPECFEM .bin format, " - "'vector': save files as NumPy .npy files, " - "'both': save as both binary and vectors]" - ) - self.required.path( - "GRAD", required=False, - default=os.path.join(self.path.WORKDIR, "scratch", "evalgrad"), - docstr="scratch path to store any models or kernels related to " - "gradient evaluations. Sub-directories will be generated " - "inside PATH.GRAD to save various stages of gradient " - "manipulation" - ) - self.required.path( - "MODEL_TRUE", required=False, - default=os.path.join(self.path.WORKDIR, "specfem", "MODEL_TRUE"), - docstr="Target model to be used for PAR.CASE == 'synthetic'. The " - "TRUE model will be used to evaluate forward simulations " - "ONCE at the beginning of the workflow, to generate 'data'." - ) + super().__init__(**kwargs) - def check(self, validate=True): - """ - Checks parameters and paths. Must be implemented by sub-class - """ - super().check(validate=validate) + self._modules = _modules + self.export_gradient = export_gradient + self.export_kernels = export_kernels - if self.par.CASE.upper() == "SYNTHETIC": - assert os.path.exists(self.path.MODEL_TRUE), \ - "CASE == SYNTHETIC requires PATH.MODEL_TRUE" + # Overwriting base class required modules list + self._required_modules = ["system", "solver", "preprocess", + "postprocess"] - if not self.path.DATA or not os.path.exists(self.path.DATA): - assert "MODEL_TRUE" in self.path, f"DATA or MODEL_TRUE must exist" + # Empty module variables that should be filled in by setup + self.postprocess = None - def setup(self, flow=None, return_flow=False): + @property + def task_list(self): """ - Override the Forward.setup() method to include new flow functions - AND run setup for a the Postprocess module which will be used to deal - with the gradient - """ - super().setup(flow=flow, return_flow=return_flow) - - postprocess = self.module("postprocess") - postprocess.setup() + USER-DEFINED TASK LIST. This property defines a list of class methods + that take NO INPUT and have NO RETURN STATEMENTS. This defines your + linear workflow, i.e., these tasks are to be run in order from start to + finish to complete a workflow. - def main(self, flow=None, return_flow=False): - """Inherits from seisflows.workflow.forward.Forward""" - flow = (self.evaluate_initial_misfit, - self.evaluate_gradient, - self.process_kernels, - self.scale_gradient - ) + This excludes 'check' (which is run during 'import_seisflows') and + 'setup' which should be run separately - self.main(flow=flow, return_flow=return_flow) - - def evaluate_initial_misfit(self): - """Inherits from seisflows.workflow.forward.Forward""" - super().evaluate_initial_misfit() + .. note:: + For workflows that require an iterative approach (e.g. inversion), + this task list will be looped over, so ensure that any setup and + teardown tasks (run once per workflow, not once per iteration) are + not included. - def evaluate_gradient(self, path=None): + :rtype: list + :return: list of methods to call in order during a workflow """ - Performs adjoint simulation to retrieve the gradient of the objective. + return [self.evaluate_initial_misfit, + self.generate_misfit_kernels, + self.postprocess_kernels + ] - .. note:: - In the terminology of seismic exploration, we are 'backprojecting' + def setup(self): """ - system = self.module("system") + Override the Forward.setup() method to include the postprocessing + module used for kernel/gradient manipulation + """ + super().setup() + self.postprocess = self._modules.postprocess - self.logger.info(msg.mnr("EVALUATING GRADIENT")) - self.logger.debug( - f"evaluating gradient {self.par.NTASK} times on system..." - ) - system.run("solver", "eval_grad", path=path or self.path.GRAD, - export_traces=self.par.SAVETRACES) + def generate_misfit_kernels(self): + """System wrapper for running adjoint simulations""" + logger.msg.mnr("GENERATING MISFIT KERNELS") + self.system.run(self.generate_misfit_kernel) - def process_kernels(self): + def generate_misfit_kernel(self): """ - System-run wrapper for postprocess.process_kernels which is meant to - sum and smooth all individual event kernels + Performs adjoint simulations for a single given event. File manipulation + to ensure kernels are discoverable by other modules """ - system = self.module("system") - self.logger.info(msg.mnr("PROCESSING KERNELS")) + if self.export_kernels: + export_kernels = os.path.join(self.path.output, "kernels", + self.solver.source_name) + else: + export_kernels = False + + # Run adjoint simulations on system. Make kernels discoverable in + # path `eval_grad`. Optionally export those kernels + self.solver.adjoint_simulation( + save_kernels=os.path.join(self.path.eval_grad, "kernels", + self.solver.source_name), + export_kernels=export_kernels + ) - # Runs kernel processing as a single parallel process - system.run("solver", "postprocess_kernels", single=True, - path_grad=self.path.GRAD) + def postprocess_kernels(self): + """System wrapper for postprocess kernels. Run with single""" + self.system.run(self._postprocess_kernels, single=True) - def scale_gradient(self): + def _postprocess_kernels(self): """ - Scale the gradient magnitude by the given model, write the gradient in - vector form and model form, and apply an optional mask to the gradient - - .. note:: - While both masking and preconditioning involve scaling the - gradient, they are fundamentally different operations: - masking is ad hoc, preconditioning is a change of variables; - For more info, see Modrak & Tromp 2016 GJI + System-run wrapper for postprocess.process_kernels which is meant to + sum and smooth all individual event kernels """ - model = Model(path=os.path.join(self.path.GRAD)) - gradient = Model(path=os.path.join(self.path.GRAD, "kernels", "sum")) - - # Merge to vector and convert to absolute perturbations: - # log dm --> dm (see Eq.13 Tromp et al 2005) - gradient.vector *= model.vector - - if self.path.MASK: - mask = Model(os.path.join(self.path.MASK)) - # Write out a non-masked gradient incase masking is not wanted - gradient.write(path=os.path.join(self.path.GRAD, "gradient_nomask")) - - gradient.vector *= mask.vector - - # Update the model values based on the vector manipulation - gradient.model = gradient.split() - - # Write the gradient out - gradient.write(path=os.path.join(self.path.GRAD, "gradient")) - gradient.save(path=os.path.join(self.path.OPTIMIZE, "g_new")) + # Combine kernels into a single volumentric quantity + self.solver.combine( + input_path=os.path.join(self.path.eval_grad, "kernels"), + output_path=os.path.join(self.path.eval_grad, "sum") + ) + if self.solver.smooth_h > 0. or self.solver.smooth_v > 0.: + # Make a distinction that we have a pre- and post-smoothed sum + unix.mv(src=os.path.join(self.path.eval_grad, "sum_nosmooth")) + self.solver.smooth( + input_path=os.path.join(self.path.eval_grad, "sum_nosmooth"), + output_path=os.path.join(self.path.eval_grad, "sum") + ) From 29835840d85141c8d3298a15551ab081f2935a4d Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Mon, 18 Jul 2022 10:28:43 -0800 Subject: [PATCH 069/195] added inversion workflow supered on top of migration workflow. Need to fix up preprocess/postprocess/optimize modules to get things working. removed core script as most of the classes there are now defunct. renamed tools.utils to tools.core as the remainder classes have been moved there --- seisflows/core.py | 258 ------------------- seisflows/tools/{utils.py => core.py} | 108 ++++---- seisflows/workflow/forward.py | 45 +--- seisflows/workflow/inversion.py | 358 ++++++++++---------------- seisflows/workflow/migration.py | 108 ++++---- 5 files changed, 264 insertions(+), 613 deletions(-) delete mode 100755 seisflows/core.py rename seisflows/tools/{utils.py => core.py} (74%) diff --git a/seisflows/core.py b/seisflows/core.py deleted file mode 100755 index b0667248..00000000 --- a/seisflows/core.py +++ /dev/null @@ -1,258 +0,0 @@ -#!/usr/bin/env python3 -""" -Core class definitions for SeisFlows. Defines some unique class objects that -define how SeisFlows works internally, or are used heavily during a Seisflows -workflow. -""" -import sys -import logging - - -class Base(object): - """ - Defines the core Base object for all SeisFlows modules. All modules MUST - inherit from the Base object to work properly. This Base class essentially - dictates the required structure of a SeisFlows class. - """ - def __init__(self): - """ - SeisFlows instantiates its required parameters through the - SeisFlowsPathsParameters class, which scaffolds a rigid framework of - how parameters and paths should be defined by the program. This is - then used to build the parameter file dynamically. - """ - self.required = SeisFlowsPathsParameters() - - def module(self, name): - """ - Access globally stored SeisFlows modules located in sys.modules - - :rtype: Class or Dict or None - :return: Returns a SeisFlows module or Dictionary containing paths or - parameters. Else None if the chosen module has not been instantiated - """ - try: - mod = sys.modules[f"seisflows_{name}"] - except KeyError: - self.logger.warning(f"seisflows_{name} has not been instantiated") - mod = None - return mod - - @property - def par(self): - """ - Quick access SeisFlows parameters from sys.modules. Throws a warning - if parameters have not been instantiated - - :rtype: Dict or None - :return: Returns a Dictionary with instantiated parameters, or None if - parameters have not been instantiated - """ - return self.module("parameters") - - @property - def path(self): - """ - Quick access SeisFlows paths from sys.modules. Throws a warning - if paths have not been instantiated - - :rtype: Dict or None - :return: Returns a Dictionary with instantiated paths, or None if - parameters have not been instantiated - """ - return self.module("paths") - - def check(self, validate=True): - """ - General check() function for each module to check the validity of the - user-input parameters and paths - """ - if validate: - self.required.validate() - - # Example of a check statement - # assert(self.par.PARAMETER == example_value), f"Parameter != example" - - def setup(self): - """ - A placeholder function for any initialization or setup tasks that - need to be run once at the beginning of any workflow. - """ - return - - def finalize(self): - """ - A placeholder function for any finalization or tear-down tasks that - need to be run at the end of any iteration or workflow. - """ - return - - -class Dict(dict): - """ - A dictionary replacement which allows for easier parameter access through - getting and setting attributes. Also has some functionality to make string - printing prettier - """ - def __str__(self): - """Pretty print dictionaries and first level nested dictionaries""" - str_ = "" - try: - longest_key = max([len(_) for _ in self.keys()]) - for key, val in self.items(): - str_ += f"{key:<{longest_key}}: {val}\n" - except ValueError: - pass - return str_ - - def __repr__(self): - """Pretty print when calling an instance of this object""" - return self.__str__() - - def __getattr__(self, key): - """Attribute-like access of the internal dictionary attributes""" - try: - return self[key] - except KeyError: - raise AttributeError(f"{key} not found in Dict") - - def __setattr__(self, key, val): - """Setting attributes can only be performed one time""" - self.__dict__[key] = val - - -class Null: - """ - A null object that always and reliably does nothing - """ - def __init__(self, *args, **kwargs): - pass - - def __call__(self, *args, **kwargs): - return self - - def __bool__(self): - return False - - def __nonzero__(self): - return False - - def __getattr__(self, key): - return self - - def __setattr__(self, key, val): - return self - - def __delattr__(self, key): - return self - - -class SeisFlowsPathsParameters: - """ - A class used to simplify defining required or optional paths and parameters - that will be globally accesible through sys.modules. This class enforces a - specific path/parameter structure, and entry point into the environment. - - .. note:: - if a path or parameter is optional it requires a default value, which is - set at the header of this class - """ - default_par = "REQUIRED PARAMETER" - default_path = "REQUIRED PATH" - - def __init__(self, base=None): - """ - We simply store paths and parameters as nested dictioanries. Due to the - use of inheritance, the class can be passed to itself on initialization - which means paths and parameters can be adopted from base class - - :type base: seisflows.config.DefinePathsParameters - :param base: paths and parameters from abstract Base class that need to - be inherited by the current child class. - """ - self.parameters, self.paths = {}, {} - if base: - self.parameters.update(base.parameters) - self.paths.update(base.paths) - - def par(self, parameter, required, docstr, par_type, default=None): - """ - Add a parameter to the internal list of parameters - - :type parameter: str - :param parameter: name of the parameter - :type required: bool - :param required: whether or not the parameter is required. If it is not - required, then a default value should be given - :type docstr: str - :param docstr: Short explanatory doc string that defines what the - parameter is used for. - :type par_type: class or str - :param par_type: the parameter type, used for doc strings and also - parameter validation - :param default: default value for the parameter, can be any type - """ - if required: - default = self.default_par - if type(par_type) == type: - par_type = par_type.__name__ - self.parameters[parameter] = {"docstr": docstr, "required": required, - "default": default, "type": par_type} - - def path(self, path, required, docstr, default=None): - """ - Add a path to the internal list of paths - - :type path: str - :param path: name of the parameter - :type required: bool - :param required: whether or not the path is required. If it is not - required, then a default value should be given - :type docstr: str - :param docstr: Short explanatory doc string that defines what the - path is used for. - :type default: str - :param default: default value for the path - - """ - if required: - default = self.default_path - self.paths[path] = {"docstr": docstr, "required": required, - "default": default} - - def validate(self, paths=True, parameters=True): - """ - Set internal paths and parameter values into sys.modules. Should be - called by each modules check() function. - - Ensures that required paths and parameters are set by the User in the - parameter file and that default values are stored for any optional - paths and parameters which are not explicitely set. - - :type paths: bool - :param paths: validate the internal path values - :type parameters: bool - :param parameters: validate the internal parameter values - :raises ParameterError: if a required path or parameter is not set by - the user. - """ - if parameters: - sys_path = sys.modules["seisflows_parameters"] - for key, attrs in self.parameters.items(): - if attrs["required"] and (key not in sys_path): - raise KeyError( - f"Required parameter '{key}' not found in parameter file" - ) - elif key not in sys_path: - setattr(sys_path, key, attrs["default"]) - - if paths: - sys_par = sys.modules["seisflows_paths"] - for key, attrs in self.paths.items(): - if attrs["required"] and (key not in sys_par): - raise KeyError( - f"Required path '{key}' not found in parameter file" - ) - elif key not in sys_par: - setattr(sys_par, key, attrs["default"]) - diff --git a/seisflows/tools/utils.py b/seisflows/tools/core.py similarity index 74% rename from seisflows/tools/utils.py rename to seisflows/tools/core.py index a1a9a794..f7d8a7ec 100644 --- a/seisflows/tools/utils.py +++ b/seisflows/tools/core.py @@ -4,13 +4,70 @@ """ import os import re -import time import yaml import numpy as np -from seisflows.core import Dict from seisflows import logger +class Dict(dict): + """ + A dictionary replacement which allows for easier parameter access through + getting and setting attributes. Also has some functionality to make string + printing prettier + """ + def __str__(self): + """Pretty print dictionaries and first level nested dictionaries""" + str_ = "" + try: + longest_key = max([len(_) for _ in self.keys()]) + for key, val in self.items(): + str_ += f"{key:<{longest_key}}: {val}\n" + except ValueError: + pass + return str_ + + def __repr__(self): + """Pretty print when calling an instance of this object""" + return self.__str__() + + def __getattr__(self, key): + """Attribute-like access of the internal dictionary attributes""" + try: + return self[key] + except KeyError: + raise AttributeError(f"{key} not found in Dict") + + def __setattr__(self, key, val): + """Setting attributes can only be performed one time""" + self.__dict__[key] = val + + +class Null: + """ + A null object that always and reliably does nothing + """ + def __init__(self, *args, **kwargs): + pass + + def __call__(self, *args, **kwargs): + return self + + def __bool__(self): + return False + + def __nonzero__(self): + return False + + def __getattr__(self, key): + return self + + def __setattr__(self, key, val): + return self + + def __delattr__(self, key): + return self + + class TaskIDError(Exception): """ A specific error that gets called when tasks are not run on system, @@ -61,53 +118,6 @@ def get_task_id(force=False): return int(_taskid) -def log_status(func): - """ - Decorator function that logs the completion status of a function to a - state file. This is used for checkpointing a workflow and resuming - failed workflows without repeating computational intense tasks - - :type func: function - """ - raise NotImplementedError("This is not working as expected") - - STATE_FILE = os.path.join(os.getcwd(), "sfstatefile") - - if not os.path.exists(STATE_FILE): - with open(STATE_FILE, "w") as f: - f.write(f"# SeisFlows State File\n") - f.write(f"# {time.asctime()}\n") - f.write(f"# =========================\n") - - def logged_func(): - """Log the completion status of the function""" - try: - output = func() - with open(STATE_FILE, "a") as f: - f.write(f"{func.__name__}\tCOMPLETED\n") - except Exception as e: - with open(STATE_FILE, "a") as f: - f.write(f"{func.__name__}\tFAILED\n") - logger.error(e) - raise - return output - - lines = open(STATE_FILE, "r").readlines() - for line in lines: - if line.startswith("#"): - continue - function, status = line.strip().split("\t") - if func.__name__ == function: - if status == "COMPLETED": - return - elif status == "FAILED": - return logged_func() - else: - return logged_func() - else: - return logged_func() - - def load_yaml(filename): """ Define how the PyYaml yaml loading function behaves. diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index 40d7718b..c3f90484 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -326,52 +326,9 @@ def evaluate_objective_function(self, path_model, **kwargs): observed=self.solver.data_filenames(choice="obs"), synthetics=self.solver.data_filenames(choice="syn"), save_adjsrcs=os.path.join(self.solver.cwd, "traces", "adj"), - save_residuals=os.path.join(self.path.evalgrad, "residuals") + save_residuals=os.path.join(self.path.eval_grad, "residuals") ) - # def alsdkjfla: - # self._write_misfit(path=path, misfit_tag=misfit_tag) - # - # def _write_model(self, path, model_tag): - # """ - # Writes model in format expected by solver - # - # :type path: str - # :param path: path to write the model to - # :type model_tag: str - # :param model_tag: name of the model to be saved, usually tagged as 'm' with - # a suffix depending on where in the inversion we are. e.g., 'm_try'. - # Expected that these tags are defined in OPTIMIZE module - # """ - # m = Model(path=os.path.join(self.path.OPTIMIZE, f"{model_tag}.npz")) - # m.write(path=os.path.join(path, "model")) - # - # self.logger.debug(f"saving model '{model_tag}'") - # - # def _write_misfit(self, path, misfit_tag): - # """ - # Writes misfit in format expected by nonlinear optimization library. - # Collects all misfit values within the given residuals directory and sums - # them in a manner chosen by the preprocess class. - # - # :type path: str - # :param path: path to write the misfit to - # :type misfit_tag: str - # :param misfit_tag: name of the model to be saved, usually tagged as - # 'f' with a suffix depending on where in the inversion we are. - # e.g., 'f_try'. Expected that these tags are defined in OPTIMIZE - # module - # """ - # preprocess = self.module("preprocess") - # optimize = self.module("optimize") - # - # self.logger.info("summing residuals with preprocess module") - # src = glob(os.path.join(path, "residuals", "*")) - # total_misfit = preprocess.sum_residuals(src) - # - # self.logger.debug(f"saving misfit {total_misfit:.3E} to '{misfit_tag}'") - # optimize.save(misfit_tag, total_misfit) - if __name__ == "__main__": # Standard SeisFlows setup, makes modules global variables to the workflow diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index d29df7a1..e360c33f 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -20,188 +20,113 @@ """ import os import sys -import numpy as np +from seisflows import logger from seisflows.workflow.migration import Migration from seisflows.tools import msg, unix class Inversion(Migration): """ - Waveform inversion base class, built on top of the Migration child class, - which in-turn is built on top of the Forward child class. + [workflow.inversion] Peforms iterative nonlinear inversion using a built-in + optimization library which stores model vectors on disk. + + :type start: int + :param start: start inversion workflow at this iteration. 1 <= start <= inf + :type end: int + :param end: end inversion workflow at this iteration. start <= end <= inf + :type export_model: bool + :param export_model: export best-fitting model from the line search to disk. + If False, new models can be discarded from scratch at any time. + :type path_eval_func: str + :param path_eval_func: scratch path to store files for line search objective + function evaluations, including models, misfit and residuals + """ + __doc__ = Migration.__doc__ + __doc__ - Peforms iterative nonlinear inversion and provides a base class on top - of which specialized strategies can be implemented. + def __init__(self, _modules=None, start=1, end=1, export_model=True, + path_eval_func=None, **kwargs): + """Instantiate Inversion-specific parameters""" - To allow customization, the inversion workflow is divided into generic - methods such as "initialize", "finalize", "evaluate_function", - "evaluate_gradient", which can be easily overloaded. + super().__init__(**kwargs) - Calls to forward and adjoint solvers are abstracted through the "solver" - interface so that various forward modeling packages canf be used - interchangeably. + self.start = start + self.end = end + self.export_model = export_model - Commands for running in serial or parallel on a workstation or cluster - are abstracted through the "system" interface. - """ - def __init__(self): - """ - Init is used to instantiate global parameters defined by the input - parameter file. - """ - super().__init__() - - self.required.par( - "BEGIN", required=False, default=1, par_type=int, - docstr="First iteration of an inversion workflow, 1 <= BEGIN <= inf" - ) - - self.required.par( - "END", required=False, default=1, par_type=int, - docstr="Last iteration of the inverison workflow," - "BEGIN <= END <= inf" - ) - self.required.par( - "RESUME_FROM", required=False, par_type=str, - docstr="Name of flow task to resume workflow from. Useful for " - "restarting failed workflows or re-trying sections of " - "workflows with new parameters. To determine available " - "options for your given workflow: > seisflows print flow" - ) - self.required.par( - "STOP_AFTER", required=False, par_type=str, - docstr="Name of flow task to stop workflow after. Useful for " - "stopping mid-workflow to look at results before " - "proceeding (e.g., to look at waveform misfits before " - "evaluating the gradient). To determine available options " - "for your given workflow: > seisflows print flow" - ) - self.required.par( - "SAVEMODEL", required=False, default=True, par_type=bool, - docstr="Save updated model files after each iteration" - ) - # Define the Paths required by this module - self.required.path( - "FUNC", required=False, - default=os.path.join(self.path.WORKDIR, "scratch", "evalfunc"), - docstr="scratch path to store data related to misfit function " - "evaluations that take place during the line search. Data " - "stored here include residuals from data-synthetic misfit, " - "and a given 'try' model being used to generate synthetics." - ) - # !!! Currently not used - self.required.path( - "HESS", required=False, - default=os.path.join(self.path.WORKDIR, "scratch", "evalhess"), - docstr="scratch path to store data related to Hessian evaluations" - ) - self.required.path( - "OPTIMIZE", required=False, - default=os.path.join(self.path.WORKDIR, "scratch", "optimize"), - docstr="scratch path to store data related to nonlinear " - "optimization library. Data stored here include model, " - "gradient, and search direction vectors (numpy arrays), and" - "additional arrays related to specific optimization " - "algorithms" - ) - - def check(self, validate=True): - """ - Checks parameters and paths - """ - super().check(validate=validate) + self.path.eval_func = path_eval_func or \ + os.path.join(self.path.workdir, "scratch", + "eval_func") - assert(1 <= self.par.BEGIN <= self.par.END), \ - f"Incorrect BEGIN or END parameter. Values must be in order: " \ - f"1 <= {self.par.BEGIN} <= {self.par.END}" + # Overwriting base class required modules list + self._required_modules = ["system", "solver", "preprocess", + "postprocess", "optimize"] - def setup(self, flow=None, return_flow=False): + # Empty module variables that should be filled in by setup + self.optimize = None + + @property + def task_list(self): """ - Lays groundwork for inversion by running setup() functions for the - involved sub-modules, generating True model synthetic data if necessary, - and generating the pre-requisite database files. + USER-DEFINED TASK LIST. This property defines a list of class methods + that take NO INPUT and have NO RETURN STATEMENTS. This defines your + linear workflow, i.e., these tasks are to be run in order from start to + finish to complete a workflow. + + This excludes 'check' (which is run during 'import_seisflows') and + 'setup' which should be run separately .. note:: - This function should only be run one time, at the start of iter 1 - """ - super().setup(flow=flow, return_flow=return_flow) + For workflows that require an iterative approach (e.g. inversion), + this task list will be looped over, so ensure that any setup and + teardown tasks (run once per workflow, not once per iteration) are + not included. - self.module("optimize").setup() + :rtype: list + :return: list of methods to call in order during a workflow + """ + return [self.evaluate_initial_misfit, + self.run_adjoint_simulations, + self.generate_misfit_kernel, + self.compute_search_direction, + self.evaluate_line_search, + self.clean_scratch_directory + ] - def finalize(self): + def check(self): """ - Saves results from current model update iteration and increment the - iteration number to set up for the next iteration. Finalization is - expected to the be LAST function in workflow.main()'s flow list. + Checks inversion-specific parameters """ - self.logger.info(msg.mjr(f"FINALIZING ITERATION {optimize.iter}")) + super().check() - self.checkpoint() - preprocess.finalize() + assert(1 <= self.start <= self.end), \ + f"Incorrect START or END parameter. Values must be in order: " \ + f"1 <= {self.start} <= {self.end}" - def main(self, flow=None, return_flow=False): + def setup(self): """ - Overwrites the forward() main function to provide the ability to run - multiple iterations in a single workflow. - - :type flow: list or tuple - :param flow: list of Class methods that will be run in the order they - are provided. - :type return_flow: bool - :param return_flow: for CLI tool, simply returns the flow function - rather than running the workflow. Used for print statements etc. + Lays groundwork for inversion by running setup() functions for the + involved sub-modules, generating True model synthetic data if necessary, + and generating the pre-requisite database files. """ - if flow is None: - flow = (self.evaluate_initial_misfit, - self.evaluate_gradient, - self.process_kernels, - self.scale_gradient, - self.compute_direction, - self.line_search, - self.export, - self.clean - ) - - self.setup(flow, return_flow) - optimize = self.module("optimize") - - # Run the workflow until from the current iteration until PAR.END - optimize.iter = self.par.BEGIN - self.logger.info(msg.mjr("STARTING INVERSION WORKFLOW")) - while True: - self.logger.info( - msg.mnr(f"ITERATION {optimize.iter} / {self.par.END}") - ) - - # Execute the functions within the flow - for func in flow[self.start:self.stop]: - func() - self.logger.info(msg.mjr(f"FINISHED FLOW EXECUTION")) - - # Reset flow for subsequent iterations - self.start, self.stop = None, None - if optimize.iter >= self.par.END: - break - optimize.iter += 1 - - self.logger.info(msg.sub(f"INCREMENT ITERATION TO {optimize.iter}")) - - self.logger.info(msg.mjr("FINISHED INVERSION WORKFLOW")) - - def evaluate_initial_misfit(self): - """Inherits from seisflows.workflow.forward.Forward""" - super().evaluate_initial_misfit() - - def compute_direction(self): + super().setup() + self.optimize = self._modules.optimize + + def run(self): + """Call the forward.run() function iteratively, from `start` to `end`""" + for i in range(self.start, self.end): + logger.info(msg.mjr(f"Running inversion iteration {i:0>2}")) + super().run() + logger.info(msg.mjr(f"Completed inversion iteration {i:0>2}")) + + def compute_search_direction(self): """ Computes search direction using the optimization library """ - optimize = self.module("optimize") - self.logger.info(msg.mnr("COMPUTING SEARCH DIRECTION")) - optimize.compute_direction() + logger.info(msg.mnr("COMPUTING SEARCH DIRECTION")) + self.optimize.compute_direction() - def line_search(self): + def evaluate_line_search(self): """ Conducts line search in given search direction @@ -210,87 +135,88 @@ def line_search(self): status == 0 : not finished status < 0 : failed """ - optimize = self.module("optimize") - # Calculate the initial step length based on optimization algorithm - if optimize.line_search.step_count == 0: - self.logger.info(msg.mjr(f"CONDUCTING LINE SEARCH: " - f"i{optimize.iter:0>2}" - f"s{optimize.line_search.step_count:0>2}") - ) - optimize.initialize_search() + if self.optimize.line_search.step_count == 0: + logger.info(msg.mjr(f"CONDUCTING LINE SEARCH: " + f"i{self.optimize.iter:0>2}" + f"s{self.optimize.line_search.step_count:0>2}") + ) + self.optimize.initialize_search() # Attempt a new trial step with the given step length - optimize.line_search.step_count += 1 - self.logger.info(msg.mnr(f"TRIAL STEP COUNT: " - f"i{optimize.iter:0>2}" - f"s{optimize.line_search.step_count:0>2}")) - self._evaluate_function(path=self.path.FUNC, suffix="try") + self.optimize.line_search.step_count += 1 + logger.info(msg.mnr(f"TRIAL STEP COUNT: " + f"i{self.optimize.iter:0>2}" + f"s{self.optimize.line_search.step_count:0>2}")) + + self.system.run(self.evaluate_objective_function, + path=self.path.eval_func, suffix="try") # Check the function evaluation against line search history - status = optimize.update_search() + status = self.optimize.update_search() # Proceed based on the outcome of the line search if status > 0: - self.logger.info("trial step successful") + logger.info("trial step successful") # Save outcome of line search to disk; reset step to 0 for next iter - optimize.finalize_search() + self.optimize.finalize_search() return elif status == 0: - self.logger.info("retrying with new trial step") + logger.info("retrying with new trial step") # Recursively call this function to attempt another trial step - self.line_search() + self.evaluate_line_search() elif status < 0: - if optimize.retry_status(): - self.logger.info("line search failed. restarting line search") + if self.optimize.retry_status(): + logger.info("line search failed. restarting line search") # Reset the line search machinery; set step count to 0 - optimize.restart() - self.line_search() + self.optimize.restart() + self.evaluate_line_search() else: - self.logger.info("line search failed. aborting inversion.") + logger.info("line search failed. aborting inversion.") sys.exit(-1) - def clean(self): + def clean_scratch_directory(self): """ Cleans directories in which function and gradient evaluations were carried out """ - self.logger.info(msg.mnr("CLEANING WORKDIR FOR NEXT ITERATION")) - - unix.rm(self.path.GRAD) - unix.rm(self.path.FUNC) - unix.mkdir(self.path.GRAD) - unix.mkdir(self.path.FUNC) - - def export(self): - """ - Exports various quantities to PATH.OUTPUT (to disk) from the SCRATCH - directory as SCRATCH is liable to be overwritten at any point of the - workflow. This takes place at the end of each iteration, before - the clean() function is called. - """ - optimize = self.module("optimize") - - if self.par.SAVEMODEL: - src = os.path.join(self.path.OPTIMIZE, "m_new") - dst = os.path.join(self.path.OUTPUT, f"model_{optimize.iter:04d}") - self.logger.debug(f"exporting model 'm_new' to disk") - unix.cp(src, dst) - - if self.par.SAVEGRADIENT: - src = os.path.join(self.path.OPTIMIZE, "g_old") - dst = os.path.join(self.path.OUTPUT, f"grad_{optimize.iter:04d}") - unix.cp(src, dst) - - if self.par.SAVEKERNELS: - src = os.path.join(self.path.GRAD, "kernels") - dst = os.path.join(self.path.OUTPUT, f"kernels_{optimize.iter:04d}") - self.logger.debug(f"saving kernels to path:\n{dst}") - unix.mv(src, dst) - - if self.par.SAVERESIDUALS: - src = os.path.join(self.path.GRAD, "residuals") - dst = os.path.join(self.path.OUTPUT, - f"residuals_{optimize.iter:04d}") - unix.mv(src, dst) + logger.info(msg.mnr("CLEANING WORKDIR FOR NEXT ITERATION")) + + unix.rm(self.path.eval_grad) + unix.rm(self.path.eval_func) + + unix.mkdir(self.path.eval_grad) + unix.mkdir(self.path.eval_func) + + # def export(self): + # """ + # Exports various quantities to PATH.OUTPUT (to disk) from the SCRATCH + # directory as SCRATCH is liable to be overwritten at any point of the + # workflow. This takes place at the end of each iteration, before + # the clean() function is called. + # """ + # optimize = self.module("optimize") + # + # if self.par.SAVEMODEL: + # src = os.path.join(self.path.OPTIMIZE, "m_new") + # dst = os.path.join(self.path.OUTPUT, f"model_{optimize.iter:04d}") + # logger.debug(f"exporting model 'm_new' to disk") + # unix.cp(src, dst) + # + # if self.par.SAVEGRADIENT: + # src = os.path.join(self.path.OPTIMIZE, "g_old") + # dst = os.path.join(self.path.OUTPUT, f"grad_{optimize.iter:04d}") + # unix.cp(src, dst) + # + # if self.par.SAVEKERNELS: + # src = os.path.join(self.path.GRAD, "kernels") + # dst = os.path.join(self.path.OUTPUT, f"kernels_{optimize.iter:04d}") + # logger.debug(f"saving kernels to path:\n{dst}") + # unix.mv(src, dst) + # + # if self.par.SAVERESIDUALS: + # src = os.path.join(self.path.GRAD, "residuals") + # dst = os.path.join(self.path.OUTPUT, + # f"residuals_{optimize.iter:04d}") + # unix.mv(src, dst) diff --git a/seisflows/workflow/migration.py b/seisflows/workflow/migration.py index f2201d6b..5f07c245 100644 --- a/seisflows/workflow/migration.py +++ b/seisflows/workflow/migration.py @@ -9,9 +9,9 @@ import os from seisflows import logger +from seisflows.config import import_seisflows from seisflows.workflow.forward import Forward from seisflows.tools import msg, unix -from seisflows.tools.specfem import Model class Migration(Forward): @@ -38,10 +38,7 @@ class Migration(Forward): def __init__(self, _modules=None, export_gradient=False, export_kernels=False, **kwargs): - """ - Init is used to instantiate global parameters defined by the input - parameter file. - """ + """Instantiate Migration-specific parameters""" super().__init__(**kwargs) self._modules = _modules @@ -76,8 +73,8 @@ def task_list(self): :return: list of methods to call in order during a workflow """ return [self.evaluate_initial_misfit, - self.generate_misfit_kernels, - self.postprocess_kernels + self.run_adjoint_simulations, + self.generate_misfit_kernel ] def setup(self): @@ -88,50 +85,69 @@ def setup(self): super().setup() self.postprocess = self._modules.postprocess - def generate_misfit_kernels(self): - """System wrapper for running adjoint simulations""" - logger.msg.mnr("GENERATING MISFIT KERNELS") - self.system.run(self.generate_misfit_kernel) - - def generate_misfit_kernel(self): + def run_adjoint_simulations(self): """ Performs adjoint simulations for a single given event. File manipulation to ensure kernels are discoverable by other modules """ - if self.export_kernels: - export_kernels = os.path.join(self.path.output, "kernels", - self.solver.source_name) - else: - export_kernels = False - - # Run adjoint simulations on system. Make kernels discoverable in - # path `eval_grad`. Optionally export those kernels - self.solver.adjoint_simulation( - save_kernels=os.path.join(self.path.eval_grad, "kernels", - self.solver.source_name), - export_kernels=export_kernels - ) - - def postprocess_kernels(self): - """System wrapper for postprocess kernels. Run with single""" - self.system.run(self._postprocess_kernels, single=True) - - def _postprocess_kernels(self): + def run_adjoint_simulation(): + """Adjoint simulation function to be run by system.run()""" + if self.export_kernels: + export_kernels = os.path.join(self.path.output, "kernels", + self.solver.source_name) + else: + export_kernels = False + + # Run adjoint simulations on system. Make kernels discoverable in + # path `eval_grad`. Optionally export those kernels + self.solver.adjoint_simulation( + save_kernels=os.path.join(self.path.eval_grad, "kernels", + self.solver.source_name), + export_kernels=export_kernels + ) + + logger.msg.mnr("running adjoint simulations to generate event kernels") + self.system.run(run_adjoint_simulation) + + def generate_misfit_kernel(self): """ - System-run wrapper for postprocess.process_kernels which is meant to - sum and smooth all individual event kernels + Combine/sum NTASK event kernels into a single volumetric kernel and + then (optionally) smooth the output misfit kernel by convolving with + a 3D Gaussian function with user-defined horizontal and vertical + half-widths. """ - # Combine kernels into a single volumentric quantity - self.solver.combine( - input_path=os.path.join(self.path.eval_grad, "kernels"), - output_path=os.path.join(self.path.eval_grad, "sum") - ) - - if self.solver.smooth_h > 0. or self.solver.smooth_v > 0.: - # Make a distinction that we have a pre- and post-smoothed sum - unix.mv(src=os.path.join(self.path.eval_grad, "sum_nosmooth")) - - self.solver.smooth( - input_path=os.path.join(self.path.eval_grad, "sum_nosmooth"), + def combine_event_kernels(): + """Combine event kernels into a misfit kernel""" + self.solver.combine( + input_path=os.path.join(self.path.eval_grad, "kernels"), output_path=os.path.join(self.path.eval_grad, "sum") ) + + def smooth_misfit_kernel(): + """Smooth the output misfit kernel """ + if self.solver.smooth_h > 0. or self.solver.smooth_v > 0.: + # Make a distinction that we have a pre- and post-smoothed sum + unix.mv(src=os.path.join(self.path.eval_grad, "sum_nosmooth")) + + self.solver.smooth( + input_path=os.path.join(self.path.eval_grad, "sum_nosmooth"), + output_path=os.path.join(self.path.eval_grad, "sum") + ) + + logger.msg.mnr("postprocessing (summing/smoothing) event kernels") + self.system.run([combine_event_kernels, smooth_misfit_kernel], + single=True) + + +if __name__ == "__main__": + # Standard SeisFlows setup, makes modules global variables to the workflow + pars, modules = import_seisflows() + + logger.info(msg.mjr("Starting migration workflow")) + + workflow = Migration(modules, **pars) + workflow.check() + workflow.setup() + workflow.run() + + logger.info(msg.mjr("Finished migration workflow")) \ No newline at end of file From d1e94ff038e94666063097426498aa6a01554466 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Mon, 18 Jul 2022 17:33:37 -0800 Subject: [PATCH 070/195] removed CFGPATHS and postprocessing module, rolled postprocessing functionality into solver and workflow up to line search in inversion workflow working. optimization library now piggybacks on supered functions during an inversion workflow, keeping forward and migration simpler removing some agency from optimization module and giving it to the workflow, i.e., controlling the line search. line search currently not working, will need to fix --- seisflows/config.py | 46 ++-- seisflows/optimize/LBFGS.py | 76 +++--- seisflows/optimize/NLCG.py | 26 +- seisflows/optimize/gradient.py | 224 +++++++++--------- seisflows/plugins/line_search/backtrack.py | 41 +--- seisflows/plugins/line_search/bracket.py | 91 ++----- seisflows/plugins/solver_io/ascii.py | 2 +- seisflows/plugins/solver_io/fortran_binary.py | 2 +- seisflows/postprocess/default.py | 8 +- seisflows/preprocess/default.py | 25 +- seisflows/seisflows.py | 12 +- seisflows/solver/specfem.py | 116 ++++----- seisflows/solver/specfem2d.py | 2 +- seisflows/solver/specfem3d.py | 4 +- seisflows/system/workstation.py | 8 +- seisflows/tests/test_config.py | 2 +- seisflows/tests/test_seisflows.py | 4 +- seisflows/tests/test_solver.py | 2 +- seisflows/tools/core.py | 51 ++-- seisflows/tools/specfem.py | 23 +- seisflows/tools/unix.py | 2 +- seisflows/workflow/base.py | 80 ------- seisflows/workflow/forward.py | 76 +++--- seisflows/workflow/inversion.py | 168 ++++++++----- seisflows/workflow/migration.py | 119 ++++++---- seisflows/workflow/test.py | 2 +- 26 files changed, 576 insertions(+), 636 deletions(-) delete mode 100644 seisflows/workflow/base.py diff --git a/seisflows/config.py b/seisflows/config.py index c3398945..710b9cd7 100755 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -25,9 +25,9 @@ from seisflows import logger -from seisflows.core import Dict, Null +from seisflows.tools.core import Dict, Null from seisflows.tools import msg, unix -from seisflows.tools.utils import load_yaml +from seisflows.tools.core import load_yaml """ @@ -40,21 +40,10 @@ """ # List of module names required by SeisFlows for imports. Order-sensitive # In sys.modules these will be prepended by 'seisflows_', e.g., seisflows_system -NAMES = ["system", "preprocess", "solver", - "postprocess", "optimize", "workflow"] +NAMES = ["system", "preprocess", "solver", "optimize", "workflow"] # The location of this config file, which is the main repository ROOT_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__))) - -# Define a package-wide default directory and file naming schema. This will -# be returned as a Dict() object, defined below. All of these files and -# directories will be created relative to the user-defined working directory -CFGPATHS = Dict( - PAR_FILE="parameters.yaml", # Default SeisFlows parameter file - LOGFILE="sfoutput.txt", # Log files for all system log - ERRLOGFILE="sferror.txt", # StdErr dump site for crash messages - LOGDIR="logs", # Dump site for previously created log files -) """ !!! ^^^ WARNING ^^^ !!! """ @@ -126,26 +115,31 @@ class names. instantiate each of the SeisFlows modules and run the workflow. This should be created by the command line argument 'seisflows configure'. Defaults to 'parameters.yaml' - :rtype: list - :return: instantiated modules that are returned in the following order + :rtype: module + :return: instantiated workflow module which contains all instantiated + sub-modules containing set parameters 'system', 'preprcess', 'solver', 'postprocess', 'optimize' """ + # Read in parameters from file. Set up the logger parameters = load_yaml(os.path.join(workdir, parameter_file)) config_logger(level=parameters.log_level, filename=parameters.path_log_file, verbose=parameters.verbose) # Instantiate SeisFlows modules dynamically based on choices and parameters # provided in the input parameter file - modules = {name: custom_import(name, parameters[name])(**parameters) for - name in NAMES} - modules = Dict(modules) - - # Drop NAMES from parameters, we don't need them anymore and they get - # muddled with the actual modules upon instantiation - for name in NAMES: - parameters.pop(name) - - return parameters, modules + modules = Dict() + for name in NAMES[:]: + # Workflow is instantiated differently + if name == "workflow": + continue + modules[name] = custom_import(name, parameters[name])(**parameters) + parameters.pop(name) # drop name so workflow doesnt instantiate it + + # Import workflow separately by providing all the instantiated modules to it + workflow = \ + custom_import("workflow", parameters["workflow"])(modules, **parameters) + + return workflow def config_logger(level="DEBUG", filename=None, filemode="a", verbose=True): diff --git a/seisflows/optimize/LBFGS.py b/seisflows/optimize/LBFGS.py index 4ae6921c..820550d8 100644 --- a/seisflows/optimize/LBFGS.py +++ b/seisflows/optimize/LBFGS.py @@ -97,19 +97,21 @@ def __init__(self, lbfgs_mem=3, lbfgs_max=np.inf, lbfgs_thresh=0., self.LBFGS_max = lbfgs_max self.LBFGS_thresh = lbfgs_thresh - # Internally used memory and path parameters + # Set new L-BFGS dependent paths for storing previous gradients + self.path["LBFGS"] = os.path.join(self.path.scratch, "LBFGS") + self.path["y_file"] = os.path.join(self.path.scratch, "LBFGS", "Y") + self.path["s_file"] = os.path.join(self.path.scratch, "LBFGS", "S") + + # Internally used memory parameters for the L-BFGS optimization algo. self._LBFGS_iter = 0 self._memory_used = 0 - self._LBFGS_dir = os.path.join(self.path, "LBFGS") - self._y_file = os.path.join(self.path, "LBFGS", "Y") - self._s_file = os.path.join(self.path, "LBFGS", "S") def setup(self): """ Set up the LBFGS optimization schema """ super().setup() - unix.mkdir(self._LBFGS_dir) + unix.mkdir(self.path.LBFGS) def compute_direction(self): """ @@ -128,20 +130,19 @@ def compute_direction(self): force a restart, search direction is inverse gradient 4. New search direction is acceptably angled from previous, becomes the new search direction - """ - unix.cd(self.path) - logger.info(f"computing search direction with L-BFGS") + :rtype: seisflows.tools.specfem.Model + :return: search direction as a Model instance + """ self._LBFGS_iter += 1 # Load the current gradient direction, which is the L-BFGS search # direction if this is the first iteration g = self.load("g_new") if self._LBFGS_iter == 1: - logger.info("first L-BFGS iteration, setting search direction " - "as inverse gradient") + logger.info("first L-BFGS iteration, default to gradient descent") p_new = -1 * g.vector - restarted = 0 + restarted = False # Restart condition or first iteration lead to setting search direction # as the inverse gradient (i.e., default to steepest descent) @@ -150,50 +151,57 @@ def compute_direction(self): "setting search direction as inverse gradient") self.restart() p_new = -1 * g.vector - restarted = 1 + restarted = True # Normal LBFGS direction computation else: # Update the search direction, apply the inverse Hessian such that # 'q' becomes the new search direction 'g' logger.info("applying inverse Hessian to gradient") - s, y = self._update() - q = self._apply(g.vector, s, y) + s, y = self._update_search_history() + q = self._apply_inverse_hessian(g.vector, s, y) # Determine if the new search direction is appropriate by checking # its angle to the previous search direction if self._check_status(g, q): logger.info("new L-BFGS search direction found") p_new = -q - restarted = 0 + restarted = False else: logger.info("new search direction not appropriate, defaulting " - "to inverse gradient") + "to gradient desceitn") self.restart() p_new = -g - restarted = 1 + restarted = True # Save values to disk and memory - self.save("p_new", p_new) self.restarted = restarted + return p_new + def restart(self): """ - Overwrite the optimization restart to restart the L-BFGS schema + Restart the L-BFGS optimization algorithm by clearing out stored + gradient memory. """ - super().restart() + logger.info("restarting L-BFGS optimization algorithm") - logger.info("restarting L-BFGS optimization algorithm by clearing " - "internal memory") + # Fall back to gradient descent for search direction + g = self.load("g_new") + self.save("p_new", -1 * g.vector) + + # Clear internal memory + self.line_search.clear_history() + self.restarted = True self._LBFGS_iter = 1 self._memory_used = 0 - unix.cd(self.path.OPTIMIZE) - s = np.memmap(filename=self._s_file, mode="r+") - y = np.memmap(filename=self._y_file, mode="r+") + # Clear out previous gradient information + s = np.memmap(filename=self.path.s_file, mode="r+") + y = np.memmap(filename=self.path.y_file, mode="r+") s[:] = 0. y[:] = 0. - def _update(self): + def _update_search_history(self): """ Updates L-BFGS algorithm history @@ -224,9 +232,9 @@ def _update(self): # Initial iteration, need to create the memory map if self._memory_used == 0: - s = np.memmap(filename=self._s_file, mode="w+", dtype="float32", + s = np.memmap(filename=self.path.s_file, mode="w+", dtype="float32", shape=(m, n)) - y = np.memmap(filename=self._y_file, mode="w+", dtype="float32", + y = np.memmap(filename=self.path.y_file, mode="w+", dtype="float32", shape=(m, n)) # Store the model and gradient differences in memmaps s[:, 0] = s_k @@ -234,9 +242,9 @@ def _update(self): self._memory_used = 1 # Subsequent iterations will append to memory maps else: - s = np.memmap(filename=self._s_file, mode="r+", dtype="float32", + s = np.memmap(filename=self.path.s_file, mode="r+", dtype="float32", shape=(m, n)) - y = np.memmap(filename=self._y_file, mode="r+", dtype="float32", + y = np.memmap(filename=self.path.y_file, mode="r+", dtype="float32", shape=(m, n)) # Shift all stored memory by one index to make room for latest mem s[:, 1:] = s[:, :-1] @@ -251,7 +259,7 @@ def _update(self): return s, y - def _apply(self, q, s=None, y=None): + def _apply_inverse_hessian(self, q, s=None, y=None): """ Applies L-BFGS inverse Hessian to given vector @@ -265,15 +273,13 @@ def _apply(self, q, s=None, y=None): :rtype r: np.array :return r: new search direction from application of L-BFGS """ - unix.cd(self.path) - # If no memmaps are given as arguments, instantiate them if s is None or y is None: m = len(q) n = self.LBFGS_mem - s = np.memmap(filename=self._s_file, mode="w+", dtype="float32", + s = np.memmap(filename=self.path.s_file, mode="w+", dtype="float32", shape=(m, n)) - y = np.memmap(filename=self._y_file, mode="w+", dtype="float32", + y = np.memmap(filename=self.path.y_file, mode="w+", dtype="float32", shape=(m, n)) # First matrix product diff --git a/seisflows/optimize/NLCG.py b/seisflows/optimize/NLCG.py index 19911d0a..645f19b5 100644 --- a/seisflows/optimize/NLCG.py +++ b/seisflows/optimize/NLCG.py @@ -68,7 +68,6 @@ def __init__(self, nlcg_max=np.inf, nlcg_thresh=np.inf, step_len_max=self.step_len_max ) - self.NLCG_max = nlcg_max self.NLCG_thresh = nlcg_thresh @@ -83,7 +82,6 @@ def __init__(self, nlcg_max=np.inf, nlcg_thresh=np.inf, self._NLCG_iter = 0 self._calc_beta = getattr(self, f"_{calc_beta}") - def compute_direction(self): """ Compute search direction using the Nonlinear Conjugate Gradient method @@ -100,9 +98,10 @@ def compute_direction(self): force restart, inverse gradient search direction 5. New NLCG search direction has conjugacy and is a descent direction and is set as the new search direction. + + :rtype: seisflows.tools.specfem.Model + :return: search direction as a Model instance """ - unix.cd(self.path) - logger.debug(f"computing search direction with NLCG") self._NLCG_iter += 1 # Load the current gradient direction @@ -110,16 +109,16 @@ def compute_direction(self): # CASE 1: If first iteration, search direction is the current gradient if self._NLCG_iter == 1: - logger.info("first NLCG iteration, setting search direction" - "as inverse gradient") + logger.info("first NLCG iteration, setting search direction " + "as inverse gradient") p_new = -1 * g_new.vector restarted = 0 # CASE 2: Force restart if the iterations have surpassed the maximum # number of allowable iter elif self._NLCG_iter > self.NLCG_max: logger.info("restarting NLCG due to periodic restart " - "condition. setting search direction as inverse " - "gradient") + "condition. setting search direction as inverse " + "gradient") self.restart() p_new = -1 * g_new.vector restarted = 1 @@ -153,14 +152,21 @@ def compute_direction(self): restarted = 0 # Save values to disk and memory - self.save("p_new", p_new) self.restarted = restarted + return p_new + def restart(self): """ Overwrite the Base restart class and include a restart of the NLCG """ - super().restart() + logger.info("restarting NLCG optimization algorithm") + + g = self.load("g_new") + self.save("p_new", -1 * g.vector) + + self.line_search.clear_history() + self.restarted = 1 self._NLCG_iter = 1 def _fletcher_reeves(self, g_new, g_old): diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index 06d0cdae..50fc0f77 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -8,13 +8,33 @@ different optimization algorithms. .. note:: - By default the base class implements a steepest descent optimization + To reduce memory overhead, vectors are read from disk rather than passed + from calling routines. For example, at the beginning of + `compute_direction` the current gradient is read from 'g_new' and the + resulting search direction is written to 'p_new'. As the inversion + progresses, other information is stored as well. + +.. note:: + The default numerical parameters for each algorithm should work well for a + range of applications without manual tuning. If the nonlinear + optimization procedure stagnates, it may be due to issues involving + data quality or the choice of data misfit, data processing, or + regularization parameters. Problems in any of these areas usually + manifest themselves through stagnation of the nonlinear optimization + algorithm. + +TODO fix line search, currently broken. Store line search variables in .npz + arrays rather than as internal attributes, that way it's easier to restart + from a broken workflow. Need to figure out how to reset and restart + line searches efficiently """ import os +import sys import numpy as np from seisflows import logger from seisflows.tools import msg, unix +from seisflows.tools.core import Dict from seisflows.tools.math import angle, dot from seisflows.tools.specfem import Model from seisflows.plugins import line_search as line_search_dir @@ -22,59 +42,34 @@ class Gradient: """ - Nonlinear optimization abstract base class poviding a gradient/steepest - descent optimization algorithm. - - Nonlinear conjugate, quasi-Newton and Newton methods can be implemented on - top of this base class. - - .. note:: - To reduce memory overhead, vectors are read from disk rather than passed - from calling routines. For example, at the beginning of - compute_direction the current gradient is read from 'g_new' and the - resulting search direction is written to 'p_new'. As the inversion - progresses, other information is stored as well. - - .. note:: - The default numerical parameters defined below should work well for a - range of applications without manual tuning. If the nonlinear - optimization procedure stagnates, it may be due to issues involving - data quality or the choice of data misfit, data processing, or - regularization parameters. Problems in any of these areas usually - manifest themselves through stagnation of the nonlinear optimization - algorithm. - + [optimization.gradient] gradient/steepest descent optimization algorithm. + + :type line_search_method: str + :param line_search_method: chosen line_search algorithm. Currently available + are 'bracket' and 'backtrack'. See seisflows.plugins.line_search + for all available options + :type preconditioner: str + :param preconditioner: algorithm for preconditioning gradients + :type step_count_max: int + :param step_count_max: maximum number of trial steps to perform during + the line search before a change in line search behavior is + considered + :type step_len_init: float + :param step_len_init: initial line search step length as a fraction of + current model parameters. + :type step_len_max: float + :param step_len_max: maximum allowable step length during the line + search. Set as a fraction of the current model parameters """ - def __init__(self, begin=1, line_search="bracket", preconditioner=False, + def __init__(self, start=1, line_search_method="bracket", preconditioner=None, step_count_max=10, step_len_init=0.05, step_len_max=0.5, - path_optimize=None, path_output=None, path_preconditioner=None, + workdir=os.getcwd(), path_optimize=None, path_output=None, + path_preconditioner=None, **kwargs): - """ - Optimization algorithm variables - - :type line_search: str - :param line_search: chosen line_search algorithm. Currently available - are 'bracket' and 'backtrack'. See seisflows.plugins.line_search - for all available options - :type preconditioner: str - :param preconditioner: algorithm for preconditioning gradients - :type step_count_max: int - :param step_count_max: maximum number of trial steps to perform during - the line search before a change in line search behavior is - considered - :type step_len_init: float - :param step_len_init: initial line search step length as a fraction of - current model parameters. - :type step_len_max: float - :param step_len_max: maximum allowable step length during the line - search. Set as a fraction of the current model parameters - :type path: str - :param path: scratch path for all optimization related procedures. - if None, defaults to $PWD/scratch/optimize - """ + """Gradient-descent input parameters""" super().__init__() - self.iteration = begin # to match PAR.BEGIN + self.iteration = start # to match PAR.BEGIN self.preconditioner = preconditioner self.step_count_max = step_count_max @@ -82,21 +77,24 @@ def __init__(self, begin=1, line_search="bracket", preconditioner=False, self.step_len_max = step_len_max # Internal check to see if the chosen line search algorithm exists - if not hasattr(line_search_dir, line_search): - logger.warning(f"{line_search} is not a valid line search " + if not hasattr(line_search_dir, line_search_method): + logger.warning(f"{line_search_method} is not a valid line search " f"algorithm, defaulting to 'bracket'") - line_search = "bracket" + line_search_method = "bracket" - self._line_search = line_search.title() - self.line_search = getattr(line_search_dir, self._line_search)( + # .title() ensures we grab the class and not the module + self.line_search = getattr(line_search_dir, line_search_method.title())( step_count_max=step_count_max, step_len_max=step_len_max ) # Set required path structure - self.path = path_optimize or \ - os.path.join(os.getcwd(), "scratch", "optimize") - self.path_output = path_output or os.path.join(os.getcwd(), "output") - self.path_preconditioner = path_preconditioner + self.path = Dict( + scratch=path_optimize or + os.path.join(os.getcwd(), "scratch", "optimize"), + output=path_output or os.path.join(workdir, "output"), + preconditioner=path_preconditioner, + + ) # Internally used parameters for checking validity self._acceptable_vectors = ["m_new", "m_old", "m_try", @@ -105,17 +103,23 @@ def __init__(self, begin=1, line_search="bracket", preconditioner=False, "f_new", "f_old", "f_try"] self._acceptable_preconditioners = ["diagonal"] + self.restarted = False + + @property + def step_count(self): + """Convenience property to access `step_count` from line search""" + return self.line_search.step_count + def check(self): """ Checks parameters, paths, and dependencies """ if self.preconditioner: # This list should match the logic in self.precondition() - assert self.preconditioner in self._acceptable_preconditioners, \ f"PRECOND must be in {self._acceptable_preconditioners}" - assert(os.path.exists(self.path_preconditioner)), ( + assert(os.path.exists(self.path.preconditioner)), ( f"preconditioner requires PATH.PRECOND pointing to a array-like" f"weight file" ) @@ -129,24 +133,7 @@ def setup(self): """ Sets up nonlinear optimization machinery """ - unix.mkdir(self.path) - - # # Read in initial model as a vector and ensure it is a valid model - # if os.path.exists(self.path.MODEL_INIT): - # m_new = Model(path=self.path.MODEL_INIT) - # m_new.save(path=os.path.join(self.path.OPTIMIZE, "m_new")) - # else: - # logger.warning( - # "PATH.MODEL_INIT not found, cannot save 'm_new'. Either ensure " - # "that 'm_new' is present in PATH.OPTIMIZE or restart with a " - # "valid PATH.MODEL_INIT" - # ) - - def finalize(self): - """ - Finalization tasks - """ - pass + unix.mkdir(self.path.scratch) def load(self, name): """ @@ -170,7 +157,16 @@ def load(self, name): :param name: name of the vector, acceptable: m, g, p, f, alpha """ assert(name in self._acceptable_vectors) - model = Model(path=os.path.join(self.path, f"{name}.npz"), load=True) + model_npz = os.path.join(self.path.scratch, f"{name}.npz") + model_txt = model_npz.replace(".npz", ".txt") + if os.path.exists(model_npz): + model = Model(path=os.path.join(self.path.scratch, + f"{name}.npz"), load=True) + elif os.path.exists(model_txt): + model = float(np.loadtxt(model_txt)) + else: + raise FileNotFoundError(f"no optimization file found for {name}") + return model def save(self, name, m): @@ -179,18 +175,20 @@ def save(self, name, m): :type name: str :param name: name of the vector to overwrite - :type m: seisflows.tools.cspefem.Model or float + :type m: seisflows.tools.specfem.Model or float :param m: Model to save to disk as npz array """ assert(name in self._acceptable_vectors) if isinstance(m, Model): - path = os.path.join(self.path, f"{name}.npz") + path = os.path.join(self.path.scratch, f"{name}.npz") m.model = m.split() # overwrite m representation m.save(path=path) elif isinstance(m, (float, int)): - path = os.path.join(self.path, f"{name}.txt") + path = os.path.join(self.path.scratch, f"{name}.txt") np.savetxt(path, [m]) + else: + raise TypeError(f"optimize.save unrecognized type error {type(m)}") def _precondition(self, q): """ @@ -202,7 +200,7 @@ def _precondition(self, q): :return: preconditioned vector """ if self.preconditioner is not None: - p = Model(path=self.path_preconditioner) + p = Model(path=self.path.preconditioner) if self.preconditioner.upper() == "DIAGONAL": logger.info("applying diagonal preconditioner") return p.vector * q @@ -220,13 +218,14 @@ def compute_direction(self): .. note:: Other optimization algorithms must overload this method - """ - logger.info(f"computing search direction") + :rtype: seisflows.tools.specfem.Model + :return: search direction as a Model instance + """ g_new = self.load("g_new") - p_new = -1 * self._precondition(g_new.vector) + p_new = g_new.update(vector=-1 * self._precondition(g_new.vector)) - self.save("p_new", p_new) + return p_new def initialize_search(self): """ @@ -241,7 +240,6 @@ def initialize_search(self): norm_m = max(abs(m.vector)) norm_p = max(abs(p.vector)) - gtg = dot(g.vector, g.vector) gtp = dot(g.vector, p.vector) @@ -255,7 +253,13 @@ def initialize_search(self): self.line_search.step_len_max = new_step_len_max logger.debug(f"max step length safeguard = {new_step_len_max:.2E}") - self.line_search.initialize(step_len=0., func_val=f, gtg=gtg, gtp=gtp) + # Initialize the line search by setting vector variables + self.line_search.step_count = 0 + self.line_search.step_lens.append(0.) + self.line_search.func_vals.append(f) + self.line_search.gtg.append(gtg) + self.line_search.gtp.append(gtp) + alpha, _ = self.line_search.calculate_step() # Alpha defines the trial step length. Optional step length override @@ -265,23 +269,24 @@ def initialize_search(self): # The new model is the old model, scaled by the step direction and # gradient threshold to remove any outlier values - m_try = m.vector + alpha * p.vector + m_try = m.update(vector=m.vector + alpha * p.vector) - self.save("m_try", m_try) - self.save("alpha", alpha) + return m_try, alpha def update_search(self): """ Updates line search status and step length and checks if the line search has been completed. - Available status codes from line_search.update(): + Available status codes from line_search.calculate_step(): status == 1 : finished status == 0 : not finished status == -1 : failed """ - self.line_search.update(step_len=np.loadtxt("alpha"), - func_val=self.load("f_try")) + # Update the line search variables for a new step calculation + self.line_search.step_lens.append(self.load("alpha")) + self.line_search.func_vals.append(self.load("f_try")) + alpha, status = self.line_search.calculate_step() # New search direction needs to be searchable on disk @@ -290,7 +295,7 @@ def update_search(self): p = self.load("p_new") self.save("alpha", alpha) - m_try = m.vector + alpha * p.vector + m_try = m.update(vector=m.vector + alpha * p.vector) self.save("m_try", m_try) return status @@ -302,7 +307,7 @@ def finalize_search(self): Removes old model/search parameters, moves current parameters to old, sets up new current parameters and writes statistic outputs """ - unix.cd(self.path) + unix.cd(self.path.scratch) logger.info(msg.sub("FINALIZING LINE SEARCH")) @@ -325,7 +330,7 @@ def finalize_search(self): unix.mv("m_try.npz", "m_new.npz") # Choose minimum misfit value as final misfit/model - f = self.line_search.search_history()[1] + f = self.line_search.get_search_history()[1] self.save("f_new", f.min()) logger.info(f"current misfit is {f.min():.3E}") @@ -358,17 +363,13 @@ def restart(self): Keeps current position in model space, but discards history of nonlinear optimization algorithm in an attempt to recover from numerical stagnation. - """ - # Steepest descent (base) does not need to be restarted - # TODO figure out how to deal with this noting inheritance from others - # if self.par.OPTIMIZE.capitalize() == "Gradient": - # return - - g = self.load("g_new") - self.save("p_new", -1 * g.vector) - self.line_search.clear_history() - self.restarted = 1 + .. note:: + steepest descent optimization algorithm does not have any restart + capabilities. This function is instantiated here to be overwritten + by child classes + """ + pass def _write_stats(self): """ @@ -378,7 +379,7 @@ def _write_stats(self): if they should be retained. """ logger.info(f"writing optimization stats") - fid = os.path.join(self.path_output, f"optim_stats.txt") + fid = os.path.join(self.path.output, f"optim_stats.txt") # First time, write header information if not os.path.exists(fid): @@ -391,8 +392,9 @@ def _write_stats(self): g = self.load("g_new") p = self.load("p_new") - x = self.line_search.search_history()[0] - f = self.line_search.search_history()[1] + x, f, *_ = self.line_search.get_search_history() + + import pdb;pdb.set_trace() # Calculated stats factors factor = -1 * dot(g.vector, g.vector) diff --git a/seisflows/plugins/line_search/backtrack.py b/seisflows/plugins/line_search/backtrack.py index d5448010..e7bb1dd1 100644 --- a/seisflows/plugins/line_search/backtrack.py +++ b/seisflows/plugins/line_search/backtrack.py @@ -2,6 +2,7 @@ """ This is the subclass class for seisflows.plugins.line_search.backtrack """ +from seisflows import logger from seisflows.plugins.line_search.bracket import Bracket from seisflows.tools import msg from seisflows.tools.math import parabolic_backtrack @@ -28,30 +29,6 @@ class Backtrack(Bracket): status == 0 : not finished status < 0 : failed """ - def __init__(self): - """Inherits from seisflows.plugins.line_search.bracket.Bracket""" - super().__init__() - - def initialize(self, step_len, func_val, gtg, gtp): - """Inherits from seisflows.plugins.line_search.bracket.Bracket""" - self.initialize(step_len, func_val, gtg, gtp) - - def update(self, step_len, func_val): - """Inherits from seisflows.plugins.line_search.bracket.Bracket""" - self.update(step_len, func_val) - - def clear_history(self): - """Inherits from seisflows.plugins.line_search.bracket.Bracket""" - self.clear_history() - - def reset(self): - """Inherits from seisflows.plugins.line_search.bracket.Bracket""" - self.reset() - - def search_history(self, sort=True): - """Inherits from seisflows.plugins.line_search.bracket.Bracket""" - return self.search_history(sort=sort) - def calculate_step(self): """ Determines step length and search status. Overwrites the Bracketing @@ -68,34 +45,34 @@ def calculate_step(self): # Assumed well scaled search direction, attempt backtracking line search # with unit step length else: - self.logger.debug(msg.sub(f"BACKTRACKING LINE SEARCH STEP" - f"{self.step_count:0>2}")) + logger.debug(msg.sub(f"BACKTRACKING LINE SEARCH STEP" + f"{self.step_count:0>2}")) x_str = ", ".join([f"{_:.2E}" for _ in x]) f_str = ", ".join([f"{_:.2E}" for _ in f]) - self.logger.debug(f"step length(s) = {x_str}") - self.logger.debug(f"misfit val(s) = {f_str}") + logger.debug(f"step length(s) = {x_str}") + logger.debug(f"misfit val(s) = {f_str}") # Initial unit step length if step_count == 0: - self.logger.info("attempting unit step length") + logger.info("attempting unit step length") alpha = min(1., self.step_len_max) status = 0 # Pass if misfit is reduced elif f.min() < f[0]: - self.logger.info("misfit decrease, pass") + logger.info("misfit decrease, pass") alpha = x[f.argmin()] status = 1 # If misfit continually increases, decrease step length elif step_count <= self.step_count_max: import pdb;pdb.set_trace() - self.logger.info("misfit increase, decreasing step length") + logger.info("misfit increase, decreasing step length") slope = gtp[-1] / gtg[-1] alpha = parabolic_backtrack(f0=f[0], g0=slope, x1=x[1], f1=f[1], b1=0.1, b2=0.5) status = 0 # Failed because step_count_max exceeded else: - self.logger.info("backtracking failed, step_count_max exceeded") + logger.info("backtracking failed, step_count_max exceeded") alpha = None status = -1 diff --git a/seisflows/plugins/line_search/bracket.py b/seisflows/plugins/line_search/bracket.py index 978ddaf3..eb97b56a 100644 --- a/seisflows/plugins/line_search/bracket.py +++ b/seisflows/plugins/line_search/bracket.py @@ -7,16 +7,15 @@ this should be completely left to the optimization algorithm to keep everything in one place) """ -import logging import numpy as np -from seisflows.core import Base +from seisflows import logger from seisflows.tools import msg from seisflows.tools.array import count_zeros from seisflows.tools.math import parabolic_backtrack, polynomial_fit -class Bracket(Base): +class Bracket: """ Abstract base class for line search @@ -44,62 +43,15 @@ def __init__(self, step_count_max, step_len_max): :param step_len_max: maximum length of the step, defaults to infinity, that is unbounded step length. set by PAR.STEP_LEN_MAX """ - super().__init__() - self.step_count_max = step_count_max self.step_len_max = step_len_max + self.func_vals = [] self.step_lens = [] self.gtg = [] self.gtp = [] self.step_count = 0 - def initialize(self, step_len, func_val, gtg, gtp): - """ - Initialize a new search from step count 0 and calculate the step - direction and length - - :type iter: int - :param iter: current iteration defined by OPTIMIZE.iter - :type step_len: float - :param step_len: initial step length determined by optimization - :type func_val: float - :param func_val: current evaluation of the objective function - :type gtg: float - :param gtg: dot product of the gradient with itself - :type gtp: float - :param gtp: dot product of gradient `g` with search direction `p` - :rtype alpha: float - :return alpha: the calculated trial step length - :rtype status: int - :return status: current status of the line search - """ - self.step_count = 0 - self.step_lens.append(step_len) - self.func_vals.append(func_val) - self.gtg.append(gtg) - self.gtp.append(gtp) - - def update(self, step_len, func_val): - """ - Update search history by appending internal attributes, writing the - current list of step lengths and function evaluations, and calculating a - new step length - - :type iter: int - :param iter: current iteration defined by OPTIMIZE.iter - :type step_len: float - :param step_len: step length determined by optimization - :type func_val: float - :param func_val: current evaluation of the objective function - :rtype alpha: float - :return alpha: the calculated rial step length - :rtype status: int - :return status: current status of the line search - """ - self.step_lens.append(step_len) - self.func_vals.append(func_val) - def clear_history(self): """ Clears internal line search history for a new line search attempt @@ -132,7 +84,7 @@ def reset(self): self.step_lens = self.step_lens[:original_idx] self.func_vals = self.func_vals[:original_idx] - def search_history(self, sort=True): + def get_search_history(self, sort=True): """ A convenience function, collects information based on the current evaluation of the line search, needed to determine search status and @@ -172,56 +124,55 @@ def calculate_step(self): Determines step length (alpha) and search status (status) """ # Determine the line search history - x, f, gtg, gtp, step_count, update_count = self.search_history() + x, f, gtg, gtp, step_count, update_count = self.get_search_history() # Print out the current line search parameters for convenience - self.logger.debug(msg.sub(f"BRACKETING LINE SEARCH STEP " - f"{self.step_count:0>2}")) + logger.debug(msg.sub(f"BRACKETING LINE SEARCH STEP " + f"{self.step_count:0>2}")) x_str = ", ".join([f"{_:.2E}" for _ in x]) f_str = ", ".join([f"{_:.2E}" for _ in f]) - self.logger.debug(f"step length(s) = {x_str}") - self.logger.debug(f"misfit val(s) = {f_str}") + logger.debug(f"step length(s) = {x_str}") + logger.debug(f"misfit val(s) = {f_str}") # For the first inversion and initial step, set alpha manually if step_count == 0 and update_count == 0: # Based on idea from Dennis and Schnabel alpha = gtg[-1] ** -1 - self.logger.info(f"first iteration, guessing trial step") + logger.info(f"first iteration, guessing trial step") status = 0 # For every i'th inversions initial step, set alpha manually elif step_count == 0: # Based on the first equation in sec 3.5 of Nocedal and Wright 2ed idx = np.argmin(self.func_vals[:-1]) alpha = self.step_lens[idx] * gtp[-2] / gtp[-1] - self.logger.info(f"first step, setting scaled step length") + logger.info(f"first step, setting scaled step length") status = 0 # If misfit is reduced and then increased, we've bracketed. Pass elif _check_bracket(x, f) and _good_enough(x, f): alpha = x[f.argmin()] - self.logger.info(f"bracket okay, step length reasonable, pass") + logger.info(f"bracket okay, step length reasonable, pass") status = 1 # If misfit is reduced but not close, set to quadratic fit elif _check_bracket(x, f): alpha = polynomial_fit(x, f) - self.logger.info(f"bracket okay, step length unreasonable, " - f"manual step") + logger.info(f"bracket okay, step length unreasonable, manual step") status = 0 # If misfit continues to step down, increase step length elif step_count <= self.step_count_max and all(f <= f[0]): alpha = 1.618034 * x[-1] # 1.618034 is the 'golden ratio' - self.logger.info(f"misfit not bracketed, increasing step length") + logger.info(f"misfit not bracketed, increasing step length") status = 0 # If misfit increases, reduce step length by backtracking elif step_count <= self.step_count_max: slope = gtp[-1] / gtg[-1] alpha = parabolic_backtrack(f0=f[0], g0=slope, x1=x[1], f1=f[1], b1=0.1, b2=0.5) - self.logger.info(f"misfit increasing, reducing step length to") + logger.info(f"misfit increasing, reducing step length to") status = 0 # step_count_max exceeded, fail else: - self.logger.info(f"bracketing failed, " - f"step_count_max={self.step_count_max} exceeded") + logger.info(f"bracketing failed, step_count_max=" + f"{self.step_count_max} exceeded") alpha = None status = -1 @@ -229,13 +180,13 @@ def calculate_step(self): if alpha is not None: if alpha > self.step_len_max and step_count == 0: alpha = 0.618034 * self.step_len_max - self.logger.info(f"initial step length safegaurd, setting " - f"manual step length") + logger.info(f"initial step length safegaurd, setting " + f"manual step length") status = 0 # Stop because safeguard prevents us from going further elif alpha > self.step_len_max: - self.logger.info(f"step_len_max={self.step_len_max:.2f} " - f"exceeded, manual set alpha") + logger.info(f"step_len_max={self.step_len_max:.2f} " + f"exceeded, manual set alpha") alpha = self.step_len_max status = 1 diff --git a/seisflows/plugins/solver_io/ascii.py b/seisflows/plugins/solver_io/ascii.py index 2de06adf..ccc2bd6a 100644 --- a/seisflows/plugins/solver_io/ascii.py +++ b/seisflows/plugins/solver_io/ascii.py @@ -5,7 +5,7 @@ import numpy as np from glob import glob from shutil import copyfile -from seisflows.tools.utils import iterable +from seisflows.tools.core import iterable def read_slice(path, parameters, iproc): diff --git a/seisflows/plugins/solver_io/fortran_binary.py b/seisflows/plugins/solver_io/fortran_binary.py index 21691fb6..9991b306 100644 --- a/seisflows/plugins/solver_io/fortran_binary.py +++ b/seisflows/plugins/solver_io/fortran_binary.py @@ -4,7 +4,7 @@ import os import numpy as np from shutil import copyfile -from seisflows.tools.utils import iterable +from seisflows.tools.core import iterable def read_slice(path, parameters, iproc): diff --git a/seisflows/postprocess/default.py b/seisflows/postprocess/default.py index 4e53e229..8ae37de9 100644 --- a/seisflows/postprocess/default.py +++ b/seisflows/postprocess/default.py @@ -84,8 +84,8 @@ def scale_gradient(self): :return: scaled gradient as a vector """ # Access the gradient information stored in as kernel files - gradient = Model(path=os.path.join(self.path, "model")) - model = Model(path=os.path.join(self.path, "kernels", "sum")) + model = Model(path=os.path.join(self.path, "model")) + gradient = Model(path=os.path.join(self.path, "kernels", "sum")) # Merge to vector and convert to absolute perturbations: # log dm --> dm (see Eq.13 Tromp et al 2005) @@ -97,10 +97,6 @@ def scale_gradient(self): # mimicking the file format in which models are stored mask = Model(self.path_mask) - # While both masking and preconditioning involve scaling the - # gradient, they are fundamentally different operations: - # masking is ad hoc, preconditioning is a change of variables; - # For more info, see Modrak & Tromp 2016 GJI gradient.write(path=os.path.join(self.path, "gradient_nomask")) gradient.vector *= mask.vector diff --git a/seisflows/preprocess/default.py b/seisflows/preprocess/default.py index c9a46843..f05521ff 100644 --- a/seisflows/preprocess/default.py +++ b/seisflows/preprocess/default.py @@ -11,8 +11,8 @@ from obspy import Stream, Trace, UTCDateTime from seisflows import logger -from seisflows.core import Dict from seisflows.tools import signal, unix +from seisflows.tools.core import Dict from seisflows.plugins.preprocess import misfit as misfit_functions from seisflows.plugins.preprocess import adjoint as adjoint_sources @@ -208,13 +208,6 @@ def setup(self): """ unix.mkdir(self.path.scratch) - def finalize(self): - """ - Any finalization processes that need to take place at the end of an - iteration - """ - pass - def read(self, fid): """ Waveform reading functionality. Imports waveforms as Obspy streams @@ -386,20 +379,18 @@ def quantify_misfit(self, observed, synthetic, fid = self._rename_as_adjoint_source(fid) self.write(st=adjsrc, fid=os.path.join(save_adjsrcs, fid)) - def sum_residuals(self, files): + @staticmethod + def sum_residuals(residuals): """ - Sums squares of residuals + Returns the summed square of residuals for each event. Following + Tape et al. 2007 - :type files: str - :param files: list of single-column text files containing residuals + :type residuals: np.array + :param residuals: list of residuals from each NTASK event :rtype: float :return: sum of squares of residuals """ - total_misfit = 0. - for filename in files: - total_misfit += np.sum(np.loadtxt(filename) ** 2.) - - return total_misfit + return np.sum(residuals ** 2.) def _apply_filter(self, st): """ diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 3a599339..0fddd568 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -23,16 +23,14 @@ import traceback import subprocess from glob import glob -from copy import copy from IPython import embed -from seisflows.core import Dict, SeisFlowsPathsParameters -from seisflows.config import (custom_import, save, NAMES, ROOT_DIR, CFGPATHS, - config_logger, import_seisflows) +from seisflows.config import (custom_import, save, NAMES, ROOT_DIR, + config_logger) from seisflows.tools import unix, msg +from seisflows.tools.core import load_yaml, Dict from seisflows.tools.specfem import (getpar, setpar, getpar_vel_model, setpar_vel_model) -from seisflows.tools.utils import load_yaml def sfparser(): @@ -77,8 +75,8 @@ def _format_action(self, action): parser.add_argument("-w", "--workdir", nargs="?", default=os.getcwd(), help="The SeisFlows working directory, default: cwd") parser.add_argument("-p", "--parameter_file", nargs="?", - default=CFGPATHS.PAR_FILE, - help=f"Parameters file, default: '{CFGPATHS.PAR_FILE}'") + default="parameters.yaml", + help=f"Parameters file, default: 'parameters.yaml'") # Initiate a sub parser to provide nested help functions and sub commands subparser = parser.add_subparsers( diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 46380695..2f25566b 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -23,10 +23,9 @@ from glob import glob from seisflows import logger -from seisflows.core import Dict from seisflows.plugins import solver_io as solver_io_dir from seisflows.tools import msg, unix -from seisflows.tools.utils import get_task_id +from seisflows.tools.core import get_task_id, Dict from seisflows.tools.specfem import getpar, setpar @@ -303,22 +302,23 @@ def data_filenames(self, choice="obs"): """ assert(choice in ["obs", "syn", "adj"]), \ f"choice must be: 'obs', 'syn' or 'adj'" - unix.cd(os.path.join(self.cwd, "traces", choice)) - filenames = [] if self.components: - for comp in self.components: - filenames = glob(self.data_wildcard(comp=comp.lower())) + comp_glob = f"[{self.components}]" # e.g., [NEZ] else: - filenames = glob(self.data_wildcard(comp="?")) + comp_glob = "?" + data_wildcard = self.data_wildcard(comp=comp_glob) + file_glob = os.path.join(self.cwd, "traces", choice, data_wildcard) + filenames = glob(file_glob) if not filenames: - print(msg.cli("The property solver.data_filenames, used to search " - "for traces in 'scratch/solver/*/traces' is empty " - "and should not be. Please check solver parameters: ", - items=[f"data_wildcard: {self.data_wildcard()}"], - header="data filenames error", border="=") - ) + logger.critical( + msg.cli("The property `solver.data_filenames`, used to search " + "for waveform files, is empty and should not be. " + "Please check solver parameters: ", + items=[f"failed wildcard: {file_glob}"], + header="data filenames error", border="=") + ) sys.exit(-1) return filenames @@ -338,6 +338,19 @@ def model_databases(self): """ return "DATA" + @property + def model_files(self): + """ + Return a list of paths to model files that match the internal parameter + list. Used to generate model vectors of the same length as gradients. + """ + _model_files = [] + for parameter in self._parameters: + _model_files += glob(os.path.join(self.path.mainsolver, + self.model_databases, + f"*{parameter}*.bin")) + return _model_files + @property def kernel_databases(self): """ @@ -371,49 +384,8 @@ def setup(self): dst = os.path.join(self.path.output, "MODEL_INIT") unix.cp(src, dst) - # def generate_data(self, save_traces=False): - # """ - # Generates observation data to be compared to synthetics. This must - # only be run once. If `PAR.CASE`=='data', then real data will be copied - # over. - # TODO move this to workflow - # - # If `PAR.CASE`=='synthetic' then the external solver will use the - # True model to generate 'observed' synthetics. Finally exports traces to - # 'cwd/traces/obs' - # - # Elif `PAR.CASE`=='DATA', will look in PATH.DATA for directories matching - # the given source name and copy ANY files that exist there. e.g., if - # source name is '001', you must store waveform data in PATH.DATA/001/* - # - # Also exports observed data to OUTPUT if desired - # """ - # # If synthetic inversion, generate 'data' with solver - # if self.case.upper() == "SYNTHETIC": - # if self.path.model_true is not None: - # if self.taskid == 0: - # logger.info("generating 'data' with MODEL_TRUE") - # - # # Generate synthetic data on the fly using the true model - # self.import_model(path_model=self.path.model_true) - # self.forward_simulation( - # save_traces=os.path.join("traces", "obs") - # ) - # # If Data provided by user, copy directly into the solver directory - # elif self.path.data is not None and os.path.exists(self.path.data): - # unix.cp( - # src=glob(os.path.join(self.path.data, self.source_name, "*")), - # dst=os.path.join(self.cwd, "traces", "obs") - # ) - # - # # Save observation data to disk - # if save_traces: - # self._export_traces( - # path=os.path.join(self.path.output, "traces", "obs") - # ) - def forward_simulation(self, executables=None, save_traces=False, - export_traces=False): + export_traces=False, **kwargs): """ Wrapper for SPECFEM binaries: 'xmeshfem?D' 'xgenerate_databases', 'xspecfem?D' @@ -533,9 +505,11 @@ def adjoint_simulation(self, executables=None, save_kernels=False, # Save and export the kernels to user-defined locations if export_kernels: + unix.mkdir(export_kernels) unix.cp(src=glob("*_kernel.bin"), dst=export_kernels) if save_kernels: + unix.mkdir(save_kernels) unix.mv(src=glob("*_kernel.bin"), dst=save_kernels) def combine(self, input_path, output_path, parameters=None): @@ -557,7 +531,7 @@ def combine(self, input_path, output_path, parameters=None): :param output_path: path to export the outputs of xcombine_sem :type parameters: list :param parameters: optional list of parameters, - defaults to `self.parameters` + defaults to `self._parameters` """ unix.cd(self.cwd) @@ -599,7 +573,7 @@ def smooth(self, input_path, output_path, parameters=None, span_h=None, :param output_path: path to export the outputs of xcombine_sem :type parameters: list :param parameters: optional list of parameters, - defaults to `self.parameters` + defaults to `self._parameters` :type span_h: float :param span_h: horizontal smoothing length in meters :type span_v: float @@ -671,8 +645,8 @@ def _run_binary(self, executable, stdout="solver.log"): # Executable may come with additional sub arguments, we only need to # check that the actually executable exists if not unix.which(executable.split(" ")[0]): - print(msg.cli(f"executable '{executable}' does not exist", - header="external solver error", border="=")) + logger.critical(msg.cli(f"executable '{executable}' does not exist", + header="external solver error", border="=")) sys.exit(-1) # Append with mpiexec if we are running with MPI @@ -683,16 +657,17 @@ def _run_binary(self, executable, stdout="solver.log"): with open(stdout, "w") as f: subprocess.run(executable, shell=True, check=True, stdout=f) except (subprocess.CalledProcessError, OSError) as e: - print(msg.cli("The external numerical solver has returned a " - "nonzero exit code (failure). Consider stopping any " - "currently running jobs to avoid wasted " - "computational resources. Check 'scratch/solver/" - "mainsolver/{stdout}' for the solvers stdout log " - "message. The failing command and error message are:", - items=[f"exc: {executable}", f"err: {e}"], - header="external solver error", - border="=") - ) + logger.critical( + msg.cli("The external numerical solver has returned a " + "nonzero exit code (failure). Consider stopping any " + "currently running jobs to avoid wasted " + "computational resources. Check 'scratch/solver/" + "mainsolver/{stdout}' for the solvers stdout log " + "message. The failing command and error message are:", + items=[f"exc: {executable}", f"err: {e}"], + header="external solver error", + border="=") + ) sys.exit(-1) @staticmethod @@ -702,6 +677,9 @@ def _exc2log(exc): e.g., binary 'xspecfem2D' will return 'solver'. Helps keep log file naming consistent and generalizable + TODO add a check here to see if the log file exists, and then use + `number_fid` to increment so that we keep all the output logs + :type exc: str :param exc: specfem executable, e.g., xspecfem2D, xgenerate_databases :rtype: str diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index 18176407..888a8436 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -70,7 +70,7 @@ def smooth(self, input_path, output_path, parameters=None, span_h=0., :param output_path: path to export the outputs of xcombine_sem :type parameters: list :param parameters: optional list of parameters, - defaults to `self.parameters` + defaults to `self._parameters` :type span_h: float :param span_h: horizontal smoothing length in meters :type span_v: float diff --git a/seisflows/solver/specfem3d.py b/seisflows/solver/specfem3d.py index 93dcc500..21357b2b 100644 --- a/seisflows/solver/specfem3d.py +++ b/seisflows/solver/specfem3d.py @@ -31,9 +31,9 @@ def __init__(self, source_prefix="CMTSOLUTION", **kwargs): # Define parameters based on material type if self.materials.upper() == "ACOUSTIC": - self._parameters += ["vp"] + self.parameters += ["vp"] elif self.materials.upper() == "ELASTIC": - self._parameters += ["vp", "vs"] + self.parameters += ["vp", "vs"] # Overwriting the base class parameters self._acceptable_source_prefixes = ["CMTSOLUTION", "FORCESOLUTION"] diff --git a/seisflows/system/workstation.py b/seisflows/system/workstation.py index 2b32bc29..fc557f61 100644 --- a/seisflows/system/workstation.py +++ b/seisflows/system/workstation.py @@ -9,10 +9,10 @@ from contextlib import redirect_stdout from seisflows import logger -from seisflows.core import Dict -from seisflows.config import CFGPATHS, save -from seisflows.tools import msg, unix -from seisflows.tools.utils import number_fid, get_task_id, set_task_id, iterable +from seisflows.tools.core import Dict +from seisflows.config import save +from seisflows.tools import unix +from seisflows.tools.core import number_fid, get_task_id, set_task_id class Workstation: diff --git a/seisflows/tests/test_config.py b/seisflows/tests/test_config.py index 51f7f2b0..03a2fa8a 100644 --- a/seisflows/tests/test_config.py +++ b/seisflows/tests/test_config.py @@ -8,7 +8,7 @@ import pytest from unittest.mock import patch from seisflows import config -from seisflows.core import SeisFlowsPathsParameters +from seisflows.tools.core import SeisFlowsPathsParameters from seisflows.seisflows import SeisFlows diff --git a/seisflows/tests/test_seisflows.py b/seisflows/tests/test_seisflows.py index 859c3305..797fb48e 100644 --- a/seisflows/tests/test_seisflows.py +++ b/seisflows/tests/test_seisflows.py @@ -11,10 +11,10 @@ import subprocess from unittest.mock import patch -from seisflows.core import Dict +from seisflows.tools.core import Dict from seisflows.seisflows import SeisFlows from seisflows.config import ROOT_DIR, NAMES, CFGPATHS -from seisflows.tools.utils import load_yaml +from seisflows.tools.core import load_yaml TEST_DIR = os.path.join(ROOT_DIR, "tests") diff --git a/seisflows/tests/test_solver.py b/seisflows/tests/test_solver.py index 7fefb361..a73b387b 100644 --- a/seisflows/tests/test_solver.py +++ b/seisflows/tests/test_solver.py @@ -6,7 +6,7 @@ import pytest from glob import glob from seisflows.config import ROOT_DIR -from seisflows.tools.utils import set_task_id +from seisflows.tools.core import set_task_id from seisflows.solver.specfem import Specfem diff --git a/seisflows/tools/core.py b/seisflows/tools/core.py index f7d8a7ec..5ccd0781 100644 --- a/seisflows/tools/core.py +++ b/seisflows/tools/core.py @@ -1,6 +1,6 @@ """ -General utility functions that are mostly concerend with file manipulation, -but also math and calling functions as well. +Core utility functions and classes which help define the working structure of +SeisFlows pacakge. """ import os import re @@ -78,20 +78,6 @@ class TaskIDError(Exception): pass -def set_task_id(task_id): - """ - Set the SEISFLOWS_TASKID in os environs - - .. note:: - Mostly used for debugging/testing purposes as a way of mimicing - system.run() assigning task ids to child processes - - :type task_id: int - :param task_id: integer task id to assign to the current working environment - """ - os.environ["SEISFLOWS_TASKID"] = str(task_id) - - def get_task_id(force=False): """ Task IDs are assigned to each child process spawned by the system module @@ -107,17 +93,34 @@ def get_task_id(force=False): """ _taskid = os.getenv("SEISFLOWS_TASKID") if _taskid is None: - if force: - _taskid = 0 - logger.warning("Environment variable 'SEISFLOWS_TASKID' not found. " - "Assigning Task ID == 0") - else: - raise TaskIDError("Environment variable 'SEISFLOWS_TASKID' not " - "found. Please make sure the process asking " - "for task id is called by system.") + logger.warning("Environment variable 'SEISFLOWS_TASKID' not found. " + "Assigning Task ID == 0") + _taskid = 0 + # if force: + # _taskid = 0 + # logger.warning("Environment variable 'SEISFLOWS_TASKID' not found. " + # "Assigning Task ID == 0") + # else: + # raise TaskIDError("Environment variable 'SEISFLOWS_TASKID' not " + # "found. Please make sure the process asking " + # "for task id is called by system.") return int(_taskid) +def set_task_id(task_id): + """ + Set the SEISFLOWS_TASKID in os environs + + .. note:: + Mostly used for debugging/testing purposes as a way of mimicing + system.run() assigning task ids to child processes + + :type task_id: int + :param task_id: integer task id to assign to the current working environment + """ + os.environ["SEISFLOWS_TASKID"] = str(task_id) + + def load_yaml(filename): """ Define how the PyYaml yaml loading function behaves. diff --git a/seisflows/tools/specfem.py b/seisflows/tools/specfem.py index 26c8b0fc..386ac123 100644 --- a/seisflows/tools/specfem.py +++ b/seisflows/tools/specfem.py @@ -6,7 +6,7 @@ import numpy as np from glob import glob from seisflows import logger -from seisflows.core import Dict +from seisflows.tools.core import Dict from seisflows.tools import unix from seisflows.tools.math import poissons_ratio @@ -224,8 +224,12 @@ def check(self, min_pr=-1., max_pr=0.5): # Tell the User min and max values of the updated model logger.info(f"model parameters") - parts = "{minval:.2E} <= {key} <= {maxval:.2E}" for key, vals in self.model.items(): + # Choose formatter based on the size of the value + if vals.min() < 1 or vals.max() > 1E4: + parts = "{minval:.2E} <= {key} <= {maxval:.2E}" + else: + parts = "{minval:.2f} <= {key} <= {maxval:.2f}" logger.info(parts.format(minval=vals.min(), key=key, maxval=vals.max())) @@ -261,6 +265,21 @@ def load(self, file): return model, ngll, str(data["fmt"]) + def update(self, model=None, vector=None): + """ + Update internal model/vector defitions. Because these two quantities + are tied to one another, updating one will update the other. This + function simply makes that easier. + """ + if model is not None: + self.model = model + self.vector = self.merge() + elif vector is not None: + self.vector = vector + self.model = self.split() + + return self + def _get_nproc_parameters(self): """ Get the number of processors and the available parameters from a list of diff --git a/seisflows/tools/unix.py b/seisflows/tools/unix.py index 1c039d89..dc1bccbf 100644 --- a/seisflows/tools/unix.py +++ b/seisflows/tools/unix.py @@ -9,7 +9,7 @@ import shutil import socket import subprocess -from seisflows.tools.utils import iterable +from seisflows.tools.core import iterable def cat(src, dst=None): diff --git a/seisflows/workflow/base.py b/seisflows/workflow/base.py deleted file mode 100644 index aa2f96c3..00000000 --- a/seisflows/workflow/base.py +++ /dev/null @@ -1,80 +0,0 @@ -#!/usr/bin/env python3 -""" -The simplest simulation workflow you can run is a large number of forward -simulations to generate synthetics from a velocity model. Therefore the -Forward class represents the BASE workflow. All other workflows will build off -of the scaffolding defined by the Forward class. -""" -from seisflows.config import import_seisflows - - -class Base(object): - """ - Defines the core Base object for all SeisFlows modules. All modules MUST - inherit from the Base object to work properly. This Base class essentially - dictates the required structure of a SeisFlows class. - """ - def __init__(self): - """ - SeisFlows instantiates its required parameters through the - SeisFlowsPathsParameters class, which scaffolds a rigid framework of - how parameters and paths should be defined by the program. This is - then used to build the parameter file dynamically. - """ - self.parameters = self.modules = import_seisflows() - (self.system, self.preprocess, self.solver, - self.postprocess, self.optimize) = self.modules - - def setup(self): - """ - - """ - - @property - def par(self): - """ - Quick access SeisFlows parameters from sys.modules. Throws a warning - if parameters have not been instantiated - - :rtype: Dict or None - :return: Returns a Dictionary with instantiated parameters, or None if - parameters have not been instantiated - """ - return self.module("parameters") - - @property - def path(self): - """ - Quick access SeisFlows paths from sys.modules. Throws a warning - if paths have not been instantiated - - :rtype: Dict or None - :return: Returns a Dictionary with instantiated paths, or None if - parameters have not been instantiated - """ - return self.module("paths") - - def check(self, validate=True): - """ - General check() function for each module to check the validity of the - user-input parameters and paths - """ - if validate: - self.required.validate() - - # Example of a check statement - # assert(self.par.PARAMETER == example_value), f"Parameter != example" - - def setup(self): - """ - A placeholder function for any initialization or setup tasks that - need to be run once at the beginning of any workflow. - """ - return - - def finalize(self): - """ - A placeholder function for any finalization or tear-down tasks that - need to be run at the end of any iteration or workflow. - """ - return \ No newline at end of file diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index c3f90484..65af4643 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -10,7 +10,7 @@ from seisflows import logger from seisflows.tools import msg, unix -from seisflows.core import Dict +from seisflows.tools.core import Dict from seisflows.config import import_seisflows @@ -64,7 +64,7 @@ def __init__(self, modules=None, data_case=None, export_traces=False, workdir=workdir, scratch=os.path.join(workdir, "scratch"), eval_grad=path_eval_grad or - os.path.join(workdir, "scratch", "evalgrad"), + os.path.join(workdir, "scratch", "eval_grad"), output=path_output or os.path.join(workdir, "output"), state_file=path_state_file or os.path.join(workdir, "statefile.txt"), @@ -163,6 +163,9 @@ def setup(self): Makes required path structure for the workflow, runs setup functions for all the required modules of this workflow. """ + logger.info(msg.mjr(f"SETTING UP {self.__class__.__name__.upper()} " + f"WORKFLOW")) + # Create the desired directory structure for path in self.path.values(): if path is not None and not os.path.splitext(path)[-1]: @@ -171,24 +174,22 @@ def setup(self): # Run setup() for each of the required modules for req_mod in self._required_modules: logger.info( - f"setup for module " + f"running setup for module " f"'{req_mod}.{self._modules[req_mod].__class__.__name__}'" ) self._modules[req_mod].setup() # Run setup() for each of the instantiated modules for opt_mod in self._optional_modules: - if self._modules[opt_mod]: + if self._modules[opt_mod] and opt_mod not in self._required_modules: logger.info( - f"setup for module " + f"running setup for module " f"'{opt_mod}.{self._modules[opt_mod].__class__.__name__}'" ) self._modules[opt_mod].setup() # Generate the state file to keep track of task completion if not os.path.exists(self.path.state_file): - logger.info(f"generating SeisFlows state file") - logger.debug(self.path.state_file) with open(self.path.state_file, "w") as f: f.write(f"# SeisFlows State File\n") f.write(f"# {asctime()}\n") @@ -225,6 +226,9 @@ def run(self): to keep track of completed tasks and avoids re-running tasks that have previously been completed (e.g., if you are restarting your workflow) """ + logger.info(msg.mjr(f"RUNNING {self.__class__.__name__.upper()} " + f"WORKFLOW")) + for func in self.task_list: # Skip over functions which have already been completed if (func.__name__ in self._states.keys()) and ( @@ -246,13 +250,22 @@ def run(self): def evaluate_initial_misfit(self): """ - System wrapper for 'evaluate_objective function' that passes in + Evaluate the initial model misfit. This requires setting up 'data' + before generating synthetics, which is either copied from user-supplied + directory or running forward simulations with a target model. Forward + simulations are then run and prepocessing compares data-synthetic misfit + + .. note:: + This is run altogether on system to save on queue time waits, + because we are potentially running two simulations back to back. """ logger.info(msg.mnr("EVALUATING MISFIT FOR INITIAL MODEL")) - self.system.run([self.prepare_data_for_solver, - self.evaluate_objective_function], - path_model=self.path.model_init - ) + self.system.run( + [self.prepare_data_for_solver, + self.evaluate_objective_function], + path_model=self.path.model_init, + save_residuals=os.path.join(self.path.eval_grad, "residuals") + ) def prepare_data_for_solver(self, **kwargs): """ @@ -264,7 +277,8 @@ def prepare_data_for_solver(self, **kwargs): Must be run by system.run() so that solvers are assigned individual task ids and working directories """ - logger.info(msg.sub("preparing data for solver")) + logger.info(f"PREPARING OBSERVATION DATA FOR SOURCE " + f"{self.solver.source_name}") if self.data_case == "data": logger.info(f"copying data from `path_data`") @@ -280,7 +294,7 @@ def prepare_data_for_solver(self, **kwargs): export_traces = False # Run the forward solver with target model and save traces the 'obs' - logger.info(f"running forward simulation for " + logger.info(f"running forward simulation w/ `MODEL_TRUE` for " f"{self.solver.source_name}") self.solver.import_model(path_model=self.path.model_true) self.solver.forward_simulation( @@ -288,7 +302,8 @@ def prepare_data_for_solver(self, **kwargs): export_traces=export_traces ) - def evaluate_objective_function(self, path_model, **kwargs): + def evaluate_objective_function(self, path_model, save_residuals=False, + **kwargs): """ Performs forward simulation for a single given event. Also evaluates the objective function and writes residuals and adjoint sources for later @@ -301,8 +316,12 @@ def evaluate_objective_function(self, path_model, **kwargs): Must be run by system.run() so that solvers are assigned individual task ids/ working directories. """ - logger.info(f"running forward simulation with " - f"'{self.solver.__class__.__name__}'") + assert(os.path.exists(path_model)), \ + f"Model path for objective function does not exist" + + logger.info("EVALUATING OBJECTIVE FUNCTION") + logger.debug(f"running forward simulation with " + f"'{self.solver.__class__.__name__}'") # Figure out where to export waveform files to, if requested if self.export_traces: @@ -320,26 +339,11 @@ def evaluate_objective_function(self, path_model, **kwargs): # (optional) Perform data-synthetic misfit quantification if self.preprocess: - logger.info(f"quantifying misfit with " - f"'{self.preprocess.__class__.__name__}'") + logger.debug(f"quantifying misfit with " + f"'{self.preprocess.__class__.__name__}'") self.preprocess.quantify_misfit( observed=self.solver.data_filenames(choice="obs"), - synthetics=self.solver.data_filenames(choice="syn"), + synthetic=self.solver.data_filenames(choice="syn"), save_adjsrcs=os.path.join(self.solver.cwd, "traces", "adj"), - save_residuals=os.path.join(self.path.eval_grad, "residuals") + save_residuals=save_residuals ) - - -if __name__ == "__main__": - # Standard SeisFlows setup, makes modules global variables to the workflow - pars, modules = import_seisflows() - - logger.info(msg.mjr("Starting forward simulation workflow")) - - workflow = Forward(modules, **pars) - workflow.check() - workflow.setup() - workflow.run() - - logger.info(msg.mjr("Finished forward simulation workflow")) - diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index e360c33f..ffc477b1 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -20,10 +20,12 @@ """ import os import sys +import numpy as np from seisflows import logger from seisflows.workflow.migration import Migration from seisflows.tools import msg, unix +from seisflows.tools.specfem import Model class Inversion(Migration): @@ -44,12 +46,13 @@ class Inversion(Migration): """ __doc__ = Migration.__doc__ + __doc__ - def __init__(self, _modules=None, start=1, end=1, export_model=True, + def __init__(self, modules=None, start=1, end=1, export_model=True, path_eval_func=None, **kwargs): """Instantiate Inversion-specific parameters""" super().__init__(**kwargs) + self._modules = modules self.start = start self.end = end self.export_model = export_model @@ -59,8 +62,7 @@ def __init__(self, _modules=None, start=1, end=1, export_model=True, "eval_func") # Overwriting base class required modules list - self._required_modules = ["system", "solver", "preprocess", - "postprocess", "optimize"] + self._required_modules = ["system", "solver", "preprocess", "optimize"] # Empty module variables that should be filled in by setup self.optimize = None @@ -87,9 +89,10 @@ def task_list(self): """ return [self.evaluate_initial_misfit, self.run_adjoint_simulations, - self.generate_misfit_kernel, + self.postprocess_event_kernels, + self.evaluate_gradient_from_kernels, self.compute_search_direction, - self.evaluate_line_search, + self.perform_line_search, self.clean_scratch_directory ] @@ -110,70 +113,129 @@ def setup(self): and generating the pre-requisite database files. """ super().setup() + + unix.mkdir(self.path.eval_func) self.optimize = self._modules.optimize def run(self): """Call the forward.run() function iteratively, from `start` to `end`""" - for i in range(self.start, self.end): - logger.info(msg.mjr(f"Running inversion iteration {i:0>2}")) + for i in range(self.start, self.end + 1): + logger.info(msg.mnr(f"RUNNING ITERATION {i:0>2}")) super().run() - logger.info(msg.mjr(f"Completed inversion iteration {i:0>2}")) + logger.info(msg.mnr(f"COMPLETED ITERATION {i:0>2}")) - def compute_search_direction(self): + def evaluate_initial_misfit(self): + """ + Overwrite `workflow.forward` just to sum residuals output by preprocess + module and save them to disk, to be discoverable by the optimization + library + """ + super().evaluate_initial_misfit() + + # Override function to sum residuals into the optimization library + residuals = np.loadtxt(os.path.join(self.path.eval_grad, "residuals")) + total_misfit = self.preprocess.sum_residuals(residuals) + self.optimize.save(name="f_new", m=total_misfit) + + def evaluate_gradient_from_kernels(self): """ - Computes search direction using the optimization library + Overwrite `workflow.migration` to convert the current model and the + gradient calculated by migration from their native SPECFEM model format + into optimization vectors that can be used for model updates. """ - logger.info(msg.mnr("COMPUTING SEARCH DIRECTION")) - self.optimize.compute_direction() + super().evaluate_gradient_from_kernels() + + model = Model(os.path.join(self.path.eval_grad, "model")) + self.optimize.save(name="m_new", m=model) + + gradient = Model(path=os.path.join(self.path.eval_grad, "gradient")) + self.optimize.save(name="g_new", m=gradient) - def evaluate_line_search(self): + def compute_search_direction(self): + """ + Computes search direction using the optimization library and performs + a sets up line search machinery to 'perform line search' + """ + logger.info(f"computing search direction with " + f"'{self.optimize.__class__.__name__}'") + p_new = self.optimize.compute_direction() + self.optimize.save(name="p_new", m=p_new) + + # Check that our search direction will actually perturb the model + if sum(p_new.vector) == 0: + logger.critical(msg.cli( + "Search direction vector 'p' is 0, meaning no model update can " + "take place. Please check your gradient and waveform misfits. " + "SeisFlows exiting prior to start of line search.", border="=", + header="line search error") + ) + sys.exit(-1) + + logger.info( + msg.mnr(f"INITALIZING LINE SEARCH: i{self.optimize.iteration:0>2}" + f"s{self.optimize.step_count:0>2}") + ) + m_try, alpha = self.optimize.initialize_search() + self.optimize.save(name="m_try", m=m_try) + self.optimize.save(name="alpha", m=alpha) + + # Expose model `m_try` to the solver by placing it in eval_func dir. + m_try.write(path=os.path.join(self.path.eval_func, "model")) + + def perform_line_search(self): """ - Conducts line search in given search direction + Conducts line search in given search direction until the objective + function is reduced acceptably, or the line search fails due to + user-defined limit criteria. Status codes: status > 0 : finished status == 0 : not finished status < 0 : failed """ - # Calculate the initial step length based on optimization algorithm - if self.optimize.line_search.step_count == 0: - logger.info(msg.mjr(f"CONDUCTING LINE SEARCH: " - f"i{self.optimize.iter:0>2}" - f"s{self.optimize.line_search.step_count:0>2}") - ) - self.optimize.initialize_search() - - # Attempt a new trial step with the given step length - self.optimize.line_search.step_count += 1 - logger.info(msg.mnr(f"TRIAL STEP COUNT: " - f"i{self.optimize.iter:0>2}" - f"s{self.optimize.line_search.step_count:0>2}")) - - self.system.run(self.evaluate_objective_function, - path=self.path.eval_func, suffix="try") - - # Check the function evaluation against line search history - status = self.optimize.update_search() - - # Proceed based on the outcome of the line search - if status > 0: - logger.info("trial step successful") - # Save outcome of line search to disk; reset step to 0 for next iter - self.optimize.finalize_search() - return - elif status == 0: - logger.info("retrying with new trial step") - # Recursively call this function to attempt another trial step - self.evaluate_line_search() - elif status < 0: - if self.optimize.retry_status(): - logger.info("line search failed. restarting line search") - # Reset the line search machinery; set step count to 0 - self.optimize.restart() - self.evaluate_line_search() - else: - logger.info("line search failed. aborting inversion.") - sys.exit(-1) + while True: + # Attempt a new trial step with the given step length + self.optimize.line_search.step_count += 1 + logger.info( + msg.sub(f"TRIAL STEP COUNT: i{self.optimize.iteration:0>2}" + f"s{self.optimize.step_count:0>2}") + ) + + # Run the forward simulation on system and calculate misfit as f_try + self.system.run( + [self.evaluate_objective_function], + path_model=os.path.join(self.path.eval_func, "model"), + save_residuals=os.path.join(self.path.eval_func, "residuals") + ) + residuals = np.loadtxt(os.path.join(self.path.eval_func, + "residuals")) + total_misfit = self.preprocess.sum_residuals(residuals) + self.optimize.save(name="f_try", m=total_misfit) + + # Check the function evaluation against line search history + status = self.optimize.update_search() + + # Proceed based on the outcome of the line search + if status > 0: + # Save outcome of line search to disk; reset step to 0 for next iter + logger.info("trial step successful. finalizing line search") + self.optimize.finalize_search() + return + elif status == 0: + logger.info("trial step unsuccessful. retrying with new trial " + "step") + # Recursively call this function to attempt another trial step + continue + elif status < 0: + if self.optimize.retry_status(): + logger.info("line search has failed. restarting " + "optimization algorithm and line search.") + # Reset the line search machinery; set step count to 0 + self.optimize.restart() + continue + else: + logger.info("line search has failed. aborting inversion.") + sys.exit(-1) def clean_scratch_directory(self): """ diff --git a/seisflows/workflow/migration.py b/seisflows/workflow/migration.py index 5f07c245..51bf5796 100644 --- a/seisflows/workflow/migration.py +++ b/seisflows/workflow/migration.py @@ -7,11 +7,12 @@ model """ import os +from glob import glob from seisflows import logger -from seisflows.config import import_seisflows -from seisflows.workflow.forward import Forward from seisflows.tools import msg, unix +from seisflows.tools.specfem import Model +from seisflows.workflow.forward import Forward class Migration(Forward): @@ -25,6 +26,16 @@ class Migration(Forward): Setting `export_kernel`==True when PAR.NTASK is large and model files are large may lead to large file overhead. + .. note:: + Migration workflow includes an option to mask the gradient. While both + masking and preconditioning involve scaling the gradient, they are + fundamentally different operations: masking is ad hoc, preconditioning + is a change of variables; For more info, see Modrak & Tromp 2016 GJI + + :type path_mask: str + :param path_mask: optional path to a masking function which is used to + mask out or scale parts of the gradient. The user-defined mask must + match the file format of the input model (e.g., .bin files). :type export_gradient: bool :param export_gradient: export the gradient after it has been generated in the scratch directory. If False, gradient can be discarded from @@ -36,21 +47,19 @@ class Migration(Forward): """ __doc__ = Forward.__doc__ + __doc__ - def __init__(self, _modules=None, export_gradient=False, + def __init__(self, modules=None, path_mask=None, export_gradient=False, export_kernels=False, **kwargs): """Instantiate Migration-specific parameters""" super().__init__(**kwargs) - self._modules = _modules + self._modules = modules self.export_gradient = export_gradient self.export_kernels = export_kernels - # Overwriting base class required modules list - self._required_modules = ["system", "solver", "preprocess", - "postprocess"] + self.path["mask"] = path_mask - # Empty module variables that should be filled in by setup - self.postprocess = None + # Overwriting base class required modules list + self._required_modules = ["system", "solver", "preprocess"] @property def task_list(self): @@ -74,17 +83,10 @@ def task_list(self): """ return [self.evaluate_initial_misfit, self.run_adjoint_simulations, - self.generate_misfit_kernel + self.postprocess_event_kernels, + self.evaluate_gradient_from_kernels ] - def setup(self): - """ - Override the Forward.setup() method to include the postprocessing - module used for kernel/gradient manipulation - """ - super().setup() - self.postprocess = self._modules.postprocess - def run_adjoint_simulations(self): """ Performs adjoint simulations for a single given event. File manipulation @@ -102,14 +104,14 @@ def run_adjoint_simulation(): # path `eval_grad`. Optionally export those kernels self.solver.adjoint_simulation( save_kernels=os.path.join(self.path.eval_grad, "kernels", - self.solver.source_name), + self.solver.source_name, ""), export_kernels=export_kernels ) - logger.msg.mnr("running adjoint simulations to generate event kernels") - self.system.run(run_adjoint_simulation) + logger.info(msg.mnr("EVALUATING EVENT KERNELS W/ ADJOINT SIMULATIONS")) + self.system.run([run_adjoint_simulation]) - def generate_misfit_kernel(self): + def postprocess_event_kernels(self): """ Combine/sum NTASK event kernels into a single volumetric kernel and then (optionally) smooth the output misfit kernel by convolving with @@ -118,36 +120,67 @@ def generate_misfit_kernel(self): """ def combine_event_kernels(): """Combine event kernels into a misfit kernel""" + logger.info("combining event kernels into single misfit kernel") self.solver.combine( input_path=os.path.join(self.path.eval_grad, "kernels"), - output_path=os.path.join(self.path.eval_grad, "sum") + output_path=os.path.join(self.path.eval_grad, "misfit_kernel") ) def smooth_misfit_kernel(): - """Smooth the output misfit kernel """ + """Smooth the output misfit kernel""" if self.solver.smooth_h > 0. or self.solver.smooth_v > 0.: - # Make a distinction that we have a pre- and post-smoothed sum - unix.mv(src=os.path.join(self.path.eval_grad, "sum_nosmooth")) - + logger.info( + f"smoothing misfit kernel: " + f"H={self.solver.smooth_h}; V={self.solver.smooth_v}" + ) + # Make a distinction that we have a pre- and post-smoothed kern. + unix.mv( + src=os.path.join(self.path.eval_grad, "misfit_kernel"), + dst=os.path.join(self.path.eval_grad, "mk_nosmooth") + ) self.solver.smooth( - input_path=os.path.join(self.path.eval_grad, "sum_nosmooth"), - output_path=os.path.join(self.path.eval_grad, "sum") + input_path=os.path.join(self.path.eval_grad, "mk_nosmooth"), + output_path=os.path.join(self.path.eval_grad, + "misfit_kernel") ) - logger.msg.mnr("postprocessing (summing/smoothing) event kernels") + logger.info(msg.mnr("GENERATING/PROCESSING MISFIT KERNEL")) self.system.run([combine_event_kernels, smooth_misfit_kernel], single=True) - -if __name__ == "__main__": - # Standard SeisFlows setup, makes modules global variables to the workflow - pars, modules = import_seisflows() - - logger.info(msg.mjr("Starting migration workflow")) - - workflow = Migration(modules, **pars) - workflow.check() - workflow.setup() - workflow.run() - - logger.info(msg.mjr("Finished migration workflow")) \ No newline at end of file + def evaluate_gradient_from_kernels(self): + """ + Generates the 'gradient' from the 'misfit kernel'. This involves + scaling the gradient by the model vector (log dm --> dm) and applying + an optional mask function to the gradient. + """ + logger.info("scaling gradient to absolute model perturbations") + gradient = Model(path=os.path.join(self.path.eval_grad, + "misfit_kernel")) + + # Set model: we only need to access parameters which will be updated + # Assuming that the model in the solver also generated the kernels + dst = os.path.join(self.path.eval_grad, "model") + unix.rm(dst) + unix.mkdir(dst) + for src in self.solver.model_files: + unix.ln(src, dst=os.path.join(dst, os.path.basename(src))) + + # Read in new model that will have been generated by `setup` or by + # optimization library + model = Model(path=dst) + + # Merge to vector and convert to absolute perturbations: + # log dm --> dm (see Eq.13 Tromp et al 2005) + gradient.update(vector=gradient.vector * model.vector) + gradient.write(path=os.path.join(self.path.eval_grad, "gradient")) + + # Apply an optional mask to the gradient + if self.path.mask: + logger.info("applying mask function to gradient") + mask = Model(path=self.path.mask) + unix.mv(src=os.path.join(self.path.eval_grad, "gradient"), + dst=os.path.join(self.path.eval_grad, "gradient_nomask")) + + gradient.update(vector=gradient.vector * mask.vector) + gradient.write(path=os.path.join(self.path.eval_grad, "gradient")) diff --git a/seisflows/workflow/test.py b/seisflows/workflow/test.py index ace180d8..e5d61635 100644 --- a/seisflows/workflow/test.py +++ b/seisflows/workflow/test.py @@ -12,7 +12,7 @@ import numpy as np from glob import glob -from seisflows.core import Base +from seisflows.tools.core import Base from seisflows.config import ROOT_DIR, CFGPATHS, save, config_logger From 2ddd9dc3b2a4e441dc631399ba17586b8d12dc77 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Tue, 19 Jul 2022 16:44:39 -0800 Subject: [PATCH 071/195] line search currently working, improved log messages for line search and optimization algo added a line_search disk file to help with restarting broken line searches need to work on restart procedure for line search and write tests to make sure its working --- seisflows/optimize/gradient.py | 206 +++++++++++++-------- seisflows/plugins/line_search/backtrack.py | 65 ++++--- seisflows/plugins/line_search/bracket.py | 197 ++++++++++++-------- seisflows/preprocess/default.py | 1 - seisflows/seisflows.py | 2 + seisflows/solver/specfem.py | 2 +- seisflows/tools/specfem.py | 32 +++- seisflows/workflow/forward.py | 25 ++- seisflows/workflow/inversion.py | 166 ++++++++--------- 9 files changed, 411 insertions(+), 285 deletions(-) diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index 50fc0f77..906005f8 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -8,29 +8,32 @@ different optimization algorithms. .. note:: - To reduce memory overhead, vectors are read from disk rather than passed - from calling routines. For example, at the beginning of - `compute_direction` the current gradient is read from 'g_new' and the - resulting search direction is written to 'p_new'. As the inversion - progresses, other information is stored as well. + To reduce memory overhead, and to enable optimization restarts due to + failed line searches, model/gradient vectors and line search history are + passed on disk rather than in memory. .. note:: The default numerical parameters for each algorithm should work well for a range of applications without manual tuning. If the nonlinear - optimization procedure stagnates, it may be due to issues involving - data quality or the choice of data misfit, data processing, or - regularization parameters. Problems in any of these areas usually - manifest themselves through stagnation of the nonlinear optimization - algorithm. + optimization procedure stagnates, it may be due to issues involving: + + 1) poor data quality + 2) choice of objective function + 3) data processing parameters (i.e., filter bandpass) + 4) regularization methods + + Problems in any of these areas usually manifest themselves through + stagnation of the nonlinear optimizationalgorithm. TODO fix line search, currently broken. Store line search variables in .npz arrays rather than as internal attributes, that way it's easier to restart from a broken workflow. Need to figure out how to reset and restart line searches efficiently + Change line search to words not numbers """ import os -import sys import numpy as np +from glob import glob from seisflows import logger from seisflows.tools import msg, unix @@ -60,10 +63,14 @@ class Gradient: :type step_len_max: float :param step_len_max: maximum allowable step length during the line search. Set as a fraction of the current model parameters + :type path_line_search: str + :param path_line_search: full path to a file used to periodically + save the line search history as a NumPy .npz file """ - def __init__(self, start=1, line_search_method="bracket", preconditioner=None, - step_count_max=10, step_len_init=0.05, step_len_max=0.5, - workdir=os.getcwd(), path_optimize=None, path_output=None, + def __init__(self, start=1, line_search_method="bracket", + preconditioner=None, step_count_max=10, step_len_init=0.05, + step_len_max=0.5, workdir=os.getcwd(), path_optimize=None, + path_line_search=None, path_output=None, path_preconditioner=None, **kwargs): """Gradient-descent input parameters""" @@ -76,6 +83,18 @@ def __init__(self, start=1, line_search_method="bracket", preconditioner=None, self.step_len_init = step_len_init self.step_len_max = step_len_max + # Set required path structure + self.path = Dict( + scratch=path_optimize or + os.path.join(os.getcwd(), "scratch", "optimize"), + output=path_output or os.path.join(workdir, "output"), + preconditioner=path_preconditioner, + ) + self.path["line_search"] = ( + path_line_search or + os.path.join(self.path.scratch, "line_search") + ) + # Internal check to see if the chosen line search algorithm exists if not hasattr(line_search_dir, line_search_method): logger.warning(f"{line_search_method} is not a valid line search " @@ -84,16 +103,8 @@ def __init__(self, start=1, line_search_method="bracket", preconditioner=None, # .title() ensures we grab the class and not the module self.line_search = getattr(line_search_dir, line_search_method.title())( - step_count_max=step_count_max, step_len_max=step_len_max - ) - - # Set required path structure - self.path = Dict( - scratch=path_optimize or - os.path.join(os.getcwd(), "scratch", "optimize"), - output=path_output or os.path.join(workdir, "output"), - preconditioner=path_preconditioner, - + step_count_max=step_count_max, step_len_max=step_len_max, + path=self.path.line_search ) # Internally used parameters for checking validity @@ -134,13 +145,15 @@ def setup(self): Sets up nonlinear optimization machinery """ unix.mkdir(self.path.scratch) + self.line_search.save_search_history() # will be empty def load(self, name): """ Convenience function to access the full paths of model and gradient vectors that are saved to disk - .. note:: the available options that can be loaded + .. note:: + Model, gradient and misfit vectors are named as follows: m_new: current model m_old: previous model m_try: line search model @@ -155,6 +168,10 @@ def load(self, name): :type name: str :param name: name of the vector, acceptable: m, g, p, f, alpha + :type check: bool + :param check: if the model is a vector, check poissons ratio and list + out min and max for all parameters. This is really only useful for + elastic models, and not for kernels, gradients etc. """ assert(name in self._acceptable_vectors) model_npz = os.path.join(self.path.scratch, f"{name}.npz") @@ -229,14 +246,14 @@ def compute_direction(self): def initialize_search(self): """ - Initialize the plugin line search machinery. Should only be run at - the beginning of line search, by the main workflow module. + Generate a trial model by perturbing the current model in the search + direction with a given step length, calculated by the chosen line + search algorithm. """ - # Vectors required to initialize a line search - m = self.load("m_new") - g = self.load("g_new") - p = self.load("p_new") - f = self.load("f_new") + m = self.load("m_new") # current model from external solver + g = self.load("g_new") # current gradient from scaled kernels + p = self.load("p_new") # current search direction from optimization + f = self.load("f_new") # current misfit value from preprocess norm_m = max(abs(m.vector)) norm_p = max(abs(p.vector)) @@ -251,51 +268,66 @@ def initialize_search(self): if self.step_len_max: new_step_len_max = self.step_len_max * norm_m / norm_p self.line_search.step_len_max = new_step_len_max - logger.debug(f"max step length safeguard = {new_step_len_max:.2E}") + logger.info(f"enforcing max step length safeguard") - # Initialize the line search by setting vector variables - self.line_search.step_count = 0 - self.line_search.step_lens.append(0.) - self.line_search.func_vals.append(f) - self.line_search.gtg.append(gtg) - self.line_search.gtp.append(gtp) + # Initialize the line search and save it to disk. + self.line_search.update_search_history(func_val=f, step_len=0., + gtg=gtg, gtp=gtp) + self.line_search.check_search_history(iteration=self.iteration) - alpha, _ = self.line_search.calculate_step() + alpha, _ = self.line_search.calculate_step_length() # Alpha defines the trial step length. Optional step length override if self.step_len_init and len(self.line_search.step_lens) <= 1: alpha = self.step_len_init * norm_m / norm_p - logger.debug(f"overwrite initial step length: {alpha:.2E}") + logger.debug(f"overwriting initial step length, " + f"alpha_new={alpha:.2E}") # The new model is the old model, scaled by the step direction and # gradient threshold to remove any outlier values m_try = m.update(vector=m.vector + alpha * p.vector) + logger.info("trial model 'm_try' parameters: ") + m_try.check() return m_try, alpha - def update_search(self): + def update_line_search(self): """ - Updates line search status and step length and checks if the line search - has been completed. + Updates line search status and step length after a forward simulation + has been run and misfit calculated. Checks misfit against line search + history to see if the line search has been completed. - Available status codes from line_search.calculate_step(): + Available status codes from line_search.calculate_step_length(): status == 1 : finished status == 0 : not finished status == -1 : failed """ - # Update the line search variables for a new step calculation - self.line_search.step_lens.append(self.load("alpha")) - self.line_search.func_vals.append(self.load("f_try")) + # Collect information on a forward evaluation that just took place + alpha = self.load("alpha") # step length + f_try = self.load("f_try") # misfit for the trial model + + # Update the line search with a new step length and misfit value + self.line_search.step_count += 1 + self.line_search.update_search_history(step_len=alpha, func_val=f_try) + self.line_search.check_search_history(iteration=self.iteration) - alpha, status = self.line_search.calculate_step() + # Calculate a new step length based on line search algorithm + alpha_try, status = self.line_search.calculate_step_length() - # New search direction needs to be searchable on disk + # Status == 0: Retry line search // Status == 1: Line search passed if status in [0, 1]: + # Save new step length to disk + self.save("alpha", alpha_try) + + # Create a new trial model based on search direction, step length + # and the initial model vector m = self.load("m_new") p = self.load("p_new") - self.save("alpha", alpha) + # Sets the latest trial model m_try = m.update(vector=m.vector + alpha * p.vector) + logger.info("trial model 'm_try' parameters: ") + m_try.check() self.save("m_try", m_try) return status @@ -306,6 +338,10 @@ def finalize_search(self): Removes old model/search parameters, moves current parameters to old, sets up new current parameters and writes statistic outputs + + TODO does moving 'm_try' to 'm_new' actually make sense with bracketing + line search where our last trial model will potentially have a + higher misfit due to the 'bracket'ing nature? """ unix.cd(self.path.scratch) @@ -313,47 +349,60 @@ def finalize_search(self): # Remove the old model parameters if self.iteration > 1: - logger.info("removing previously accepted model files (old)") + logger.info("removing previously accepted model files (?_old)") for fid in ["m_old", "f_old", "g_old", "p_old"]: - unix.rm(fid) + unix.rm(os.path.join(self.path.scratch, fid)) # Needs to be run before shifting model in next step self._write_stats() - logger.info("shifting current model (new) to previous model (old)") - unix.mv("m_new.npz", "m_old.npz") - unix.mv("f_new.npz", "f_old.npz") - unix.mv("g_new.npz", "g_old.npz") - unix.mv("p_new.npz", "p_old.npz") + logger.info("renaming current (new) optimization vectors as " + "previous model (old)") + # e.g., m_new.npz -> m_old.npz + for src in glob(os.path.join(self.path.scratch, "*_new.*")): + dst = src.replace("_new.", "_old.") + unix.mv(src, dst) - logger.info("setting accepted line search model as current model") - unix.mv("m_try.npz", "m_new.npz") + logger.info("setting accepted trial model (try) as current model (new)") + unix.mv(src=os.path.join(self.path.scratch, "m_try.npz"), + dst=os.path.join(self.path.scratch, "m_new.npz")) - # Choose minimum misfit value as final misfit/model + # Choose minimum misfit value as final misfit/model. index 0 is initial f = self.line_search.get_search_history()[1] self.save("f_new", f.min()) - logger.info(f"current misfit is {f.min():.3E}") + logger.info(f"misfit of accepted trial model is f={f.min():.3E}") logger.info("resetting line search step count to 0") self.line_search.step_count = 0 - def retry_status(self): + def attempt_line_search_restart(self, threshold=1E-3): """ After a failed line search, this determines if restart is worthwhile - by checking, in effect, if the search direction was the same as gradient - direction + by checking, in effect, if the search direction was the same as the + negative gradientdirection. + + Essentially checking if this is a steepest-descent optimization, which + cannot and should not be restarted. If the search direction is calc'ed + by another optimization schema, the search direction and gradient should + differ + + :type threshold: float + :param threshold: angle threshold for the angle between the search + direction and the gradient. + :rtype: int + :return: pass (1) fail (0) status for retrying line search """ g = self.load("g_new") p = self.load("p_new") - theta = angle(p.vector, -1 * g.vector) - logger.debug(f"theta: {theta:6.3f}") + theta = angle(p.vector, -1 * g.vector) + logger.debug(f"checking gradient/search direction angle, " + f"theta: {theta:6.3f}") - thresh = 1.e-3 - if abs(theta) < thresh: - return 0 + if abs(theta) < threshold: + return 0 # Do not restart else: - return 1 + return 1 # Go for restart def restart(self): """ @@ -384,7 +433,8 @@ def _write_stats(self): # First time, write header information if not os.path.exists(fid): with open(fid, "w") as f: - for header in ["ITER", "FACTOR", "GRAD_NORM_L1", "GRAD_NORM_L2", + for header in ["ITER", # "FACTOR", + "GRAD_NORM_L1", "GRAD_NORM_L2", "MISFIT", "RESTART", "SLOPE", "STEP", "LENGTH", "THETA"]: f.write(f"{header.upper()},") @@ -394,11 +444,11 @@ def _write_stats(self): p = self.load("p_new") x, f, *_ = self.line_search.get_search_history() - import pdb;pdb.set_trace() - # Calculated stats factors - factor = -1 * dot(g.vector, g.vector) - factor = factor ** -0.5 * (f[1] - f[0]) / (x[1] - x[0]) + # TODO What is this? It was returning a RuntimeError for value too small + # for double precision. Do we need to keep it? + # factor = -1 * dot(g.vector, g.vector) + # factor = factor ** -0.5 * (f[1] - f[0]) / (x[1] - x[0]) grad_norm_L1 = np.linalg.norm(g.vector, 1) grad_norm_L2 = np.linalg.norm(g.vector, 2) @@ -408,11 +458,11 @@ def _write_stats(self): slope = (f[1] - f[0]) / (x[1] - x[0]) step_count = self.line_search.step_count step_length = x[f.argmin()] - theta = 180. * np.pi ** -1 * angle(p, -1 * g.vector) + theta = 180. * np.pi ** -1 * angle(p.vector, -1 * g.vector) with open(fid, "a") as f: f.write(f"{self.iteration:0>2}," - f"{factor:6.3E}," + # f"{factor:6.3E}," f"{grad_norm_L1:6.3E}," f"{grad_norm_L2:6.3E}," f"{misfit:6.3E}," diff --git a/seisflows/plugins/line_search/backtrack.py b/seisflows/plugins/line_search/backtrack.py index e7bb1dd1..a4a8cc71 100644 --- a/seisflows/plugins/line_search/backtrack.py +++ b/seisflows/plugins/line_search/backtrack.py @@ -1,20 +1,23 @@ #!/usr/bin/env python3 """ -This is the subclass class for seisflows.plugins.line_search.backtrack +Backtracking line search class plugin to be used with an L-BFGS optimization + +https://en.wikipedia.org/wiki/Backtracking_line_search """ from seisflows import logger from seisflows.plugins.line_search.bracket import Bracket -from seisflows.tools import msg from seisflows.tools.math import parabolic_backtrack class Backtrack(Bracket): """ - Implements backtracking linesearch. A backtracking line search is used - for L-BFGS optimization, where a unit step length is attempted, if this - does not satisfy the misfit reduction criteria, the step length is - `backtracked` to a smaller value. If the backtracked value becomes too small - the backtracking line search defaults to a Bracketing line search. + [line_search.backtrack] Backtracking line search assumes the + gradient is well scaled from the L-BFGS optimization algorithm, such + that a unit step length (1) will provide a decrease in misfit. If + misfit does not decrease, the backtracking step count follows a + parabolic backtrack from 1 -> 0 in search of a decreased misfit. If the + backtracked value becomes too small the backtracking line search defaults to + a Bracketing line search. Variables Descriptions: x: list of step lenths from current line search @@ -25,54 +28,62 @@ class Backtrack(Bracket): gtp: dot product of gradient and search direction Status codes - status > 0 : finished - status == 0 : not finished - status < 0 : failed + status == 1 : PASS, line search finished + status == 0 : TRY/RETRY, attempt line search w/ new step length + status == -1 : FAIL, line search exceeds internal criteria """ - def calculate_step(self): + def calculate_step_length(self): """ - Determines step length and search status. Overwrites the Bracketing - line search step calculation + Determines step length and search status. Defaults to 'Bracket'ing + line search during the first evaluation (waiting for the L-BFGS to scale + properly). + + .. note:: + Search history variable descriptions: + x: list of step lenths from current line search + f: correpsonding list of function values + m: number of step lengths in current line search + n: number of model updates in optimization problem + gtg: dot product of gradient with itself + gtp: dot product of gradient and search direction """ # Determine the line search history - x, f, gtg, gtp, step_count, update_count = self.search_history() - + x, f, gtg, gtp, step_count, update_count = self.get_search_history() + # quasi-Newton direction is not yet scaled properly, so instead # of a bactracking line perform a bracketing line search if update_count == 0: - alpha, status = super().calculate_step() + alpha, status = super().calculate_step_length() # Assumed well scaled search direction, attempt backtracking line search # with unit step length else: - logger.debug(msg.sub(f"BACKTRACKING LINE SEARCH STEP" - f"{self.step_count:0>2}")) - x_str = ", ".join([f"{_:.2E}" for _ in x]) - f_str = ", ".join([f"{_:.2E}" for _ in f]) - logger.debug(f"step length(s) = {x_str}") - logger.debug(f"misfit val(s) = {f_str}") + self._print_stats(x, f) # Initial unit step length if step_count == 0: - logger.info("attempting unit step length") alpha = min(1., self.step_len_max) + logger.info(f"try: attempt unit step length w/ alpha={alpha}") status = 0 # Pass if misfit is reduced elif f.min() < f[0]: - logger.info("misfit decrease, pass") alpha = x[f.argmin()] + logger.info(f"pass: misfit decreased, line search " + f"successful w/ alpha={alpha}") status = 1 # If misfit continually increases, decrease step length elif step_count <= self.step_count_max: - import pdb;pdb.set_trace() - logger.info("misfit increase, decreasing step length") slope = gtp[-1] / gtg[-1] alpha = parabolic_backtrack(f0=f[0], g0=slope, x1=x[1], f1=f[1], b1=0.1, b2=0.5) + logger.info(f"try: misfit increasing, attempting " + f"to decrease step length to alpha={alpha}") status = 0 # Failed because step_count_max exceeded else: - logger.info("backtracking failed, step_count_max exceeded") + logger.info(f"fail: backtracking line search has " + f"failed because the maximum allowable step counts " + f"({self.step_count_max}) has been exceeded") alpha = None status = -1 diff --git a/seisflows/plugins/line_search/bracket.py b/seisflows/plugins/line_search/bracket.py index eb97b56a..7a3fea45 100644 --- a/seisflows/plugins/line_search/bracket.py +++ b/seisflows/plugins/line_search/bracket.py @@ -1,58 +1,70 @@ #!/usr/bin/env python3 """ -This is the Bracketing line search class for seisflows +A bracketing line search (a.k.a direct line search) attempts to find an +appropriate step length by identifying two points between which the minimum +misfit lies. Contains some functionality for saving line search history to disk +so that a line search may be resumed in the case of a failure/reset. -Line search is called on by the optimization procedure and should not really -have any agency (i.e. it should not be able to iterate its own step count etc., -this should be completely left to the optimization algorithm to keep everything -in one place) +https://en.wikipedia.org/wiki/Line_search + +.. note:: + Line search is called on by the optimization procedure and should not + really have any agency (i.e. it should not be able to iterate its own step + count etc., this should be completely left to the optimization algorithm to + keep everything in one place) """ +import os import numpy as np from seisflows import logger -from seisflows.tools import msg from seisflows.tools.array import count_zeros from seisflows.tools.math import parabolic_backtrack, polynomial_fit class Bracket: """ - Abstract base class for line search - - Variables Descriptions: - x: list of step lenths from current line search - f: correpsonding list of function values - m: number of step lengths in current line search - n: number of model updates in optimization problem - gtg: dot product of gradient with itself - gtp: dot product of gradient and search direction - - Status codes - status > 0 : finished - status == 0 : not finished - status < 0 : failed + [line_search.bracket] The bracketing line search identifies two points + between which the minimum misfit lies between. + + :type step_count_max: int + :param step_count_max: maximum number of step counts before changing + line search behavior. set by PAR.STEP_COUNT_MAX + :type step_len_max: int + :param step_len_max: maximum length of the step, defaults to infinity, + that is unbounded step length. set by PAR.STEP_LEN_MAX """ - def __init__(self, step_count_max, step_len_max): + def __init__(self, step_count_max, step_len_max, path=None): """ - Initiate the line search machinery - - :type step_count_max: int - :param step_count_max: maximum number of step counts before changing - line search behavior. set by PAR.STEP_COUNT_MAX - :type step_len_max: int - :param step_len_max: maximum length of the step, defaults to infinity, - that is unbounded step length. set by PAR.STEP_LEN_MAX + Instantiate max criteria for line search """ self.step_count_max = step_count_max self.step_len_max = step_len_max + if path is None: + self.path = os.path.join(os.getcwd(), "line_search") + else: + self.path = path + self.func_vals = [] self.step_lens = [] self.gtg = [] self.gtp = [] self.step_count = 0 - def clear_history(self): + def update_search_history(self, func_val, step_len, gtg=None, gtp=None): + """ + Update the internal list of search history attributes. Lists like + `func_vals` get appended to, while values like step_count are + overwritten. Allowed to increment func_val and step_len by themselves + """ + self.func_vals.append(func_val) + self.step_lens.append(step_len) + if gtg: + self.gtg.append(gtg) + if gtp: + self.gtp.append(gtp) + + def clear_search_history(self): """ Clears internal line search history for a new line search attempt """ @@ -62,27 +74,51 @@ def clear_history(self): self.gtp = [] self.step_count = 0 - def reset(self): + def check_search_history(self, iteration): """ - If a line search fails mid-search, and the User wants to resume from - the line search function. Initialize will be called again. This function - undos the progress made by the previous line search so that a new line - search can be called without problem. + Since the line search is just a wrapper for list of numbers, check that + search history hasn't been muddled up by ensuring that internal lists + are the correct length for the given evaluation - output.optim needs to have its lines cleared manually + :type iteration: int + :param iteration: current iteration of the workflow """ - # First step treated differently - if len(self.step_lens) <= 1: - self.clear_history() - else: - # Wind back dot products by one - self.gtg = self.gtg[:-1] - self.gtp = self.gtp[:-1] - - # Move step lens and function evaluations by number of step count - original_idx = -1 * self.step_count - 1 - self.step_lens = self.step_lens[:original_idx] - self.func_vals = self.func_vals[:original_idx] + assert(len(self.gtg) == iteration), f"too many entries for 'gtg'" + assert(len(self.gtp) == iteration), f"too many entries for 'gtp'" + assert(len(self.func_vals) == len(self.step_lens)), \ + f"number of function evaluations does not match step lengths" + assert(self.step_count + 1 == len(self.func_vals)), \ + f"current step coutn doesn't match the number of function evals" + + def save_search_history(self, file=None): + """ + Save the current line search history to disk. Used to re-load a line + search from disk in the case of a failed search + """ + if file is None: + file = self.path + + dict_out = dict(func_vals=self.func_vals, step_lens=self.step_lens, + gtg=self.gtg, gtp=self.gtp, step_count=self.step_count) + np.savez(file=file, **dict_out) + + def load_search_history(self, file=None): + """ + Load line search history from disk. Used to re-load line search in the + case of failed line searches. + """ + if file is None: + file = self.path + + # Numpy will append .npz to saved files, just ensure we honor that + if not file.endswith(".npz"): + file = f"{file}.npz" + + dict_in = np.load(file=file) + self.step_count = int(dict_in["step_count"]) # only var thats not list + for key in ["func_vals", "step_lens", "gtg", "gtp"]: + assert(key in dict_in), f"line search .npz file has no key {key}" + setattr(self, key, list(dict_in[key])) def get_search_history(self, sort=True): """ @@ -104,13 +140,14 @@ def get_search_history(self, sort=True): :rtype i: int :return i: step_count :rtype j: int - :return j: number of iterations corresponding to 0 step length + :return j: number of iterations corresponding to 0 step length, + i.e., the update count """ i = self.step_count - j = count_zeros(self.step_lens) - 1 k = len(self.step_lens) x = np.array(self.step_lens[k - i - 1:k]) f = np.array(self.func_vals[k - i - 1:k]) + j = count_zeros(self.step_lens) - 1 # update count # Sort by step length if sort: @@ -119,60 +156,70 @@ def get_search_history(self, sort=True): return x, f, self.gtg, self.gtp, i, j - def calculate_step(self): - """ - Determines step length (alpha) and search status (status) - """ - # Determine the line search history - x, f, gtg, gtp, step_count, update_count = self.get_search_history() - - # Print out the current line search parameters for convenience - logger.debug(msg.sub(f"BRACKETING LINE SEARCH STEP " - f"{self.step_count:0>2}")) + def _print_stats(self, x, f): + """Print out misfit values and step lengths to the logger""" x_str = ", ".join([f"{_:.2E}" for _ in x]) f_str = ", ".join([f"{_:.2E}" for _ in f]) logger.debug(f"step length(s) = {x_str}") logger.debug(f"misfit val(s) = {f_str}") + def calculate_step_length(self): + """ + Determines step length (alpha) and search status (status) using a + bracketing line search. + """ + # Determine the line search history + x, f, gtg, gtp, step_count, update_count = self.get_search_history() + self._print_stats(x, f) + # For the first inversion and initial step, set alpha manually if step_count == 0 and update_count == 0: # Based on idea from Dennis and Schnabel alpha = gtg[-1] ** -1 - logger.info(f"first iteration, guessing trial step") + logger.info(f"try: first evaluation, attempt guess step length, " + f"alpha={alpha:.2E}") status = 0 - # For every i'th inversions initial step, set alpha manually + # For every iteration's initial step, set alpha manually elif step_count == 0: # Based on the first equation in sec 3.5 of Nocedal and Wright 2ed idx = np.argmin(self.func_vals[:-1]) alpha = self.step_lens[idx] * gtp[-2] / gtp[-1] - logger.info(f"first step, setting scaled step length") + logger.info(f"try: first step count of iteration, " + f"setting scaled step length, alpha={alpha:.2E}") status = 0 # If misfit is reduced and then increased, we've bracketed. Pass elif _check_bracket(x, f) and _good_enough(x, f): alpha = x[f.argmin()] - logger.info(f"bracket okay, step length reasonable, pass") + logger.info(f"pass: bracket acceptable and step length " + f"reasonable.") status = 1 # If misfit is reduced but not close, set to quadratic fit elif _check_bracket(x, f): alpha = polynomial_fit(x, f) - logger.info(f"bracket okay, step length unreasonable, manual step") + logger.info(f"try: bracket acceptable but step length unreasonable " + f"attempting to re-adjust step length " + f"alpha={alpha:.2E}") status = 0 # If misfit continues to step down, increase step length elif step_count <= self.step_count_max and all(f <= f[0]): alpha = 1.618034 * x[-1] # 1.618034 is the 'golden ratio' - logger.info(f"misfit not bracketed, increasing step length") + logger.info(f"try: misfit not bracketed, increasing step length " + f"using golden ratio, alpha={alpha:.2E}") status = 0 # If misfit increases, reduce step length by backtracking elif step_count <= self.step_count_max: slope = gtp[-1] / gtg[-1] alpha = parabolic_backtrack(f0=f[0], g0=slope, x1=x[1], f1=f[1], b1=0.1, b2=0.5) - logger.info(f"misfit increasing, reducing step length to") + logger.info(f"try: misfit increasing, attempting " + f"to reduce step length using parabloic backtrack, " + f"alpha={alpha:.2E}") status = 0 # step_count_max exceeded, fail else: - logger.info(f"bracketing failed, step_count_max=" - f"{self.step_count_max} exceeded") + logger.info(f"fail: bracketing line search has failed " + f"to reduce the misfit before exceeding " + f"`step_count_max`={self.step_count_max}") alpha = None status = -1 @@ -180,15 +227,17 @@ def calculate_step(self): if alpha is not None: if alpha > self.step_len_max and step_count == 0: alpha = 0.618034 * self.step_len_max - logger.info(f"initial step length safegaurd, setting " - f"manual step length") + logger.info(f"try: applying initial step length " + f"safegaurd as alpha has exceeded maximum step " + f"length, alpha_new={alpha:.2E}") status = 0 # Stop because safeguard prevents us from going further elif alpha > self.step_len_max: - logger.info(f"step_len_max={self.step_len_max:.2f} " - f"exceeded, manual set alpha") alpha = self.step_len_max - status = 1 + logger.info(f"try: applying initial step length " + f"safegaurd as alpha has exceeded maximum step " + f"length, alpha_new={alpha:.2E}") + status = 1 # TODO shouldn't this be 0 or -1? return alpha, status diff --git a/seisflows/preprocess/default.py b/seisflows/preprocess/default.py index f05521ff..000d59b6 100644 --- a/seisflows/preprocess/default.py +++ b/seisflows/preprocess/default.py @@ -524,7 +524,6 @@ def _read_ascii(fid, origintime=None): data = np.array(data) if origintime is None: - print("No origintime given, setting to default 1970-01-01T00:00:00") origintime = UTCDateTime("1970-01-01T00:00:00") # We assume that dt is constant after 'precision' decimal points diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 0fddd568..199cfd38 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -1239,6 +1239,8 @@ def _inspect_module_hierarchy(name=None, **kwargs): def _reset_line_search(self, **kwargs): """ + TODO Delete me + Reset the machinery of the line search. This is useful for if a line search fails or stagnates but the User does not want to re-run the entire iteration. They can reset the line search and resume the workflow diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 2f25566b..751e18a4 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -494,7 +494,7 @@ def adjoint_simulation(self, executables=None, save_kernels=False, # Rename kernels to work w/ conflicting name conventions unix.cd(self.kernel_databases) - logger.info(f"renaming event kernels for {self.source_name}") + logger.debug(f"renaming event kernels for {self.source_name}") for tag in ["alpha", "alpha[hv]", "reg1_alpha", "reg1_alpha[hv]"]: names = glob(f"*proc??????_{tag}_kernel.bin") unix.rename(old="alpha", new="vp", names=names) diff --git a/seisflows/tools/specfem.py b/seisflows/tools/specfem.py index 386ac123..222fd516 100644 --- a/seisflows/tools/specfem.py +++ b/seisflows/tools/specfem.py @@ -33,6 +33,11 @@ def __init__(self, path, fmt=None, parameters=None, load=False): `fmt` can be provided by the user or guessed based on available file formats + .. note:: + The `vector` representation is based completely on the `model` + attribute. In order to update the model based on vector manipulation + you must use the update() function. + :type path: str :param path: path to SPECFEM model/kernel/gradient files :type fmt: str @@ -63,8 +68,6 @@ def __init__(self, path, fmt=None, parameters=None, load=False): self.model, self.ngll = self.read(parameters=parameters) self.parameters = self.model.keys() - self.vector = self.merge() - self.check() @staticmethod def fnfmt(i="*", val="*", ext="*"): @@ -92,6 +95,12 @@ def fnfmt(i="*", val="*", ext="*"): filename_format = f"proc{i}_{val}{ext}" return filename_format + @property + def vector(self): + """conveience property to access the merge() property which creates a + linear vector defining all model parameters""" + return self.merge() + def read(self, parameters=None): """ Utility function to load in SPECFEM models/kernels/gradients saved in @@ -179,21 +188,27 @@ def write(self, path, fmt=None): save_fx(path=path) - def split(self): + def split(self, vector=None): """ Converts internal vector representation `m` to dictionary representation `model`. Does this by separating the vector based on how it was constructed, parameter-wise and processor-wise + :type vector: np.array + :param vector: allow Model to split an input vector. If none given, + will split the internal vector representation :rtype: Dict of np.array :return: dictionary of model parameters split up by number of processors """ + if vector is None: + vector = self.vector + model = Dict({key: [] for key in self.parameters}) for idim, key in enumerate(self.parameters): for iproc in range(self.nproc): imin = sum(self.ngll) * idim + sum(self.ngll[:iproc]) imax = sum(self.ngll) * idim + sum(self.ngll[:iproc + 1]) - model[key].extend([self.vector[imin:imax]]) + model[key].extend([vector[imin:imax]]) model[key] = np.array(model[key]) return model @@ -223,10 +238,9 @@ def check(self, min_pr=-1., max_pr=0.5): logger.warning(f"Vp minimum is negative {self.model.vp.min()}") # Tell the User min and max values of the updated model - logger.info(f"model parameters") for key, vals in self.model.items(): - # Choose formatter based on the size of the value - if vals.min() < 1 or vals.max() > 1E4: + # Choose formatter based on the magnitude of the value + if vals.min() < 1 or (vals.max() > 1E4): parts = "{minval:.2E} <= {key} <= {maxval:.2E}" else: parts = "{minval:.2f} <= {key} <= {maxval:.2f}" @@ -273,10 +287,8 @@ def update(self, model=None, vector=None): """ if model is not None: self.model = model - self.vector = self.merge() elif vector is not None: - self.vector = vector - self.model = self.split() + self.model = self.split(vector=vector) return self diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index 65af4643..4f3c7e25 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -11,7 +11,7 @@ from seisflows import logger from seisflows.tools import msg, unix from seisflows.tools.core import Dict -from seisflows.config import import_seisflows +from seisflows.tools.specfem import Model class Forward: @@ -67,7 +67,7 @@ def __init__(self, modules=None, data_case=None, export_traces=False, os.path.join(workdir, "scratch", "eval_grad"), output=path_output or os.path.join(workdir, "output"), state_file=path_state_file or - os.path.join(workdir, "statefile.txt"), + os.path.join(workdir, ".statefile.txt"), data=path_data, model_init=path_model_init, model_true=path_model_true @@ -173,7 +173,7 @@ def setup(self): # Run setup() for each of the required modules for req_mod in self._required_modules: - logger.info( + logger.debug( f"running setup for module " f"'{req_mod}.{self._modules[req_mod].__class__.__name__}'" ) @@ -182,7 +182,7 @@ def setup(self): # Run setup() for each of the instantiated modules for opt_mod in self._optional_modules: if self._modules[opt_mod] and opt_mod not in self._required_modules: - logger.info( + logger.debug( f"running setup for module " f"'{opt_mod}.{self._modules[opt_mod].__class__.__name__}'" ) @@ -196,6 +196,16 @@ def setup(self): f.write(f"# Acceptable states: 'completed', 'failed'\n") f.write(f"# =======================================\n") + # Load in the initial model and check its poissons ratio + if self.path.model_init: + logger.info("checking initial model parameters") + _model = Model(os.path.join(self.path.model_init)) + _model.check() + if self.path.model_true: + logger.info("checking true/target model parameters") + _model = Model(os.path.join(self.path.model_true)) + _model.check() + # Distribute modules to the class namespace. We don't do this at init # incase _modules was set as NoneType self.solver = self._modules.solver @@ -277,7 +287,7 @@ def prepare_data_for_solver(self, **kwargs): Must be run by system.run() so that solvers are assigned individual task ids and working directories """ - logger.info(f"PREPARING OBSERVATION DATA FOR SOURCE " + logger.info(f"preparing observation data for source " f"{self.solver.source_name}") if self.data_case == "data": @@ -294,7 +304,7 @@ def prepare_data_for_solver(self, **kwargs): export_traces = False # Run the forward solver with target model and save traces the 'obs' - logger.info(f"running forward simulation w/ `MODEL_TRUE` for " + logger.info(f"running forward simulation w/ target model for " f"{self.solver.source_name}") self.solver.import_model(path_model=self.path.model_true) self.solver.forward_simulation( @@ -319,7 +329,8 @@ def evaluate_objective_function(self, path_model, save_residuals=False, assert(os.path.exists(path_model)), \ f"Model path for objective function does not exist" - logger.info("EVALUATING OBJECTIVE FUNCTION") + logger.info(f"evaluating objective function for source " + f"{self.solver.source_name}") logger.debug(f"running forward simulation with " f"'{self.solver.__class__.__name__}'") diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index ffc477b1..a2cd11fa 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -91,7 +91,7 @@ def task_list(self): self.run_adjoint_simulations, self.postprocess_event_kernels, self.evaluate_gradient_from_kernels, - self.compute_search_direction, + self.initialize_line_search, self.perform_line_search, self.clean_scratch_directory ] @@ -117,6 +117,14 @@ def setup(self): unix.mkdir(self.path.eval_func) self.optimize = self._modules.optimize + def checkpoint(self): + """ + Override checkpoint and add the optimization iteration parameter + """ + super().checkpoint() + with open(self.path.state_file, "a") as f: + f.write(f"iteration: {self.optimize.iteration}") + def run(self): """Call the forward.run() function iteratively, from `start` to `end`""" for i in range(self.start, self.end + 1): @@ -151,17 +159,22 @@ def evaluate_gradient_from_kernels(self): gradient = Model(path=os.path.join(self.path.eval_grad, "gradient")) self.optimize.save(name="g_new", m=gradient) - def compute_search_direction(self): + def initialize_line_search(self): """ - Computes search direction using the optimization library and performs - a sets up line search machinery to 'perform line search' + Computes search direction using the optimization library and sets up + line search machinery to 'perform line search' by placing correct files + on disk for each of the modules to find. + + Optimization module perturbs the current model (m_new) by the search + direction (p_new) to recover the trial model (m_try). This model is + then exposed on disk to the solver. """ - logger.info(f"computing search direction with " - f"'{self.optimize.__class__.__name__}'") - p_new = self.optimize.compute_direction() - self.optimize.save(name="p_new", m=p_new) + logger.info(f"initializing " + f"'{self.optimize.line_search.__class__.__name__}'ing " + f"line search") - # Check that our search direction will actually perturb the model + # 'p' is the search direction used to perturb the initial model + p_new = self.optimize.compute_direction() if sum(p_new.vector) == 0: logger.critical(msg.cli( "Search direction vector 'p' is 0, meaning no model update can " @@ -170,14 +183,13 @@ def compute_search_direction(self): header="line search error") ) sys.exit(-1) + self.optimize.save(name="p_new", m=p_new) - logger.info( - msg.mnr(f"INITALIZING LINE SEARCH: i{self.optimize.iteration:0>2}" - f"s{self.optimize.step_count:0>2}") - ) + # Scale search direction with step length alpha generate a model update m_try, alpha = self.optimize.initialize_search() self.optimize.save(name="m_try", m=m_try) self.optimize.save(name="alpha", m=alpha) + self.optimize.line_search.save_search_history() # Expose model `m_try` to the solver by placing it in eval_func dir. m_try.write(path=os.path.join(self.path.eval_func, "model")) @@ -188,54 +200,65 @@ def perform_line_search(self): function is reduced acceptably, or the line search fails due to user-defined limit criteria. + .. note:: + Starts on step_count == 1 because step_count == 0 will be the + misfit of the starting model + Status codes: status > 0 : finished status == 0 : not finished status < 0 : failed """ - while True: - # Attempt a new trial step with the given step length - self.optimize.line_search.step_count += 1 - logger.info( - msg.sub(f"TRIAL STEP COUNT: i{self.optimize.iteration:0>2}" - f"s{self.optimize.step_count:0>2}") - ) - - # Run the forward simulation on system and calculate misfit as f_try - self.system.run( - [self.evaluate_objective_function], - path_model=os.path.join(self.path.eval_func, "model"), - save_residuals=os.path.join(self.path.eval_func, "residuals") - ) - residuals = np.loadtxt(os.path.join(self.path.eval_func, - "residuals")) - total_misfit = self.preprocess.sum_residuals(residuals) - self.optimize.save(name="f_try", m=total_misfit) - - # Check the function evaluation against line search history - status = self.optimize.update_search() - - # Proceed based on the outcome of the line search - if status > 0: - # Save outcome of line search to disk; reset step to 0 for next iter - logger.info("trial step successful. finalizing line search") - self.optimize.finalize_search() - return - elif status == 0: - logger.info("trial step unsuccessful. retrying with new trial " - "step") - # Recursively call this function to attempt another trial step - continue - elif status < 0: - if self.optimize.retry_status(): - logger.info("line search has failed. restarting " - "optimization algorithm and line search.") - # Reset the line search machinery; set step count to 0 - self.optimize.restart() - continue - else: - logger.info("line search has failed. aborting inversion.") - sys.exit(-1) + # self.optimize.line_search.load_search_history() + + logger.info(msg.sub(f"LINE SEARCH STEP COUNT " + f"{self.optimize.step_count + 1:0>2}")) + + # Run fwd solver with the model 'm_try'. Corresponding misfit is 'f_try' + self._evaluate_line_search_misfit() + + # Increment step count, calculate new step length/model, check misfit + status = self.optimize.update_line_search() + self.optimize.line_search.save_search_history() + + # Proceed based on the outcome of the line search + if status == 1: + # Save outcome of line search to disk; reset step to 0 for next iter + logger.info("trial step successful. finalizing line search") + self.optimize.finalize_search() + self.optimize.line_search.save_search_history() + return + elif status == 0: + logger.info("trial step unsuccessful. re-attempting line search") + self.perform_line_search() # RECURSIVE CALL + elif status == -1: + if self.optimize.attempt_line_search_restart(): + logger.info("line search has failed. restarting " + "optimization algorithm and line search.") + # Reset the line search machinery; set step count to 0 + self.optimize.restart() + self.perform_line_search() # RECURSIVE CALL + else: + logger.critical( + msg.cli("Line search has failed to reduce the misfit and " + "has run out of fallback options. Aborting " + "inversion.", border="=", + header="line search failed") + ) + sys.exit(-1) + + def _evaluate_line_search_misfit(self): + """Convenience fuinction to wrap forward solver and misfit calc""" + self.system.run( + [self.evaluate_objective_function], + path_model=os.path.join(self.path.eval_func, "model"), + save_residuals=os.path.join(self.path.eval_func, "residuals") + ) + residuals = np.loadtxt(os.path.join(self.path.eval_func, + "residuals")) + total_misfit = self.preprocess.sum_residuals(residuals) + logger.debug(f"misfit for trial model (f_try) == {total_misfit:.2E}") + self.optimize.save(name="f_try", m=total_misfit) def clean_scratch_directory(self): """ @@ -250,35 +273,4 @@ def clean_scratch_directory(self): unix.mkdir(self.path.eval_grad) unix.mkdir(self.path.eval_func) - # def export(self): - # """ - # Exports various quantities to PATH.OUTPUT (to disk) from the SCRATCH - # directory as SCRATCH is liable to be overwritten at any point of the - # workflow. This takes place at the end of each iteration, before - # the clean() function is called. - # """ - # optimize = self.module("optimize") - # - # if self.par.SAVEMODEL: - # src = os.path.join(self.path.OPTIMIZE, "m_new") - # dst = os.path.join(self.path.OUTPUT, f"model_{optimize.iter:04d}") - # logger.debug(f"exporting model 'm_new' to disk") - # unix.cp(src, dst) - # - # if self.par.SAVEGRADIENT: - # src = os.path.join(self.path.OPTIMIZE, "g_old") - # dst = os.path.join(self.path.OUTPUT, f"grad_{optimize.iter:04d}") - # unix.cp(src, dst) - # - # if self.par.SAVEKERNELS: - # src = os.path.join(self.path.GRAD, "kernels") - # dst = os.path.join(self.path.OUTPUT, f"kernels_{optimize.iter:04d}") - # logger.debug(f"saving kernels to path:\n{dst}") - # unix.mv(src, dst) - # - # if self.par.SAVERESIDUALS: - # src = os.path.join(self.path.GRAD, "residuals") - # dst = os.path.join(self.path.OUTPUT, - # f"residuals_{optimize.iter:04d}") - # unix.mv(src, dst) From a3cc003699ac3a1a14e59e17f1c984ff90e33180 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Tue, 19 Jul 2022 17:26:41 -0800 Subject: [PATCH 072/195] adding doc inheritance for future seisflows configure command, udpating docstring to class definition for all modules missed before --- seisflows/optimize/LBFGS.py | 104 +++++++----------- seisflows/optimize/NLCG.py | 56 +++------- seisflows/optimize/gradient.py | 4 - seisflows/postprocess/__init__.py | 5 - seisflows/postprocess/default.py | 138 ------------------------ seisflows/solver/specfem.py | 3 +- seisflows/system/cluster.py | 39 +++---- seisflows/system/slurm.py | 26 ++--- seisflows/system/workstation.py | 60 +++++------ seisflows/tests/test_config.py | 92 ++-------------- seisflows/tests/test_optimize.py | 2 + seisflows/tests/test_preprocess.py | 8 +- seisflows/tests/test_solver.py | 17 +-- seisflows/tools/specfem.py | 4 +- seisflows/workflow/migration.py | 1 - seisflows/workflow/thrifty_inversion.py | 86 +++++++-------- 16 files changed, 184 insertions(+), 461 deletions(-) delete mode 100644 seisflows/postprocess/__init__.py delete mode 100644 seisflows/postprocess/default.py create mode 100644 seisflows/tests/test_optimize.py diff --git a/seisflows/optimize/LBFGS.py b/seisflows/optimize/LBFGS.py index 820550d8..80e12b6f 100644 --- a/seisflows/optimize/LBFGS.py +++ b/seisflows/optimize/LBFGS.py @@ -1,7 +1,30 @@ #!/usr/bin/env python3 """ -This is the custom class for an LBFGS optimization schema. -It supercedes the `seisflows.optimize.base` class +L-BFGS (Limited memory Broyden–Fletcher–Goldfarb–Shanno) algorithm for solving +nonlinear optimization problems. + +L-BFGS Variables: + s: memory of model differences + y: memory of gradient differences + +Optimization Variables: + m: model + f: objective function value + g: gradient direction + p: search direction + +Line Search Variables: + x: list of step lenths from current line search + f: correpsonding list of function values + m: number of step lengths in current line search + n: number of model updates in optimization problem + gtg: dot product of gradient with itself + gtp: dot product of gradient and search direction + +Status codes + status > 0 : finished + status == 0 : not finished + status < 0 : failed """ import os import numpy as np @@ -16,75 +39,26 @@ class LBFGS(Gradient): """ - The Limited memory BFGS algorithm - Calls upon seisflows.plugin.optimize.LBFGS to accomplish LBFGS algorithm - - Includes optional safeguards: periodic restarting and descent conditions. - - To conserve memory, most vectors are read from disk rather than passed - from a calling routine. - - L-BFGS Variables: - s: memory of model differences - y: memory of gradient differences - - Optimization Variables: - m: model - f: objective function value - g: gradient direction - p: search direction - - Line Search Variables: - x: list of step lenths from current line search - f: correpsonding list of function values - m: number of step lengths in current line search - n: number of model updates in optimization problem - gtg: dot product of gradient with itself - gtp: dot product of gradient and search direction - - Status codes - status > 0 : finished - status == 0 : not finished - status < 0 : failed + [optimize.lbfgs] Limited memory BFGS nonlienar optimization algorithm + + :type lbfgs_mem: int + :param lbfgs_mem: L-BFGS memory. Max number of previous gradients to + retain in local memory for approximating the objective function. + :type lbfgs_max: L-BFGS periodic restart interval. Must be + 1 <= lbfgs_max <= infinity. + :type lbfgs_thresh: L-BFGS angle restart threshold. If the angle between + the current and previous search direction exceeds this value, + optimization algorithm will be restarted. """ + __doc__ = Gradient.__doc__ + __doc__ + def __init__(self, lbfgs_mem=3, lbfgs_max=np.inf, lbfgs_thresh=0., **kwargs): - """ - These parameters should not be set by the user. - Attributes are initialized as NoneTypes for clarity and docstrings. - - :type lbfgs_mem: int - :param lbfgs_mem: L-BFGS memory. Max number of previous gradients to - retain in local memory for approximating the objective function. - :type lbfgs_max: L-BFGS periodic restart interval. Must be - 1 <= lbfgs_max <= infinity. - :type lbfgs_thresh: L-BFGS angle restart threshold. If the angle between - the current and previous search direction exceeds this value, - optimization algorithm will be restarted. - - - :type LBFGS_iter: int - :param LBFGS_iter: an internally used iteration that differs from - optimization iter. Keeps track of internal LBFGS memory of previous - gradients. If LBFGS is restarted, the LBFGS_iter iteration is reset, - but the optization iteration. - :type memory_used: int - :param memory_used: bookkeeping to see how many previous - gradients have been stored to internal memory. Should not exceed - PAR.LBFGSMEM - :type LBFGS_dir: str - :param LBFGS_dir: location to store LBFGS internal memory - :type y_file: str - :param y_file: path to store memory of the gradient differences - i.e., `g_new - g_old` - :type s_file: str - :param s_file: path to store memory of the model differences - i.e., `m_new - m_old` - """ + """Instantiate L-BFGS specific parameters""" super().__init__(**kwargs) # Overwrite user-chosen line search. L-BFGS requires 'Backtrack'ing LS - if self._line_search.title != "Backtrack": + if self._line_search.title() != "Backtrack": logger.warning(f"L-BFGS optimization requires 'backtrack'ing line " f"search. Overwritng {self._line_search}") self._line_search = "Backtrack" diff --git a/seisflows/optimize/NLCG.py b/seisflows/optimize/NLCG.py index 645f19b5..d49704ec 100644 --- a/seisflows/optimize/NLCG.py +++ b/seisflows/optimize/NLCG.py @@ -1,61 +1,33 @@ #!/usr/bin/env python3 """ -This is the custom class for an NLCG optimization schema. -It inherits from the `seisflows.optimize.gradient.Gradient` class +Nonlinear conjugate gradient method for optimization """ import numpy as np from seisflows import logger from seisflows.optimize.gradient import Gradient -from seisflows.tools import unix from seisflows.tools.math import dot from seisflows.plugins import line_search as line_search_dir class NLCG(Gradient): """ - Nonlinear conjugate gradient method - - Optimization Variables: - m: model - f: objective function value - g: gradient direction - p: search direction - - Line Search Variables: - x: list of step lenths from current line search - f: correpsonding list of function values - m: number of step lengths in current line search - n: number of model updates in optimization problem - gtg: dot product of gradient with itself - gtp: dot product of gradient and search direction - - Status codes - status > 0 : finished - status == 0 : not finished - status < 0 : failed + [optimize.NLCG] Nonlinear conjugate gradient method + + :type nlcg_max: int + :param nlcg_max: NLCG periodic restart interval, should be between 1 + and infinity + :type nlcg_thresh: NLCG conjugacy restart threshold, should be + between 1 and infinity + :type calc_beta: str + :param calc_beta: method to calculate the parameter 'beta' in the + NLCG algorithm. Available: 'pollak_ribere', 'fletcher_reeves' """ + __doc__ = Gradient.__doc__ + __doc__ + def __init__(self, nlcg_max=np.inf, nlcg_thresh=np.inf, calc_beta="pollak_ribere", **kwargs): - """ - These parameters should not be set by the user. - Attributes are initialized as NoneTypes for clarity and docstrings. - - - :type nlcg_max: int - :param nlcg_max: NLCG periodic restart interval, should be between 1 - and infinity - :type nlcg_thresh: NLCG conjugacy restart threshold, should be - between 1 and infinity - :type calc_beta: str - :param calc_beta: method to calculate the parameter 'beta' in the - NLCG algorithm. Available: 'pollak_ribere', 'fletcher_reeves' - - - :type _NLCG_iter: Class - :param _NLCG_iter: an internally used iteration that differs from - optimization iter. Keeps track of internal NLCG memory. - """ + """NLCG-specific input parameters""" super().__init__(**kwargs) # Overwrite user-chosen line search. L-BFGS requires 'Backtrack'ing LS diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index 906005f8..95b208e2 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -168,10 +168,6 @@ def load(self, name): :type name: str :param name: name of the vector, acceptable: m, g, p, f, alpha - :type check: bool - :param check: if the model is a vector, check poissons ratio and list - out min and max for all parameters. This is really only useful for - elastic models, and not for kernels, gradients etc. """ assert(name in self._acceptable_vectors) model_npz = os.path.join(self.path.scratch, f"{name}.npz") diff --git a/seisflows/postprocess/__init__.py b/seisflows/postprocess/__init__.py deleted file mode 100644 index 5f49d2b7..00000000 --- a/seisflows/postprocess/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -import logging -from pkgutil import extend_path -__path__ = extend_path(__path__, __name__) -logger = logging.getLogger(__name__) - diff --git a/seisflows/postprocess/default.py b/seisflows/postprocess/default.py deleted file mode 100644 index 8ae37de9..00000000 --- a/seisflows/postprocess/default.py +++ /dev/null @@ -1,138 +0,0 @@ -#!/usr/bin/env python3 -""" -This class provides the core utilities for the SeisFlows postprocessing -functionalities, including kernel/gradient smoothing and masking as well as -kernel summation -""" -import os - -from seisflows import logger -from seisflows.tools.specfem import Model - - -class Default: - """ - Postprocessing in a Seisflows workflow includes tasks such as - regularization, smoothing, sharpening, masking and related operations - on models or gradients - """ - def __init__(self, smooth_h=0., smooth_v=0., tasktime_smooth=1, - path_postprocess=None, path_mask=None, **kwargs): - """ - Establish Postprocessing parameters - - :type smooth_h: float - :param smooth_h: Gaussian half-width for horizontal smoothing in units - of meters. If 0., no smoothing applied - :type smooth_h: float - :param smooth_v: Gaussian half-width for vertical smoothing in units - of meters. - :type tasktime_smooth: float - :param tasktime_smooth: Large radii smoothing may take longer than - normal tasks. Allocate additional smoothing task time as a multiple - of system.tasktime - :type path_postprocess: str - :param path_postprocess: scratch path to perform all postprocessing - tasks such as smoothing, kernel combination etc. - :type path_mask: str - :param path_mask: Directory to mask files for gradient masking. Format - of the mask files MUST match the format of the input model. - """ - super().__init__() - - self.smooth_h = smooth_h - self.smooth_v = smooth_v - self.tasktime_smooth = tasktime_smooth - self.path = path_postprocess or \ - os.path.join(os.getcwd(), "scratch", "evalgrad") - self.path_mask = path_mask - - def check(self): - """ - Checks parameters and paths - """ - if self.path_mask: - assert os.path.exists(self.path_mask), \ - "`postprocess.path_mask` provided but does not exist" - - def setup(self): - """ - Setup tasks - """ - pass - - def finalize(self): - """ - Finalization tasks - """ - pass - - def scale_gradient(self): - """ - Combines contributions from individual sources and material parameters - to get the gradient, and optionally applies user-supplied scaling - - .. note:: - Because processing operations can be quite expensive, they must be - run through the HPC system interface; processing does not involve - embarassingly parallel tasks, we use run(single=True) - - :type input_path: str - :param input_path: directory from which kernels are read and to which - gradient is written. Should probably point to PATH.GRAD - :rtype: np.array - :return: scaled gradient as a vector - """ - # Access the gradient information stored in as kernel files - model = Model(path=os.path.join(self.path, "model")) - gradient = Model(path=os.path.join(self.path, "kernels", "sum")) - - # Merge to vector and convert to absolute perturbations: - # log dm --> dm (see Eq.13 Tromp et al 2005) - gradient.vector *= model.vector - - if self.path_mask: - logger.info(f"masking gradient") - # to scale the gradient, users can supply "masks" by exactly - # mimicking the file format in which models are stored - mask = Model(self.path_mask) - - gradient.write(path=os.path.join(self.path, "gradient_nomask")) - - gradient.vector *= mask.vector - - return gradient - - def sum_smooth_kernels(self, solver): - """ - Sums kernels from individual sources, with optional smoothing - - .. note:: - This function needs to be run on system, i.e., called by - system.run(single=True) - - :type solver: solver instance - :param solver: SeisFlows solver which will be used for its combine and - smooth functions - """ - # If specified, smooth the kernels in the vertical and horizontal and - # save both (summed, summed+smoothed) to separate output directories - path_kernel = os.path.join(self.path, "kernels") - path_sum_nosmooth = os.path.join(path_kernel, "sum_nosmooth") - path_sum = os.path.join(path_kernel, "sum") - - if (self.smooth_h > 0) or (self.smooth_v > 0): - logger.debug(f"saving un-smoothed and summed kernels to:\n" - f"{path_sum_nosmooth}") - solver.combine(input_path=path_kernel, output_path=path_sum_nosmooth) - - logger.info(f"smoothing gradient: " - f"H={self.smooth_h}m; V={self.smooth_v}m") - logger.debug(f"saving smoothed kernels to:\n{path_sum}") - solver.smooth(input_path=path_sum_nosmooth, output_path=path_sum, - span_h=self.smooth_h, span_v=self.smooth_v) - - # Combine all the input kernels, generating the unscaled gradient - else: - logger.debug(f"saving summed kernels to:\n{path_sum}") - solver.combine(input_path=path_kernel, output_path=path_sum) diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 751e18a4..87db83c8 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -655,7 +655,8 @@ def _run_binary(self, executable, stdout="solver.log"): try: with open(stdout, "w") as f: - subprocess.run(executable, shell=True, check=True, stdout=f) + subprocess.run(executable, shell=True, check=True, stdout=f, + stderr=f) except (subprocess.CalledProcessError, OSError) as e: logger.critical( msg.cli("The external numerical solver has returned a " diff --git a/seisflows/system/cluster.py b/seisflows/system/cluster.py index 83888596..c9eaf145 100644 --- a/seisflows/system/cluster.py +++ b/seisflows/system/cluster.py @@ -3,33 +3,36 @@ The Cluster class provides the core utilities interaction with HPC systems which must be overloaded by subclasses for specific workload managers, or specific clusters. + +.. warning:: + The Cluster class is an abstract base class for the Systems module which and + MUST be overwritten by system-specific child classes, it cannot be used to + run jobs by itself. """ import subprocess -from seisflows.config import save from seisflows.system.workstation import Workstation class Cluster(Workstation): """ - Abstract base class for the Systems module which controls interaction with - compute systems such as HPC clusters. + [system.cluster] Generic or common HPC/cluster interfacing commands + + :type walltime: int + :param walltime: maximum job time in minutes for the master SeisFlows + job submitted to cluster + :type tasktime: int + :param tasktime: maximum job time in minutes for each job spawned by + the SeisFlows master job during a workflow. These include, e.g., + running the forward solver + :type environs: str + :param environs: Optional environment variables to be provided in the + following format VAR1=var1,VAR2=var2... Will be set using + os.environs """ + __doc__ = Workstation.__doc__ + __doc__ + def __init__(self, walltime=10, tasktime=1, environs="", **kwargs): - """ - Instantiate the Cluster System class - - :type walltime: int - :param walltime: maximum job time in minutes for the master SeisFlows - job submitted to cluster - :type tasktime: int - :param tasktime: maximum job time in minutes for each job spawned by - the SeisFlows master job during a workflow. These include, e.g., - running the forward solver - :type environs: str - :param environs: Optional environment variables to be provided in the - following format VAR1=var1,VAR2=var2... Will be set using - os.environs - """ + """Instantiate the Cluster System class""" super().__init__(**kwargs) self.walltime = walltime diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index 6ebe6574..d700a440 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -27,21 +27,21 @@ class Slurm(Cluster): """ - Generalized interface for submitting jobs to and interfacing with a SLURM - workload management system. + [system.slurm] Interface for submitting jobs to Simple Linux Utility for + Resource Management (SLURM) system. + + :type ntask_max: int + :param ntask_max: limit the number of concurrent tasks in a given + array job + :type slurm_args: str + :param slurm_args: Any optional, additional SLURM arguments that will + be passed to the SBATCH scripts. Should be in the form: + '--key1=value1 --key2=value2" """ + __doc__ = Cluster.__doc__ + __doc__ + def __init__(self, ntask_max=100, slurm_args="", **kwargs): - """ - Slurm-specific setup parameters - - :type ntask_max: int - :param ntask_max: limit the number of concurrent tasks in a given - array job - :type slurm_args: str - :param slurm_args: Any optional, additional SLURM arguments that will - be passed to the SBATCH scripts. Should be in the form: - '--key1=value1 --key2=value2" - """ + """Slurm-specific setup parameters""" super().__init__(**kwargs) # Overwrite the existing 'mpiexec' diff --git a/seisflows/system/workstation.py b/seisflows/system/workstation.py index fc557f61..6d4f22b8 100644 --- a/seisflows/system/workstation.py +++ b/seisflows/system/workstation.py @@ -17,40 +17,37 @@ class Workstation: """ - Run tasks in a serial fashion on a single local machine. Also serves as the - Base System module, upon which all other System classes should be built. + [system.workstation] runs tasks in serial on a local machine. + + :type title: str + :param title: The name used to submit jobs to the system, defaults + to the name of the current working directory + :type mpiexec: str + :param mpiexec: Function used to invoke executables on the system. + For example 'srun' on SLURM systems. If None this will default to + './' for calling executables. + :type ntask: int + :param ntask: number of individual tasks/events to run during workflow + :type nproc: int + :param nproc: number of processors to use for each simulation + :type log_level: str + :param log_level: logger level to pass to logging module. + Available: 'debug', 'info', 'warning' + :type verbose: bool + :param verbose: if True, formats the log messages to include the file + name, line number and message type. Useful for debugging but + also very verbose + :type path_output: str + :param path_output: path to save files permanently to disk + :type path_system: str + :param path_system: scratch path to save any system related files """ def __init__(self, title=None, mpiexec=None, ntask=1, nproc=1, log_level="DEBUG", verbose=False, workdir=os.getcwd(), - path_output=None, - path_system=None, path_output_log=None, path_error_log=None, - path_log_files=None, path_par_file=None, **kwargs): - """ - Workstation System Class Parameters - - :type title: str - :param title: The name used to submit jobs to the system, defaults - to the name of the current working directory - :type mpiexec: str - :param mpiexec: Function used to invoke executables on the system. - For example 'srun' on SLURM systems. If None this will default to - './' for calling executables. - :type ntask: int - :param ntask: number of individual tasks/events to run during workflow - :type nproc: int - :param nproc: number of processors to use for each simulation - :type log_level: str - :param log_level: logger level to pass to logging module. - Available: 'debug', 'info', 'warning' - :type verbose: bool - :param verbose: if True, formats the log messages to include the file - name, line number and message type. Useful for debugging but - also very verbose - :type path_output: str - :param path_output: path to save files permanently to disk - :type path_system: str - :param path_system: scratch path to save any system related files - """ + path_output=None, path_system=None, path_output_log=None, + path_error_log=None, path_log_files=None, path_par_file=None, + **kwargs): + """Workstation System Class Parameters""" self.title = title self.mpiexec = mpiexec self.ntask = ntask @@ -66,7 +63,6 @@ def __init__(self, title=None, mpiexec=None, ntask=1, nproc=1, log_files=path_log_files or os.path.join(workdir, "logs"), output_log=path_output_log or os.path.join(workdir, "sfoutput.log"), error_log=path_error_log or os.path.join(workdir, "sferror.log"), - ) def check(self): diff --git a/seisflows/tests/test_config.py b/seisflows/tests/test_config.py index 03a2fa8a..cc725546 100644 --- a/seisflows/tests/test_config.py +++ b/seisflows/tests/test_config.py @@ -3,13 +3,10 @@ system and the working environment required for SF to run properly """ import os -import sys import shutil import pytest -from unittest.mock import patch + from seisflows import config -from seisflows.tools.core import SeisFlowsPathsParameters -from seisflows.seisflows import SeisFlows TEST_DIR = os.path.join(config.ROOT_DIR, "tests") @@ -27,24 +24,6 @@ def copy_par_file(tmpdir): shutil.copy(src, dst) -@pytest.fixture -def sfinit(tmpdir, copy_par_file): - """ - Re-used function that will initate a SeisFlows working environment in - sys modules - :return: - """ - copy_par_file - os.chdir(tmpdir) - with patch.object(sys, "argv", ["seisflows"]): - sf = SeisFlows() - sf._register_parameters() - sf._register_modules() - sf._check_parameters() - - return sf - - def test_seisflows_constants(): """ Ensure that the constants set in the Config file have not changed @@ -52,8 +31,7 @@ def test_seisflows_constants(): because the rest of the package depends on these being accesible and the same """ - names_check = ["system", "preprocess", "solver", - "postprocess", "optimize", "workflow"] + names_check = ["system", "preprocess", "solver", "optimize", "workflow"] root_dir_check = os.path.join( os.path.dirname(os.path.abspath(__file__)), ".." @@ -63,69 +41,11 @@ def test_seisflows_constants(): assert(os.path.samefile(config.ROOT_DIR, root_dir_check)) -def test_register_modules(sfinit): - """ - Make sure that initiation of the modular approach of seisflows works - as expected. That is, that system-wide accessible modules are - instantiated with the accepted naming schema - - .. note:: - This assumes that the parameter file is set up correctly, which it - should be if it's coming from the test data directory - :return: - """ - sf = sfinit - # Ensure that all the modules in NAMES have been instantiated in sys.modules - for name in config.NAMES: - assert(f"seisflows_{name}" in sys.modules) - - -def test_save_and_load(sfinit): - """ - Test saving the current session to disk - :return: - """ - # Instantiate sys modules and save to disk - sfinit - config.save(path="./output") - # Now remove seisflows sys modules so we can try load them back - for name in config.NAMES: - sys.modules.pop(f"seisflows_{name}") - config.load(path="./output") - for name in config.NAMES: - assert(f"seisflows_{name}" in sys.modules) - - -def test_seisflows_paths_parameters(sfinit): - """ - Test the class that makes inputting and checking paths and parameters easier - Recreates the required() function at the top of each class. - """ - sfinit - sfpp = SeisFlowsPathsParameters() - - # All of these parameters are defined in the test parameter file - sfpp.par("SOLVER", required=True, par_type=str, - docstr="This is a required parameter") - sfpp.par("MIN_PERIOD", required=False, default=10., par_type=float, - docstr="This is an optional parameter") - sfpp.path("SPECFEM_BIN", required=True, docstr="This is a required path") - sfpp.path("LOCAL", required=False, docstr="This is an optional path") - sfpp.validate() - - # These parameters are not defined and are expected to throw parameter error - sfpp.path("UNDEFINED", required=True, - docstr="This path is not in the test parameter file") - with pytest.raises(KeyError): - sfpp.validate() - - -def test_custom_import(sfinit): +def test_custom_import(): """ Test that importing based on internal modules works for various inputs :return: """ - sfinit with pytest.raises(SystemExit): config.custom_import() with pytest.raises(SystemExit): @@ -136,8 +56,8 @@ def test_custom_import(sfinit): assert(module.__module__ == "seisflows.optimize.LBFGS") # Check one more to be safe - module = config.custom_import(name="preprocess", module="pyatoa") - assert(module.__name__ == "Pyatoa") - assert(module.__module__ == "seisflows.preprocess.pyatoa") + module = config.custom_import(name="preprocess", module="default") + assert(module.__name__ == "Default") + assert(module.__module__ == "seisflows.preprocess.default") diff --git a/seisflows/tests/test_optimize.py b/seisflows/tests/test_optimize.py new file mode 100644 index 00000000..139597f9 --- /dev/null +++ b/seisflows/tests/test_optimize.py @@ -0,0 +1,2 @@ + + diff --git a/seisflows/tests/test_preprocess.py b/seisflows/tests/test_preprocess.py index aace73ae..6702e12e 100644 --- a/seisflows/tests/test_preprocess.py +++ b/seisflows/tests/test_preprocess.py @@ -77,16 +77,16 @@ def test_quantify_misfit(tmpdir): data_filenames = glob(os.path.join(TEST_DATA, "*semd")) preprocess.quantify_misfit( observed=data_filenames, synthetic=data_filenames, - write_residuals=os.path.join(tmpdir, "residuals_ascii"), - write_adjsrcs=tmpdir + save_residuals=os.path.join(tmpdir, "residuals_ascii"), + save_adjsrcs=tmpdir ) preprocess.data_format = "SU" data_filenames = glob(os.path.join(TEST_DATA, "*su")) preprocess.quantify_misfit( observed=data_filenames, synthetic=data_filenames, - write_residuals=os.path.join(tmpdir, "residuals_su"), - write_adjsrcs=tmpdir + save_residuals=os.path.join(tmpdir, "residuals_su"), + save_adjsrcs=tmpdir ) assert(len(glob(os.path.join(tmpdir, "*"))) == 4) diff --git a/seisflows/tests/test_solver.py b/seisflows/tests/test_solver.py index a73b387b..075b2ed6 100644 --- a/seisflows/tests/test_solver.py +++ b/seisflows/tests/test_solver.py @@ -6,6 +6,7 @@ import pytest from glob import glob from seisflows.config import ROOT_DIR +from seisflows.tools import unix from seisflows.tools.core import set_task_id from seisflows.solver.specfem import Specfem @@ -54,7 +55,6 @@ def test_initialize_working_directory(tmpdir): path_specfem_bin=specfem_bin, source_prefix="CMTSOLUTION", workdir=tmpdir ) - assert(not os.path.exists(solver.path.mainsolver)) # Set the environment task id so that the logger doesn't throw warnings @@ -62,7 +62,9 @@ def test_initialize_working_directory(tmpdir): set_task_id(0) # Generate the required directory structure - solver._initialize_working_directory(cwd=tmpdir) + solver._initialize_working_directory( + cwd=os.path.join(solver.path.scratch, "001") + ) # Simple checks to make sure the directory structure was set up properly assert(os.path.islink(solver.path.mainsolver)) @@ -74,19 +76,22 @@ def test_initialize_working_directory(tmpdir): assert(event_line == "EVENT 1") -def test_run_binary(): +def test_run_binary(tmpdir): """ Just run a known and intentially incorrect binary with the run binary function to check that we can, and that error catching is working """ solver = Specfem() - solver._run_binary(executable="echo hello world") + solver._run_binary(executable="echo hello world", + stdout=os.path.join(tmpdir, "log.txt")) # Executables that don't exist will not run with pytest.raises(SystemExit): - solver._run_binary(executable="gobbledigook") + solver._run_binary(executable="gobbledigook", + stdout=os.path.join(tmpdir, "log.txt")) # Executables that do exist but error out (e.g., with invalid options) will # also throw an error with pytest.raises(SystemExit): - solver._run_binary(executable="ls -//daflkjeaf") + solver._run_binary(executable="ls -//daflkjeaf", + stdout=os.path.join(tmpdir, "log.txt")) diff --git a/seisflows/tools/specfem.py b/seisflows/tools/specfem.py index 222fd516..532a58f4 100644 --- a/seisflows/tools/specfem.py +++ b/seisflows/tools/specfem.py @@ -67,7 +67,9 @@ def __init__(self, path, fmt=None, parameters=None, load=False): self.nproc, self.available_parameters = self._get_nproc_parameters() self.model, self.ngll = self.read(parameters=parameters) - self.parameters = self.model.keys() + # .sorted() enforces parameter order every time, otherwise things can + # get screwy if keys returns different each time + self.parameters = sorted(self.model.keys()) @staticmethod def fnfmt(i="*", val="*", ext="*"): diff --git a/seisflows/workflow/migration.py b/seisflows/workflow/migration.py index 51bf5796..b7c73681 100644 --- a/seisflows/workflow/migration.py +++ b/seisflows/workflow/migration.py @@ -7,7 +7,6 @@ model """ import os -from glob import glob from seisflows import logger from seisflows.tools import msg, unix diff --git a/seisflows/workflow/thrifty_inversion.py b/seisflows/workflow/thrifty_inversion.py index aa5e4f13..b8322075 100644 --- a/seisflows/workflow/thrifty_inversion.py +++ b/seisflows/workflow/thrifty_inversion.py @@ -7,41 +7,40 @@ the previous iteration's line search can be used in the current one. Otherwise it performs the same as the Inversion workflow """ -import sys -import logging - +from seisflows import logger from seisflows.workflow.inversion import Inversion from seisflows.tools import unix, msg class ThriftyInversion(Inversion): """ - Thrifty inversion which attempts to save resources by re-using previous - line search results for the current iteration. + [workflow.thrifty_inversion] an inversion that attempts to save resources + by re-using previous line search results for the current iteration. + + :type line_search_method: str + :param line_search_method: chosen line_search algorithm. Currently available + are 'bracket' and 'backtrack'. See seisflows.plugins.line_search + for all available options """ - def __init__(self): - """ - :type thrifty: bool - :param thrifty: the current status of the inversion. - if False: assumed to be first iteration, a restart, or some other - condition has been met which means inversion is defaulting to normal - behavior - if True: A well-scaled inversion can skip the function evaluation - of the next iteration by using the line search results of the - previous iteration + __doc__ = Inversion.__doc__ + __doc__ + + def __init__(self, line_search_method): + """Thrifty does not require input parameters + """ super().__init__() - self.thrifty = False + self.line_search_method = line_search_method + self._thrifty_status = False - def check(self, validate=True): + def check(self): """ - Checks parameters and paths + Checks that we have the correct line search """ - super().check(validate=validate) + super().check() - assert self.par.LINESEARCH.upper() == "BACKTRACK", ( - "Thrifty inversion requires PAR.LINESEARCH == 'backtrack'" + assert(self.line_search_method.title() == "Backtrack"), ( + "Thrifty inversion requires `line_search_method` == 'backtrack'" ) def evaluate_initial_misfit(self): @@ -49,53 +48,50 @@ def evaluate_initial_misfit(self): If line search can be carried over, skip initialization step Or if manually starting a new run, start with normal inversion init """ - optimize = self.module("optimize") - - if not self.thrifty or optimize.iter == self.par.BEGIN: + if not self._thrifty_status or (self.optimize.iteration == self.start): super().evaluate_initial_misfit() else: - self.logger.info(msg.mjr("SKIPPING INITIAL MISFIT EVALUATION")) + logger.info(msg.mnr("THRIFTY INVERSION, SKIPPING INITIAL MISFIT " + "EVALUATION")) - def clean(self): + def clean_scratch_directory(self): """ Determine if forward simulation from line search can be carried over. We assume clean() is the final flow() argument so that we can update the thrifty status here. """ - self._update_status() + self._thrifty_status = self._update_status() - if self.thrifty: - self.logger.info( + if self._thrifty_status: + logger.info( msg.mnr("THRIFTY CLEANING WORKDIR FOR NEXT ITERATION") ) - unix.rm(self.path.GRAD) + unix.rm(self.path.eval_grad) # Last line search evaluation becomes the new gradient evaluation - unix.mv(self.path.FUNC, self.path.GRAD) - unix.mkdir(self.path.FUNC) + unix.mv(self.path.eval_func, self.path.eval_grad) + unix.mkdir(self.path.eval_func) else: - super().clean() + super().clean_scratch_directory() def _update_status(self): """ Determine if line search forward simulation can be carried over based on a variety of criteria relating to location in the inversion. """ - optimize = self.module("optimize") - - self.logger.info("updating thrifty inversion status") - if optimize.iter == self.par.BEGIN: - self.logger.info("1st iteration, defaulting to inversion workflow") + logger.info("updating thrifty inversion status") + if self.optimize.iter == self.start: + logger.info("1st iteration, defaulting to inversion workflow") thrifty = False - elif optimize.restarted: - self.logger.info("optimization has been restarted, defaulting to " - "inversion workflow") + elif self.optimize.restarted: + logger.info("optimization has been restarted, defaulting to " + "inversion workflow") thrifty = False - elif optimize.iter == self.par.END: - self.logger.info("final iteration, defaulting to inversion workflow") + elif self.optimize.iter == self.end: + logger.info("final iteration, defaulting to inversion workflow") thrifty = False else: - self.logger.info("continuing with thrifty inversion workflow") + logger.info("continuing with thrifty inversion workflow") thrifty = True - self.thrifty = thrifty + return thrifty From 9513931a2691dc2318e79670dff6ba1308876c7b Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Wed, 20 Jul 2022 16:46:29 -0800 Subject: [PATCH 073/195] implementing a new cluster run system which saves individual methods to be 'run' as pickle files, and then loads and runs those from system. this is opposed to the old system of saving all modules as pickle files, and reloading to sys modules, finally detaching seisflows from the old sys modules implementation rewrote the slurm job status checker to be simpler to call and use. makes use of some additional sbatch parameters to keep stdout output of submitted jobs cleaner removed remnants of the old sys modules approach, including all the register() functions in the seisflows CLI tool working on seisflows CLI tool, getting 'configure' to work again but this time calling the __doc__ attributes of instances rather than trying to build from the old seisflowspathsparameters class --- seisflows/__init__.py | 45 +++- seisflows/config.py | 172 +++++++------ seisflows/examples/parameters.yaml | 2 +- seisflows/seisflows.py | 184 ++------------ seisflows/solver/specfem.py | 81 +++--- seisflows/solver/specfem2d.py | 7 +- seisflows/solver/specfem3d.py | 8 +- seisflows/solver/specfem3d_globe.py | 4 +- seisflows/system/cluster.py | 122 +++++---- seisflows/system/runscripts/run | 2 +- seisflows/system/runscripts/run_funcs.py | 97 ++++++++ seisflows/system/runscripts/run_function.py | 121 --------- seisflows/system/slurm.py | 260 ++++++-------------- seisflows/system/workstation.py | 82 ++---- seisflows/tests/test_solver.py | 13 - seisflows/tests/test_system.py | 0 seisflows/tools/core.py | 43 ++-- seisflows/workflow/forward.py | 3 + 18 files changed, 492 insertions(+), 754 deletions(-) create mode 100755 seisflows/system/runscripts/run_funcs.py delete mode 100644 seisflows/system/runscripts/run_function.py create mode 100644 seisflows/tests/test_system.py diff --git a/seisflows/__init__.py b/seisflows/__init__.py index 0ebf0e99..681646e4 100644 --- a/seisflows/__init__.py +++ b/seisflows/__init__.py @@ -1,4 +1,7 @@ +import copyreg import logging +import types + from pkgutil import extend_path # Extend the search path for the modules which comprise a package. @@ -7,6 +10,46 @@ # different parts of a single logical package as multiple directories. __path__ = extend_path(__path__, __name__) - # Set up the SeisFlows Logging environment logger = logging.getLogger(__name__) + + +def _pickle_method(method): + """ + The following code changes how instance methods are handled by pickle. + Placing it in this module ensures that pickle changes will be in + effect for all SeisFlows workflows + + Note: For relevant discussion, see stackoverflow thread: + "Can't pickle when using python's + multiprocessing Pool.map()" + + Relevant Links (last accessed 01.20.2020): + https://stackoverflow.com/questions/7016567/ + picklingerror-when-using-multiprocessing + + https://bytes.com/topic/python/answers/ + 552476-why-cant-you-pickle-instancemethods + """ + func_name = method.im_func.__name__ + obj = method.im_self + cls = method.im_class + return _unpickle_method, (func_name, obj, cls) + + +def _unpickle_method(func_name, obj, cls): + """ + The unpickling counterpart to the above function + """ + for cls in cls.mro(): + try: + func = cls.__dict__[func_name] + except KeyError: + pass + else: + break + return func.__get__(obj, cls) + + +copyreg.pickle(types.MethodType, _pickle_method, _unpickle_method) + diff --git a/seisflows/config.py b/seisflows/config.py index 710b9cd7..bfe0b642 100755 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -40,7 +40,8 @@ """ # List of module names required by SeisFlows for imports. Order-sensitive # In sys.modules these will be prepended by 'seisflows_', e.g., seisflows_system -NAMES = ["system", "preprocess", "solver", "optimize", "workflow"] +# NAMES = ["system", "preprocess", "solver", "optimize", "workflow"] +NAMES = ["workflow", "system", "solver", "preprocess", "optimize"] # The location of this config file, which is the main repository ROOT_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__))) @@ -49,52 +50,6 @@ """ -def save(path): - """ - Export the current Python environment to disk as Pickle and JSON files, - which allows us to checkpoint a current workflow and resume without - loss of information. - - :type path: str - :param path: path to save the current session - """ - if not os.path.exists(path): - unix.mkdir(path) - - # Save the paths and parameters into a JSON file - for name in ["seisflows_parameters", "seisflows_paths"]: - fullfile = os.path.join(path, f"{name}.json") - with open(fullfile, "w") as f: - json.dump(sys.modules[name], f, sort_keys=True, indent=4) - - # Save the current workflow as pickle objects - for name in NAMES: - fullfile = os.path.join(path, f"seisflows_{name}.p") - with open(fullfile, "wb") as f: - pickle.dump(sys.modules[f"seisflows_{name}"], f) - - -def load(path): - """ - Imports a previously saved session from disk by reading in JSON and - Pickle files which define a saved Python environment - - :type path: str - :param path: path to the previously saved session - """ - # Load parameters and paths from a JSON file - for name in ["seisflows_parameters", "seisflows_paths"]: - fullfile = os.path.join(os.path.abspath(path), f"{name}.json") - with open(fullfile, "r") as f: - sys.modules[name] = Dict(json.load(f)) - - # Load the saved workflow from pickle objects - for name in NAMES: - fullfile = os.path.join(os.path.abspath(path), f"seisflows_{name}.p") - with open(fullfile, "rb") as f: - sys.modules[f"seisflows_{name}"] = pickle.load(f) - - def import_seisflows(workdir=os.getcwd(), parameter_file="parameters.yaml"): """ Standard SeisFlows workflow setup block which runs some standard setup @@ -236,7 +191,6 @@ class 'Inversion'. "is implemented correctly, where name must be in the following:", items=NAMES, header="custom import error", border="=")) sys.exit(-1) - sys.exit(-1) # Attempt to retrieve currently assigned classname from parameters if module is None: try: @@ -285,43 +239,87 @@ class 'Inversion'. f"class: seisflows.{name}.{module}.{classname}")) sys.exit(-1) - -def _pickle_method(method): - """ - The following code changes how instance methods are handled by pickle. - Placing it in this module ensures that pickle changes will be in - effect for all SeisFlows workflows - - Note: For relevant discussion, see stackoverflow thread: - "Can't pickle when using python's - multiprocessing Pool.map()" - - Relevant Links (last accessed 01.20.2020): - https://stackoverflow.com/questions/7016567/ - picklingerror-when-using-multiprocessing - - https://bytes.com/topic/python/answers/ - 552476-why-cant-you-pickle-instancemethods - """ - func_name = method.im_func.__name__ - obj = method.im_self - cls = method.im_class - return _unpickle_method, (func_name, obj, cls) - - -def _unpickle_method(func_name, obj, cls): - """ - The unpickling counterpart to the above function - """ - for cls in cls.mro(): - try: - func = cls.__dict__[func_name] - except KeyError: - pass - else: - break - return func.__get__(obj, cls) - - -copyreg.pickle(types.MethodType, _pickle_method, _unpickle_method) +# def save(path): +# """ +# Export the current Python environment to disk as Pickle and JSON files, +# which allows us to checkpoint a current workflow and resume without +# loss of information. +# +# :type path: str +# :param path: path to save the current session +# """ +# if not os.path.exists(path): +# unix.mkdir(path) +# +# # Save the paths and parameters into a JSON file +# for name in ["seisflows_parameters", "seisflows_paths"]: +# fullfile = os.path.join(path, f"{name}.json") +# with open(fullfile, "w") as f: +# json.dump(sys.modules[name], f, sort_keys=True, indent=4) +# +# # Save the current workflow as pickle objects +# for name in NAMES: +# fullfile = os.path.join(path, f"seisflows_{name}.p") +# with open(fullfile, "wb") as f: +# pickle.dump(sys.modules[f"seisflows_{name}"], f) +# +# +# def load(path): +# """ +# Imports a previously saved session from disk by reading in JSON and +# Pickle files which define a saved Python environment +# +# :type path: str +# :param path: path to the previously saved session +# """ +# # Load parameters and paths from a JSON file +# for name in ["seisflows_parameters", "seisflows_paths"]: +# fullfile = os.path.join(os.path.abspath(path), f"{name}.json") +# with open(fullfile, "r") as f: +# sys.modules[name] = Dict(json.load(f)) +# +# # Load the saved workflow from pickle objects +# for name in NAMES: +# fullfile = os.path.join(os.path.abspath(path), f"seisflows_{name}.p") +# with open(fullfile, "rb") as f: +# sys.modules[f"seisflows_{name}"] = pickle.load(f) + +# def _pickle_method(method): +# """ +# The following code changes how instance methods are handled by pickle. +# Placing it in this module ensures that pickle changes will be in +# effect for all SeisFlows workflows +# +# Note: For relevant discussion, see stackoverflow thread: +# "Can't pickle when using python's +# multiprocessing Pool.map()" +# +# Relevant Links (last accessed 01.20.2020): +# https://stackoverflow.com/questions/7016567/ +# picklingerror-when-using-multiprocessing +# +# https://bytes.com/topic/python/answers/ +# 552476-why-cant-you-pickle-instancemethods +# """ +# func_name = method.im_func.__name__ +# obj = method.im_self +# cls = method.im_class +# return _unpickle_method, (func_name, obj, cls) +# +# +# def _unpickle_method(func_name, obj, cls): +# """ +# The unpickling counterpart to the above function +# """ +# for cls in cls.mro(): +# try: +# func = cls.__dict__[func_name] +# except KeyError: +# pass +# else: +# break +# return func.__get__(obj, cls) + + +# copyreg.pickle(types.MethodType, _pickle_method, _unpickle_method) diff --git a/seisflows/examples/parameters.yaml b/seisflows/examples/parameters.yaml index 003090d1..2caee39d 100644 --- a/seisflows/examples/parameters.yaml +++ b/seisflows/examples/parameters.yaml @@ -25,7 +25,7 @@ # POSTPROCESS (str): Postprocessing schema for kernels and gradients # ============================================================================== SYSTEM: workstation -WORKFLOW: inversion +WORKFLOW: forward SOLVER: specfem2d OPTIMIZE: gradient PREPROCESS: default diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 199cfd38..8e740cb6 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -15,7 +15,6 @@ """ import os import sys -import pickle import inspect import logging import warnings @@ -25,8 +24,8 @@ from glob import glob from IPython import embed -from seisflows.config import (custom_import, save, NAMES, ROOT_DIR, - config_logger) +from seisflows import logger +from seisflows.config import custom_import, NAMES, ROOT_DIR, config_logger from seisflows.tools import unix, msg from seisflows.tools.core import load_yaml, Dict from seisflows.tools.specfem import (getpar, setpar, getpar_vel_model, @@ -389,153 +388,6 @@ def _public_methods(self): """ return [_ for _ in dir(self) if not _.startswith("_")] - def _register_parameters(self): - """ - Load the paths and parameters from file into sys.modules, set the - default parameters if they are missing from the file, and expand all - paths to absolute pathnames. Also configure the logger. - - .. note:: - This is ideally the FIRST thing that happens everytime SeisFlows - is initiated. The package cannot do anything without the resulting - PATH and PARAMETER variables. - """ - # Check if the filepaths exist - if not os.path.exists(self._args.parameter_file): - print(msg.cli(f"SeisFlows parameter file not found: " - f"'{self._args.parameter_file}'. Run 'seisflows " - f"setup' to create a new parameter file.") - ) - sys.exit(-1) - - # Register parameters from the parameter file - try: - parameters = load_yaml(self._args.parameter_file) - except Exception as e: - print(msg.cli(f"Please check that your parameter file is properly " - f"formatted in the YAML format. The read error is:", - items=[str(e)], header="parameter file read error", - border="=")) - sys.exit(-1) - - # Distribute the paths and parameters internally and separately - # If we are running seisflows configure, paths will be empty - try: - paths = parameters.pop("PATHS") - except KeyError: - paths = {} - - # WORKDIR needs to be set here as it's expected by most modules - if "WORKDIR" not in paths: - paths["WORKDIR"] = self._args.workdir - - # Parameter file is set here as well so that it can be user-defined - if "PAR_FILE" not in paths: - paths["PAR_FILE"] = self._args.parameter_file - - # Expand all paths to be absolute on the filesystem - for key, val in paths.items(): - try: - paths[key] = os.path.expanduser(os.path.abspath(val)) - except TypeError: - continue - - # Register parameters to sys and internally - sys.modules["seisflows_parameters"] = Dict(parameters) - sys.modules["seisflows_paths"] = Dict(paths) - self._paths = Dict(paths) - self._parameters = Dict(parameters) - - def _register_modules(self): - """ - First time setup procedure which loads in the user-chosen modules - and registers them into sys.modules so that they are globally accessible - to the program. - - :type check: bool - :param check: run the check() function for each of the instantiated - modules, which essentially checks the validity of all the - user-defined parameters. This is typically wanted, but sometimes - you don't want to check, e.g., during testing when you know - some parameters are set incorrectly - """ - assert(self._paths is not None), ( - f"seisflows._register_parameters() must be run before " - f"_register_modules()" - ) - assert(self._parameters is not None), ( - f"seisflows._register_parameters() must be run before " - f"_register_modules()" - ) - # Check if current workflow exists on disk, exit so as to not overwrite - if "OUTPUT" in self._paths and os.path.exists(self._paths.OUTPUT): - print(msg.cli( - "Data from previous workflow found in working directory.", - items=["> seisflows restart: delete data, start new workflow", - "> seisflows resume: resume existing workflow"], - header="warning", border="=") - ) - sys.exit(-1) - - # Instantiate and register objects - for name in NAMES: - sys.modules[f"seisflows_{name}"] = custom_import(name)() - - # Bare minimum Module requirements for SeisFlows - req_modules = ["WORKFLOW", "SYSTEM"] - for req in req_modules: - if not hasattr(self._parameters, req): - print(msg.cli(f"SeisFlows requires modules: {req_modules}." - "Please specify these in the parameter file. Use " - "'seisflows print module' to determine suitable " - "choices.", header="error", border="=")) - sys.exit(-1) - - def _check_parameters(self): - """ - Runs the .check() function on each of the modules, which validates the - given parameters in a parameter file to ensure that a workflow will not - break unexpectedly - """ - errors = [] - for name in NAMES: - try: - sys.modules[f"seisflows_{name}"].check() - except AssertionError as e: - errors.append(f"{name}: {e}") - if errors: - print(msg.cli("seisflows.config module check failed with:", - items=errors, header="module check error", - border="=")) - sys.exit(-1) - - def _load_modules(self): - """ - A function to load and check each of the SeisFlows modules, - re-initiating the SeisFlows environment. All modules are reliant on one - another so any access to SeisFlows requires loading everything - simultaneously and in correct order. - - .. note:: - This is similar to config.load() except it doesn't load paths - and parameters. This allows the User to OVERLOAD the currently - defined paths and parameters anytime they call 'seisflows resume' - """ - for NAME in NAMES: - fid = os.path.join(self._paths.OUTPUT, f"seisflows_{NAME}.p") - - if not os.path.exists(fid): - print(msg.cli("Not a SeisFlows working directory (no state " - "files found). Run 'seisflows init' or " - "'seisflows submit' to instantiate a working " - "directory.") - ) - sys.exit(-1) - - with open(fid, "rb") as f: - sys.modules[f"seisflows_{NAME}"] = pickle.load(f) - self._check_parameters() - def setup(self, force=False, **kwargs): """ Initiate a SeisFlows working directory from scratch by establishing a @@ -549,7 +401,7 @@ def setup(self, force=False, **kwargs): :type force: bool :param force: flag to force parameter file overwriting """ - PAR_FILE = os.path.join(ROOT_DIR, "examples", "parameters.yaml") + par_file = os.path.join(ROOT_DIR, "examples", "parameters.yaml") if os.path.exists(self._args.parameter_file): if force: @@ -565,7 +417,7 @@ def setup(self, force=False, **kwargs): else: sys.exit(0) - unix.cp(PAR_FILE, self._args.workdir) + unix.cp(par_file, self._args.workdir) print(msg.cli(f"creating parameter file: {self._args.parameter_file}")) def configure(self, absolute_paths=False, **kwargs): @@ -582,8 +434,7 @@ def configure(self, absolute_paths=False, **kwargs): # Load in a barebones parameter file and instantiate specific classes parameters = load_yaml(os.path.join(self._args.workdir, self._args.parameter_file)) - classes = [custom_import(name, parameters[name])() for name in NAMES] - + modules = [custom_import(name, parameters[name])() for name in NAMES] # If writing to parameter file fails for any reason, the file will be # mangled, create a temporary copy that can be re-instated upon failure @@ -591,13 +442,30 @@ def configure(self, absolute_paths=False, **kwargs): unix.cp(self._args.parameter_file, temp_par_file) try: - import pdb;pdb.set_trace() - except Exception as e: + written = [] + f = open(self._args.parameter_file, "a") + for module in modules: + # Write the docstring + f.write(f"# {'=' * 77}\n#") + f.write(module.__doc__.replace("\n", "\n#")) + f.write(f"\n# {'=' * 77}\n") + # Write the parameters, make sure to not have the same one twice + for key, val in vars(module).items(): + # Skip already written, hidden vars, and paths + if (key in written) or key.startswith("_"): + continue + if val is None: + val = "null" # required by YAML + f.write(f"{key}: {val}\n") + written.append(key) + except Exception: unix.rm(self._args.parameter_file) unix.cp(temp_par_file, self._args.parameter_file) - print(msg.cli(text="seisflows configure traceback", header="error")) + logger.critical( + msg.cli(text="seisflows configure traceback", header="error") + ) print(traceback.format_exc()) - sys.exit(-1) + raise else: unix.rm(temp_par_file) diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 87db83c8..d388b86a 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -131,8 +131,9 @@ def __init__(self, data_format="ascii", materials="acoustic", if self.density: self._parameters.append("rho") - self._source_names = None - self._io = getattr(solver_io_dir, self.solver_io) + self._source_names = None # for property source_names + self._ext = None # for database file extensions + self._io = getattr(solver_io_dir, self.solver_io) # for database IO # Define available choices for check parameters self._available_model_types = ["gll"] @@ -158,6 +159,7 @@ def check(self): ) # Make sure we can read in the model/kernel/gradient files + # TODO is this even used? Can we remove? assert hasattr(solver_io_dir, self.solver_io) assert hasattr(self._io, "read_slice"), \ "IO method has no attribute 'read'" @@ -170,7 +172,7 @@ def check(self): assert(dir_ is not None), f"SPECFEM path '{name}' cannot be None" assert(os.path.exists(dir_)), f"SPECFEM path '{name}' must exist" - # Check that the required SPECFEM files are available + # Check that the required SPECFEM files are available in DATA/ for fid in ["STATIONS", "Par_file"]: assert(os.path.exists(os.path.join(self.path.specfem_data, fid))), ( f"DATA/{fid} does not exist but is required by SeisFlows solver" @@ -195,29 +197,26 @@ def check(self): assert(model_type in self._available_model_types), \ f"{model_type} not in available types {self._available_model_types}" + # Assign file extensions to be used for database file searching + if model_type == "gll": + self._ext = ".bin" + + # Make sure the initial model is set and actually contains files assert(self.path.model_init is not None and os.path.exists(self.path.model_init)), \ f"`path_model_init` is required for the solver, but does not exist" - # Check that the number of tasks/events matches the number of events - self._source_names = self._check_source_names() + assert(len(glob(os.path.join(self.path.model_init, "*")))), \ + f"`path_model_init` is empty but should have model files" - @property - def taskid(self): - """ - Returns the currently running process for embarassingly parallelized - tasks. Task IDs are assigned to the environment by system.run(). - Task IDs are simply integer values from 0 to the number of - simultaneously running tasks. + if self.path.model_true is not None: + assert(os.path.exists(self.path.model_true)), \ + f"`path_model_true` is provided but does not exist" + assert(len(glob(os.path.join(self.path.model_true, "*")))), \ + f"`path_model_true` is empty but should have model files" - .. note:: - Dependent on environment variable 'SEISFLOWS_TASKID' which is - assigned by system.run() to each individually running process. - - :rtype: int - :return: task id for given solver - """ - return get_task_id() + # Check that the number of tasks/events matches the number of events + self._source_names = self._check_source_names() @property def source_names(self): @@ -249,7 +248,7 @@ def source_name(self): :rtype: str :return: given source name for given task id """ - return self.source_names[self.taskid] + return self.source_names[get_task_id()] @property def cwd(self): @@ -334,7 +333,8 @@ def model_databases(self): versions must overwrite this function :rtype: str - :return: path where SPECFEM2D database files are stored + :return: path where SPECFEM2D database files are stored, relative to + `solver.cwd` """ return "DATA" @@ -343,12 +343,16 @@ def model_files(self): """ Return a list of paths to model files that match the internal parameter list. Used to generate model vectors of the same length as gradients. + + :rtype: list + :return: a list of full paths to model files that matches the internal + list of solver parameters """ _model_files = [] for parameter in self._parameters: _model_files += glob(os.path.join(self.path.mainsolver, self.model_databases, - f"*{parameter}*.bin")) + f"*{parameter}{self._ext}")) return _model_files @property @@ -362,7 +366,8 @@ def kernel_databases(self): versions must overwrite this function :rtype: str - :return: path where SPECFEM2D database files are stored + :return: path where SPECFEM2D database files are stored, relative to + `solver.cwd` """ return "OUTPUT_FILES" @@ -372,16 +377,14 @@ def setup(self): Sets up directory structure expected by SPECFEM and copies or generates seismic data to be inverted or migrated - - TODO the .bin during model export assumes GLL file format, more general? """ self._initialize_working_directories() # Export the initial model to the SeisFlows output directory - unix.mkdir(self.path.output) + dst = os.path.join(self.path.output, "MODEL_INIT", "") + unix.mkdir(dst) for key in self._parameters: - src = glob(os.path.join(self.path.model_init, f"*{key}.bin")) - dst = os.path.join(self.path.output, "MODEL_INIT") + src = glob(os.path.join(self.path.model_init, f"*{key}{self._ext}")) unix.cp(src, dst) def forward_simulation(self, executables=None, save_traces=False, @@ -493,24 +496,26 @@ def adjoint_simulation(self, executables=None, save_kernels=False, self._run_binary(executable=exc, stdout=stdout) # Rename kernels to work w/ conflicting name conventions + # Change directory so that the rename doesn't affect the full path unix.cd(self.kernel_databases) - logger.debug(f"renaming event kernels for {self.source_name}") + logger.debug(f"renaming output event kernels: 'alpha' -> 'vp'") for tag in ["alpha", "alpha[hv]", "reg1_alpha", "reg1_alpha[hv]"]: - names = glob(f"*proc??????_{tag}_kernel.bin") + names = glob(f"*proc??????_{tag}_kernel{self._ext}") unix.rename(old="alpha", new="vp", names=names) + logger.debug(f"renaming output event kernels: 'alpha' -> 'vp'") for tag in ["beta", "beta[hv]", "reg1_beta", "reg1_beta[hv]"]: - names = glob(f"*proc??????_{tag}_kernel.bin") + names = glob(f"*proc??????_{tag}_kernel{self._ext}") unix.rename(old="beta", new="vs", names=names) # Save and export the kernels to user-defined locations if export_kernels: unix.mkdir(export_kernels) - unix.cp(src=glob("*_kernel.bin"), dst=export_kernels) + unix.cp(src=glob(f"*_kernel{self._ext}"), dst=export_kernels) if save_kernels: unix.mkdir(save_kernels) - unix.mv(src=glob("*_kernel.bin"), dst=save_kernels) + unix.mv(src=glob(f"*_kernel{self._ext}"), dst=save_kernels) def combine(self, input_path, output_path, parameters=None): """ @@ -518,7 +523,7 @@ def combine(self, input_path, output_path, parameters=None): Sums kernels from individual source contributions to create gradient. .. note:: - The binary xcombine_sem simply sums matching databases (.bin) + The binary xcombine_sem simply sums matching databases .. note:: It is ASSUMED that this function is being called by @@ -708,7 +713,7 @@ def import_model(self, path_model): unix.cd(self.cwd) # Copy the model files (ex: proc000023_vp.bin ...) into database dir - src = glob(os.path.join(path_model, "*")) + src = glob(os.path.join(path_model, f"*{self._ext}")) dst = os.path.join(self.cwd, self.model_databases, "") unix.cp(src, dst) @@ -753,10 +758,8 @@ def _initialize_working_directory(self, cwd=None): if cwd is None: cwd = self.cwd source_name = self.source_name - taskid = self.taskid else: source_name = os.path.basename(cwd) - taskid = self.source_names.index(source_name) logger.debug(f"initializing solver directory source: {source_name}") @@ -784,7 +787,7 @@ def _initialize_working_directory(self, cwd=None): unix.ln(src, dst) # Symlink TaskID==0 as mainsolver in solver directory for convenience - if taskid == 0: + if self.source_names.index(source_name) == 0: if not os.path.exists(self.path.mainsolver): logger.debug(f"linking source '{source_name}' as 'mainsolver'") unix.ln(cwd, self.path.mainsolver) diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index 888a8436..e60e902e 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -1,9 +1,7 @@ #!/usr/bin/env python3 """ -This is the subclass seisflows.solver.specfem2d - This class provides utilities for the Seisflows solver interactions with -Specfem2D. It inherits all attributes from seisflows.solver.Base, +Specfem2D. """ import os from glob import glob @@ -89,4 +87,5 @@ def smooth(self, input_path, output_path, parameters=None, span_h=0., unix.cp(src=src, dst=input_path) super().smooth(input_path=input_path, output_path=output_path, - parameters=parameters, span_h=span_h, span_v=span_v) + parameters=parameters, span_h=span_h, span_v=span_v, + use_gpu=use_gpu) diff --git a/seisflows/solver/specfem3d.py b/seisflows/solver/specfem3d.py index 21357b2b..1715e03f 100644 --- a/seisflows/solver/specfem3d.py +++ b/seisflows/solver/specfem3d.py @@ -1,9 +1,7 @@ #!/usr/bin/env python3 """ -This is the subclass seisflows.solver.Specfem3D This class provides utilities for the Seisflows solver interactions with -Specfem3D Cartesian. It inherits all attributes from seisflows.solver.Base, -and overwrites these functions to provide specified interaction with Specfem3D +Specfem3D Cartesian. """ import os @@ -31,9 +29,9 @@ def __init__(self, source_prefix="CMTSOLUTION", **kwargs): # Define parameters based on material type if self.materials.upper() == "ACOUSTIC": - self.parameters += ["vp"] + self._parameters += ["vp"] elif self.materials.upper() == "ELASTIC": - self.parameters += ["vp", "vs"] + self._parameters += ["vp", "vs"] # Overwriting the base class parameters self._acceptable_source_prefixes = ["CMTSOLUTION", "FORCESOLUTION"] diff --git a/seisflows/solver/specfem3d_globe.py b/seisflows/solver/specfem3d_globe.py index 8fcbf027..7e8a5561 100644 --- a/seisflows/solver/specfem3d_globe.py +++ b/seisflows/solver/specfem3d_globe.py @@ -1,9 +1,7 @@ #!/usr/bin/env python3 """ -This is the subclass seisflows.solver.specfem3d_globe This class provides utilities for the Seisflows solver interactions with -Specfem3D Globe. It inherits all attributes from seisflows.solver.specfem3d, -and overwrites these functions to provide specified interaction with Specfem3D. +Specfem3D Globe. SPECFEM3D_Globe specfic notes: - does not allow SU seismogram outputs, only ASCII, SAC, ASDF, 3D_Array diff --git a/seisflows/system/cluster.py b/seisflows/system/cluster.py index c9eaf145..f74973e0 100644 --- a/seisflows/system/cluster.py +++ b/seisflows/system/cluster.py @@ -9,14 +9,24 @@ MUST be overwritten by system-specific child classes, it cannot be used to run jobs by itself. """ +import os +import dill import subprocess +from seisflows import logger +from seisflows.config import ROOT_DIR from seisflows.system.workstation import Workstation class Cluster(Workstation): """ - [system.cluster] Generic or common HPC/cluster interfacing commands - + [system.cluster] generic or common HPC/cluster interfacing commands + + :type title: str + :param title: The name used to submit jobs to the system, defaults + to the name of the current working directory + :type mpiexec: str + :param mpiexec: Function used to invoke executables on the system. + For example 'srun' on SLURM systems. :type walltime: int :param walltime: maximum job time in minutes for the master SeisFlows job submitted to cluster @@ -31,67 +41,87 @@ class Cluster(Workstation): """ __doc__ = Workstation.__doc__ + __doc__ - def __init__(self, walltime=10, tasktime=1, environs="", **kwargs): + def __init__(self, title=None, mpiexec="", walltime=10, tasktime=1, + environs="", **kwargs): """Instantiate the Cluster System class""" super().__init__(**kwargs) + if title is None: + self.title = os.path.basename(os.getcwd()) + else: + self.title = title + self.mpiexec = mpiexec self.walltime = walltime self.tasktime = tasktime self.environs = environs - def submit(self, workflow, submit_call=None): + def _pickle_func_list(self, funcs, **kwargs): """ - Main insertion point of SeisFlows onto the compute system. - - .. rubric:: - $ seisflows submit + Save a list of functions and their keyword arguments as pickle files. + Return the names of the files for the run() function. .. note:: - The expected behavior of the submit() function is to: - 1) run system setup, creating directory structure, - 2) execute workflow by submitting workflow.main() - - :type submit_call: str - :param submit_call: the command line workload manager call to be run by - subprocess. These need to be passed in by specific workload manager - subclasses. + The idea here is that we need this list of functions to be + discoverable by a system separate to the one that defined them. To + do this we can pickle Python objects on disk, and have the new + system read in the pickle files and evaluate the objects. We use + 'dill' because Pickle can't serialize methods/functions + + :type funcs: list of methods + :param funcs: a list of functions that should be run in order. All + kwargs passed to run() will be passed into the functions. """ - self.setup() - workflow.checkpoint() - # check==True: subprocess will wait for workflow.main() to finish - subprocess.run(submit_call, shell=True, check=True) + # Save the instances that define the functions as a pickle object + func_names = "_".join([_.__name__ for _ in funcs]) # unique identifier + fid_funcs_pickle = os.path.join(self.path.scratch, f"{func_names}.p") - def run(self, classname, method, single=False, **kwargs): - """ - Runs a task multiple times in parallel + with open(fid_funcs_pickle, "wb") as f: + dill.dump(obj=funcs, file=f) - .. note:: - The expected behavior of the run() function is to: submit N jobs to - the system in parallel. For example, in a simulation step, run() - submits N jobs to the compute system where N is the number of - events requiring an adjoint simulation. - - :type classname: str - :param classname: the class to run - :type method: str - :param method: the method from the given `classname` to run + # Save the kwargs as a separate pickle object + fid_kwargs_pickle = os.path.join(self.path.scratch, + f"{func_names}_kwargs.p") + with open(fid_kwargs_pickle, "wb") as f: + dill.dump(obj=kwargs, file=f) + + return fid_funcs_pickle, fid_kwargs_pickle + + def run(self, funcs, single=False, run_call=None, **kwargs): + """ + Runs tasks multiple times in parallel by submitting NTASK new jobs to + system. The list of functions and its kwargs are saved as pickles files, + and then re-loaded by each submitted process with specific environment + variables. Each spawned process will run the list of functions. + + :type funcs: list of methods + :param funcs: a list of functions that should be run in order. All + kwargs passed to run() will be passed into the functions. :type single: bool :param single: run a single-process, non-parallel task, such as smoothing the gradient, which only needs to be run by once. This will change how the job array and the number of tasks is defined, such that the job is submitted as a single-core job to the system. + :type run_call: str + :param run_call: the call used to submit the run script. If None, + attempts default run call which should be suited for the given + system """ - raise NotImplementedError('Must be implemented by subclass.') - - def taskid(self): - """ - Provides a unique identifier for each running task. This is - compute system specific. - - :rtype: int - :return: this function is expected to return a unique numerical - identifier. - """ - raise NotImplementedError('Must be implemented by subclass.') - + funcs_fid, kwargs_fid = self._pickle_func_list(funcs, **kwargs) + logger.info(f"running functions {[_.__name__ for _ in funcs]} on " + f"system {self.ntask} times") + + if run_call is None: + run_call = " ".join([ + f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'run')}", + f"--funcs {funcs_fid}", + f"--kwargs {kwargs_fid}", + f"--environment SEISFLOWS_TASKID={{task_id}},{self.environs}" + ]) + logger.debug(run_call) + + for task_id in range(self.ntask): + logger.debug(f"running task id {task_id} " + f"(job {task_id + 1}/{self.ntask})") + # Subprocess waits for the process to end before running the next + subprocess.run(run_call.format(task_id=task_id), shell=True) diff --git a/seisflows/system/runscripts/run b/seisflows/system/runscripts/run index e862dedf..e017cbf0 120000 --- a/seisflows/system/runscripts/run +++ b/seisflows/system/runscripts/run @@ -1 +1 @@ -run_function.py \ No newline at end of file +run_funcs.py \ No newline at end of file diff --git a/seisflows/system/runscripts/run_funcs.py b/seisflows/system/runscripts/run_funcs.py new file mode 100755 index 00000000..f3cb61d1 --- /dev/null +++ b/seisflows/system/runscripts/run_funcs.py @@ -0,0 +1,97 @@ +#!/usr/bin/env python3 +""" +Only required when system==cluster (or any subclass of cluster) + +This script is a wrapper for running tasks on systems during an active workflow. +Loads in functions/methods and their keyword arguments from pickle files which +have been written by system.run(). Runs functions in order of list. + +.. note:: + Not to be called by the user, this script is to be called by system.run() + +.. rubric:: + >> python run --funcs function_list.p --kwargs kwarg_dict.p +""" +import argparse +import dill +import os + + +def parse_args(): + """ + Get command line arguments + """ + parser = argparse.ArgumentParser("Run arguments for system submitted tasks") + + parser.add_argument("-f", "--funcs", type=str, nargs="?", required=True, + help="path to pickle file containing a list of " + "functions/methods that should be run by the " + "submitted process" + ) + parser.add_argument("-k", "--kwargs", type=str, nargs="?", required=False, + default=None, + help="path to pickle file containing a dictionary of " + "keyword argumnets that should be passed to the " + "functions") + parser.add_argument("-e", "--environment", type=str, nargs="?", + required=False, + help="Optional comma-separated environment variables, " + "which should be given as " + "VARNAME1=value1,VARNAME2=value2 and so on. These " + "will be separated and instantiated into Python's " + "os.environ") + + return parser.parse_args() + + +def export(myenv): + """ + Exports comma delimited list of environment variables also allows deleting + environment variables by providing VARNAME with no corresponding value + + e.g. VARNAME1=value1,VARNAME2=value2,VARNAME3 + will add VARNAME1 and VARNAME2 to the environment with corresponding values, + and remove VARNAME3 from the environment + + .. note:: + The ability to delete environment variables came from the Maui upgrade + to Slurm 21.08, which enforced mutually exclusivity of --mem-per-cpu + and --mem-per-node, which are both defined on cross-cluster submissions. + We needed a mechanism to remove one of these + + :type myenv: str + :param myenv: the system environment to take variables from + """ + for item in myenv.split(","): + if item: + try: + key, val = item.split("=") + os.environ[key] = val + # Variables to be deleted will not split on '=', throwing ValueError + except ValueError: + del os.environ[item] + + +if __name__ == '__main__': + """Runs task within a currently executing workflow """ + args = parse_args() + + if args.environment: + export(args.environment) + + # Load the functions + with open(args.funcs, "rb") as f: + funcs = dill.load(f) + + # Load the kwargs. Optional, if No kwargs then funcs will be run bare + if args.kwargs is not None: + with open(args.kwargs, "rb") as f: + kwargs = dill.load(f) + else: + kwargs = {} + + # Evaluate the function with given keyword arguments + for func in funcs: + func(**kwargs) + + diff --git a/seisflows/system/runscripts/run_function.py b/seisflows/system/runscripts/run_function.py deleted file mode 100644 index 164dc289..00000000 --- a/seisflows/system/runscripts/run_function.py +++ /dev/null @@ -1,121 +0,0 @@ -#!/usr/bin/env python3 -""" -Only required when system==cluster (or any subclass of cluster) - -This script is a wrapper for running tasks on systems during an active workflow. -Acts as a Python script to submit certain SeisFlows functions or tasks to a -compute system. - -.. note:: - Not to be called by the user, this script is to be called by system.run() - -.. rubric:: - >> python run --output ./OUTPUT --classname solver \ - --funcname eval_func - OR - >> sbatch run --output ./OUTPUT --classname solver --funcname eval_func -""" -import os -import sys -import pickle -import argparse - -from seisflows.config import load, config_logger - - -def parse_args(): - """ - Get command line arguments - """ - parser = argparse.ArgumentParser("Run arguments for system submitted tasks") - parser.add_argument("-o", "--output", type=str, nargs="?", required=True, - help="the SeisFlows output directory used to load the " - "active working state from inside the compute node" - ) - parser.add_argument("-c", "--classname", type=str, nargs="?", required=True, - help="the SeisFlows class from within which the " - "desired function is defined. Available options " - "are defined in seisflows.config.NAMES" - ) - parser.add_argument("-f", "--funcname", type=str, nargs="?", required=True, - help="the function name from the chosen `classname`. " - "This function will be executed on the compute " - "node") - parser.add_argument("-e", "--environment", type=str, nargs="?", - required=False, - help="Optional comma-separated environment variables, " - "which should be given as " - "VARNAME1=value1,VARNAME2=value2 and so on. These " - "will be separated and instantiated into Python's " - "os.environ") - - return parser.parse_args() - - -def export(myenv): - """ - Exports comma delimited list of environment variables also allows deleting - environment variables by providing VARNAME with no corresponding value - - e.g. VARNAME1=value1,VARNAME2=value2,VARNAME3 - will add VARNAME1 and VARNAME2 to the environment with corresponding values, - and remove VARNAME3 from the environment - - .. note:: - The ability to delete environment variables came from the Maui upgrade - to Slurm 21.08, which enforced mutually exclusivity of --mem-per-cpu - and --mem-per-node, which are both defined on cross-cluster submissions. - We needed a mechanism to remove one of these - - :type myenv: str - :param myenv: the system environment to take variables from - """ - for item in myenv.split(","): - try: - key, val = item.split("=") - os.environ[key] = val - # Variables to be deleted will not split on '=', throwing the ValueError - except ValueError: - del os.environ[item] - - -if __name__ == '__main__': - """ - Runs task within a currently executing workflow - """ - args = parse_args() - - if args.environment: - export(args.environment) - - # Load the last checkpointed working state from the 'seisflows_?.p` files - # Allowing access through sys.modules - load(args.output) - - # Load keyword arguments required by this function - # Files will be something like: 'solver_eval_func.p' - kwargs_fid = f"{args.classname}_{args.funcname}.p" - kwargs_path = os.path.join(args.output, "kwargs", kwargs_fid) - with open(kwargs_path, "rb") as f: - kwargs = pickle.load(f) - - # Load in some of the working state from sys.modules - PAR = sys.modules["seisflows_parameters"] - PATH = sys.modules["seisflows_paths"] - system = sys.modules["seisflows_system"] - - # Configure the CPU-dependent logger which will log to stdout only - # But mainsolver will log to the main log file as well - if system.taskid == 0: - filename = PATH.LOGFILE - else: - filename = None - config_logger(level=PAR.LOG_LEVEL, verbose=PAR.VERBOSE, filename=filename) - - # Get the actual function so we can evaluate it - func = getattr(sys.modules[f"seisflows_{args.classname}"], args.funcname) - - # Evaluate the function with given keyword arguments - func(**kwargs) - - diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index d700a440..f18e4b43 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -72,34 +72,29 @@ def submit(self, submit_call=None): if submit_call is None: submit_call = " ".join([ f"sbatch", - f"{self.slurmargs or ''}", + f"{self.slurm_args or ''}", f"--job-name={self.title}", - f"--output={self.path_output_log}", - f"--error={self.path_error_log}", + f"--output={self.path.output_log}", + f"--error={self.path.error_log}", f"--ntasks-per-node={self.node_size}", f"--nodes=1", f"--time={self.walltime:d}", f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'submit')}", - f"--output {self.path_output}" + f"--output {self.path.output}" ]) logger.debug(submit_call) super().submit(submit_call=submit_call) - def run(self, classname, method, single=False, run_call=None, **kwargs): + def run(self, funcs, single=False, run_call=None, **kwargs): """ Runs task multiple times in embarrassingly parallel fasion on a SLURM cluster. Executes classname.method(*args, **kwargs) `NTASK` times, each time on `NPROC` CPU cores - .. note:: - The actual CLI call structure looks something like this - $ sbatch --args scripts/run OUTPUT class method environs - - :type classname: str - :param classname: the class to run - :type method: str - :param method: the method from the given `classname` to run + :type funcs: list of methods + :param funcs: a list of functions that should be run in order. All + kwargs passed to run() will be passed into the functions. :type single: bool :param single: run a single-process, non-parallel task, such as smoothing the gradient, which only needs to be run by once. @@ -111,7 +106,9 @@ def run(self, classname, method, single=False, run_call=None, **kwargs): can overload the sbatch command line input by setting run_call. If set to None, default run_call will be set here. """ - self.save_kwargs_to_disk(self.path_output, classname, method, kwargs) + funcs_fid, kwargs_fid = self._pickle_func_list(funcs, **kwargs) + logger.info(f"running functions {[_.__name__ for _ in funcs]} on " + f"system {self.ntask} times") # Default sbatch command line input, can be overloaded by subclasses # Copy-paste this default run_call and adjust accordingly for subclass @@ -124,15 +121,14 @@ def run(self, classname, method, single=False, run_call=None, **kwargs): f"--ntasks-per-node={self.node_size:d}", f"--ntasks={self.nproc:d}", f"--time={self.tasktime:d}", - f"--output={os.path.join(self.path_log_files, '%A_%a')}", - f"--array=0-{self.natsk-1 % self.ntaskmax}", + f"--output={os.path.join(self.path.log_files, '%A_%a')}", + f"--array=0-{self.ntask-1}%{self.ntask_max}", + f"--parsable", # keeps stdout cleaner f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'run')}", - f"--output {self.path_output}", - f"--classname {classname}", - f"--funcname {method}", + f"--funcs {funcs_fid}", + f"--kwargs {kwargs_fid}", f"--environment {self.environs or ''}" ]) - logger.debug(run_call) # Single-process jobs simply need to replace a few sbatch arguments. @@ -143,196 +139,86 @@ def run(self, classname, method, single=False, run_call=None, **kwargs): "process job") run_call = _modify_run_call_single_proc(run_call) - # The standard response from SLURM when submitting jobs - # is something like 'Submitted batch job 441636', we want job number - stdout = subprocess.run(run_call, stdout=subprocess.PIPE, + # Stdout will be job number. Federated clusters will return job # and + # cluster name (e.g., 1234;Cluster1), so split that off, only want job # + job_id = subprocess.run(run_call, stdout=subprocess.PIPE, text=True, shell=True).stdout - - # Continuously check for job completion on ALL running array jobs - job_ids = self._job_id_list(stdout, single) - job_id, status = self._check_job_status(job_ids) - if status != "OKAY": - print(msg.cli((f"Stopping workflow for {status} job. " - f"Please check log file for details."), - items=[f"TASK: {classname}.{method}", - f"TASK ID: {job_id}", - f"LOG: logs/{job_id}", - f"SBATCH: {run_call}"], - header="slurm run error", border="=")) + job_id = str(job_id).split(";")[0] + + # Monitor the job queue until all jobs have completed, or any one fails + status = check_job_status(job_id) + if status == -1: # Failed job + logger.critical( + msg.cli(f"Stopping workflow. Please check logs for details.", + items=[f"TASKS: {[_.__name_ for _ in funcs]}", + f"SBATCH: {run_call}"], + header="slurm run error", border="=") + ) sys.exit(-1) - - logger.info(f"task {classname}.{method} finished successfully") - - def taskid(self): - """ - Provides a unique identifier for each running task - - :rtype: int - :return: identifier for a given task - """ - # If not set, this environment variable will return None - sftaskid = os.getenv("SEISFLOWS_TASKID") - - if sftaskid is None: - sftaskid = os.getenv("SLURM_ARRAY_TASK_ID") - if sftaskid is None: - print(msg.cli("system.taskid() environment variable not found. " - "Assuming DEBUG mode and returning taskid==0. " - "If not DEBUG mode, please check SYSTEM.run()", - header="warning", border="=")) - sftaskid = 0 - - return int(sftaskid) - - def _check_job_status(self, job_ids): - """ - Repeatedly check the status of a currently running job using 'sacct'. - If the job goes into a bad state like 'FAILED', return the failing - job's id and the state. If all jobs complete nominally, - return state=="OKAY" - - :type job_ids: list - :param job_ids: list of running jobs to check using SACCT - :rtype: tuple (int, str) - :return: (job_id, state) state=="OKAY" if all jobs complete, else it - will be a bad state. - """ - is_done = False - count = 0 - bad_states = ["TIMEOUT", "FAILED", "NODE_FAIL", "OUT_OF_MEMORY", - "CANCELLED"] - while not is_done: - is_done, states = job_array_status(job_ids) - # EXIT CONDITION: if any of the jobs provide job failure codes - if not is_done: - for i, state in enumerate(states): - # Sometimes states can be something like 'CANCELLED+', so - # we can't do exact string matching, check partial matches - if any([check in state for check in bad_states]): - return job_ids[i], state - # WAIT CONDITION: if sacct is not working, we'll get stuck in a loop - if "UNDEFINED" in states: - count += 1 - # Every 10 counts, warn the user this is unexpected behavior - if not count % 10: - job_id = job_ids[states.index("UNDEFINED")] - logger.warning(f"SLURM command 'sacct {job_id}' has " - f"returned unexpected response {count} " - f"times. This job may have failed " - f"unexpectedly. Consider checking " - f"manually") - # Wait a bit to avoid rapidly querying sacct - time.sleep(5) - - return None, "OKAY" - - def _job_id_list(self, stdout, single): - """ - Parses job id list from sbatch standard output. Stdout typically looks - like: 'Submitted batch job 441636', but if submitting jobs cross-cluster - (e.g., like on Maui), stdout might be: - 'Submitted batch job 441636 on cluster Maui' - - .. note:: - In order to find the job number, we just scan each word in stdout - until we find the number, ASSUMING that there is only one number in - the string - - TODO Should failing to return job_id break in reasonable way? - - The output job arrays will look something like: - [44163_0, 44163_1, ..., 44163_self.par.NTASK] - - :type stdout: str - :param stdout: the text response from running 'sbatch' on SLURM, which - should be returned by subprocess.run(stdout=PIPE) - :type single: bool - :param single: if running a single process job, returns a list of length - 1 with a single job id, else returns a list of length self.par.NTASK - for all arrayed jobs - :rtype: list - :return: a list of array jobs that should be currently running - """ - if single: - ntask = 1 else: - ntask = self.ntask - - # Splitting e.g.,: 'Submitted batch job 441636\n' - for part in stdout.strip().split(): - try: - # The int will keep throwing ValueError until we find the num - job_id = int(part) - break - except ValueError: - continue - return [f"{job_id}_{i}" for i in range(ntask)] + logger.info(f"tasks finished successfully") -def job_array_status(job_ids): +def check_job_status(job_id): """ - Determines current status of job or job array - - :type job_ids: list - :param job_ids: list of SLURM job id numbers to check completion of - Will not return unless all jobs have completed - :rtype is_done: bool - :return is_done: True if all jobs in the array have been completed - :rtype states: list - :return states: list of states returned from sacct - """ - states = [] - for job_id in job_ids: - state = check_job_state(job_id) - states.append(state.upper()) - - # All array jobs must be completed to return is_done == True - is_done = all([state.upper() == "COMPLETED" for state in states]) + Repeatedly check the status of a currently running job using 'sacct'. + If the job goes into a bad state like 'FAILED', log the failing + job's id and their states. If all jobs complete nominally, return - return is_done, states + :type job_id: str + :param job_id: main job id to query, returned from the subprocess.run that + ran the jobs + :rtype: int + :return: status of all running jobs. 1 for pass (all jobs COMPLETED). -1 for + fail (one or more jobs returned failing status) + """ + bad_states = ["TIMEOUT", "FAILED", "NODE_FAIL", + "OUT_OF_MEMORY", "CANCELLED"] + while True: + job_ids, states = query_job_states(job_id) + if [_ == "COMPLETED" for _ in states]: + return 1 # Pass + elif any([check in states for check in bad_states]): + logger.info("atleast 1 system job returned a failing exit code") + for job_id, state in zip(job_ids, states): + if state in bad_states: + logger.debug(f"{job_id}: {state}") + return -1 # Fail + else: + time.sleep(5) # Don't overload 'sacct' command -def check_job_state(job_id): +def query_job_states(job_id): """ - Queries completion status of a single job by running: - $ sacct -nL -o jobid,state -j {job_id} + Queries completion status of an array job by running: + $ sacct -nLX -o jobid,state -j {job_id} # Example outputs from this sacct command - # JOB_ID STATUS - 441630_0 PENDING # array job will have the array number - 441630 COMPLETED # if --array=0-0, jobs will not have suffix - 441628.batch COMPLETED # we don't want to check these + 441630_0 PENDING + 441630_1 COMPLETED Available job states: https://slurm.schedmd.com/sacct.html .. note:: - -L flag in sacct queries all available clusters, not just the - cluster that ran the `sacct` call - -X supress the .batch and .extern jobname + -L: queries all available clusters, not just the cluster that ran the + `sacct` call + -X: supress the .batch and .extern jobnames that are normally returned + but don't represent that actual running job :type job_id: str - :param job_id: job id to query + :param job_id: main job id to query, returned from the subprocess.run that + ran the jobs """ + job_ids, job_states = [], [] cmd = f"sacct -nLX -o jobid,state -j {job_id}" stdout = subprocess.run(cmd, stdout=subprocess.PIPE, text=True, shell=True).stdout + for job_line in str(stdout).strip().split("\n"): + job_id, job_state = job_line.split() + job_ids.append(job_id) + job_states.append(job_state) - # Undefined status will be retured if we cannot match the job id with - # the sacct output - state = "UNDEFINED" - lines = stdout.strip().split("\n") - for line in lines: - # expecting e.g., 441628 COMPLETED - try: - job_id_check, state = line.split() - # str.split() will throw ValueError on non-matching strings - except ValueError: - continue - # Use in to allow for array jobs to match job ids - if job_id in job_id_check: - break - - return state + return job_ids, job_states def _modify_run_call_single_proc(run_call): diff --git a/seisflows/system/workstation.py b/seisflows/system/workstation.py index 6d4f22b8..04f9eb94 100644 --- a/seisflows/system/workstation.py +++ b/seisflows/system/workstation.py @@ -4,13 +4,10 @@ Provides utilities for submitting jobs in serial on a single machine """ import os -import sys -import pickle from contextlib import redirect_stdout from seisflows import logger from seisflows.tools.core import Dict -from seisflows.config import save from seisflows.tools import unix from seisflows.tools.core import number_fid, get_task_id, set_task_id @@ -19,13 +16,6 @@ class Workstation: """ [system.workstation] runs tasks in serial on a local machine. - :type title: str - :param title: The name used to submit jobs to the system, defaults - to the name of the current working directory - :type mpiexec: str - :param mpiexec: Function used to invoke executables on the system. - For example 'srun' on SLURM systems. If None this will default to - './' for calling executables. :type ntask: int :param ntask: number of individual tasks/events to run during workflow :type nproc: int @@ -42,14 +32,11 @@ class Workstation: :type path_system: str :param path_system: scratch path to save any system related files """ - def __init__(self, title=None, mpiexec=None, ntask=1, nproc=1, - log_level="DEBUG", verbose=False, workdir=os.getcwd(), - path_output=None, path_system=None, path_output_log=None, - path_error_log=None, path_log_files=None, path_par_file=None, - **kwargs): + def __init__(self, ntask=1, nproc=1, log_level="DEBUG", verbose=False, + workdir=os.getcwd(), path_output=None, path_system=None, + path_output_log=None, path_error_log=None, path_log_files=None, + path_par_file=None, **kwargs): """Workstation System Class Parameters""" - self.title = title - self.mpiexec = mpiexec self.ntask = ntask self.nproc = nproc self.log_level = log_level @@ -118,22 +105,22 @@ def setup(self): logger.debug(f"copying par/log file to: {dst}") unix.cp(src=src, dst=dst) - def submit(self, workflow, submit_call=None): - """ - Submits the main workflow job as a serial job submitted directly to - the compute node that is running the master job - - TO DO fix this - - :type submit_call: str or None - :param submit_call: the command line workload manager call to be run by - subprocess. This is only needed for overriding classes, it has no - effect on the Workstation class - """ - self.setup() - workflow = sys.modules["seisflows_workflow"] - workflow.checkpoint() - workflow.main() + # def submit(self, workflow, submit_call=None): + # """ + # Submits the main workflow job as a serial job submitted directly to + # the compute node that is running the master job + # + # TO DO fix this + # + # :type submit_call: str or None + # :param submit_call: the command line workload manager call to be run by + # subprocess. This is only needed for overriding classes, it has no + # effect on the Workstation class + # """ + # self.setup() + # workflow = sys.modules["seisflows_workflow"] + # workflow.checkpoint() + # workflow.main() def run(self, funcs, single=False, **kwargs): """ @@ -142,10 +129,9 @@ def run(self, funcs, single=False, **kwargs): .. note:: kwargs will be passed to the underlying `method` that is called - :type classname: str - :param classname: the class to run - :type method: str - :param method: the method from the given `classname` to run + :type funcs: list of methods + :param funcs: a list of functions that should be run in order. All + kwargs passed to run() will be passed into the functions. :type single: bool :param single: run a single-process, non-parallel task, such as smoothing the gradient, which only needs to be run by once. @@ -178,25 +164,3 @@ def run(self, funcs, single=False, **kwargs): with redirect_stdout(f): for func in funcs: func(**kwargs) - - def save_kwargs_to_disk(self, path, classname, method, kwargs): - """ - Writes keyword arguments for a given method to disk - - :type path: str - :param path: path to save the checkpointed pickle files to - :type classname: str - :param classname: name of the class to save - :type method: str - :param method: the specific function to be checkpointed - :type kwargs: dict - :param kwargs: dictionary to pass to object saving - """ - argspath = os.path.join(path, "kwargs") - argsfile = os.path.join(argspath, f"{classname}_{method}.p") - - unix.mkdir(argspath) - with open(argsfile, "wb") as f: - pickle.dump(kwargs, f) - - save(path=self.path.output) \ No newline at end of file diff --git a/seisflows/tests/test_solver.py b/seisflows/tests/test_solver.py index 075b2ed6..377d418f 100644 --- a/seisflows/tests/test_solver.py +++ b/seisflows/tests/test_solver.py @@ -6,7 +6,6 @@ import pytest from glob import glob from seisflows.config import ROOT_DIR -from seisflows.tools import unix from seisflows.tools.core import set_task_id from seisflows.solver.specfem import Specfem @@ -14,18 +13,6 @@ TEST_DATA = os.path.join(ROOT_DIR, "tests", "test_data", "test_solver") -def test_taskid(): - """ - Make sure that task id returns correctly - - TODO move this into test_utils - """ - solver = Specfem() - assert(solver.taskid == 0) - os.environ["SEISFLOWS_TASKID"] = "9" - assert(solver.taskid == 9) - - def test_source_names(): """ Check that source names are established correctly diff --git a/seisflows/tests/test_system.py b/seisflows/tests/test_system.py new file mode 100644 index 00000000..e69de29b diff --git a/seisflows/tools/core.py b/seisflows/tools/core.py index 5ccd0781..c895e19d 100644 --- a/seisflows/tools/core.py +++ b/seisflows/tools/core.py @@ -8,6 +8,10 @@ import numpy as np from seisflows import logger +# Acceptable environment variables assigned to individually running tasks when +# running SeisFlows on a system +ENV_VARIABLES = ["SEISFLOWS_TASKID", "SLURM_ARRAY_TASK_ID"] + class Dict(dict): """ @@ -68,48 +72,29 @@ def __delattr__(self, key): return self -class TaskIDError(Exception): - """ - A specific error that gets called when tasks are not run on system, - i.e., when we can't find 'SEISFLOWS_TASKID' in the environment variables. - This means we are attempting to access child process variables inside - the parent process. - """ - pass - - -def get_task_id(force=False): +def get_task_id(): """ Task IDs are assigned to each child process spawned by the system module during a SeisFlows workflow. SeisFlows modules use this Task ID to keep track of embarassingly parallel process, e.g., solver uses the Task ID to determine which source is being considered. - :type force: bool - :param force: If no task id is found, force set it to 0 :rtype: int :return: task id for given solver - :raises TaskIDError: if no environment variable is found """ - _taskid = os.getenv("SEISFLOWS_TASKID") - if _taskid is None: - logger.warning("Environment variable 'SEISFLOWS_TASKID' not found. " - "Assigning Task ID == 0") - _taskid = 0 - # if force: - # _taskid = 0 - # logger.warning("Environment variable 'SEISFLOWS_TASKID' not found. " - # "Assigning Task ID == 0") - # else: - # raise TaskIDError("Environment variable 'SEISFLOWS_TASKID' not " - # "found. Please make sure the process asking " - # "for task id is called by system.") - return int(_taskid) + for env_var in ENV_VARIABLES: + _taskid = os.getenv(env_var) + if _taskid is not None: + return int(_taskid) + else: + logger.warning("Environment Task ID variable not found. Assigning 0") + return 0 def set_task_id(task_id): """ - Set the SEISFLOWS_TASKID in os environs + Set the SEISFLOWS_TASKID in os environs for local workflows. If running + on HPC systems, running array jobs will assign the Task ID .. note:: Mostly used for debugging/testing purposes as a way of mimicing diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index 4f3c7e25..3d255497 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -230,6 +230,9 @@ def checkpoint(self): for key, val in self._states.items(): f.write(f"{key}: {val}\n") + # Pickle the current working state so that system can load it during run + + def run(self): """ Call the Task List in order to 'run' the workflow. Contains logic for From b7c17eb6700031b937ae483313b0589a1b3fac4d Mon Sep 17 00:00:00 2001 From: bch0w Date: Thu, 21 Jul 2022 11:37:15 -0800 Subject: [PATCH 074/195] removing agency from optimization module. optimization no longer keeps track of iteration count, this respossibility has been moved to the workflow. lowercasing default parameter file keys optimization line search attribute now stored as hidden variable to keep it from being exposed to the parameter file --- seisflows/config.py | 42 ++---- seisflows/examples/parameters.yaml | 22 ++- seisflows/optimize/gradient.py | 81 ++++++----- seisflows/plugins/line_search/bracket.py | 8 +- seisflows/seisflows.py | 174 ++++------------------- seisflows/tools/core.py | 2 +- seisflows/tools/msg.py | 100 +------------ seisflows/workflow/inversion.py | 8 +- seisflows/workflow/thrifty_inversion.py | 4 +- 9 files changed, 101 insertions(+), 340 deletions(-) diff --git a/seisflows/config.py b/seisflows/config.py index bfe0b642..0ebd7df5 100755 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -4,8 +4,6 @@ throughout the Seisflows workflow. It also (re)defines some important functions that are used extensively by the machinery of Seisflows. -SeisFlows consists of interacting objects: -'system', 'preprocess', 'solver', 'postprocess', 'optimize', 'workflow' Each corresponds simultaneously to a module in the SeisFlows source code, a class that is instantiated and made accessible via sys.modules, and a @@ -23,44 +21,27 @@ from pkgutil import find_loader from importlib import import_module - from seisflows import logger from seisflows.tools.core import Dict, Null -from seisflows.tools import msg, unix +from seisflows.tools import msg from seisflows.tools.core import load_yaml -""" -!!! WARNING !!! - -The following constants are (some of the only) hardwired components -of the package. The naming, order, case, etc., of each constant may be -important, and any changes to these will more-than-likely break the underlying -mechanics of the package. Do not touch unless you know what you're doing! -""" # List of module names required by SeisFlows for imports. Order-sensitive -# In sys.modules these will be prepended by 'seisflows_', e.g., seisflows_system -# NAMES = ["system", "preprocess", "solver", "optimize", "workflow"] NAMES = ["workflow", "system", "solver", "preprocess", "optimize"] # The location of this config file, which is the main repository ROOT_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__))) -""" -!!! ^^^ WARNING ^^^ !!! -""" def import_seisflows(workdir=os.getcwd(), parameter_file="parameters.yaml"): """ - Standard SeisFlows workflow setup block which runs some standard setup - tasks including: setting the working directory, instantiating the logging - module and dynmically importing each of the SeisFlows modules by specific - class names. - - .. note:: - This should be called in the exact way each time: - > pars, modules = import_seisflows() - > system, preprocess, solver, postprocess, optimize = modules + Standard SeisFlows workflow setup block which runs a number of setup + tasks including: loading a user-defiend parameter file, configuring the + package-wide logger based on user-input path to log file and desired + verbosity, and instantiating all modules in a generic fashion based on user + choice. Returns the 'workflow' module, which contains all other submodules + as attributes. :type workdir: str :param workdir: the current working directory in which to perform a @@ -71,9 +52,8 @@ class names. should be created by the command line argument 'seisflows configure'. Defaults to 'parameters.yaml' :rtype: module - :return: instantiated workflow module which contains all instantiated - sub-modules containing set parameters - 'system', 'preprcess', 'solver', 'postprocess', 'optimize' + :return: instantiated 'workflow' module which contains all sub-modules which + have been instantiated with user-defined parameters """ # Read in parameters from file. Set up the logger parameters = load_yaml(os.path.join(workdir, parameter_file)) @@ -104,6 +84,7 @@ def config_logger(level="DEBUG", filename=None, filemode="a", verbose=True): to stdout, and a file logger which writes to `filename`. Two levels of verbosity and three levels of log messages allow the user to determine how much output they want to see. + :type level: str :param level: log level to be passed to logger, available are 'CRITICAL', 'WARNING', 'INFO', 'DEBUG' @@ -155,7 +136,8 @@ def config_logger(level="DEBUG", filename=None, filemode="a", verbose=True): def custom_import(name=None, module=None, classname=None): """ Imports SeisFlows module and extracts class that is the camelcase version - of the module name + of the module name. Used to dynamically import sub-modules by name only, + avoiding the need to hardcode import statements. For example: custom_import('workflow', 'inversion') diff --git a/seisflows/examples/parameters.yaml b/seisflows/examples/parameters.yaml index 2caee39d..d637bf70 100644 --- a/seisflows/examples/parameters.yaml +++ b/seisflows/examples/parameters.yaml @@ -17,16 +17,14 @@ # # MODULES # /////// -# WORKFLOW (str): The method for running SeisFlows; equivalent to main() -# SOLVER (str): External numerical solver to use for waveform simulations -# SYSTEM (str): Computer architecture of the system being used -# OPTIMIZE (str): Optimization algorithm for the inverse problem -# PREPROCESS (str): Preprocessing schema for waveform data -# POSTPROCESS (str): Postprocessing schema for kernels and gradients +# workflow (str): The types and order of functions for running SeisFlows +# system (str): Computer architecture of the system being used +# solver (str): External numerical solver to use for waveform simulations +# preprocess (str): Preprocessing schema for waveform data +# optimize (str): Optimization algorithm for the inverse problem # ============================================================================== -SYSTEM: workstation -WORKFLOW: forward -SOLVER: specfem2d -OPTIMIZE: gradient -PREPROCESS: default -POSTPROCESS: default +workflow: forward +system: workstation +solver: specfem2d +preprocess: default +optimize: gradient diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index 95b208e2..d7526f57 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -24,12 +24,6 @@ Problems in any of these areas usually manifest themselves through stagnation of the nonlinear optimizationalgorithm. - -TODO fix line search, currently broken. Store line search variables in .npz - arrays rather than as internal attributes, that way it's easier to restart - from a broken workflow. Need to figure out how to reset and restart - line searches efficiently - Change line search to words not numbers """ import os import numpy as np @@ -67,18 +61,15 @@ class Gradient: :param path_line_search: full path to a file used to periodically save the line search history as a NumPy .npz file """ - def __init__(self, start=1, line_search_method="bracket", - preconditioner=None, step_count_max=10, step_len_init=0.05, - step_len_max=0.5, workdir=os.getcwd(), path_optimize=None, - path_line_search=None, path_output=None, - path_preconditioner=None, + def __init__(self, line_search_method="bracket", preconditioner=None, + step_count_max=10, step_len_init=0.05, step_len_max=0.5, + workdir=os.getcwd(), path_optimize=None, path_line_search=None, + path_output=None, path_preconditioner=None, **kwargs): """Gradient-descent input parameters""" super().__init__() - self.iteration = start # to match PAR.BEGIN self.preconditioner = preconditioner - self.step_count_max = step_count_max self.step_len_init = step_len_init self.step_len_max = step_len_max @@ -101,25 +92,27 @@ def __init__(self, start=1, line_search_method="bracket", f"algorithm, defaulting to 'bracket'") line_search_method = "bracket" - # .title() ensures we grab the class and not the module - self.line_search = getattr(line_search_dir, line_search_method.title())( - step_count_max=step_count_max, step_len_max=step_len_max, - path=self.path.line_search - ) + self.line_search_method = line_search_method - # Internally used parameters for checking validity + # Internally used parameters for keeping track of optimization + self._restarted = False self._acceptable_vectors = ["m_new", "m_old", "m_try", "g_new", "g_old", "g_try", "p_new", "p_old", "alpha", "f_new", "f_old", "f_try"] self._acceptable_preconditioners = ["diagonal"] - self.restarted = False + # .title() ensures we grab the class and not the module + self._line_search = getattr( + line_search_dir, line_search_method.title())( + step_count_max=step_count_max, step_len_max=step_len_max, + path=self.path.line_search + ) @property def step_count(self): """Convenience property to access `step_count` from line search""" - return self.line_search.step_count + return self._line_search.step_count def check(self): """ @@ -145,7 +138,7 @@ def setup(self): Sets up nonlinear optimization machinery """ unix.mkdir(self.path.scratch) - self.line_search.save_search_history() # will be empty + self.checkpoint_line_search() # will be empty def load(self, name): """ @@ -203,6 +196,14 @@ def save(self, name, m): else: raise TypeError(f"optimize.save unrecognized type error {type(m)}") + def checkpoint_line_search(self): + """ + Convenience wrapper of the underlying _line_search.save_search_history + to avoid accessing the private attr. _line_search from outside the class + """ + self._line_search.check_search_history() + self._line_search.save_search_history() + def _precondition(self, q): """ Apply available preconditioner to a given gradient @@ -258,23 +259,23 @@ def initialize_search(self): # Restart plugin line search if the optimization library restarts if self.restarted: - self.line_search.clear_history() + self._line_search.clear_history() # Optional safeguard to prevent step length from getting too large if self.step_len_max: new_step_len_max = self.step_len_max * norm_m / norm_p - self.line_search.step_len_max = new_step_len_max + self._line_search.step_len_max = new_step_len_max logger.info(f"enforcing max step length safeguard") # Initialize the line search and save it to disk. - self.line_search.update_search_history(func_val=f, step_len=0., + self._line_search.update_search_history(func_val=f, step_len=0., gtg=gtg, gtp=gtp) - self.line_search.check_search_history(iteration=self.iteration) + self._line_search.check_search_history() - alpha, _ = self.line_search.calculate_step_length() + alpha, _ = self._line_search.calculate_step_length() # Alpha defines the trial step length. Optional step length override - if self.step_len_init and len(self.line_search.step_lens) <= 1: + if self.step_len_init and len(self._line_search.step_lens) <= 1: alpha = self.step_len_init * norm_m / norm_p logger.debug(f"overwriting initial step length, " f"alpha_new={alpha:.2E}") @@ -303,12 +304,12 @@ def update_line_search(self): f_try = self.load("f_try") # misfit for the trial model # Update the line search with a new step length and misfit value - self.line_search.step_count += 1 - self.line_search.update_search_history(step_len=alpha, func_val=f_try) - self.line_search.check_search_history(iteration=self.iteration) + self._line_search.step_count += 1 + self._line_search.update_search_history(step_len=alpha, func_val=f_try) + self._line_search.check_search_history() # Calculate a new step length based on line search algorithm - alpha_try, status = self.line_search.calculate_step_length() + alpha_try, status = self._line_search.calculate_step_length() # Status == 0: Retry line search // Status == 1: Line search passed if status in [0, 1]: @@ -344,7 +345,7 @@ def finalize_search(self): logger.info(msg.sub("FINALIZING LINE SEARCH")) # Remove the old model parameters - if self.iteration > 1: + if glob("?_old"): logger.info("removing previously accepted model files (?_old)") for fid in ["m_old", "f_old", "g_old", "p_old"]: unix.rm(os.path.join(self.path.scratch, fid)) @@ -364,12 +365,12 @@ def finalize_search(self): dst=os.path.join(self.path.scratch, "m_new.npz")) # Choose minimum misfit value as final misfit/model. index 0 is initial - f = self.line_search.get_search_history()[1] + f = self._line_search.get_search_history()[1] self.save("f_new", f.min()) logger.info(f"misfit of accepted trial model is f={f.min():.3E}") logger.info("resetting line search step count to 0") - self.line_search.step_count = 0 + self._line_search.step_count = 0 def attempt_line_search_restart(self, threshold=1E-3): """ @@ -438,7 +439,7 @@ def _write_stats(self): g = self.load("g_new") p = self.load("p_new") - x, f, *_ = self.line_search.get_search_history() + x, f, *_ = self._line_search.get_search_history() # Calculated stats factors # TODO What is this? It was returning a RuntimeError for value too small @@ -450,19 +451,17 @@ def _write_stats(self): grad_norm_L2 = np.linalg.norm(g.vector, 2) misfit = f[0] - restarted = self.restarted slope = (f[1] - f[0]) / (x[1] - x[0]) - step_count = self.line_search.step_count + step_count = self._line_search.step_count step_length = x[f.argmin()] theta = 180. * np.pi ** -1 * angle(p.vector, -1 * g.vector) with open(fid, "a") as f: - f.write(f"{self.iteration:0>2}," - # f"{factor:6.3E}," + f.write(# f"{factor:6.3E}," f"{grad_norm_L1:6.3E}," f"{grad_norm_L2:6.3E}," f"{misfit:6.3E}," - f"{restarted:6.3E}," + f"{self._restarted:6.3E}," f"{slope:6.3E}," f"{step_count:0>2}," f"{step_length:6.3E}," diff --git a/seisflows/plugins/line_search/bracket.py b/seisflows/plugins/line_search/bracket.py index 7a3fea45..917f52f7 100644 --- a/seisflows/plugins/line_search/bracket.py +++ b/seisflows/plugins/line_search/bracket.py @@ -74,17 +74,13 @@ def clear_search_history(self): self.gtp = [] self.step_count = 0 - def check_search_history(self, iteration): + def check_search_history(self): """ Since the line search is just a wrapper for list of numbers, check that search history hasn't been muddled up by ensuring that internal lists are the correct length for the given evaluation - - :type iteration: int - :param iteration: current iteration of the workflow """ - assert(len(self.gtg) == iteration), f"too many entries for 'gtg'" - assert(len(self.gtp) == iteration), f"too many entries for 'gtp'" + assert(len(self.gtg) == len(self.gtp)), f"too many entries for 'gtg'" assert(len(self.func_vals) == len(self.step_lens)), \ f"number of function evaluations does not match step lengths" assert(self.step_count + 1 == len(self.func_vals)), \ diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 8e740cb6..801a545f 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -25,7 +25,7 @@ from IPython import embed from seisflows import logger -from seisflows.config import custom_import, NAMES, ROOT_DIR, config_logger +from seisflows.config import custom_import, NAMES, ROOT_DIR, config_logger, import_seisflows from seisflows.tools import unix, msg from seisflows.tools.core import load_yaml, Dict from seisflows.tools.specfem import (getpar, setpar, getpar_vel_model, @@ -444,18 +444,22 @@ def configure(self, absolute_paths=False, **kwargs): try: written = [] f = open(self._args.parameter_file, "a") + print(f"writing parameters for modules: ") for module in modules: + print(f"{module.__class__.__name__}") # Write the docstring f.write(f"# {'=' * 77}\n#") f.write(module.__doc__.replace("\n", "\n#")) f.write(f"\n# {'=' * 77}\n") # Write the parameters, make sure to not have the same one twice for key, val in vars(module).items(): + print(key) # Skip already written, hidden vars, and paths - if (key in written) or key.startswith("_"): + if (key in written) or key.startswith("_") or key == "path": continue + # YAML wants NoneType to be 'null' if val is None: - val = "null" # required by YAML + val = "null" f.write(f"{key}: {val}\n") written.append(key) except Exception: @@ -469,71 +473,7 @@ def configure(self, absolute_paths=False, **kwargs): else: unix.rm(temp_par_file) - # def configure(self, absolute_paths=False, **kwargs): - # """ - # Dynamically generate the parameter file by writing out docstrings and - # default values for each of the SeisFlows module parameters. - # This function writes files manually, consistent with the .yaml format. - # - # :type absolute_paths: bool - # :param absolute_paths: if True, expand pathnames to absolute paths, - # else if False, use path names relative to the working directory. - # Defaults to False, uses relative paths. - # """ - # self._register_parameters() - # - # # Check if the User set turn off any modules (if None, dont instantiate) - # names = copy(NAMES) - # for name, choice in self._parameters.items(): - # if choice is None: - # names.remove(name.lower()) - # - # # Need to attempt importing all modules before we access their par/paths - # for NAME in NAMES: - # sys.modules[f"seisflows_{NAME}"] = custom_import(NAME)() - # - # # System defines foundational directory structure required by other - # # modules. Don't validate the parameters because they aren't yet set - # sys.modules["seisflows_system"].required.validate(paths=True, - # parameters=False) - # - # # If writing to parameter file fails for any reason, the file will be - # # mangled, create a temporary copy that can be re-instated upon failure - # temp_par_file = f".{self._args.parameter_file}" - # unix.cp(self._args.parameter_file, temp_par_file) - # - # try: - # # Paths are collected for each but written at the end - # seisflows_paths = {} - # with open(self._args.parameter_file, "a") as f: - # for name in names: - # req = sys.modules[f"seisflows_{name}"].required - # seisflows_paths.update(req.paths) - # - # # Write the docstring header and then the parameters in YAML - # msg.write_par_file_header(f, req.parameters, name) - # msg.write_par_file_paths_pars(f, req.parameters) - # - # # Write the paths in the same format as parameters - # msg.write_par_file_header(f, seisflows_paths, name="PATHS") - # f.write("PATHS:\n") - # - # # If requested, set the paths relative to the current dir - # if not absolute_paths: - # for key, attrs in seisflows_paths.items(): - # if attrs["default"]: - # seisflows_paths[key]["default"] = os.path.relpath( - # attrs["default"]) - # msg.write_par_file_paths_pars(f, seisflows_paths, indent=4) - # # General error catch as anything can happen here - # except Exception as e: - # unix.rm(self._args.parameter_file) - # unix.cp(temp_par_file, self._args.parameter_file) - # print(msg.cli(text="seisflows configure traceback", header="error")) - # print(traceback.format_exc()) - # sys.exit(-1) - # else: - # unix.rm(temp_par_file) + f.close() def swap(self, module, classname, **kwargs): """ @@ -584,29 +524,18 @@ def swap(self, module, classname, **kwargs): unix.rm(f"_{self._args.parameter_file}") - def init(self, **kwargs): + def validate(self, **kwargs): """ - Establish a SeisFlows working environment on disk. Instantiates a - working state in memory (sys.modules) and then writes this state as \ - pickle files to the OUTPUT directory for User inspection and debug - purposes. + Run check() functions for a given parameter file and each of the + SeisFlows modules, ensuring that parameters are acceptable for the + given set of user-defined parameters """ unix.mkdir(self._args.workdir) unix.cd(self._args.workdir) - self._register_parameters() - self._register_modules() - self._check_parameters() - - save(path=self._paths.OUTPUT) - - # Ensure that all parameters and paths that need to be instantiated - # are present in sys modules - for NAME in NAMES: - sys.modules[f"seisflows_{NAME}"].required.validate() - - print(msg.cli(f"instantiating SeisFlows working state in: " - f"{self._paths.OUTPUT}")) + workflow = import_seisflows(workdir=self._args.workdir, + parameter_file=self._args.parameter_file) + workflow.check() def submit(self, **kwargs): """ @@ -618,25 +547,9 @@ def submit(self, **kwargs): unix.mkdir(self._args.workdir) unix.cd(self._args.workdir) - # If parameter `RESUME_FROM` is set, unset it because submit and restart - # should start from a fresh workflow - try: - self.par(parameter="RESUME_FROM", value="", skip_print=True) - except SystemExit as e: - pass - - # Read in the Parameter file and set parameters into sys.modules. - self._register_parameters() - self._register_modules() - self._check_parameters() - - config_logger(level=self._parameters.LOG_LEVEL, - verbose=self._parameters.VERBOSE, - filename=self._paths.LOGFILE) - - # Submit workflow.main() to the system - system = sys.modules["seisflows_system"] - system.submit() + workflow = import_seisflows(workdir=self._args.workdir, + parameter_file=self._args.parameter_file) + workflow.system.submit() def clean(self, force=False, **kwargs): """ @@ -679,21 +592,6 @@ def clean(self, force=False, **kwargs): continue print(msg.cli(items=items, header="clean", border="=")) - def resume(self, **kwargs): - """ - Resume a previously started workflow by loading the module pickle files - and submitting the workflow from where it left off. - """ - self._register_parameters() - self._load_modules() - - config_logger(level=self._parameters.LOG_LEVEL, - verbose=self._parameters.VERBOSE, - filename=self._paths.LOGFILE) - - system = sys.modules["seisflows_system"] - system.submit() - def restart(self, force=False, **kwargs): """ Restart simply means clean the workding dir and submit a new workflow. @@ -708,18 +606,18 @@ def debug(self, **kwargs): interactive environment allowing exploration of the package space. Does not allow stepping through of code (not a breakpoint). """ - self._register_parameters() - self._load_modules() + workflow = import_seisflows(workdir=self._args.workdir, + parameter_file=self._args.parameter_file) - # Distribute modules to common names for easy access during debug mode - PATH = sys.modules["seisflows_paths"] - PAR = sys.modules["seisflows_parameters"] - system = sys.modules["seisflows_system"] - preprocess = sys.modules["seisflows_preprocess"] - solver = sys.modules["seisflows_solver"] - postprocess = sys.modules["seisflows_postprocess"] - optimize = sys.modules["seisflows_optimize"] - workflow = sys.modules["seisflows_workflow"] + # Break out sub-modules and parameters so they're more easily accesible + parameters = load_yaml(self._args.parameter_file) + system = workflow.system + solver = workflow.solver + preprocess = workflow.preprocess + optimize = workflow.optimize + + for module in [workflow, system, solver, preprocess, optimize]: + print(module) print(msg.cli("SeisFlows's debug mode is an embedded IPython " "environment. All modules are loaded by default. " @@ -897,20 +795,6 @@ def par(self, parameter, value=None, skip_print=False, required=False, if not skip_print: print(msg.cli(f"{key}: {cur_val} -> {value}")) - def _par_required(self): - """ - Only list parameters which have not been set as a default value. - Filled in with default values defined in SeisFlowsPathParameters - """ - sf = SeisFlowsPathsParameters - with open(self._args.parameter_file, "r") as f: - lines = f.readlines() - for check in [sf.default_par, sf.default_path]: - print(f"{check}\n{'='*len(check)}") - for line in lines: - if check in line: - print(f"\t{line.split(':')[0].strip()}") - def examples(self, run=None, choice=None, **kwargs): """ List or run a SeisFlows example problem diff --git a/seisflows/tools/core.py b/seisflows/tools/core.py index c895e19d..7927200d 100644 --- a/seisflows/tools/core.py +++ b/seisflows/tools/core.py @@ -166,7 +166,7 @@ def number_fid(fid, i=0): output_000.txt, output_001.txt, output_002.txt, ouput_003.txt ... .. note:: - Replace statement is catch all so we assume that there is only one \ + Replace statement is catch-all, so we assume that there is only one instance of the file extension in the entire path. :type fid: str diff --git a/seisflows/tools/msg.py b/seisflows/tools/msg.py index f0b770e9..f5ccb4aa 100644 --- a/seisflows/tools/msg.py +++ b/seisflows/tools/msg.py @@ -155,105 +155,7 @@ def cli(text="", items=None, wraplen=80, header=None, border=None, hchar="/"): return output_str -def write_par_file_header(f, paths_or_parameters, name="", tabsize=4, - border="=", uline="/"): - """ - Re-usable function to write docstring comments inside the SeisFlows - parameter file. Used by seisflows.SeisFlows.configure() - - Headers look something like this - - # =========================== - # MODULE NAME - # /////////// - # PAR (type): - # description of par - # =========================== - PAR: val - - :type f: _io.TextIO - :param f: open text file to write to - :type paths_or_parameters: dict - :param paths_or_parameters: the paths or parameters that should be written - to the header - :type name: str - :param name: the name of the module that is being written, will be used as - the header of the docstring - :type tabsize: int - :param tabsize: how large to expand tab character '\t' as spaces - :type border: str - :param border: character to use as the header and footer border - :type uline: str - :param uline: how to underline the header - """ - # Some aesthetically pleasing dividers to separate sections - # Length 77 ensure that total line width is no more than 80 characters - # including the '#' and spaces - top = (f"\n# {border * 77}" - f"\n# {name.upper():^77}" - f"\n# {uline * len(name):^77}" - f"\n" - ) - bot = f"# {border * 77}\n" - - # Write top header, all parameters, types and descriptions, and then footer - f.write(top) - for key, attrs in paths_or_parameters.items(): - if "type" in attrs: - f.write(f"# {key} ({attrs['type']}):\n") - else: - f.write(f"# {key}:\n") - docstrs = wrap(attrs["docstr"], width=77 - tabsize, - break_long_words=False) - for line, docstr in enumerate(docstrs): - f.write(f"#\t{docstr}\n".expandtabs(tabsize=tabsize)) - f.write(bot) - - -def write_par_file_paths_pars(f, paths_or_parameters, indent=0, tabsize=4): - """ - Re-usable function to write paths or parameters in yaml format to the - SeisFlows parameter file. Used by seisflows.SeisFlows.configure() - - Parameters are written something like: - - PAR1: val1 - PAR2: val2 - Par3: - - val3a - - val3b - - val3c - - :type f: _io.TextIO - :param f: open text file to write to - :type paths_or_parameters: dict - :param paths_or_parameters: the paths or parameters that should be written - to the header - :type indent: int - :param indent: level of indentation to match yaml style. passed to - str.expandtabs(tabsize=`indent`) - :type tabsize: int - :param tabsize: how large to expand tab character '\t' as spaces - """ - for key, attrs in paths_or_parameters.items(): - # Lists need to be treated differently in yaml format - if isinstance(attrs["default"], list): - if len(attrs["default"]) == 0: - f.write(f"{key}: []\n") - else: - f.write(f"{key}:\n") - for val in attrs["default"]: - f.write(f"\t- {val}\n".expandtabs(tabsize=tabsize)) - else: - # Yaml saves NoneType values as 'null' or blank lines - if attrs["default"] is None: - f.write(f"\t{key}:\n".expandtabs(tabsize=indent)) - else: - f.write( - f"\t{key}: {attrs['default']}\n".expandtabs(tabsize=indent) - ) - - +# SeisFlows 'Globe' logo in ASCII. Used for CLI print statements ascii_logo = """ @@@@@@@@@@@@@@@@@@@@@@@ diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index a2cd11fa..70d39c8a 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -170,7 +170,7 @@ def initialize_line_search(self): then exposed on disk to the solver. """ logger.info(f"initializing " - f"'{self.optimize.line_search.__class__.__name__}'ing " + f"'{self.optimize.line_search_method}'ing " f"line search") # 'p' is the search direction used to perturb the initial model @@ -189,7 +189,7 @@ def initialize_line_search(self): m_try, alpha = self.optimize.initialize_search() self.optimize.save(name="m_try", m=m_try) self.optimize.save(name="alpha", m=alpha) - self.optimize.line_search.save_search_history() + self.optimize.checkpoint_line_search() # Expose model `m_try` to the solver by placing it in eval_func dir. m_try.write(path=os.path.join(self.path.eval_func, "model")) @@ -219,14 +219,14 @@ def perform_line_search(self): # Increment step count, calculate new step length/model, check misfit status = self.optimize.update_line_search() - self.optimize.line_search.save_search_history() + self.optimize.checkpoint_line_search() # Proceed based on the outcome of the line search if status == 1: # Save outcome of line search to disk; reset step to 0 for next iter logger.info("trial step successful. finalizing line search") self.optimize.finalize_search() - self.optimize.line_search.save_search_history() + self.optimize.checkpoint_line_search() return elif status == 0: logger.info("trial step unsuccessful. re-attempting line search") diff --git a/seisflows/workflow/thrifty_inversion.py b/seisflows/workflow/thrifty_inversion.py index b8322075..7f7183b8 100644 --- a/seisflows/workflow/thrifty_inversion.py +++ b/seisflows/workflow/thrifty_inversion.py @@ -30,7 +30,7 @@ def __init__(self, line_search_method): """ super().__init__() - self.line_search_method = line_search_method + self._line_search_method = line_search_method self._thrifty_status = False def check(self): @@ -39,7 +39,7 @@ def check(self): """ super().check() - assert(self.line_search_method.title() == "Backtrack"), ( + assert(self._line_search_method.title() == "Backtrack"), ( "Thrifty inversion requires `line_search_method` == 'backtrack'" ) From 006340840fab6ccba54ab0984827f23da8a6b61c Mon Sep 17 00:00:00 2001 From: bch0w Date: Thu, 21 Jul 2022 14:35:32 -0800 Subject: [PATCH 075/195] adjusting docstrings to fit the new seisflows configure command which builds a parameter file based on the class docstrings of each of the sub moduels used --- seisflows/config.py | 3 +- seisflows/optimize/LBFGS.py | 10 ++-- seisflows/optimize/NLCG.py | 10 ++-- seisflows/optimize/gradient.py | 46 ++++++++++------ seisflows/preprocess/default.py | 19 ++++--- seisflows/seisflows.py | 98 +++++++++++++++++++++------------ seisflows/solver/specfem.py | 69 ++++++++++++++--------- seisflows/system/cluster.py | 10 +--- seisflows/system/slurm.py | 36 ++++++------ seisflows/system/workstation.py | 94 +++++++++++++++++-------------- seisflows/workflow/forward.py | 51 ++++++++++++----- seisflows/workflow/inversion.py | 24 ++++---- seisflows/workflow/migration.py | 42 ++++++++------ 13 files changed, 307 insertions(+), 205 deletions(-) diff --git a/seisflows/config.py b/seisflows/config.py index 0ebd7df5..ba8d9295 100755 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -57,7 +57,8 @@ def import_seisflows(workdir=os.getcwd(), parameter_file="parameters.yaml"): """ # Read in parameters from file. Set up the logger parameters = load_yaml(os.path.join(workdir, parameter_file)) - config_logger(level=parameters.log_level, filename=parameters.path_log_file, + config_logger(level=parameters.log_level, + filename=parameters.path_output_log, verbose=parameters.verbose) # Instantiate SeisFlows modules dynamically based on choices and parameters diff --git a/seisflows/optimize/LBFGS.py b/seisflows/optimize/LBFGS.py index 80e12b6f..0a45e581 100644 --- a/seisflows/optimize/LBFGS.py +++ b/seisflows/optimize/LBFGS.py @@ -61,8 +61,8 @@ def __init__(self, lbfgs_mem=3, lbfgs_max=np.inf, lbfgs_thresh=0., if self._line_search.title() != "Backtrack": logger.warning(f"L-BFGS optimization requires 'backtrack'ing line " f"search. Overwritng {self._line_search}") - self._line_search = "Backtrack" - self.line_search = getattr(line_search_dir, self._line_search)( + self.line_search_method = "Backtrack" + self._line_search = getattr(line_search_dir, self._line_search)( step_count_max=self.step_count_max, step_len_max=self.step_len_max ) @@ -148,7 +148,7 @@ def compute_direction(self): restarted = True # Save values to disk and memory - self.restarted = restarted + self._restarted = restarted return p_new @@ -164,8 +164,8 @@ def restart(self): self.save("p_new", -1 * g.vector) # Clear internal memory - self.line_search.clear_history() - self.restarted = True + self._line_search.clear_history() + self._restarted = True self._LBFGS_iter = 1 self._memory_used = 0 diff --git a/seisflows/optimize/NLCG.py b/seisflows/optimize/NLCG.py index d49704ec..8c801521 100644 --- a/seisflows/optimize/NLCG.py +++ b/seisflows/optimize/NLCG.py @@ -34,8 +34,8 @@ def __init__(self, nlcg_max=np.inf, nlcg_thresh=np.inf, if self._line_search.title != "Bracket": logger.warning(f"NLCG optimization requires 'bracket'ing line " f"search. Overwritng {self._line_search}") - self._line_search = "Bracket" - self.line_search = getattr(line_search_dir, self._line_search)( + self.line_search_method = "Bracket" + self._line_search = getattr(line_search_dir, self._line_search)( step_count_max=self.step_count_max, step_len_max=self.step_len_max ) @@ -124,7 +124,7 @@ def compute_direction(self): restarted = 0 # Save values to disk and memory - self.restarted = restarted + self._restarted = restarted return p_new @@ -137,8 +137,8 @@ def restart(self): g = self.load("g_new") self.save("p_new", -1 * g.vector) - self.line_search.clear_history() - self.restarted = 1 + self._line_search.clear_history() + self._restarted = 1 self._NLCG_iter = 1 def _fletcher_reeves(self, g_new, g_old): diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index d7526f57..9cc6b7f0 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -46,27 +46,45 @@ class Gradient: are 'bracket' and 'backtrack'. See seisflows.plugins.line_search for all available options :type preconditioner: str - :param preconditioner: algorithm for preconditioning gradients + :param preconditioner: algorithm for preconditioning gradients. Currently + available: 'diagonal'. Requires `path_preconditioner` to point to a + set of files that define the preconditioner, formatted the same as the + input model :type step_count_max: int :param step_count_max: maximum number of trial steps to perform during the line search before a change in line search behavior is - considered + considered, or a line search is considered to have failed. :type step_len_init: float - :param step_len_init: initial line search step length as a fraction of - current model parameters. + :param step_len_init: initial line search step length guess, provided + as a fraction of current model parameters. :type step_len_max: float :param step_len_max: maximum allowable step length during the line search. Set as a fraction of the current model parameters - :type path_line_search: str - :param path_line_search: full path to a file used to periodically - save the line search history as a NumPy .npz file + + [path structure] + :type path_preconditioner: str + :param path_preconditioner: optional path to a set of preconditioner files + formatted the same as the input model (or output model of solver). + Required to exist and contain files if `preconditioner`==True """ def __init__(self, line_search_method="bracket", preconditioner=None, step_count_max=10, step_len_init=0.05, step_len_max=0.5, - workdir=os.getcwd(), path_optimize=None, path_line_search=None, - path_output=None, path_preconditioner=None, - **kwargs): - """Gradient-descent input parameters""" + workdir=os.getcwd(), path_optimize=None, path_output=None, + path_preconditioner=None, **kwargs): + """ + Gradient-descent input parameters. + + .. note:: + Paths listed here are shared with `workflow.forward` and so are not + included in the class docstring. + + :type workdir: str + :param workdir: working directory in which to look for data and store + results. Defaults to current working directory + :type path_output: str + :param path_output: path to directory used for permanent storage on disk. + Results and exported scratch files are saved here. + """ super().__init__() self.preconditioner = preconditioner @@ -81,10 +99,6 @@ def __init__(self, line_search_method="bracket", preconditioner=None, output=path_output or os.path.join(workdir, "output"), preconditioner=path_preconditioner, ) - self.path["line_search"] = ( - path_line_search or - os.path.join(self.path.scratch, "line_search") - ) # Internal check to see if the chosen line search algorithm exists if not hasattr(line_search_dir, line_search_method): @@ -106,7 +120,7 @@ def __init__(self, line_search_method="bracket", preconditioner=None, self._line_search = getattr( line_search_dir, line_search_method.title())( step_count_max=step_count_max, step_len_max=step_len_max, - path=self.path.line_search + path=os.path.join(self.path.scratch, "line_search") ) @property diff --git a/seisflows/preprocess/default.py b/seisflows/preprocess/default.py index 000d59b6..f78f2eef 100644 --- a/seisflows/preprocess/default.py +++ b/seisflows/preprocess/default.py @@ -20,9 +20,8 @@ class Default: """ - [preprocess.default] SeisFlows preprocessing module provides data processing - functions for seismic traces, with options for data misfit, filtering, - normalization and muting. + [preprocess.default] Data processing for seismic traces, with options for + data misfit, filtering, normalization and muting. :type data_format: str :param data_format: data format for reading traces into memory. For @@ -70,9 +69,6 @@ class Default: LATE: mute late arrivals; SHORT: mute short source-receiver distances; LONG: mute long source-receiver distances - :type path_preprocess: str - :param path_preprocess: scratch path for all preprocessing processes, - including saving files """ def __init__(self, data_format="ascii", misfit="waveform", adjoint="waveform", normalize=None, filter=None, @@ -83,7 +79,16 @@ def __init__(self, data_format="ascii", misfit="waveform", """ Preprocessing module parameters - + .. note:: + Paths listed here are shared with `workflow.forward` and so are not + included in the class docstring. + + :type workdir: str + :param workdir: working directory in which to look for data and store + results. Defaults to current working directory + :type path_preprocess: str + :param path_preprocess: scratch path for all preprocessing processes, + including saving files """ self.data_format = data_format.upper() self.misfit = misfit diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 801a545f..273b2d70 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -232,10 +232,10 @@ def _format_action(self, action): """, help="Check state of an active environment") - check.add_argument("choice", type=str, nargs="?", - help="Parameter, state, or value to check") - check.add_argument("args", type=str, nargs="*", - help="Generic arguments passed to check functions") + # check.add_argument("choice", type=str, nargs="?", + # help="Parameter, state, or value to check") + # check.add_argument("args", type=str, nargs="*", + # help="Generic arguments passed to check functions") # ========================================================================= print_ = subparser.add_parser( "print", formatter_class=argparse.RawDescriptionHelpFormatter, @@ -442,18 +442,16 @@ def configure(self, absolute_paths=False, **kwargs): unix.cp(self._args.parameter_file, temp_par_file) try: - written = [] + written, path_docstrings = [], [] f = open(self._args.parameter_file, "a") - print(f"writing parameters for modules: ") + # Write all module parameters and corresponding docstrings for module in modules: - print(f"{module.__class__.__name__}") - # Write the docstring - f.write(f"# {'=' * 77}\n#") - f.write(module.__doc__.replace("\n", "\n#")) - f.write(f"\n# {'=' * 77}\n") + docstring = module.__doc__.replace("\n", "\n#") + docstring = docstring.split("[path structure]")[0] + f.write(f"# {'=' * 77}\n#{docstring}\n# {'=' * 77}\n") + # Write the parameters, make sure to not have the same one twice for key, val in vars(module).items(): - print(key) # Skip already written, hidden vars, and paths if (key in written) or key.startswith("_") or key == "path": continue @@ -462,6 +460,31 @@ def configure(self, absolute_paths=False, **kwargs): val = "null" f.write(f"{key}: {val}\n") written.append(key) + # Write docstrings for publically accesible path structure + f.write(f"# {'=' * 77}\n") + f.write("#\n") + f.write("#\t [path structure] SeisFlows internal/external paths") + for module in modules: + docstring = module.__doc__.strip().replace("\n", "\n#") + docstring = docstring.split("[path structure]") + try: + # The extra split is to catch any inherited docstrings + f.write(docstring[1].split("[")[0]) + # IndexError means no path docstring to write out + except IndexError as e: + continue + f.write(f"\n# {'=' * 77}\n") + + # Write values for publically accessible path structure + written = [] + for module in modules: + for key, val in module.path.items(): + if key in written: + continue + if val is None: + val = "null" + f.write(f"path_{key}: {val}\n") + written.append(key) except Exception: unix.rm(self._args.parameter_file) unix.cp(temp_par_file, self._args.parameter_file) @@ -524,7 +547,7 @@ def swap(self, module, classname, **kwargs): unix.rm(f"_{self._args.parameter_file}") - def validate(self, **kwargs): + def check(self, **kwargs): """ Run check() functions for a given parameter file and each of the SeisFlows modules, ensuring that parameters are acceptable for the @@ -535,7 +558,10 @@ def validate(self, **kwargs): workflow = import_seisflows(workdir=self._args.workdir, parameter_file=self._args.parameter_file) - workflow.check() + try: + workflow.check() + except AssertionError as e: + print(msg.cli(str(e), border="=", header="parameter errror")) def submit(self, **kwargs): """ @@ -549,7 +575,7 @@ def submit(self, **kwargs): workflow = import_seisflows(workdir=self._args.workdir, parameter_file=self._args.parameter_file) - workflow.system.submit() + workflow.system.submit(workflow) def clean(self, force=False, **kwargs): """ @@ -864,27 +890,27 @@ def examples(self, run=None, choice=None, **kwargs): )) print(msg.cli(items=items)) - def check(self, choice=None, **kwargs): - """ - Check parameters, state or values of an active SeisFlows environment. - Type 'seisflows check --help' for a detailed help message. - - :type choice: str - :param choice: underlying sub-function to choose - """ - acceptable_args = {"model": self._check_model_parameters, - "iter": self._check_current_iteration, - "src": self._check_source_names, - "isrc": self._check_source_index} - - # Ensure that help message is thrown for empty commands - if choice not in acceptable_args.keys(): - self._subparser.print_help() - sys.exit(0) - - self._register_parameters() - self._load_modules() - acceptable_args[choice](*self._args.args, **kwargs) + # def check(self, choice=None, **kwargs): + # """ + # Check parameters, state or values of an active SeisFlows environment. + # Type 'seisflows check --help' for a detailed help message. + # + # :type choice: str + # :param choice: underlying sub-function to choose + # """ + # acceptable_args = {"model": self._check_model_parameters, + # "iter": self._check_current_iteration, + # "src": self._check_source_names, + # "isrc": self._check_source_index} + # + # # Ensure that help message is thrown for empty commands + # if choice not in acceptable_args.keys(): + # self._subparser.print_help() + # sys.exit(0) + # + # self._register_parameters() + # self._load_modules() + # acceptable_args[choice](*self._args.args, **kwargs) def print(self, choice=None, **kwargs): """ diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index d388b86a..f41b3ae9 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -31,14 +31,12 @@ class Specfem: """ - [solver.specfem] SPECFEM interface shared between 2D/3D/3D_GLOBE - implementations providing generalized interface to establish SPECFEM - working directories, call SPECFEM binaries, and keep track of a number of - parallel processes. + [solver.specfem] Generalized SPECFEM interface to manipulate + SPECFEM2D/3D/3D_GLOBE via Python. :type data_format: str :param data_format: data format for reading traces into memory. - Available: ['SU' seismic unix format, 'ASCII' human-readable ascii] + Available: ['SU': seismic unix format, 'ASCII': human-readable ascii] :type materials: str :param materials: Material parameters used to define model. Available: ['ELASTIC': Vp, Vs, 'ACOUSTIC': Vp, 'ISOTROPIC', 'ANISOTROPIC'] @@ -69,11 +67,8 @@ class Specfem: :type mpiexec: str :param mpiexec: MPI executable used to run parallel processes. Should also be defined for the system module - :type workdir: str - :param workdir: working directory in which to look for data and store - results. Defaults to current working directory - :type path_solver: str - :param path_solver: scratch path for all solver related tasks + + [path structure] :type path_data: str :param path_data: path to any externally stored data required by the solver :type path_specfem_bin: str @@ -83,12 +78,6 @@ class Specfem: :param path_specfem_data: path to SPECFEM DATA/ directory which must contain the CMTSOLUTION, STATIONS and Par_file files used for running SPECFEM - :type path_model_true: str - :param path_model_true: path to a target model if `case`=='synthetic' and - a set of synthetic 'observations' are required for workflow. - :type path_output: str - :param path_output: shared output directory on disk for more permanent - storage of solver related files such as traces, kernels, gradients. """ def __init__(self, data_format="ascii", materials="acoustic", density=False, nproc=1, ntask=1, attenuation=False, @@ -98,7 +87,29 @@ def __init__(self, data_format="ascii", materials="acoustic", path_data=None, path_specfem_bin=None, path_specfem_data=None, path_model_init=None, path_model_true=None, path_output=None, **kwargs): - """Set default SPECFEM interface parameters""" + """ + Set default SPECFEM interface parameters + + .. note:: + Paths listed here are shared with `workflow.forward` and so are not + included in the class docstring. + + :type workdir: str + :param workdir: working directory in which to look for data and store + results. Defaults to current working directory + :type path_solver: str + :param path_solver: scratch path for all solver related tasks + :type path_model_init: str + :param path_model_init: path to the starting model used to calculate the + initial misfit. Must match the expected `solver_io` format. + :type path_model_true: str + :param path_model_true: path to a target model if `case`=='synthetic' and + a set of synthetic 'observations' are required for workflow. + :type path_output: str + :param path_output: shared output directory on disk for more permanent + storage of solver related files such as traces, kernels, gradients. + """ + # Publically accessible parameters self.data_format = data_format self.materials = materials self.nproc = nproc @@ -109,7 +120,6 @@ def __init__(self, data_format="ascii", materials="acoustic", self.smooth_v = smooth_v self.components = components self.solver_io = solver_io - self.mpiexec = mpiexec self.source_prefix = source_prefix or "SOURCE" # Define internally used directory structure @@ -126,11 +136,12 @@ def __init__(self, data_format="ascii", materials="acoustic", ) self.path.mainsolver = os.path.join(self.path.scratch, "mainsolver") - # Establish internally defined parameter system + # Private internal parameters for keeping track of solver requirements self._parameters = [] if self.density: self._parameters.append("rho") + self._mpiexec = mpiexec self._source_names = None # for property source_names self._ext = None # for database file extensions self._io = getattr(solver_io_dir, self.solver_io) # for database IO @@ -167,10 +178,18 @@ def check(self): "IO method has no attribute 'write'" # Check that User has provided appropriate bin/ and DATA/ directories - for name, dir_ in zip(["bin/", "DATA/"], - [self.path.specfem_bin, self.path.specfem_data]): - assert(dir_ is not None), f"SPECFEM path '{name}' cannot be None" - assert(os.path.exists(dir_)), f"SPECFEM path '{name}' must exist" + assert(self.path.specfem_bin is not None), ( + f"`path_specfem_bin` cannot be NoneType. Must point to directory " + f"containing SPECFEM executables" + ) + assert(os.path.exists(self.path.specfem_bin)), ( + f"`path_specfem_bin` must exist and point to directory " + f"containing SPECFEM executables" + ) + assert(glob(os.path.join(self.path.specfem_bin, "*"))), ( + f"`path_specfem_bin` is empty but is expected to contain " + f"executable " \ + f"binary files") # Check that the required SPECFEM files are available in DATA/ for fid in ["STATIONS", "Par_file"]: @@ -655,8 +674,8 @@ def _run_binary(self, executable, stdout="solver.log"): sys.exit(-1) # Append with mpiexec if we are running with MPI - if self.mpiexec: - executable = f"{self.mpiexec} {executable}" + if self._mpiexec: + executable = f"{self._mpiexec} {executable}" try: with open(stdout, "w") as f: diff --git a/seisflows/system/cluster.py b/seisflows/system/cluster.py index f74973e0..fd40f89d 100644 --- a/seisflows/system/cluster.py +++ b/seisflows/system/cluster.py @@ -3,11 +3,6 @@ The Cluster class provides the core utilities interaction with HPC systems which must be overloaded by subclasses for specific workload managers, or specific clusters. - -.. warning:: - The Cluster class is an abstract base class for the Systems module which and - MUST be overwritten by system-specific child classes, it cannot be used to - run jobs by itself. """ import os import dill @@ -26,14 +21,15 @@ class Cluster(Workstation): to the name of the current working directory :type mpiexec: str :param mpiexec: Function used to invoke executables on the system. - For example 'srun' on SLURM systems. + For example 'mpirun', 'mpiexec', 'srun', 'ibrun' :type walltime: int :param walltime: maximum job time in minutes for the master SeisFlows job submitted to cluster :type tasktime: int :param tasktime: maximum job time in minutes for each job spawned by the SeisFlows master job during a workflow. These include, e.g., - running the forward solver + running the forward solver, adjoint solver, smoother, kernel combiner. + All spawned tasks receive the same task time. :type environs: str :param environs: Optional environment variables to be provided in the following format VAR1=var1,VAR2=var2... Will be set using diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index f18e4b43..98f391f3 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -31,21 +31,21 @@ class Slurm(Cluster): Resource Management (SLURM) system. :type ntask_max: int - :param ntask_max: limit the number of concurrent tasks in a given - array job + :param ntask_max: limit the number of concurrent tasks in a given array job :type slurm_args: str - :param slurm_args: Any optional, additional SLURM arguments that will + :param slurm_args: Any (optional) additional SLURM arguments that will be passed to the SBATCH scripts. Should be in the form: '--key1=value1 --key2=value2" """ __doc__ = Cluster.__doc__ + __doc__ - def __init__(self, ntask_max=100, slurm_args="", **kwargs): + def __init__(self, ntask_max=100, slurm_args="", **kwargs): """Slurm-specific setup parameters""" super().__init__(**kwargs) # Overwrite the existing 'mpiexec' - self.mpiexec = "srun -u" + if self.mpiexec is None: + self.mpiexec = "srun -u" self.ntask_max = ntask_max self.slurm_args = slurm_args @@ -75,7 +75,7 @@ def submit(self, submit_call=None): f"{self.slurm_args or ''}", f"--job-name={self.title}", f"--output={self.path.output_log}", - f"--error={self.path.error_log}", + f"--error={self.path.output_log}", f"--ntasks-per-node={self.node_size}", f"--nodes=1", f"--time={self.walltime:d}", @@ -139,8 +139,8 @@ def run(self, funcs, single=False, run_call=None, **kwargs): "process job") run_call = _modify_run_call_single_proc(run_call) - # Stdout will be job number. Federated clusters will return job # and - # cluster name (e.g., 1234;Cluster1), so split that off, only want job # + # Stdout will be job number (e.g., 1234). Federated clusters will return + # job # and cluster name (e.g., 1234;Cluster1). We only want job # job_id = subprocess.run(run_call, stdout=subprocess.PIPE, text=True, shell=True).stdout job_id = str(job_id).split(";")[0] @@ -176,32 +176,33 @@ def check_job_status(job_id): "OUT_OF_MEMORY", "CANCELLED"] while True: job_ids, states = query_job_states(job_id) - if [_ == "COMPLETED" for _ in states]: + if [state == "COMPLETED" for state in states]: return 1 # Pass - elif any([check in states for check in bad_states]): + elif any([check in states for check in bad_states]): # Any bad states? logger.info("atleast 1 system job returned a failing exit code") for job_id, state in zip(job_ids, states): if state in bad_states: logger.debug(f"{job_id}: {state}") return -1 # Fail else: - time.sleep(5) # Don't overload 'sacct' command + time.sleep(5) # Don't query 'sacct' command too often def query_job_states(job_id): """ - Queries completion status of an array job by running: - $ sacct -nLX -o jobid,state -j {job_id} + Queries completion status of an array job by running the SLURM cmd `sacct` + Available job states are listed here: https://slurm.schedmd.com/sacct.html - # Example outputs from this sacct command + .. note:: + The actual command line call wil look something like this + $ sacct -nLX -o jobid,state -j 441630 441630_0 PENDING 441630_1 COMPLETED - Available job states: https://slurm.schedmd.com/sacct.html - .. note:: + SACCT flag options are described as follows: -L: queries all available clusters, not just the cluster that ran the - `sacct` call + `sacct` call. Used for federated clusters -X: supress the .batch and .extern jobnames that are normally returned but don't represent that actual running job @@ -224,6 +225,7 @@ def query_job_states(job_id): def _modify_run_call_single_proc(run_call): """ Modifies a SLURM SBATCH command to use only 1 processor as a single run + by replacing the --array and --ntasks options :type run_call: str :param run_call: The SBATCH command to modify diff --git a/seisflows/system/workstation.py b/seisflows/system/workstation.py index 04f9eb94..aa75fac4 100644 --- a/seisflows/system/workstation.py +++ b/seisflows/system/workstation.py @@ -17,29 +17,52 @@ class Workstation: [system.workstation] runs tasks in serial on a local machine. :type ntask: int - :param ntask: number of individual tasks/events to run during workflow + :param ntask: number of individual tasks/events to run during workflow. + Must be <= the number of source files in `path_specfem_data` :type nproc: int :param nproc: number of processors to use for each simulation :type log_level: str :param log_level: logger level to pass to logging module. - Available: 'debug', 'info', 'warning' + Available: 'debug', 'info', 'warning', 'critical' :type verbose: bool :param verbose: if True, formats the log messages to include the file name, line number and message type. Useful for debugging but - also very verbose - :type path_output: str - :param path_output: path to save files permanently to disk - :type path_system: str - :param path_system: scratch path to save any system related files + also very verbose. + + [path structure] + :type path_output_log: str + :param path_output_log: path to a text file used to store the outputs of + the package wide logger, which are also written to stdout + :type path_par_file: str + :param path_par_file: path to parameter file which is used to instantiate + the package + :type path_log_files: str + :param path_log_files: path to a directory where individual log files are + saved whenever a number of parallel tasks are run on the system. """ def __init__(self, ntask=1, nproc=1, log_level="DEBUG", verbose=False, workdir=os.getcwd(), path_output=None, path_system=None, - path_output_log=None, path_error_log=None, path_log_files=None, - path_par_file=None, **kwargs): - """Workstation System Class Parameters""" + path_par_file=None, path_output_log=None, path_log_files=None, + **kwargs): + """ + Workstation System Class Parameters + + .. note:: + Paths listed here are shared with `workflow.forward` and so are not + included in the class docstring. + + :type workdir: str + :param workdir: working directory in which to look for data and store + results. Defaults to current working directory + :type path_output: str + :param path_output: path to directory used for permanent storage on disk. + Results and exported scratch files are saved here. + :type path_system: str + :param path_system: scratch path to save any system related files + """ self.ntask = ntask self.nproc = nproc - self.log_level = log_level + self.log_level = log_level.upper() self.verbose = verbose # Define internal path system @@ -49,8 +72,8 @@ def __init__(self, ntask=1, nproc=1, log_level="DEBUG", verbose=False, output=path_output or os.path.join(workdir, "output"), log_files=path_log_files or os.path.join(workdir, "logs"), output_log=path_output_log or os.path.join(workdir, "sfoutput.log"), - error_log=path_error_log or os.path.join(workdir, "sferror.log"), ) + self._acceptable_log_levels = ["CRITICAL", "WARNING", "INFO", "DEBUG"] def check(self): """ @@ -59,17 +82,10 @@ def check(self): assert(os.path.exists(self.path.par_file)), \ f"parameter file does not exist but should" - def taskid(self): - """ - Provides a unique identifier for each running task, which should be set - by the 'run'' command. - - :rtype: int - :return: returns the os environment variable SEISFLOWS_TASKID which is - set by run() to label each of the currently - running processes on the SYSTEM. - """ - return get_task_id() + assert(self.ntask > 0), f"number of events/tasks `ntask` cannot be neg'" + assert(self.nproc == 1), f"system.workstation rqeuires `nproc`==1" + assert(self.log_level) in self._acceptable_log_levels, \ + f"`system.log_level` must be in {self._acceptable_log_levels}" def setup(self): """ @@ -94,8 +110,7 @@ def setup(self): # If resuming, move old log files to keep them out of the way. Number # in ascending order, so we don't end up overwriting things - for src in [self.path.output_log, self.path.error_log, - self.path.par_file]: + for src in [self.path.output_log, self.path.par_file]: i = 1 if os.path.exists(src): dst = os.path.join(self.path.log_files, number_fid(src, i)) @@ -105,22 +120,19 @@ def setup(self): logger.debug(f"copying par/log file to: {dst}") unix.cp(src=src, dst=dst) - # def submit(self, workflow, submit_call=None): - # """ - # Submits the main workflow job as a serial job submitted directly to - # the compute node that is running the master job - # - # TO DO fix this - # - # :type submit_call: str or None - # :param submit_call: the command line workload manager call to be run by - # subprocess. This is only needed for overriding classes, it has no - # effect on the Workstation class - # """ - # self.setup() - # workflow = sys.modules["seisflows_workflow"] - # workflow.checkpoint() - # workflow.main() + def submit(self, workflow, submit_call=None): + """ + Submits the main workflow job as a serial job submitted directly to + the system that is running the master job + + :type submit_call: str or None + :param submit_call: the command line workload manager call to be run by + subprocess. This is only needed for overriding classes, it has no + effect on the Workstation class + """ + workflow.setup() + workflow.check() + workflow.run() def run(self, funcs, single=False, **kwargs): """ diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index 3d255497..80049c47 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -16,7 +16,7 @@ class Forward: """ - [workflow.forward] Run forward solver in parallel and optionally calculate + [workflow.forward] Run forward solver in parallel and (optionally) calculate data-synthetic misfit and adjoint sources. :type modules: list of module @@ -39,19 +39,43 @@ class Forward: :param export_residuals: export all residuals (data-synthetic misfit) that are generated by the external solver to `path_output`. If False, residuals stored in scratch may be discarded at any time in the workflow + + [path structure] + :type workdir: str :param workdir: working directory in which to look for data and store results. Defaults to current working directory + :type path_output: str + :param path_output: path to directory used for permanent storage on disk. + Results and exported scratch files are saved here. + :type path_data: str + :param path_data: path to any externally stored data required by the solver + :type path_state_file: str + :param path_state_file: path to a text file used to track the current + status of a workflow (i.e., what functions have already been completed), + used for checkpointing and resuming workflows + :type path_model_init: str + :param path_model_init: path to the starting model used to calculate the + initial misfit. Must match the expected `solver_io` format. + :type path_model_true: str + :param path_model_true: path to a target model if `case`=='synthetic' and + a set of synthetic 'observations' are required for workflow. :type path_eval_grad: str :param path_eval_grad: scratch path to store files for gradient evaluation, including models, kernels, gradient and residuals. """ - def __init__(self, modules=None, data_case=None, export_traces=False, - export_residuals=False, workdir=os.getcwd(), - path_eval_grad=None, path_output=None, path_data=None, - path_state_file=None, path_model_init=None, - path_model_true=None, **kwargs): - """Set default forward workflow parameters""" + def __init__(self, modules=None, data_case="data", export_traces=False, + export_residuals=False, workdir=os.getcwd(), path_output=None, + path_data=None, path_state_file=None, path_model_init=None, + path_model_true=None, path_eval_grad=None, **kwargs): + """ + Set default forward workflow parameters + + :type modules: list + :param modules: list of sub-modules that will be established as class + attributes by the setup() function. Should not need to be set by the + user + """ # Keep modules hidden so that seisflows configure doesnt count them # as 'parameters' self._modules = modules @@ -66,22 +90,17 @@ def __init__(self, modules=None, data_case=None, export_traces=False, eval_grad=path_eval_grad or os.path.join(workdir, "scratch", "eval_grad"), output=path_output or os.path.join(workdir, "output"), + model_init=path_model_init, + model_true=path_model_true, state_file=path_state_file or os.path.join(workdir, ".statefile.txt"), data=path_data, - model_init=path_model_init, - model_true=path_model_true ) self._required_modules = ["system", "solver"] self._acceptable_data_cases = ["data", "synthetic"] self._optional_modules = ["preprocess"] - # Empty module variables that should be filled in by setup - self.system = None - self.solver = None - self.preprocess = None - # Read in any existing state file which keeps track of workflow tasks self._states = {} if os.path.exists(self.path.state_file): @@ -160,6 +179,9 @@ def check(self): def setup(self): """ + Assigns modules as attributes of the workflow. I.e., `self.solver` to + access the solver module (or `workflow.solver` from outside class) + Makes required path structure for the workflow, runs setup functions for all the required modules of this workflow. """ @@ -232,7 +254,6 @@ def checkpoint(self): # Pickle the current working state so that system can load it during run - def run(self): """ Call the Task List in order to 'run' the workflow. Contains logic for diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index 70d39c8a..9d5cc7f7 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -30,8 +30,8 @@ class Inversion(Migration): """ - [workflow.inversion] Peforms iterative nonlinear inversion using a built-in - optimization library which stores model vectors on disk. + [workflow.inversion] Peforms iterative nonlinear inversion using the + built-in optimization library. :type start: int :param start: start inversion workflow at this iteration. 1 <= start <= inf @@ -40,6 +40,9 @@ class Inversion(Migration): :type export_model: bool :param export_model: export best-fitting model from the line search to disk. If False, new models can be discarded from scratch at any time. + + [path structure] + :type path_eval_func: str :param path_eval_func: scratch path to store files for line search objective function evaluations, including models, misfit and residuals @@ -61,12 +64,10 @@ def __init__(self, modules=None, start=1, end=1, export_model=True, os.path.join(self.path.workdir, "scratch", "eval_func") - # Overwriting base class required modules list + # Internal attribute for keeping track of inversion + self._iteration = start self._required_modules = ["system", "solver", "preprocess", "optimize"] - # Empty module variables that should be filled in by setup - self.optimize = None - @property def task_list(self): """ @@ -108,6 +109,9 @@ def check(self): def setup(self): """ + Assigns modules as attributes of the workflow. I.e., `self.solver` to + access the solver module (or `workflow.solver` from outside class) + Lays groundwork for inversion by running setup() functions for the involved sub-modules, generating True model synthetic data if necessary, and generating the pre-requisite database files. @@ -117,14 +121,6 @@ def setup(self): unix.mkdir(self.path.eval_func) self.optimize = self._modules.optimize - def checkpoint(self): - """ - Override checkpoint and add the optimization iteration parameter - """ - super().checkpoint() - with open(self.path.state_file, "a") as f: - f.write(f"iteration: {self.optimize.iteration}") - def run(self): """Call the forward.run() function iteratively, from `start` to `end`""" for i in range(self.start, self.end + 1): diff --git a/seisflows/workflow/migration.py b/seisflows/workflow/migration.py index b7c73681..9508c064 100644 --- a/seisflows/workflow/migration.py +++ b/seisflows/workflow/migration.py @@ -5,6 +5,17 @@ simulation to derive the gradient of the objective function. This workflow sets up the machinery to derive a scaled, smoothed gradient from an initial model + +.. warning:: + Misfit kernels require large amounts of disk space for storage. + Setting `export_kernel`==True when PAR.NTASK is large and model files + are large may lead to large file overhead. + +.. note:: + Migration workflow includes an option to mask the gradient. While both + masking and preconditioning involve scaling the gradient, they are + fundamentally different operations: masking is ad hoc, preconditioning + is a change of variables; For more info, see Modrak & Tromp 2016 GJI """ import os @@ -20,21 +31,6 @@ class Migration(Forward): event-dependent misfit kernels. Sum and postprocess kernels to produce gradient. In seismic exploration this is 'reverse time migration'. - .. warning:: - Misfit kernels require large amounts of disk space for storage. - Setting `export_kernel`==True when PAR.NTASK is large and model files - are large may lead to large file overhead. - - .. note:: - Migration workflow includes an option to mask the gradient. While both - masking and preconditioning involve scaling the gradient, they are - fundamentally different operations: masking is ad hoc, preconditioning - is a change of variables; For more info, see Modrak & Tromp 2016 GJI - - :type path_mask: str - :param path_mask: optional path to a masking function which is used to - mask out or scale parts of the gradient. The user-defined mask must - match the file format of the input model (e.g., .bin files). :type export_gradient: bool :param export_gradient: export the gradient after it has been generated in the scratch directory. If False, gradient can be discarded from @@ -43,12 +39,26 @@ class Migration(Forward): :param export_kernels: export each sources event kernels after they have been generated in the scratch directory. If False, gradient can be discarded from scratch at any time in the workflow + + [path structure] + + :type path_mask: str + :param path_mask: optional path to a masking function which is used to + mask out or scale parts of the gradient. The user-defined mask must + match the file format of the input model (e.g., .bin files). """ __doc__ = Forward.__doc__ + __doc__ def __init__(self, modules=None, path_mask=None, export_gradient=False, export_kernels=False, **kwargs): - """Instantiate Migration-specific parameters""" + """ + Instantiate Migration-specific parameters + + :type modules: list + :param modules: list of sub-modules that will be established as class + attributes by the setup() function. Should not need to be set by the + user + """ super().__init__(**kwargs) self._modules = modules From ed7c34bfadbc6c4d34b7a7fa6d8905f47bf964c0 Mon Sep 17 00:00:00 2001 From: bch0w Date: Thu, 21 Jul 2022 14:40:10 -0800 Subject: [PATCH 076/195] fixed some check statements in solver --- seisflows/solver/specfem.py | 33 +++++++++++++++------------------ 1 file changed, 15 insertions(+), 18 deletions(-) diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index f41b3ae9..33bcf9d1 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -177,21 +177,24 @@ def check(self): assert hasattr(self._io, "write_slice"), \ "IO method has no attribute 'write'" - # Check that User has provided appropriate bin/ and DATA/ directories - assert(self.path.specfem_bin is not None), ( - f"`path_specfem_bin` cannot be NoneType. Must point to directory " + # Check that User has provided appropriate binary files to run SPECFEM + assert(self.path.specfem_bin is not None and + os.path.exists(self.path.specfem_bin)), ( + f"`path_specfem_bin` must exist and must point to directory " f"containing SPECFEM executables" ) - assert(os.path.exists(self.path.specfem_bin)), ( - f"`path_specfem_bin` must exist and point to directory " - f"containing SPECFEM executables" - ) - assert(glob(os.path.join(self.path.specfem_bin, "*"))), ( - f"`path_specfem_bin` is empty but is expected to contain " - f"executable " \ - f"binary files") + for fid in self._required_binaries: + assert(os.path.exists(os.path.join(self.path.specfem_bin, fid))), ( + f"`path_specfem_bin`/{fid} does not exist but is required by " + f"SeisFlows solver module" + ) - # Check that the required SPECFEM files are available in DATA/ + # Check that SPECFEM/DATA directory exists + assert(self.path.specfem_data is not None and + os.path.exists(self.path.specfem_data)), ( + f"`path_specfem_data` must exist and must point to directory " + f"containing SPECFEM input files" + ) for fid in ["STATIONS", "Par_file"]: assert(os.path.exists(os.path.join(self.path.specfem_data, fid))), ( f"DATA/{fid} does not exist but is required by SeisFlows solver" @@ -203,12 +206,6 @@ def check(self): f"{self.source_prefix}*"))), ( f"No source files with prefix {self.source_prefix} found in DATA/") - # Check that required binary files exist which are called upon by solver - for fid in self._required_binaries: - assert(os.path.exists(os.path.join(self.path.specfem_bin, fid))), ( - f"bin/{fid} does not exist but is required by SeisFlows solver" - ) - # Check that model type is set correctly in the Par_file model_type = getpar(key="MODEL", file=os.path.join(self.path.specfem_data, From 147e15b6d62ed8d7175b33461472d251ac13a0e2 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Fri, 22 Jul 2022 16:08:17 -0800 Subject: [PATCH 077/195] fixed up seisflows CLI tools based on the new workflow/parameter system seisflows configure still doesn't work as advertised, need to rethink system line search check failing, working to implement optimization checkpointing back in --- seisflows/__init__.py | 42 --------- seisflows/config.py | 6 +- seisflows/optimize/LBFGS.py | 19 ++-- seisflows/optimize/NLCG.py | 7 +- seisflows/optimize/gradient.py | 9 +- seisflows/plugins/line_search/bracket.py | 5 +- seisflows/seisflows.py | 36 +++----- seisflows/system/cluster.py | 25 ++++- seisflows/system/runscripts/run_funcs.py | 6 +- seisflows/system/workstation.py | 4 +- seisflows/workflow/forward.py | 8 +- seisflows/workflow/inversion.py | 113 +++++++++++++++++++---- 12 files changed, 165 insertions(+), 115 deletions(-) diff --git a/seisflows/__init__.py b/seisflows/__init__.py index 681646e4..45c157f4 100644 --- a/seisflows/__init__.py +++ b/seisflows/__init__.py @@ -1,6 +1,4 @@ -import copyreg import logging -import types from pkgutil import extend_path @@ -13,43 +11,3 @@ # Set up the SeisFlows Logging environment logger = logging.getLogger(__name__) - -def _pickle_method(method): - """ - The following code changes how instance methods are handled by pickle. - Placing it in this module ensures that pickle changes will be in - effect for all SeisFlows workflows - - Note: For relevant discussion, see stackoverflow thread: - "Can't pickle when using python's - multiprocessing Pool.map()" - - Relevant Links (last accessed 01.20.2020): - https://stackoverflow.com/questions/7016567/ - picklingerror-when-using-multiprocessing - - https://bytes.com/topic/python/answers/ - 552476-why-cant-you-pickle-instancemethods - """ - func_name = method.im_func.__name__ - obj = method.im_self - cls = method.im_class - return _unpickle_method, (func_name, obj, cls) - - -def _unpickle_method(func_name, obj, cls): - """ - The unpickling counterpart to the above function - """ - for cls in cls.mro(): - try: - func = cls.__dict__[func_name] - except KeyError: - pass - else: - break - return func.__get__(obj, cls) - - -copyreg.pickle(types.MethodType, _pickle_method, _unpickle_method) - diff --git a/seisflows/config.py b/seisflows/config.py index ba8d9295..8711dc24 100755 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -12,11 +12,7 @@ """ import os import sys -import json -import types -import pickle import logging -import copyreg import traceback from pkgutil import find_loader from importlib import import_module @@ -69,7 +65,7 @@ def import_seisflows(workdir=os.getcwd(), parameter_file="parameters.yaml"): if name == "workflow": continue modules[name] = custom_import(name, parameters[name])(**parameters) - parameters.pop(name) # drop name so workflow doesnt instantiate it + # parameters.pop(name) # drop name so workflow doesnt instantiate it # Import workflow separately by providing all the instantiated modules to it workflow = \ diff --git a/seisflows/optimize/LBFGS.py b/seisflows/optimize/LBFGS.py index 0a45e581..e5fbd6ed 100644 --- a/seisflows/optimize/LBFGS.py +++ b/seisflows/optimize/LBFGS.py @@ -58,11 +58,12 @@ def __init__(self, lbfgs_mem=3, lbfgs_max=np.inf, lbfgs_thresh=0., super().__init__(**kwargs) # Overwrite user-chosen line search. L-BFGS requires 'Backtrack'ing LS - if self._line_search.title() != "Backtrack": + if self.line_search_method.title() != "Backtrack": logger.warning(f"L-BFGS optimization requires 'backtrack'ing line " - f"search. Overwritng {self._line_search}") + f"search. Overwriting '{self.line_search_method}'") self.line_search_method = "Backtrack" - self._line_search = getattr(line_search_dir, self._line_search)( + self._line_search = getattr( + line_search_dir, self.line_search_method)( step_count_max=self.step_count_max, step_len_max=self.step_len_max ) @@ -113,8 +114,9 @@ def compute_direction(self): # Load the current gradient direction, which is the L-BFGS search # direction if this is the first iteration g = self.load("g_new") + if self._LBFGS_iter == 1: - logger.info("first L-BFGS iteration, default to gradient descent") + logger.info("first L-BFGS iteration, default to 'Gradient' descent") p_new = -1 * g.vector restarted = False @@ -138,16 +140,19 @@ def compute_direction(self): # its angle to the previous search direction if self._check_status(g, q): logger.info("new L-BFGS search direction found") - p_new = -q + p_new = q.update(vector=-1 * q.vector) restarted = False else: logger.info("new search direction not appropriate, defaulting " "to gradient desceitn") self.restart() - p_new = -g + p_new = g.update(vector=-1 * g.vector) restarted = True - # Save values to disk and memory + # Assign newly computed 'p_new' vector to a Model instance + p_new = g.update(vector=p_new) + + # Assign restart condition to internal memory self._restarted = restarted return p_new diff --git a/seisflows/optimize/NLCG.py b/seisflows/optimize/NLCG.py index 8c801521..4d25e11f 100644 --- a/seisflows/optimize/NLCG.py +++ b/seisflows/optimize/NLCG.py @@ -31,11 +31,12 @@ def __init__(self, nlcg_max=np.inf, nlcg_thresh=np.inf, super().__init__(**kwargs) # Overwrite user-chosen line search. L-BFGS requires 'Backtrack'ing LS - if self._line_search.title != "Bracket": + if self.line_search_method.title != "Bracket": logger.warning(f"NLCG optimization requires 'bracket'ing line " - f"search. Overwritng {self._line_search}") + f"search. Overwritng {self.line_search_method}") self.line_search_method = "Bracket" - self._line_search = getattr(line_search_dir, self._line_search)( + self._line_search = getattr( + line_search_dir, self.line_search_method)( step_count_max=self.step_count_max, step_len_max=self.step_len_max ) diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index 9cc6b7f0..1ccba9da 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -152,7 +152,7 @@ def setup(self): Sets up nonlinear optimization machinery """ unix.mkdir(self.path.scratch) - self.checkpoint_line_search() # will be empty + self.checkpoint() # will be empty def load(self, name): """ @@ -210,7 +210,7 @@ def save(self, name, m): else: raise TypeError(f"optimize.save unrecognized type error {type(m)}") - def checkpoint_line_search(self): + def checkpoint(self): """ Convenience wrapper of the underlying _line_search.save_search_history to avoid accessing the private attr. _line_search from outside the class @@ -218,6 +218,9 @@ def checkpoint_line_search(self): self._line_search.check_search_history() self._line_search.save_search_history() + # TODO add in checkpointing for optimization, saving iteration, + # restarted condition, etc? + def _precondition(self, q): """ Apply available preconditioner to a given gradient @@ -272,7 +275,7 @@ def initialize_search(self): gtp = dot(g.vector, p.vector) # Restart plugin line search if the optimization library restarts - if self.restarted: + if self._restarted: self._line_search.clear_history() # Optional safeguard to prevent step length from getting too large diff --git a/seisflows/plugins/line_search/bracket.py b/seisflows/plugins/line_search/bracket.py index 917f52f7..9ade581d 100644 --- a/seisflows/plugins/line_search/bracket.py +++ b/seisflows/plugins/line_search/bracket.py @@ -83,8 +83,9 @@ def check_search_history(self): assert(len(self.gtg) == len(self.gtp)), f"too many entries for 'gtg'" assert(len(self.func_vals) == len(self.step_lens)), \ f"number of function evaluations does not match step lengths" - assert(self.step_count + 1 == len(self.func_vals)), \ - f"current step coutn doesn't match the number of function evals" + if self.func_vals: + assert(self.step_count + 1 == len(self.func_vals)), \ + f"current step count doesn't match the number of function evals" def save_search_history(self, file=None): """ diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 273b2d70..4dd0ea1e 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -16,7 +16,6 @@ import os import sys import inspect -import logging import warnings import argparse import traceback @@ -25,7 +24,7 @@ from IPython import embed from seisflows import logger -from seisflows.config import custom_import, NAMES, ROOT_DIR, config_logger, import_seisflows +from seisflows.config import custom_import, NAMES, ROOT_DIR, import_seisflows from seisflows.tools import unix, msg from seisflows.tools.core import load_yaml, Dict from seisflows.tools.specfem import (getpar, setpar, getpar_vel_model, @@ -321,8 +320,6 @@ class SeisFlows: any checks we must load the entire SeisFlows environment, which is slow but provides the most flexibility when accessing internal information """ - logger = logging.getLogger(__name__).getChild(__qualname__) - def __init__(self): """ Parse user-defined arguments and establish internal parameters used to @@ -575,7 +572,8 @@ def submit(self, **kwargs): workflow = import_seisflows(workdir=self._args.workdir, parameter_file=self._args.parameter_file) - workflow.system.submit(workflow) + system = workflow._modules.system + system.submit(workflow) def clean(self, force=False, **kwargs): """ @@ -602,21 +600,11 @@ def clean(self, force=False, **kwargs): "(y/[n])", header="clean", border="=")) if check == "y": - # CFGPATHS defines the outermost directory structure of SeisFlows - # We safeguard below against deleting the parameter file - items = [] - for fid_ in CFGPATHS.values(): - for fid in glob(os.path.join(self._args.workdir, fid_)): - # Safeguards against deleting files that should not be dltd - try: - assert("yaml" not in fid) - assert(not os.path.islink(fid)) - unix.rm(fid) - items.append(f"- deleting file/folder: {fid}") - except AssertionError: - items.append(f"+ skipping over: {fid}") - continue - print(msg.cli(items=items, header="clean", border="=")) + pars = load_yaml(self._args.parameter_file) + unix.rm(pars.path_scratch) + unix.rm(pars.path_output) + unix.rm(pars.path_log_files) + unix.rm(pars.path_state_file) def restart(self, force=False, **kwargs): """ @@ -637,13 +625,11 @@ def debug(self, **kwargs): # Break out sub-modules and parameters so they're more easily accesible parameters = load_yaml(self._args.parameter_file) - system = workflow.system - solver = workflow.solver - preprocess = workflow.preprocess - optimize = workflow.optimize + system, solver, preprocess, optimize = workflow._modules.values() + print("Loaded SeisFlows Modules:") for module in [workflow, system, solver, preprocess, optimize]: - print(module) + print(f"{module.__class__}") print(msg.cli("SeisFlows's debug mode is an embedded IPython " "environment. All modules are loaded by default. " diff --git a/seisflows/system/cluster.py b/seisflows/system/cluster.py index fd40f89d..fe42f3b8 100644 --- a/seisflows/system/cluster.py +++ b/seisflows/system/cluster.py @@ -3,8 +3,15 @@ The Cluster class provides the core utilities interaction with HPC systems which must be overloaded by subclasses for specific workload managers, or specific clusters. + +The `Cluster` class acts as a base class for more specific cluster +implementations (like SLURM). However it can be used standalone. When running +jobs on the `Cluster` system, jobs will be submitted to the master system +using `subprocess.run`, mimicing how jobs would be run on a cluster but not +actually submitting to any job scheduler. """ import os +import sys import dill import subprocess from seisflows import logger @@ -49,7 +56,7 @@ def __init__(self, title=None, mpiexec="", walltime=10, tasktime=1, self.mpiexec = mpiexec self.walltime = walltime self.tasktime = tasktime - self.environs = environs + self.environs = environs or "" def _pickle_func_list(self, funcs, **kwargs): """ @@ -103,11 +110,18 @@ def run(self, funcs, single=False, run_call=None, **kwargs): attempts default run call which should be suited for the given system """ + # Single tasks only need to be run one time, as `TASK_ID` === 0 + if single: + ntasks = 1 + else: + ntasks = self.ntask + funcs_fid, kwargs_fid = self._pickle_func_list(funcs, **kwargs) logger.info(f"running functions {[_.__name__ for _ in funcs]} on " f"system {self.ntask} times") if run_call is None: + # e.g., run --funcs func.p --kwargs kwargs.p --environment ... run_call = " ".join([ f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'run')}", f"--funcs {funcs_fid}", @@ -116,8 +130,13 @@ def run(self, funcs, single=False, run_call=None, **kwargs): ]) logger.debug(run_call) - for task_id in range(self.ntask): + for task_id in range(ntasks): logger.debug(f"running task id {task_id} " f"(job {task_id + 1}/{self.ntask})") # Subprocess waits for the process to end before running the next - subprocess.run(run_call.format(task_id=task_id), shell=True) + try: + subprocess.run(run_call.format(task_id=task_id), shell=True) + except subprocess.CalledProcessError as e: + logger.critical(f"run task_id {task_id} has failed with error " + f"message {e}") + sys.exit(-1) diff --git a/seisflows/system/runscripts/run_funcs.py b/seisflows/system/runscripts/run_funcs.py index f3cb61d1..6369a3a8 100755 --- a/seisflows/system/runscripts/run_funcs.py +++ b/seisflows/system/runscripts/run_funcs.py @@ -69,7 +69,11 @@ def export(myenv): os.environ[key] = val # Variables to be deleted will not split on '=', throwing ValueError except ValueError: - del os.environ[item] + try: + del os.environ[item] + # If a NoneType sneaks through, it will throw TypeEror on 'del' + except TypeError: + continue if __name__ == '__main__': diff --git a/seisflows/system/workstation.py b/seisflows/system/workstation.py index aa75fac4..299aabd2 100644 --- a/seisflows/system/workstation.py +++ b/seisflows/system/workstation.py @@ -71,7 +71,7 @@ def __init__(self, ntask=1, nproc=1, log_level="DEBUG", verbose=False, par_file=path_par_file or os.path.join(workdir, "parameters.yaml"), output=path_output or os.path.join(workdir, "output"), log_files=path_log_files or os.path.join(workdir, "logs"), - output_log=path_output_log or os.path.join(workdir, "sfoutput.log"), + output_log=path_output_log or os.path.join(workdir, "sflog.txt"), ) self._acceptable_log_levels = ["CRITICAL", "WARNING", "INFO", "DEBUG"] @@ -130,8 +130,8 @@ def submit(self, workflow, submit_call=None): subprocess. This is only needed for overriding classes, it has no effect on the Workstation class """ - workflow.setup() workflow.check() + workflow.setup() workflow.run() def run(self, funcs, single=False, **kwargs): diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index 80049c47..baae46f9 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -93,7 +93,7 @@ def __init__(self, modules=None, data_case="data", export_traces=False, model_init=path_model_init, model_true=path_model_true, state_file=path_state_file or - os.path.join(workdir, ".statefile.txt"), + os.path.join(workdir, "sfstate.txt"), data=path_data, ) @@ -230,9 +230,9 @@ def setup(self): # Distribute modules to the class namespace. We don't do this at init # incase _modules was set as NoneType - self.solver = self._modules.solver - self.system = self._modules.system - self.preprocess = self._modules.preprocess + self.solver = self._modules.solver # NOQA + self.system = self._modules.system # NOQA + self.preprocess = self._modules.preprocess # NOQA def checkpoint(self): """ diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index 9d5cc7f7..e7abf157 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -37,6 +37,11 @@ class Inversion(Migration): :param start: start inversion workflow at this iteration. 1 <= start <= inf :type end: int :param end: end inversion workflow at this iteration. start <= end <= inf + :type thrifty: bool + :param thrifty: a thrifty inversion skips the costly intialization step + (i.e., forward simulations and misfit quantification) if the final + forward simulations from the previous iterations line search can be + used in the current one. Requires L-BFGS optimization. :type export_model: bool :param export_model: export best-fitting model from the line search to disk. If False, new models can be discarded from scratch at any time. @@ -49,23 +54,36 @@ class Inversion(Migration): """ __doc__ = Migration.__doc__ + __doc__ - def __init__(self, modules=None, start=1, end=1, export_model=True, - path_eval_func=None, **kwargs): - """Instantiate Inversion-specific parameters""" - + def __init__(self, modules=None, start=1, end=1, thrifty=False, + optimize="LBFGS", export_model=True, path_eval_func=None, + **kwargs): + """ + Instantiate Inversion-specific parameters. Non-essential parameters are + listed here, rather than in the class docstring. + + :type optimize: str + :param optimize: Name of the optimization module chosen by the user. + This should be instantiated by default when using `import_seisflows` + Used to check that the correct module is set when performing a + `thrifty` inversion. + """ super().__init__(**kwargs) self._modules = modules self.start = start self.end = end self.export_model = export_model + self.thrifty = thrifty - self.path.eval_func = path_eval_func or \ - os.path.join(self.path.workdir, "scratch", + # Append an additional path for line search function evaluations + self.path["eval_func"] = path_eval_func or \ + os.path.join(self.path.workdir, "scratch", "eval_func") # Internal attribute for keeping track of inversion self._iteration = start + self._optimize_name = optimize + self._thrifty_status = False self._required_modules = ["system", "solver", "preprocess", "optimize"] @property @@ -107,6 +125,12 @@ def check(self): f"Incorrect START or END parameter. Values must be in order: " \ f"1 <= {self.start} <= {self.end}" + if self.thrifty: + assert(self._optimize_name == "LBFGS"), ( + f"a `thrifty` inversion requires the optimization module to be " + f"set as 'LBFGS'" + ) + def setup(self): """ Assigns modules as attributes of the workflow. I.e., `self.solver` to @@ -130,11 +154,32 @@ def run(self): def evaluate_initial_misfit(self): """ - Overwrite `workflow.forward` just to sum residuals output by preprocess - module and save them to disk, to be discoverable by the optimization - library + Overwrite `workflow.forward` to skip over initial misfit evaluation + (using `MODEL_INIT`) if we are past iteration 1. Additionally, sum + residuals output by preprocess module and save float to disk, to be + discoverable by the optimization library """ - super().evaluate_initial_misfit() + if self._iteration == 1: + super().evaluate_initial_misfit() + else: + # Thrifty inversion SKIPS initial misfit evaluation, re-using final + # model from previous line search. Can only happen mid-workflow + if self.thrifty and ( + self._thrifty_status or self._iteration == self.start): + logger.info(msg.mnr("THRIFTY INVERSION; SKIP MISFIT EVAL")) + else: + logger.info(msg.mnr("EVALUATING MISFIT FOR MODEL `m_new`")) + # Previous line search will have saved `m_new` as the initial model, + # export in SPECFEM format to a path discoverable by all solvers + path_model = os.path.join(self.path.eval_grad, "model") + m_new = self.optimize.load("m_new") + m_new.write(path=path_model) + # Run forward simulation/misfit quantification with previous model + self.system.run( + [self.evaluate_objective_function], + path_model=path_model, + save_residuals=os.path.join(self.path.eval_grad, "residuals") + ) # Override function to sum residuals into the optimization library residuals = np.loadtxt(os.path.join(self.path.eval_grad, "residuals")) @@ -185,7 +230,7 @@ def initialize_line_search(self): m_try, alpha = self.optimize.initialize_search() self.optimize.save(name="m_try", m=m_try) self.optimize.save(name="alpha", m=alpha) - self.optimize.checkpoint_line_search() + self.optimize.checkpoint() # Expose model `m_try` to the solver by placing it in eval_func dir. m_try.write(path=os.path.join(self.path.eval_func, "model")) @@ -215,14 +260,14 @@ def perform_line_search(self): # Increment step count, calculate new step length/model, check misfit status = self.optimize.update_line_search() - self.optimize.checkpoint_line_search() + self.optimize.checkpoint() # Proceed based on the outcome of the line search if status == 1: # Save outcome of line search to disk; reset step to 0 for next iter logger.info("trial step successful. finalizing line search") self.optimize.finalize_search() - self.optimize.checkpoint_line_search() + self.optimize.checkpoint() return elif status == 0: logger.info("trial step unsuccessful. re-attempting line search") @@ -259,14 +304,46 @@ def _evaluate_line_search_misfit(self): def clean_scratch_directory(self): """ Cleans directories in which function and gradient evaluations were - carried out + carried out. Contains some logic to consider whether or not to continue + with a thrifty inversion. """ logger.info(msg.mnr("CLEANING WORKDIR FOR NEXT ITERATION")) - unix.rm(self.path.eval_grad) - unix.rm(self.path.eval_func) + self._thrifty_status = self._update_thrifty_status() + if self._thrifty_status: + unix.rm(self.path.eval_grad) + # Eval func model now defines the current model 'm_new' + unix.mv(self.path.eval_func, self.path.eval_grad) + unix.mkdir(self.path.eval_func) + else: + unix.rm(self.path.eval_grad) + unix.rm(self.path.eval_func) - unix.mkdir(self.path.eval_grad) - unix.mkdir(self.path.eval_func) + unix.mkdir(self.path.eval_grad) + unix.mkdir(self.path.eval_func) + def _update_thrifty_status(self): + """ + Determine if line search forward simulation can be carried over to the + next iteration. Checks criteria related to the current iteration and + its position relative to the start and end of the workflow. + """ + if self._iteration == self.start: + logger.info("thrifty inversion encountering first iteration, " + "defaulting to standard inversion workflow") + _thrifty_status = False + elif self.optimize._restarted: # NOQA + logger.info("optimization has been restarted, defaulting to " + "standard inversion workflow") + _thrifty_status = False + elif self.optimize.iter == self.end: + logger.info("thrifty inversion encountering final iteration, " + "defaulting to inversion workflow") + _thrifty_status = False + else: + logger.info("acceptable conditions for thrifty inverison, " + "continuing with thrifty inversion") + _thrifty_status = True + + return _thrifty_status From 487675a3d5bb52dce098bbf55787170bb960033a Mon Sep 17 00:00:00 2001 From: bch0w Date: Mon, 25 Jul 2022 15:09:15 -0800 Subject: [PATCH 078/195] inversion workflow now working with checkpointing of optimization module, has ability to resume failed workflow mid-iteratidue to state file starting to remove old tests, starting to write new tests --- seisflows/config.py | 85 ------------------- seisflows/optimize/gradient.py | 103 ++++++++++++++++------- seisflows/plugins/line_search/bracket.py | 30 ------- seisflows/seisflows.py | 4 +- seisflows/solver/specfem.py | 8 +- seisflows/tests/test_config.py | 33 -------- seisflows/tools/specfem.py | 8 +- seisflows/workflow/forward.py | 7 +- seisflows/workflow/inversion.py | 96 ++++++++++++++++----- 9 files changed, 159 insertions(+), 215 deletions(-) diff --git a/seisflows/config.py b/seisflows/config.py index 8711dc24..fa49286b 100755 --- a/seisflows/config.py +++ b/seisflows/config.py @@ -217,88 +217,3 @@ class 'Inversion'. print(msg.cli(f"The following method was not found in the imported " f"class: seisflows.{name}.{module}.{classname}")) sys.exit(-1) - -# def save(path): -# """ -# Export the current Python environment to disk as Pickle and JSON files, -# which allows us to checkpoint a current workflow and resume without -# loss of information. -# -# :type path: str -# :param path: path to save the current session -# """ -# if not os.path.exists(path): -# unix.mkdir(path) -# -# # Save the paths and parameters into a JSON file -# for name in ["seisflows_parameters", "seisflows_paths"]: -# fullfile = os.path.join(path, f"{name}.json") -# with open(fullfile, "w") as f: -# json.dump(sys.modules[name], f, sort_keys=True, indent=4) -# -# # Save the current workflow as pickle objects -# for name in NAMES: -# fullfile = os.path.join(path, f"seisflows_{name}.p") -# with open(fullfile, "wb") as f: -# pickle.dump(sys.modules[f"seisflows_{name}"], f) -# -# -# def load(path): -# """ -# Imports a previously saved session from disk by reading in JSON and -# Pickle files which define a saved Python environment -# -# :type path: str -# :param path: path to the previously saved session -# """ -# # Load parameters and paths from a JSON file -# for name in ["seisflows_parameters", "seisflows_paths"]: -# fullfile = os.path.join(os.path.abspath(path), f"{name}.json") -# with open(fullfile, "r") as f: -# sys.modules[name] = Dict(json.load(f)) -# -# # Load the saved workflow from pickle objects -# for name in NAMES: -# fullfile = os.path.join(os.path.abspath(path), f"seisflows_{name}.p") -# with open(fullfile, "rb") as f: -# sys.modules[f"seisflows_{name}"] = pickle.load(f) - -# def _pickle_method(method): -# """ -# The following code changes how instance methods are handled by pickle. -# Placing it in this module ensures that pickle changes will be in -# effect for all SeisFlows workflows -# -# Note: For relevant discussion, see stackoverflow thread: -# "Can't pickle when using python's -# multiprocessing Pool.map()" -# -# Relevant Links (last accessed 01.20.2020): -# https://stackoverflow.com/questions/7016567/ -# picklingerror-when-using-multiprocessing -# -# https://bytes.com/topic/python/answers/ -# 552476-why-cant-you-pickle-instancemethods -# """ -# func_name = method.im_func.__name__ -# obj = method.im_self -# cls = method.im_class -# return _unpickle_method, (func_name, obj, cls) -# -# -# def _unpickle_method(func_name, obj, cls): -# """ -# The unpickling counterpart to the above function -# """ -# for cls in cls.mro(): -# try: -# func = cls.__dict__[func_name] -# except KeyError: -# pass -# else: -# break -# return func.__get__(obj, cls) - - -# copyreg.pickle(types.MethodType, _pickle_method, _unpickle_method) - diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index 1ccba9da..87f76c30 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -67,10 +67,10 @@ class Gradient: formatted the same as the input model (or output model of solver). Required to exist and contain files if `preconditioner`==True """ - def __init__(self, line_search_method="bracket", preconditioner=None, - step_count_max=10, step_len_init=0.05, step_len_max=0.5, - workdir=os.getcwd(), path_optimize=None, path_output=None, - path_preconditioner=None, **kwargs): + def __init__(self, line_search_method="bracket", + preconditioner=None, step_count_max=10, step_len_init=0.05, + step_len_max=0.5, workdir=os.getcwd(), path_optimize=None, + path_output=None, path_preconditioner=None, **kwargs): """ Gradient-descent input parameters. @@ -100,6 +100,10 @@ def __init__(self, line_search_method="bracket", preconditioner=None, preconditioner=path_preconditioner, ) + # Hidden paths to store checkpoint file in scratch directory + self.path["_checkpoint"] = os.path.join(self.path.scratch, + "checkpoint") + # Internal check to see if the chosen line search algorithm exists if not hasattr(line_search_dir, line_search_method): logger.warning(f"{line_search_method} is not a valid line search " @@ -120,7 +124,6 @@ def __init__(self, line_search_method="bracket", preconditioner=None, self._line_search = getattr( line_search_dir, line_search_method.title())( step_count_max=step_count_max, step_len_max=step_len_max, - path=os.path.join(self.path.scratch, "line_search") ) @property @@ -152,9 +155,12 @@ def setup(self): Sets up nonlinear optimization machinery """ unix.mkdir(self.path.scratch) + + # Load checkpoint (if resuming) or save current checkpoint + self.load_checkpoint() self.checkpoint() # will be empty - def load(self, name): + def load_vector(self, name): """ Convenience function to access the full paths of model and gradient vectors that are saved to disk @@ -189,7 +195,7 @@ def load(self, name): return model - def save(self, name, m): + def save_vector(self, name, m): """ Convenience function to save/overwrite vectors on disk @@ -212,14 +218,47 @@ def save(self, name, m): def checkpoint(self): """ - Convenience wrapper of the underlying _line_search.save_search_history - to avoid accessing the private attr. _line_search from outside the class + The optimization module (and its underlying `line_search` attribute) + requires continuity across runs of the same workflow (e.g., in the + event of a failed job). This function saves internal attributes of + the optimization module to disk so that a resumed workflow does not + lose information from its previous version. + + User can checkpoint other variables by adding kwargs """ - self._line_search.check_search_history() - self._line_search.save_search_history() + dict_out = dict(restarted=self._restarted, + func_vals=self._line_search.func_vals, + step_lens=self._line_search.step_lens, + gtg=self._line_search.gtg, + gtp=self._line_search.gtp, + step_count=self._line_search.step_count) + + np.savez(file=self.path._checkpoint, **dict_out) - # TODO add in checkpointing for optimization, saving iteration, - # restarted condition, etc? + def load_checkpoint(self): + """ + Counterpart to `optimize.checkpoint`. Loads a checkpointed optimization + module from disk and sets internal tracking attributes. + """ + # NumPy appends '.npz' when saving. Make sure we honor that. + if not self.path._checkpoint.endswith(".npz"): + fid = f"{self.path._checkpoint}.npz" + else: + fid = self.path._checkpoint + + if os.path.exists(fid): + logger.info("re-loading optimization module from checkpoint") + dict_in = np.load(file=fid) + + self._restarted = bool(dict_in["restarted"]) + self._line_search.func_vals = list(dict_in["func_vals"]) + self._line_search.step_lens = list(dict_in["step_lens"]) + self._line_search.gtg = list(dict_in["gtg"]) + self._line_search.gtp = list(dict_in["gtp"]) + self._line_search.step_count = int(dict_in["step_count"]) + else: + logger.info("no optimization checkpoint found, assuming first run") + self.checkpoint() def _precondition(self, q): """ @@ -253,7 +292,7 @@ def compute_direction(self): :rtype: seisflows.tools.specfem.Model :return: search direction as a Model instance """ - g_new = self.load("g_new") + g_new = self.load_vector("g_new") p_new = g_new.update(vector=-1 * self._precondition(g_new.vector)) return p_new @@ -264,10 +303,10 @@ def initialize_search(self): direction with a given step length, calculated by the chosen line search algorithm. """ - m = self.load("m_new") # current model from external solver - g = self.load("g_new") # current gradient from scaled kernels - p = self.load("p_new") # current search direction from optimization - f = self.load("f_new") # current misfit value from preprocess + m = self.load_vector("m_new") # current model from external solver + g = self.load_vector("g_new") # current gradient from scaled kernels + p = self.load_vector("p_new") # current search direction + f = self.load_vector("f_new") # current misfit value from preprocess norm_m = max(abs(m.vector)) norm_p = max(abs(p.vector)) @@ -287,7 +326,7 @@ def initialize_search(self): # Initialize the line search and save it to disk. self._line_search.update_search_history(func_val=f, step_len=0., gtg=gtg, gtp=gtp) - self._line_search.check_search_history() + # self._line_search.check_search_history() alpha, _ = self._line_search.calculate_step_length() @@ -317,13 +356,13 @@ def update_line_search(self): status == -1 : failed """ # Collect information on a forward evaluation that just took place - alpha = self.load("alpha") # step length - f_try = self.load("f_try") # misfit for the trial model + alpha = self.load_vector("alpha") # step length + f_try = self.load_vector("f_try") # misfit for the trial model # Update the line search with a new step length and misfit value self._line_search.step_count += 1 self._line_search.update_search_history(step_len=alpha, func_val=f_try) - self._line_search.check_search_history() + # self._line_search.check_search_history() # Calculate a new step length based on line search algorithm alpha_try, status = self._line_search.calculate_step_length() @@ -331,18 +370,18 @@ def update_line_search(self): # Status == 0: Retry line search // Status == 1: Line search passed if status in [0, 1]: # Save new step length to disk - self.save("alpha", alpha_try) + self.save_vector("alpha", alpha_try) # Create a new trial model based on search direction, step length # and the initial model vector - m = self.load("m_new") - p = self.load("p_new") + m = self.load_vector("m_new") + p = self.load_vector("p_new") # Sets the latest trial model m_try = m.update(vector=m.vector + alpha * p.vector) logger.info("trial model 'm_try' parameters: ") m_try.check() - self.save("m_try", m_try) + self.save_vector("m_try", m_try) return status @@ -383,7 +422,7 @@ def finalize_search(self): # Choose minimum misfit value as final misfit/model. index 0 is initial f = self._line_search.get_search_history()[1] - self.save("f_new", f.min()) + self.save_vector("f_new", f.min()) logger.info(f"misfit of accepted trial model is f={f.min():.3E}") logger.info("resetting line search step count to 0") @@ -406,8 +445,8 @@ def attempt_line_search_restart(self, threshold=1E-3): :rtype: int :return: pass (1) fail (0) status for retrying line search """ - g = self.load("g_new") - p = self.load("p_new") + g = self.load_vector("g_new") + p = self.load_vector("p_new") theta = angle(p.vector, -1 * g.vector) logger.debug(f"checking gradient/search direction angle, " @@ -447,15 +486,15 @@ def _write_stats(self): # First time, write header information if not os.path.exists(fid): with open(fid, "w") as f: - for header in ["ITER", # "FACTOR", + for header in [# "ITER", "FACTOR", "GRAD_NORM_L1", "GRAD_NORM_L2", "MISFIT", "RESTART", "SLOPE", "STEP", "LENGTH", "THETA"]: f.write(f"{header.upper()},") f.write("\n") - g = self.load("g_new") - p = self.load("p_new") + g = self.load_vector("g_new") + p = self.load_vector("p_new") x, f, *_ = self._line_search.get_search_history() # Calculated stats factors diff --git a/seisflows/plugins/line_search/bracket.py b/seisflows/plugins/line_search/bracket.py index 9ade581d..64f7744f 100644 --- a/seisflows/plugins/line_search/bracket.py +++ b/seisflows/plugins/line_search/bracket.py @@ -87,36 +87,6 @@ def check_search_history(self): assert(self.step_count + 1 == len(self.func_vals)), \ f"current step count doesn't match the number of function evals" - def save_search_history(self, file=None): - """ - Save the current line search history to disk. Used to re-load a line - search from disk in the case of a failed search - """ - if file is None: - file = self.path - - dict_out = dict(func_vals=self.func_vals, step_lens=self.step_lens, - gtg=self.gtg, gtp=self.gtp, step_count=self.step_count) - np.savez(file=file, **dict_out) - - def load_search_history(self, file=None): - """ - Load line search history from disk. Used to re-load line search in the - case of failed line searches. - """ - if file is None: - file = self.path - - # Numpy will append .npz to saved files, just ensure we honor that - if not file.endswith(".npz"): - file = f"{file}.npz" - - dict_in = np.load(file=file) - self.step_count = int(dict_in["step_count"]) # only var thats not list - for key in ["func_vals", "step_lens", "gtg", "gtp"]: - assert(key in dict_in), f"line search .npz file has no key {key}" - setattr(self, key, list(dict_in[key])) - def get_search_history(self, sort=True): """ A convenience function, collects information based on the current diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 4dd0ea1e..59c2e86f 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -476,7 +476,8 @@ def configure(self, absolute_paths=False, **kwargs): written = [] for module in modules: for key, val in module.path.items(): - if key in written: + # '_key' means hidden path so don't include in par file + if key in written or key.startswith("_"): continue if val is None: val = "null" @@ -605,6 +606,7 @@ def clean(self, force=False, **kwargs): unix.rm(pars.path_output) unix.rm(pars.path_log_files) unix.rm(pars.path_state_file) + unix.rm(pars.path_output_log) def restart(self, force=False, **kwargs): """ diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 33bcf9d1..bcac58d0 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -519,7 +519,7 @@ def adjoint_simulation(self, executables=None, save_kernels=False, names = glob(f"*proc??????_{tag}_kernel{self._ext}") unix.rename(old="alpha", new="vp", names=names) - logger.debug(f"renaming output event kernels: 'alpha' -> 'vp'") + logger.debug(f"renaming output event kernels: 'beta' -> 'vs'") for tag in ["beta", "beta[hv]", "reg1_beta", "reg1_beta[hv]"]: names = glob(f"*proc??????_{tag}_kernel{self._ext}") unix.rename(old="beta", new="vs", names=names) @@ -670,7 +670,7 @@ def _run_binary(self, executable, stdout="solver.log"): header="external solver error", border="=")) sys.exit(-1) - # Append with mpiexec if we are running with MPI + # Prepend with `mpiexec` if we are running with MPI if self._mpiexec: executable = f"{self._mpiexec} {executable}" @@ -684,7 +684,7 @@ def _run_binary(self, executable, stdout="solver.log"): "nonzero exit code (failure). Consider stopping any " "currently running jobs to avoid wasted " "computational resources. Check 'scratch/solver/" - "mainsolver/{stdout}' for the solvers stdout log " + f"mainsolver/{stdout}' for the solvers stdout log " "message. The failing command and error message are:", items=[f"exc: {executable}", f"err: {e}"], header="external solver error", @@ -742,8 +742,6 @@ def _initialize_working_directories(self): for source_name in self.source_names: cwd = os.path.join(self.path.scratch, source_name) if os.path.exists(cwd): - logger.warning(f"solver scratch path for source {source_name} " - f"already exists") continue self._initialize_working_directory(cwd=cwd) diff --git a/seisflows/tests/test_config.py b/seisflows/tests/test_config.py index cc725546..c200878f 100644 --- a/seisflows/tests/test_config.py +++ b/seisflows/tests/test_config.py @@ -2,44 +2,11 @@ Test the SeisFlows configuration script, which configures the compute system and the working environment required for SF to run properly """ -import os -import shutil import pytest from seisflows import config -TEST_DIR = os.path.join(config.ROOT_DIR, "tests") - - -@pytest.fixture -def copy_par_file(tmpdir): - """ - Copy the template parameter file into the temporary test directory - :rtype: str - :return: location of the parameter file - """ - src = os.path.join(TEST_DIR, "test_data", "test_filled_parameters.yaml") - dst = os.path.join(tmpdir, "parameters.yaml") - shutil.copy(src, dst) - - -def test_seisflows_constants(): - """ - Ensure that the constants set in the Config file have not changed - Essentially a double check to make sure these things haven't been edited - because the rest of the package depends on these being accesible and - the same - """ - names_check = ["system", "preprocess", "solver", "optimize", "workflow"] - - root_dir_check = os.path.join( - os.path.dirname(os.path.abspath(__file__)), ".." - ) - - assert(config.NAMES == names_check) - assert(os.path.samefile(config.ROOT_DIR, root_dir_check)) - def test_custom_import(): """ diff --git a/seisflows/tools/specfem.py b/seisflows/tools/specfem.py index 532a58f4..6fec3297 100644 --- a/seisflows/tools/specfem.py +++ b/seisflows/tools/specfem.py @@ -533,9 +533,13 @@ def setpar(key, val, file, delim="=", match_partial=False): lines = open(file, "r").readlines() - # Replace value in place + # Replace value in place. We only want to replace the 'LAST' occurrence + # otherwise we risk replacing the actual key (e.g., 'data_case' == 'data') + # will replace 'data' twice with a normal .replace() if val_out != "": - lines[i] = lines[i].replace(val_out, str(val)) + line_reverse = lines[i][::-1].replace(val_out[::-1], str(val)[::-1], 1) + lines[i] = line_reverse[::-1] + # lines[i] = lines[i].replace(val_out, str(val)) else: # Special case where the initial parameter is empty so we just replace # the newline formatter at the end diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index baae46f9..f3e6aba4 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -165,12 +165,12 @@ def check(self): if self.data_case.lower() == "data": assert(self.path.data is not None and os.path.exists(self.path.data)), \ - f"importing data with `data_case`=='import' requires " \ + f"importing data with `data_case`=='data' requires " \ f"'path_data' to exist" elif self.data_case.lower() == "synthetic": assert(self.path.model_true is not None and os.path.exists(self.path.model_true)), \ - f"creating data with `data_case`=='create' requires " \ + f"creating data with `data_case`=='synthetic' requires " \ f"'path_model_true' to exist and point to a target model" else: logger.warning(f"`workflow.data_case` is None, SeisFlows will not " @@ -252,8 +252,6 @@ def checkpoint(self): for key, val in self._states.items(): f.write(f"{key}: {val}\n") - # Pickle the current working state so that system can load it during run - def run(self): """ Call the Task List in order to 'run' the workflow. Contains logic for @@ -275,6 +273,7 @@ def run(self): try: func() self._states[func.__name__] = "completed" + self.checkpoint() except Exception as e: self._states[func.__name__] = "failed" self.checkpoint() diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index e7abf157..1a2f5269 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -37,6 +37,10 @@ class Inversion(Migration): :param start: start inversion workflow at this iteration. 1 <= start <= inf :type end: int :param end: end inversion workflow at this iteration. start <= end <= inf + :type iteration: int + :param iteration: The current iteration of the workflow. If NoneType, takes + the value of `start` (i.e., first iteration of the workflow). User can + also set between `start` and `end` to resume a failed workflow. :type thrifty: bool :param thrifty: a thrifty inversion skips the costly intialization step (i.e., forward simulations and misfit quantification) if the final @@ -54,8 +58,9 @@ class Inversion(Migration): """ __doc__ = Migration.__doc__ + __doc__ - def __init__(self, modules=None, start=1, end=1, thrifty=False, - optimize="LBFGS", export_model=True, path_eval_func=None, + def __init__(self, modules=None, start=1, end=1, + thrifty=False, optimize="LBFGS", export_model=True, + path_eval_func=None, **kwargs): """ Instantiate Inversion-specific parameters. Non-essential parameters are @@ -81,11 +86,17 @@ def __init__(self, modules=None, start=1, end=1, thrifty=False, "eval_func") # Internal attribute for keeping track of inversion - self._iteration = start self._optimize_name = optimize self._thrifty_status = False self._required_modules = ["system", "solver", "preprocess", "optimize"] + # Grab iteration from state file + if "iteration" in self._states: + self.iteration = int(self._states["iteration"]) + logger.debug(f"setting iteration=={self.iteration} from state file") + else: + self.iteration = start + @property def task_list(self): """ @@ -112,7 +123,7 @@ def task_list(self): self.evaluate_gradient_from_kernels, self.initialize_line_search, self.perform_line_search, - self.clean_scratch_directory + self.finalize_iteration ] def check(self): @@ -125,6 +136,18 @@ def check(self): f"Incorrect START or END parameter. Values must be in order: " \ f"1 <= {self.start} <= {self.end}" + assert(self.start <= self.iteration <= self.end), \ + f"`workflow.iteration` must be between `start` and `end`" + + if self.iteration > 1: + assert(os.path.exists(self.path.eval_grad)), \ + f"scratch path `eval_grad` does not exist but should for a " \ + f"workflow with `iteration` >= 1" + + if self.iteration >= self.end: + logger.warning(f"current `iteration` is >= chosen `end` point. " + f"Inversion workflow will not `run`") + if self.thrifty: assert(self._optimize_name == "LBFGS"), ( f"a `thrifty` inversion requires the optimization module to be " @@ -143,14 +166,28 @@ def setup(self): super().setup() unix.mkdir(self.path.eval_func) + self.optimize = self._modules.optimize + # If optimization has been run before, re-load from checkpoint + self.optimize.load_checkpoint() def run(self): """Call the forward.run() function iteratively, from `start` to `end`""" - for i in range(self.start, self.end + 1): - logger.info(msg.mnr(f"RUNNING ITERATION {i:0>2}")) - super().run() - logger.info(msg.mnr(f"COMPLETED ITERATION {i:0>2}")) + while self.iteration < self.end: + logger.info(msg.mnr(f"RUNNING ITERATION {self.iteration:0>2}")) + super().run() # Runs task list + logger.info(msg.mnr(f"COMPLETED ITERATION {self.iteration:0>2}")) + # Clear the state file for new iteration + self._states = {} + self.checkpoint() + + def checkpoint(self): + """ + Add an additional line in the state file to keep track of iteration, + """ + super().checkpoint() + with open(self.path.state_file, "a") as f: + f.write(f"iteration: {self.iteration}") def evaluate_initial_misfit(self): """ @@ -159,20 +196,20 @@ def evaluate_initial_misfit(self): residuals output by preprocess module and save float to disk, to be discoverable by the optimization library """ - if self._iteration == 1: + if self.iteration == 1: super().evaluate_initial_misfit() else: # Thrifty inversion SKIPS initial misfit evaluation, re-using final # model from previous line search. Can only happen mid-workflow if self.thrifty and ( - self._thrifty_status or self._iteration == self.start): + self._thrifty_status or self.iteration == self.start): logger.info(msg.mnr("THRIFTY INVERSION; SKIP MISFIT EVAL")) else: logger.info(msg.mnr("EVALUATING MISFIT FOR MODEL `m_new`")) # Previous line search will have saved `m_new` as the initial model, # export in SPECFEM format to a path discoverable by all solvers path_model = os.path.join(self.path.eval_grad, "model") - m_new = self.optimize.load("m_new") + m_new = self.optimize.load_vector("m_new") m_new.write(path=path_model) # Run forward simulation/misfit quantification with previous model self.system.run( @@ -184,7 +221,7 @@ def evaluate_initial_misfit(self): # Override function to sum residuals into the optimization library residuals = np.loadtxt(os.path.join(self.path.eval_grad, "residuals")) total_misfit = self.preprocess.sum_residuals(residuals) - self.optimize.save(name="f_new", m=total_misfit) + self.optimize.save_vector(name="f_new", m=total_misfit) def evaluate_gradient_from_kernels(self): """ @@ -195,10 +232,10 @@ def evaluate_gradient_from_kernels(self): super().evaluate_gradient_from_kernels() model = Model(os.path.join(self.path.eval_grad, "model")) - self.optimize.save(name="m_new", m=model) + self.optimize.save_vector(name="m_new", m=model) gradient = Model(path=os.path.join(self.path.eval_grad, "gradient")) - self.optimize.save(name="g_new", m=gradient) + self.optimize.save_vector(name="g_new", m=gradient) def initialize_line_search(self): """ @@ -224,12 +261,12 @@ def initialize_line_search(self): header="line search error") ) sys.exit(-1) - self.optimize.save(name="p_new", m=p_new) + self.optimize.save_vector(name="p_new", m=p_new) # Scale search direction with step length alpha generate a model update m_try, alpha = self.optimize.initialize_search() - self.optimize.save(name="m_try", m=m_try) - self.optimize.save(name="alpha", m=alpha) + self.optimize.save_vector(name="m_try", m=m_try) + self.optimize.save_vector(name="alpha", m=alpha) self.optimize.checkpoint() # Expose model `m_try` to the solver by placing it in eval_func dir. @@ -250,8 +287,6 @@ def perform_line_search(self): status == 0 : not finished status < 0 : failed """ - # self.optimize.line_search.load_search_history() - logger.info(msg.sub(f"LINE SEARCH STEP COUNT " f"{self.optimize.step_count + 1:0>2}")) @@ -271,6 +306,7 @@ def perform_line_search(self): return elif status == 0: logger.info("trial step unsuccessful. re-attempting line search") + self.optimize.checkpoint() self.perform_line_search() # RECURSIVE CALL elif status == -1: if self.optimize.attempt_line_search_restart(): @@ -278,6 +314,7 @@ def perform_line_search(self): "optimization algorithm and line search.") # Reset the line search machinery; set step count to 0 self.optimize.restart() + self.optimize.checkpoint() self.perform_line_search() # RECURSIVE CALL else: logger.critical( @@ -299,9 +336,9 @@ def _evaluate_line_search_misfit(self): "residuals")) total_misfit = self.preprocess.sum_residuals(residuals) logger.debug(f"misfit for trial model (f_try) == {total_misfit:.2E}") - self.optimize.save(name="f_try", m=total_misfit) + self.optimize.save_vector(name="f_try", m=total_misfit) - def clean_scratch_directory(self): + def finalize_iteration(self): """ Cleans directories in which function and gradient evaluations were carried out. Contains some logic to consider whether or not to continue @@ -309,6 +346,7 @@ def clean_scratch_directory(self): """ logger.info(msg.mnr("CLEANING WORKDIR FOR NEXT ITERATION")) + # Clear out the scratch directory self._thrifty_status = self._update_thrifty_status() if self._thrifty_status: unix.rm(self.path.eval_grad) @@ -322,13 +360,25 @@ def clean_scratch_directory(self): unix.mkdir(self.path.eval_grad) unix.mkdir(self.path.eval_func) + # Export scratch files to output if requested + if self.export_model: + model = self.optimize.load_vector("m_new") + model.write(path=os.path.join(self.path.output, + f"M{self.iteration:0>2}") + ) + + # Update optimization + self.iteration += 1 + logger.info(f"setting current iteration to: {self.iteration}") + self.optimize.checkpoint() + def _update_thrifty_status(self): """ Determine if line search forward simulation can be carried over to the next iteration. Checks criteria related to the current iteration and its position relative to the start and end of the workflow. """ - if self._iteration == self.start: + if self.iteration == self.start: logger.info("thrifty inversion encountering first iteration, " "defaulting to standard inversion workflow") _thrifty_status = False @@ -336,7 +386,7 @@ def _update_thrifty_status(self): logger.info("optimization has been restarted, defaulting to " "standard inversion workflow") _thrifty_status = False - elif self.optimize.iter == self.end: + elif self.iteration == self.end: logger.info("thrifty inversion encountering final iteration, " "defaulting to inversion workflow") _thrifty_status = False From 55d054b1add048a263ec54b8a6fa7f63c90d9add Mon Sep 17 00:00:00 2001 From: bch0w Date: Mon, 25 Jul 2022 15:15:13 -0800 Subject: [PATCH 079/195] moved config.py to tools/ rather than main directory. Moved some of the constants (ROOT_DIR and NAMES) into __init__ rather than config --- seisflows/__init__.py | 6 + seisflows/examples/sfexample2d.py | 2 +- seisflows/preprocess/pyatoa.py | 2 +- seisflows/seisflows.py | 4 +- seisflows/system/cluster.py | 2 +- seisflows/system/frontera.py | 2 +- seisflows/system/lsf.py | 2 +- seisflows/system/maui.py | 2 +- .../system/runscripts/submit_workflow.py | 2 +- seisflows/system/slurm.py | 2 +- seisflows/tests/test_config.py | 30 --- .../scripts/make_test_directory_structure.py | 2 +- seisflows/tests/test_modules.py | 174 ------------------ seisflows/tests/test_preprocess.py | 2 +- seisflows/tests/test_seisflows.py | 4 +- seisflows/tests/test_solver.py | 2 +- seisflows/tests/test_tools.py | 23 ++- seisflows/{ => tools}/config.py | 9 +- seisflows/workflow/inversion.py | 28 +-- seisflows/workflow/test.py | 2 +- 20 files changed, 61 insertions(+), 241 deletions(-) delete mode 100644 seisflows/tests/test_config.py delete mode 100644 seisflows/tests/test_modules.py rename seisflows/{ => tools}/config.py (96%) diff --git a/seisflows/__init__.py b/seisflows/__init__.py index 45c157f4..eadb0c3d 100644 --- a/seisflows/__init__.py +++ b/seisflows/__init__.py @@ -1,3 +1,4 @@ +import os import logging from pkgutil import extend_path @@ -11,3 +12,8 @@ # Set up the SeisFlows Logging environment logger = logging.getLogger(__name__) +# The location of this config file, which is the main repository +ROOT_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__))) + +# List of module names required by SeisFlows for imports. Order-sensitive +NAMES = ["workflow", "system", "solver", "preprocess", "optimize"] \ No newline at end of file diff --git a/seisflows/examples/sfexample2d.py b/seisflows/examples/sfexample2d.py index 72166c0e..c4ed3e0e 100644 --- a/seisflows/examples/sfexample2d.py +++ b/seisflows/examples/sfexample2d.py @@ -34,7 +34,7 @@ import numpy as np from seisflows.tools import msg -from seisflows.config import Dict +from seisflows.tools.config import Dict from seisflows.seisflows import SeisFlows from seisflows.tools.unix import cd, cp, rm, ln, mv, mkdir diff --git a/seisflows/preprocess/pyatoa.py b/seisflows/preprocess/pyatoa.py index 4b99ee51..f938be87 100644 --- a/seisflows/preprocess/pyatoa.py +++ b/seisflows/preprocess/pyatoa.py @@ -16,7 +16,7 @@ from seisflows import logger from seisflows.tools import unix, msg -from seisflows.config import CFGPATHS +from seisflows.tools.config import CFGPATHS class Pyatoa: diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 59c2e86f..78862bfa 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -23,10 +23,10 @@ from glob import glob from IPython import embed -from seisflows import logger -from seisflows.config import custom_import, NAMES, ROOT_DIR, import_seisflows +from seisflows import logger, ROOT_DIR, NAMES from seisflows.tools import unix, msg from seisflows.tools.core import load_yaml, Dict +from seisflows.tools.config import custom_import, import_seisflows from seisflows.tools.specfem import (getpar, setpar, getpar_vel_model, setpar_vel_model) diff --git a/seisflows/system/cluster.py b/seisflows/system/cluster.py index fe42f3b8..4d25be83 100644 --- a/seisflows/system/cluster.py +++ b/seisflows/system/cluster.py @@ -15,7 +15,7 @@ import dill import subprocess from seisflows import logger -from seisflows.config import ROOT_DIR +from seisflows.tools.config import ROOT_DIR from seisflows.system.workstation import Workstation diff --git a/seisflows/system/frontera.py b/seisflows/system/frontera.py index 40e5d903..303f613c 100644 --- a/seisflows/system/frontera.py +++ b/seisflows/system/frontera.py @@ -8,7 +8,7 @@ """ import os import numpy as np -from seisflows.config import ROOT_DIR +from seisflows.tools.config import ROOT_DIR from seisflows.system.slurm import Slurm diff --git a/seisflows/system/lsf.py b/seisflows/system/lsf.py index 4bcc277f..3a5fddf9 100644 --- a/seisflows/system/lsf.py +++ b/seisflows/system/lsf.py @@ -10,7 +10,7 @@ import subprocess from seisflows.system.cluster import Cluster -from seisflows.config import ROOT_DIR +from seisflows.tools.config import ROOT_DIR class Lsf(Cluster): diff --git a/seisflows/system/maui.py b/seisflows/system/maui.py index dafaaa36..3ff3fddc 100644 --- a/seisflows/system/maui.py +++ b/seisflows/system/maui.py @@ -21,7 +21,7 @@ import numpy as np from seisflows import logger from seisflows.system.slurm import Slurm -from seisflows.config import ROOT_DIR +from seisflows.tools.config import ROOT_DIR class Maui(Slurm): diff --git a/seisflows/system/runscripts/submit_workflow.py b/seisflows/system/runscripts/submit_workflow.py index 802ac22e..023a79e8 100644 --- a/seisflows/system/runscripts/submit_workflow.py +++ b/seisflows/system/runscripts/submit_workflow.py @@ -19,7 +19,7 @@ import argparse from seisflows.tools import unix -from seisflows.config import load, config_logger +from seisflows.tools.config import load, config_logger def parse_args(): diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index 98f391f3..ab9acaa6 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -22,7 +22,7 @@ from seisflows import logger from seisflows.system.cluster import Cluster from seisflows.tools import msg -from seisflows.config import ROOT_DIR +from seisflows.tools.config import ROOT_DIR class Slurm(Cluster): diff --git a/seisflows/tests/test_config.py b/seisflows/tests/test_config.py deleted file mode 100644 index c200878f..00000000 --- a/seisflows/tests/test_config.py +++ /dev/null @@ -1,30 +0,0 @@ -""" -Test the SeisFlows configuration script, which configures the compute -system and the working environment required for SF to run properly -""" -import pytest - -from seisflows import config - - - -def test_custom_import(): - """ - Test that importing based on internal modules works for various inputs - :return: - """ - with pytest.raises(SystemExit): - config.custom_import() - with pytest.raises(SystemExit): - config.custom_import(name="NOT A VALID NAME") - - module = config.custom_import(name="optimize", module="LBFGS") - assert(module.__name__ == "LBFGS") - assert(module.__module__ == "seisflows.optimize.LBFGS") - - # Check one more to be safe - module = config.custom_import(name="preprocess", module="default") - assert(module.__name__ == "Default") - assert(module.__module__ == "seisflows.preprocess.default") - - diff --git a/seisflows/tests/test_data/scripts/make_test_directory_structure.py b/seisflows/tests/test_data/scripts/make_test_directory_structure.py index 41c9df08..2527d830 100644 --- a/seisflows/tests/test_data/scripts/make_test_directory_structure.py +++ b/seisflows/tests/test_data/scripts/make_test_directory_structure.py @@ -3,7 +3,7 @@ """ import os from seisflows.tools import unix -from seisflows.config import CFGPATHS, ROOT_DIR +from seisflows.tools.config import CFGPATHS, ROOT_DIR testdir = os.path.join(ROOT_DIR, "tests", "test_data") diff --git a/seisflows/tests/test_modules.py b/seisflows/tests/test_modules.py deleted file mode 100644 index 3b77581a..00000000 --- a/seisflows/tests/test_modules.py +++ /dev/null @@ -1,174 +0,0 @@ -""" -General test suite for all SeisFlows modules. Defines required parameters and -functions for each of the modules, tests importing each of the modules and that -each of the required parameters and functions exist. A sort of first-pass test -which makes sure the package is set up correctly. -""" -import os -import sys -import shutil -import pytest -from unittest.mock import patch -from seisflows import config -from seisflows.seisflows import SeisFlows, return_modules - - -# Define dictionary dictating the bare-minimum SeisFlows structure. -# Each subclass will be checked to see if it meets these requirements which -# ensure that the package will work as intended. The required functions are -# determined by whether or not other submodules call for these functions, e.g., -# an inversion workflow will call solver.eval_func() -required_structure = { - "system": { - "parameters": [], - "functions": ["submit", "run", "taskid", "checkpoint"] - }, - "preprocess": { - "parameters": [], - "functions": ["prepare_eval_grad", "sum_residuals", "finalize"] - }, - "solver": { - "parameters": ["MATERIALS", "DENSITY", "ATTENUATION"], - "functions": ["generate_data", "eval_func", - "eval_grad", "source_names", "parameters"] - }, - "postprocess": { - "parameters": [], - "functions": ["sum_smooth_kernels", "scale_gradient"] - }, - "optimize": { - "parameters": [], - "functions": ["compute_direction", "initialize_search", "update_search", - "finalize_search", "retry_status", "restart"] - }, - "workflow": { - "parameters": [], - "functions": ["main", "checkpoint"] - }, -} - -# Just make sure that the structure here is dictated by the Config -assert(set(required_structure.keys()) == set(config.NAMES)) - -# Define some re-used paths -TEST_DIR = os.path.join(config.ROOT_DIR, "tests") -REPO_DIR = os.path.abspath(os.path.join(config.ROOT_DIR, "..")) - - -@pytest.fixture -def copy_par_file(tmpdir): - """ - Copy the template parameter file into the temporary test directory - :rtype: str - :return: location of the parameter file - """ - src = os.path.join(TEST_DIR, "test_data", "test_filled_parameters.yaml") - dst = os.path.join(tmpdir, "parameters.yaml") - shutil.copy(src, dst) - - -@pytest.fixture -def sfinit(tmpdir, copy_par_file): - """ - Re-used function that will initate a SeisFlows working environment in - sys modules - :return: - """ - copy_par_file - os.chdir(tmpdir) - with patch.object(sys, "argv", ["seisflows"]): - sf = SeisFlows() - sf._register_parameters() - sf._register_modules() - sf._check_parameters() - - return sf - - -def test_import(sfinit): - """ - Test code by importing all available classes for this module. - If any of these fails then the module itself has some code error - (e.g., syntax errors, inheritance errors). - """ - sfinit - for name in config.NAMES: - modules = return_modules()[name] - for module in modules: - config.custom_import(name, module)() - - -def test_required_parameters_exist(sfinit): - """ - Ensure that the required parameters are set in all the classes/subclasses - That is, that the parameters defined above in REQUIRED_PARAMETERS have been - defined by each SYSTEM class - """ - sfinit - for name in config.NAMES: - modules = return_modules()[name] - for module in modules: - loaded_module = config.custom_import(name, module)() - sf_pp = loaded_module.required - # Check that required parameters are set - for req_par in required_structure[name]["parameters"]: - assert(req_par in sf_pp.parameters.keys()), \ - f"{req_par} is a required parameter for module: " \ - f"{name}.{module}" - - -def test_required_functions_exist(sfinit): - """ - Make sure that the named, required functions exist within the class - Do not execute just make sure they're defined, because they will be - expected by other modules - """ - sfinit - for name in config.NAMES: - modules = return_modules()[name] - for module in modules: - loaded_module = config.custom_import(name, module)() - # Check that required parameters are set - for func in required_structure[name]["functions"]: - assert(func in dir(loaded_module)), \ - f"{func} is a required function for module: " \ - f"{name}.{module}" - - -@pytest.mark.skip("test not working as expected") -def test_setup(sfinit): - """ - Test the expected behavior of each of the required functions. - - Setup: make sure that setup creates the necessary directory structure - - :param sfinit: - :param modules: - :return: - """ - sfinit - PATH = sys.modules["seisflows_paths"] - SETUP_CREATES = [PATH.SCRATCH, PATH.SYSTEM, PATH.OUTPUT] - - for name in config.NAMES: - modules = return_modules()[name] - for module in modules: - loaded_module = config.custom_import(name, module)() - - # Make sure these don't already exist - for path_ in SETUP_CREATES: - assert(not os.path.exists(path_)) - - loaded_module.setup() - - # Check that the minimum required directories were created - for path_ in SETUP_CREATES: - pytest.set_trace() - assert(os.path.exists(path_)) - - # Remove created paths so we can check the next module - for path_ in SETUP_CREATES: - if os.path.isdir(path_): - shutil.rmtree(path_) - else: - os.remove(path_) diff --git a/seisflows/tests/test_preprocess.py b/seisflows/tests/test_preprocess.py index 6702e12e..9e09471e 100644 --- a/seisflows/tests/test_preprocess.py +++ b/seisflows/tests/test_preprocess.py @@ -5,7 +5,7 @@ import os import pytest from glob import glob -from seisflows.config import ROOT_DIR +from seisflows.tools.config import ROOT_DIR from seisflows.preprocess.default import Default diff --git a/seisflows/tests/test_seisflows.py b/seisflows/tests/test_seisflows.py index 797fb48e..43c94079 100644 --- a/seisflows/tests/test_seisflows.py +++ b/seisflows/tests/test_seisflows.py @@ -13,7 +13,7 @@ from seisflows.tools.core import Dict from seisflows.seisflows import SeisFlows -from seisflows.config import ROOT_DIR, NAMES, CFGPATHS +from seisflows.tools.config import ROOT_DIR, NAMES, CFGPATHS from seisflows.tools.core import load_yaml TEST_DIR = os.path.join(ROOT_DIR, "tests") @@ -306,7 +306,7 @@ def test_cmd_configure(tmpdir, setup_par_file, conf_par_file): assert (len(lines_conf) == len(lines_fill)) # My attempt to flush sys.modules which did NOT work - # from seisflows.config import NAMES, PAR, PATH + # from seisflows.tools.config import NAMES, PAR, PATH # for name in NAMES: # del sys.modules[f"seisflows_{name}"] # del sys.modules[PAR] diff --git a/seisflows/tests/test_solver.py b/seisflows/tests/test_solver.py index 377d418f..c7bb6d8c 100644 --- a/seisflows/tests/test_solver.py +++ b/seisflows/tests/test_solver.py @@ -5,7 +5,7 @@ import os import pytest from glob import glob -from seisflows.config import ROOT_DIR +from seisflows.tools.config import ROOT_DIR from seisflows.tools.core import set_task_id from seisflows.solver.specfem import Specfem diff --git a/seisflows/tests/test_tools.py b/seisflows/tests/test_tools.py index cccd1fc9..88d0c5cd 100644 --- a/seisflows/tests/test_tools.py +++ b/seisflows/tests/test_tools.py @@ -4,8 +4,9 @@ import os import pytest from glob import glob +from seisflows import ROOT_DIR from seisflows.tools.specfem import Model -from seisflows.config import ROOT_DIR, NAMES, CFGPATHS +from seisflows.tools.config import custom_import TEST_DIR = os.path.join(ROOT_DIR, "tests") @@ -46,3 +47,23 @@ def test_specfem_model(tmpdir): # Check that writing fortran binary works m.write(path=tmpdir) assert(len(glob(os.path.join(tmpdir, f"*{m.fmt}"))) == 2) + + +def test_custom_import(): + """ + Test that importing based on internal modules works for various inputs + :return: + """ + with pytest.raises(SystemExit): + custom_import() + with pytest.raises(SystemExit): + custom_import(name="NOT A VALID NAME") + + module = custom_import(name="optimize", module="LBFGS") + assert(module.__name__ == "LBFGS") + assert(module.__module__ == "seisflows.optimize.LBFGS") + + # Check one more to be safe + module = custom_import(name="preprocess", module="default") + assert(module.__name__ == "Default") + assert(module.__module__ == "seisflows.preprocess.default") \ No newline at end of file diff --git a/seisflows/config.py b/seisflows/tools/config.py similarity index 96% rename from seisflows/config.py rename to seisflows/tools/config.py index fa49286b..08f346b2 100755 --- a/seisflows/config.py +++ b/seisflows/tools/config.py @@ -17,19 +17,12 @@ from pkgutil import find_loader from importlib import import_module -from seisflows import logger +from seisflows import logger, NAMES from seisflows.tools.core import Dict, Null from seisflows.tools import msg from seisflows.tools.core import load_yaml -# List of module names required by SeisFlows for imports. Order-sensitive -NAMES = ["workflow", "system", "solver", "preprocess", "optimize"] - -# The location of this config file, which is the main repository -ROOT_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__))) - - def import_seisflows(workdir=os.getcwd(), parameter_file="parameters.yaml"): """ Standard SeisFlows workflow setup block which runs a number of setup diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index 1a2f5269..0a2371fa 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -346,6 +346,18 @@ def finalize_iteration(self): """ logger.info(msg.mnr("CLEANING WORKDIR FOR NEXT ITERATION")) + # Export scratch files to output if requested + if self.export_model: + model = self.optimize.load_vector("m_new") + model.write(path=os.path.join(self.path.output, + f"M{self.iteration:0>2}") + ) + + # Update optimization + self.iteration += 1 + logger.info(f"setting current iteration to: {self.iteration}") + self.optimize.checkpoint() + # Clear out the scratch directory self._thrifty_status = self._update_thrifty_status() if self._thrifty_status: @@ -360,23 +372,15 @@ def finalize_iteration(self): unix.mkdir(self.path.eval_grad) unix.mkdir(self.path.eval_func) - # Export scratch files to output if requested - if self.export_model: - model = self.optimize.load_vector("m_new") - model.write(path=os.path.join(self.path.output, - f"M{self.iteration:0>2}") - ) - - # Update optimization - self.iteration += 1 - logger.info(f"setting current iteration to: {self.iteration}") - self.optimize.checkpoint() - def _update_thrifty_status(self): """ Determine if line search forward simulation can be carried over to the next iteration. Checks criteria related to the current iteration and its position relative to the start and end of the workflow. + + .. note:: + Resumed, failed workflows will not re-load `_thrifty_status` so + initial misfit will always be evaluated in that case. """ if self.iteration == self.start: logger.info("thrifty inversion encountering first iteration, " diff --git a/seisflows/workflow/test.py b/seisflows/workflow/test.py index e5d61635..42f18b61 100644 --- a/seisflows/workflow/test.py +++ b/seisflows/workflow/test.py @@ -13,7 +13,7 @@ from glob import glob from seisflows.tools.core import Base -from seisflows.config import ROOT_DIR, CFGPATHS, save, config_logger +from seisflows.tools.config import ROOT_DIR, CFGPATHS, save, config_logger class Test(Base): From eda9b84f288354492e4d4661a3edd2265b600562 Mon Sep 17 00:00:00 2001 From: bch0w Date: Mon, 25 Jul 2022 15:48:00 -0800 Subject: [PATCH 080/195] working on optimization test problem --- seisflows/optimize/gradient.py | 6 ++ seisflows/tests/test_optimize.py | 98 ++++++++++++++++++++++++++++++++ seisflows/tests/test_solver.py | 2 +- seisflows/tests/test_system.py | 0 4 files changed, 105 insertions(+), 1 deletion(-) delete mode 100644 seisflows/tests/test_system.py diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index 87f76c30..22001523 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -184,10 +184,13 @@ def load_vector(self, name): """ assert(name in self._acceptable_vectors) model_npz = os.path.join(self.path.scratch, f"{name}.npz") + model_npy = model_npz.replace(".npz", ".npy") model_txt = model_npz.replace(".npz", ".txt") if os.path.exists(model_npz): model = Model(path=os.path.join(self.path.scratch, f"{name}.npz"), load=True) + elif os.path.exists(model_npy): + model = np.load(model_npy) elif os.path.exists(model_txt): model = float(np.loadtxt(model_txt)) else: @@ -210,6 +213,9 @@ def save_vector(self, name, m): path = os.path.join(self.path.scratch, f"{name}.npz") m.model = m.split() # overwrite m representation m.save(path=path) + elif isinstance(m, np.array): + path = os.path.join(self.path.scratch, f"{name}.npy") + np.save(path=path) elif isinstance(m, (float, int)): path = os.path.join(self.path.scratch, f"{name}.txt") np.savetxt(path, [m]) diff --git a/seisflows/tests/test_optimize.py b/seisflows/tests/test_optimize.py index 139597f9..3a5c4387 100644 --- a/seisflows/tests/test_optimize.py +++ b/seisflows/tests/test_optimize.py @@ -1,2 +1,100 @@ +""" +Test the optimization module by setting up a Rosenbrock optimization problem +and running line search +""" +import os +import pytest +import numpy as np +from seisflows.tools.specfem import Model +from seisflows.optimize.gradient import Gradient +from seisflows.optimize.LBFGS import LBFGS +from seisflows.optimize.NLCG import NLCG +@pytest.fixture +def rosenbrock(): + """ + Rosenbrock test problem for optimization library testing + + https://en.wikipedia.org/wiki/Rosenbrock_function + """ + model_init = np.array([-1.2, 1]) # This is the guess for the global min + model_true = np.array([1, 1]) # This is the actual minimum + + def objective_function(x): + """ + Rosenbrock objective function which is defined mathematically as: + + f(x,y) = (a-x)^2 + b(y-x^2)^2 + + where the global minimum is at (x,y) == (a, a^2) + and typical constant values are: a==1, b==100 + """ + return np.array([((1 - x[0]) ** 2 + 100 * (-x[0] ** 2 + x[1]) ** 2)]) + + def gradient(x): + """ + Gradient of the objective function for Rosenbrock test + """ + return np.array([-2*(1-x[0]) - 400*x[0]*(-x[0]**2+x[1]), + 200*(- x[0]**2+x[1])]) + + return model_init, model_true, objective_function, gradient + + +def rosenbrock_n(n=1E5): + """ + N dimensional Rosenbrock test problem for optimization library testing + + https://en.wikipedia.org/wiki/Rosenbrock_function + """ + model_init = 0.1 * np.ones(int(n)) # This is a guess for the global min + model_true = np.ones(int(n)) + + def objective_function(x): + """ + Rosenbrock objective function + """ + return sum(100 * (x[:-1]**2. - x[1:])**2. + (x[:-1] - 1.)**2) + + def gradient(x): + """ + Gradient of the objective function for Rosenbrock test + """ + g = np.zeros(int(n)) + g[1:-1] = -200 * (x[:-2] ** 2. - x[1:-1]) + \ + 400. * x[1:-1] * (x[1:-1] ** 2. - x[2:]) + \ + 2. * (x[1:-1] - 1.) + + g[0] = 400. * x[0] * (x[0] ** 2. - x[1]) + \ + 2. * (x[0] - 1) + + g[-1] = -200. * (x[-2] ** 2. - x[-1]) + + return g + + return model_init, model_true, objective_function, gradient + + +def test_gradient_descent_w_bracket(tmpdir, rosenbrock): + """ + Test Gradient class with Rosenbrock problem + """ + m_new, m_true, evaluate_objective_function, evaluate_gradient = rosenbrock() + + optimize = Gradient(workdir=tmpdir, line_search_method="bracket") + optimize.setup() + optimize.check() + + # Set up the optimization problem + optimize.save_vector(name="m_new", m=m_new) + optimize.initialize_search() + + while True: + f_try = evaluate_objective_function(x=m_new) + optimize.save_vector(name="f_try", m=f_try) + + g_new = evaluate_gradient(x=m_new) + optimize.save_vector(name="g_new", m=g_new) + + optimize. diff --git a/seisflows/tests/test_solver.py b/seisflows/tests/test_solver.py index c7bb6d8c..0ad93992 100644 --- a/seisflows/tests/test_solver.py +++ b/seisflows/tests/test_solver.py @@ -5,7 +5,7 @@ import os import pytest from glob import glob -from seisflows.tools.config import ROOT_DIR +from seisflows import ROOT_DIR from seisflows.tools.core import set_task_id from seisflows.solver.specfem import Specfem diff --git a/seisflows/tests/test_system.py b/seisflows/tests/test_system.py deleted file mode 100644 index e69de29b..00000000 From 19637c44037390a2daef732ca3018d792b37695a Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Tue, 26 Jul 2022 13:10:31 -0800 Subject: [PATCH 081/195] Model class can now set a model attribute and derive internal attributes from the model dictionary. This is opposed to old behavior where all of the internal attributes were set in __init__ and would not adjust if model vector was changed --- seisflows/tests/test_tools.py | 11 +++- seisflows/tools/specfem.py | 121 +++++++++++++++++++++++----------- 2 files changed, 94 insertions(+), 38 deletions(-) diff --git a/seisflows/tests/test_tools.py b/seisflows/tests/test_tools.py index 88d0c5cd..3e8c5ff0 100644 --- a/seisflows/tests/test_tools.py +++ b/seisflows/tests/test_tools.py @@ -3,8 +3,10 @@ """ import os import pytest +import numpy as np from glob import glob from seisflows import ROOT_DIR +from seisflows.tools.core import Dict from seisflows.tools.specfem import Model from seisflows.tools.config import custom_import @@ -40,7 +42,7 @@ def test_specfem_model(tmpdir): # Check that saving and loading npz file works m.save(path=os.path.join(tmpdir, "test.npz")) - m_new = Model(path=os.path.join(tmpdir, "test.npz"), load=True) + m_new = Model(path=os.path.join(tmpdir, "test.npz")) assert(m_new.ngll[0] == m.ngll[0]) assert(m_new.fmt == m.fmt) @@ -48,6 +50,13 @@ def test_specfem_model(tmpdir): m.write(path=tmpdir) assert(len(glob(os.path.join(tmpdir, f"*{m.fmt}"))) == 2) + # Check that we can instantiate a model from an input vector + m = Model() + m.model = Dict(x=[np.array([-1.2, 1.])]) + assert(m.nproc == 1) + assert(m.ngll == [2]) + assert(m.parameters == ["x"]) + def test_custom_import(): """ diff --git a/seisflows/tools/specfem.py b/seisflows/tools/specfem.py index 6fec3297..8f038fb1 100644 --- a/seisflows/tools/specfem.py +++ b/seisflows/tools/specfem.py @@ -27,7 +27,7 @@ class Model: "vpv", "vph", "vsv", "vsh", "eta"] acceptable_parameters.extend([f"{_}_kernel" for _ in acceptable_parameters]) - def __init__(self, path, fmt=None, parameters=None, load=False): + def __init__(self, path=None, fmt=None, parameters=None): """ Model only needs path to model to determine model parameters. Format `fmt` can be provided by the user or guessed based on available file @@ -44,38 +44,38 @@ def __init__(self, path, fmt=None, parameters=None, load=False): :param fmt: expected format of the files (e.g., '.bin'), if None, will attempt to guess based on the file extensions found in `path` Available formats are: .bin, .dat - :type load: bool - :param load: load the model into disk as dictionary and vector - representations. If False, will guess how to import data based on - file extensions or lack thereof """ - assert os.path.exists(path), f"specfem model path {path} does not exist" self.path = path - self.parameters = parameters - - # Load an existing model - if os.path.splitext(path)[-1] == ".npz" or load: - self.model, self.ngll, self.fmt = self.load(file=self.path) - _first_key = list(self.model.keys())[0] - self.nproc = len(self.model[_first_key]) - # Read a model from files - else: - if fmt is None: - self.fmt = self._guess_file_format() + self.fmt = fmt + self.model = None + self._parameters = None + self._ngll = None + self._nproc = None + + # Load an existing model if a valid path is given + if self.path and os.path.exists(path): + # Read existing model from a previously saved .npz file + if os.path.splitext(path)[-1] == ".npz": + self.model, self._ngll, self.fmt = self.load(file=self.path) + _first_key = list(self.model.keys())[0] + self._nproc = len(self.model[_first_key]) + # Read a SPECFEM model from its native output files else: - self.fmt = fmt - self.nproc, self.available_parameters = self._get_nproc_parameters() - self.model, self.ngll = self.read(parameters=parameters) + if self.fmt is None: + self.fmt = self._guess_file_format() + self._nproc, self.available_parameters = \ + self._get_nproc_parameters() + self.model = self.read(parameters=parameters) - # .sorted() enforces parameter order every time, otherwise things can - # get screwy if keys returns different each time - self.parameters = sorted(self.model.keys()) + # .sorted() enforces parameter order every time, otherwise things + # can get screwy if keys returns different each time + self._parameters = sorted(self.model.keys()) @staticmethod def fnfmt(i="*", val="*", ext="*"): """ - Expected SPECFEM filename format with some checks to ensure that - wildcards and numbers are accepted. An example filename is: + Expected SPECFEM filename format with some checks to ensure that + wildcards and numbers are accepted. An example filename is: 'proc000001_vs.bin' :type i: int or str @@ -97,21 +97,73 @@ def fnfmt(i="*", val="*", ext="*"): filename_format = f"proc{i}_{val}{ext}" return filename_format + @property + def parameters(self): + """ + Returns a list of parameters which defines the model. + """ + if not self._parameters: + self._parameters = list(self.model.keys()) + return self._parameters + + @property + def ngll(self): + """ + Provide the number of GLL (Gauss Lobatto Legendre) points per processor + chunk. Access hidden attribute `_ngll` in the case that the model + is very large, we only want to count the GLL points once. + + :rtype: list of float + :return: each float represents the number of GLL points for the chunk + number that corresponds to its index + """ + if not self._ngll: + self._update_ngll_from_model() + return self._ngll + + def _update_ngll_from_model(self): + """Convenience function to count NGLL points as length of data arrays + for each parameter processor chunk""" + self._ngll = [] + for proc in self.model[self.parameters[0]]: + self._ngll.append(len(proc)) + + @property + def nproc(self): + """ + Returns the number of processors that define the model/gradient/kernel. + + :rtype: int + :return: number of processors that define the model + """ + if not self._nproc: + self._nproc = len(self.model[self.parameters[0]]) + return self._nproc + @property def vector(self): - """conveience property to access the merge() property which creates a - linear vector defining all model parameters""" - return self.merge() + """ + Conveience property to access the merge() function which creates a + linear vector defining all model parameters + + :rtype: np.array + :return: a linear vector of all model parameters + """ + try: + return self.merge() + except TypeError as e: + raise TypeError("Model cannot merge files into continous " + "vector") from e def read(self, parameters=None): """ Utility function to load in SPECFEM models/kernels/gradients saved in - various formats. Will try to guess format of model. Assumes that models + various formats. Will try to guess format of model. Assumes that models are saved using the following filename format: proc{num}_{val}.{format} where `num` is usually a 6 digit number representing the processor number (e.g., 000000), `val` is the parameter - value of the model/kernel/gradient and `format` is the format of the + value of the model/kernel/gradient and `format` is the format of the file (e.g., bin) :type parameters: list of str @@ -140,12 +192,7 @@ def read(self, parameters=None): for parameter in parameters: parameter_dict[parameter] = load_fx(parameter=parameter) - # Save some metadata to be able to manipulate model slices freely - ngll = [] - for array in parameter_dict[parameters[0]]: - ngll.append(len(array)) - - return parameter_dict, ngll + return parameter_dict def merge(self, parameter=None): """ @@ -305,7 +352,7 @@ def _get_nproc_parameters(self): fids = glob(os.path.join(self.path, self.fnfmt(val="*", ext=self.fmt))) fids = [os.path.basename(_) for _ in fids] # drop full path fids = [os.path.splitext(_)[0] for _ in fids] # drop extension - + if self.fmt == ".bin": avail_par = list(set(["_".join(_.split("_")[1:]) for _ in fids])) nproc = len(glob(os.path.join( From a47928d0f7633cdab06f04bc653a4dbd3a4f75e1 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Tue, 26 Jul 2022 18:07:53 -0800 Subject: [PATCH 082/195] created a relatively mature optimization test module which runs both line searches and full inversion workflows with the Rosenbrock test function. Made edites to the optimization module along the way, the most significant being that the optimization module will pass vectors as outputs, rather than saving to disk. It is then the workflows responsibility to save these to disk. This makes the optimization module more transparent (i.e., we know what it takes and returns), and shifts more of the agency into workflow and out of the individual sub-modules --- seisflows/optimize/LBFGS.py | 100 +++-- seisflows/optimize/NLCG.py | 2 +- seisflows/optimize/gradient.py | 107 +++-- seisflows/plugins/line_search/backtrack.py | 18 +- seisflows/plugins/line_search/bracket.py | 28 +- .../tests/test_data/test_optimize/g_new.npz | 1 + .../test_optimize/gradient_rosenbrock.npz | Bin 0 -> 526 bytes .../test_optimize/m_init_rosenbrock.npz | Bin 0 -> 526 bytes .../test_optimize/m_true_rosenbrock.npz | Bin 0 -> 526 bytes .../tests/test_data/test_optimize/m_try.npz | 1 + seisflows/tests/test_optimize.py | 382 +++++++++++++++--- seisflows/tools/math.py | 9 +- seisflows/tools/specfem.py | 2 +- seisflows/workflow/inversion.py | 23 +- seisflows/workflow/test.py | 1 - 15 files changed, 517 insertions(+), 157 deletions(-) create mode 120000 seisflows/tests/test_data/test_optimize/g_new.npz create mode 100644 seisflows/tests/test_data/test_optimize/gradient_rosenbrock.npz create mode 100644 seisflows/tests/test_data/test_optimize/m_init_rosenbrock.npz create mode 100644 seisflows/tests/test_data/test_optimize/m_true_rosenbrock.npz create mode 120000 seisflows/tests/test_data/test_optimize/m_try.npz diff --git a/seisflows/optimize/LBFGS.py b/seisflows/optimize/LBFGS.py index e5fbd6ed..1f2e158a 100644 --- a/seisflows/optimize/LBFGS.py +++ b/seisflows/optimize/LBFGS.py @@ -25,6 +25,8 @@ status > 0 : finished status == 0 : not finished status < 0 : failed + +TODO store LBFGS_iter during checkpointing """ import os import numpy as np @@ -73,9 +75,9 @@ def __init__(self, lbfgs_mem=3, lbfgs_max=np.inf, lbfgs_thresh=0., self.LBFGS_thresh = lbfgs_thresh # Set new L-BFGS dependent paths for storing previous gradients - self.path["LBFGS"] = os.path.join(self.path.scratch, "LBFGS") - self.path["y_file"] = os.path.join(self.path.scratch, "LBFGS", "Y") - self.path["s_file"] = os.path.join(self.path.scratch, "LBFGS", "S") + self.path["_LBFGS"] = os.path.join(self.path.scratch, "LBFGS") + self.path["_y_file"] = os.path.join(self.path["_LBFGS"], "Y.dat") + self.path["_s_file"] = os.path.join(self.path["_LBFGS"], "S.dat") # Internally used memory parameters for the L-BFGS optimization algo. self._LBFGS_iter = 0 @@ -86,16 +88,44 @@ def setup(self): Set up the LBFGS optimization schema """ super().setup() - unix.mkdir(self.path.LBFGS) + unix.mkdir(self.path._LBFGS) + + def checkpoint(self): + """ + Overwrite default checkpointing to store internal L-BFGS Attributes + """ + super().checkpoint() + checkpoint_dict = np.load(self.path._checkpoint) + checkpoint_dict["LBFGS_iter"] = self._LBFGS_iter + checkpoint_dict["memory_used"] = self._memory_used + + np.savez(file=self.path._checkpoint, **dict_out) # NOQA + + def load_checkpoint(self): + """ + Counterpart to `optimize.checkpoint`. Loads a checkpointed optimization + module from disk and sets internal tracking attributes. Adds additional + functionality to restore internal L-BFGS attributes + """ + super().load_checkpoint() + + # NumPy appends '.npz' when saving. Make sure we honor that. + if not self.path._checkpoint.endswith(".npz"): + fid = f"{self.path._checkpoint}.npz" + else: + fid = self.path._checkpoint + + if os.path.exists(fid): + dict_in = np.load(file=fid) + self._LBFGS_iter = int(dict_in["LBFGS_iter"]) + self._memory_used = int(dict_in["memory_used"]) def compute_direction(self): """ Call on the L-BFGS optimization machinery to compute a search direction using internally stored memory of previous gradients. - The potential outcomes when computing direction with L-BFGS - - TODO do we need to precondition L-BFGS? + The potential outcomes when computing direction with L-BFGS: 1. First iteration of L-BFGS optimization, search direction is defined as the inverse gradient 2. L-BFGS internal iteration ticks over the maximum allowable number of @@ -106,6 +136,8 @@ def compute_direction(self): 4. New search direction is acceptably angled from previous, becomes the new search direction + TODO do we need to precondition L-BFGS? + :rtype: seisflows.tools.specfem.Model :return: search direction as a Model instance """ @@ -113,20 +145,19 @@ def compute_direction(self): # Load the current gradient direction, which is the L-BFGS search # direction if this is the first iteration - g = self.load("g_new") + g = self.load_vector("g_new") if self._LBFGS_iter == 1: logger.info("first L-BFGS iteration, default to 'Gradient' descent") - p_new = -1 * g.vector + p_new = g.update(vector=-1 * g.vector) restarted = False - # Restart condition or first iteration lead to setting search direction # as the inverse gradient (i.e., default to steepest descent) elif self._LBFGS_iter > self.LBFGS_max: logger.info("restarting L-BFGS due to periodic restart condition. " "setting search direction as inverse gradient") self.restart() - p_new = -1 * g.vector + p_new = g.update(vector=-1 * g.vector) restarted = True # Normal LBFGS direction computation else: @@ -134,11 +165,12 @@ def compute_direction(self): # 'q' becomes the new search direction 'g' logger.info("applying inverse Hessian to gradient") s, y = self._update_search_history() - q = self._apply_inverse_hessian(g.vector, s, y) + _q_vector = self._apply_inverse_hessian(g.vector, s, y) + q = g.update(vector=_q_vector) # Determine if the new search direction is appropriate by checking # its angle to the previous search direction - if self._check_status(g, q): + if self._check_status(g.vector, q.vector): logger.info("new L-BFGS search direction found") p_new = q.update(vector=-1 * q.vector) restarted = False @@ -149,9 +181,6 @@ def compute_direction(self): p_new = g.update(vector=-1 * g.vector) restarted = True - # Assign newly computed 'p_new' vector to a Model instance - p_new = g.update(vector=p_new) - # Assign restart condition to internal memory self._restarted = restarted @@ -165,18 +194,19 @@ def restart(self): logger.info("restarting L-BFGS optimization algorithm") # Fall back to gradient descent for search direction - g = self.load("g_new") - self.save("p_new", -1 * g.vector) + g = self.load_vector("g_new") + p_new = g.update(vector=-1 * g.vector) + self.save_vector("p_new", p_new) # Clear internal memory - self._line_search.clear_history() + self._line_search.clear_search_history() self._restarted = True self._LBFGS_iter = 1 self._memory_used = 0 # Clear out previous gradient information - s = np.memmap(filename=self.path.s_file, mode="r+") - y = np.memmap(filename=self.path.y_file, mode="r+") + s = np.memmap(filename=self.path._s_file, mode="r+") + y = np.memmap(filename=self.path._y_file, mode="r+") s[:] = 0. y[:] = 0. @@ -199,11 +229,11 @@ def _update_search_history(self): :rtype y: np.memmap :return y: memory of the gradient differences `g_new - g_old` """ - unix.cd(self.path) - # Determine the iterates for model m and gradient g - s_k = self.load("m_new").vector - self.load("m_old").vector - y_k = self.load("g_new").vector - self.load("g_old").vector + s_k = \ + self.load_vector("m_new").vector - self.load_vector("m_old").vector + y_k = \ + self.load_vector("g_new").vector - self.load_vector("g_old").vector # Determine the shape of the memory map (length of model, length of mem) m = len(s_k) @@ -211,20 +241,20 @@ def _update_search_history(self): # Initial iteration, need to create the memory map if self._memory_used == 0: - s = np.memmap(filename=self.path.s_file, mode="w+", dtype="float32", - shape=(m, n)) - y = np.memmap(filename=self.path.y_file, mode="w+", dtype="float32", - shape=(m, n)) + s = np.memmap(filename=self.path._s_file, mode="w+", + dtype="float32", shape=(m, n)) + y = np.memmap(filename=self.path._y_file, mode="w+", + dtype="float32", shape=(m, n)) # Store the model and gradient differences in memmaps s[:, 0] = s_k y[:, 0] = y_k self._memory_used = 1 # Subsequent iterations will append to memory maps else: - s = np.memmap(filename=self.path.s_file, mode="r+", dtype="float32", - shape=(m, n)) - y = np.memmap(filename=self.path.y_file, mode="r+", dtype="float32", - shape=(m, n)) + s = np.memmap(filename=self.path._s_file, mode="r+", + dtype="float32", shape=(m, n)) + y = np.memmap(filename=self.path._y_file, mode="r+", + dtype="float32", shape=(m, n)) # Shift all stored memory by one index to make room for latest mem s[:, 1:] = s[:, :-1] y[:, 1:] = y[:, :-1] @@ -256,9 +286,9 @@ def _apply_inverse_hessian(self, q, s=None, y=None): if s is None or y is None: m = len(q) n = self.LBFGS_mem - s = np.memmap(filename=self.path.s_file, mode="w+", dtype="float32", + s = np.memmap(filename=self.path._s_file, mode="w+", dtype="float32", shape=(m, n)) - y = np.memmap(filename=self.path.y_file, mode="w+", dtype="float32", + y = np.memmap(filename=self.path._y_file, mode="w+", dtype="float32", shape=(m, n)) # First matrix product diff --git a/seisflows/optimize/NLCG.py b/seisflows/optimize/NLCG.py index 4d25e11f..762f7c5d 100644 --- a/seisflows/optimize/NLCG.py +++ b/seisflows/optimize/NLCG.py @@ -138,7 +138,7 @@ def restart(self): g = self.load("g_new") self.save("p_new", -1 * g.vector) - self._line_search.clear_history() + self._line_search.clear_search_history() self._restarted = 1 self._NLCG_iter = 1 diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index 22001523..08e356ae 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -95,7 +95,7 @@ def __init__(self, line_search_method="bracket", # Set required path structure self.path = Dict( scratch=path_optimize or - os.path.join(os.getcwd(), "scratch", "optimize"), + os.path.join(workdir, "scratch", "optimize"), output=path_output or os.path.join(workdir, "output"), preconditioner=path_preconditioner, ) @@ -103,6 +103,8 @@ def __init__(self, line_search_method="bracket", # Hidden paths to store checkpoint file in scratch directory self.path["_checkpoint"] = os.path.join(self.path.scratch, "checkpoint") + self.path["_stats_file"] = os.path.join(self.path.scratch, + "output_optim.txt") # Internal check to see if the chosen line search algorithm exists if not hasattr(line_search_dir, line_search_method): @@ -183,18 +185,19 @@ def load_vector(self, name): :param name: name of the vector, acceptable: m, g, p, f, alpha """ assert(name in self._acceptable_vectors) + model_npz = os.path.join(self.path.scratch, f"{name}.npz") model_npy = model_npz.replace(".npz", ".npy") model_txt = model_npz.replace(".npz", ".txt") + if os.path.exists(model_npz): - model = Model(path=os.path.join(self.path.scratch, - f"{name}.npz"), load=True) + model = Model(path=model_npz) elif os.path.exists(model_npy): model = np.load(model_npy) elif os.path.exists(model_txt): model = float(np.loadtxt(model_txt)) else: - raise FileNotFoundError(f"no optimization file found for {name}") + raise FileNotFoundError(f"no optimization file found for '{name}'") return model @@ -213,7 +216,7 @@ def save_vector(self, name, m): path = os.path.join(self.path.scratch, f"{name}.npz") m.model = m.split() # overwrite m representation m.save(path=path) - elif isinstance(m, np.array): + elif isinstance(m, np.ndarray): path = os.path.join(self.path.scratch, f"{name}.npy") np.save(path=path) elif isinstance(m, (float, int)): @@ -239,7 +242,7 @@ def checkpoint(self): gtp=self._line_search.gtp, step_count=self._line_search.step_count) - np.savez(file=self.path._checkpoint, **dict_out) + np.savez(file=self.path._checkpoint, **dict_out) # NOQA def load_checkpoint(self): """ @@ -308,6 +311,9 @@ def initialize_search(self): Generate a trial model by perturbing the current model in the search direction with a given step length, calculated by the chosen line search algorithm. + + :rtype: tuple + :return: (Model, float) or (m_try==trial model, alpha=step length) """ m = self.load_vector("m_new") # current model from external solver g = self.load_vector("g_new") # current gradient from scaled kernels @@ -321,7 +327,7 @@ def initialize_search(self): # Restart plugin line search if the optimization library restarts if self._restarted: - self._line_search.clear_history() + self._line_search.clear_search_history() # Optional safeguard to prevent step length from getting too large if self.step_len_max: @@ -331,8 +337,7 @@ def initialize_search(self): # Initialize the line search and save it to disk. self._line_search.update_search_history(func_val=f, step_len=0., - gtg=gtg, gtp=gtp) - # self._line_search.check_search_history() + gtg=gtg, gtp=gtp) alpha, _ = self._line_search.calculate_step_length() @@ -356,10 +361,24 @@ def update_line_search(self): has been run and misfit calculated. Checks misfit against line search history to see if the line search has been completed. - Available status codes from line_search.calculate_step_length(): - status == 1 : finished - status == 0 : not finished - status == -1 : failed + .. note:: + This is a bit confusing as it calculates the step length `alpha` for + the NEXT line search step, while working with the `alpha` value that + was calculated from the LAST line search step. + + If line search returns a passing exit code (0 or 1), sets up for a + subsequent line search evaluation by saving a new step length (alpha), + and creating a new trial model (m_try). + + .. note: + Available status returns are: + 'TRY': try/re-try the line search as conditions have not been met + 'PASS': line search was successful, you can terminate the search + 'FAIL': line search has failed for one or more reasons. + + :rtype: tuple + :return: (Model, float, bool) or (m_try==trial model, alpha=step length, + status==how to proceed with line search) """ # Collect information on a forward evaluation that just took place alpha = self.load_vector("alpha") # step length @@ -368,28 +387,30 @@ def update_line_search(self): # Update the line search with a new step length and misfit value self._line_search.step_count += 1 self._line_search.update_search_history(step_len=alpha, func_val=f_try) - # self._line_search.check_search_history() - # Calculate a new step length based on line search algorithm + # Calculate a new step length based on the step length and corresponding + # misfit that we should have just calculated alpha_try, status = self._line_search.calculate_step_length() + # Vectors are saved to disk immediately to avoid passing them in memory # Status == 0: Retry line search // Status == 1: Line search passed - if status in [0, 1]: - # Save new step length to disk - self.save_vector("alpha", alpha_try) - + if status.upper() in ["TRY", "PASS"]: # Create a new trial model based on search direction, step length # and the initial model vector - m = self.load_vector("m_new") - p = self.load_vector("p_new") + _m = self.load_vector("m_new") + _p = self.load_vector("p_new") - # Sets the latest trial model - m_try = m.update(vector=m.vector + alpha * p.vector) + # Sets the latest trial model using the current `alpha` value + m_try = _m.update(vector=_m.vector + alpha * _p.vector) logger.info("trial model 'm_try' parameters: ") m_try.check() - self.save_vector("m_try", m_try) - return status + # Newly calculated `alpha` value overwrites original `alpha` + alpha = alpha_try + else: + m_try = None + + return m_try, alpha, status def finalize_search(self): """ @@ -448,8 +469,8 @@ def attempt_line_search_restart(self, threshold=1E-3): :type threshold: float :param threshold: angle threshold for the angle between the search direction and the gradient. - :rtype: int - :return: pass (1) fail (0) status for retrying line search + :rtype: bool + :return: pass (True) fail (False) status for retrying line search """ g = self.load_vector("g_new") p = self.load_vector("p_new") @@ -459,9 +480,9 @@ def attempt_line_search_restart(self, threshold=1E-3): f"theta: {theta:6.3f}") if abs(theta) < threshold: - return 0 # Do not restart + return False # Do not restart else: - return 1 # Go for restart + return True # Go for restart def restart(self): """ @@ -485,19 +506,19 @@ def _write_stats(self): Used because stats line search information can be overwritten by subsequent iterations so we need to append values to text files if they should be retained. + + .. note:: + This CSV file can be easily read and plotted using np.genfromtxt + >>> np.genfromtxt("optim_stats.txt", delimiter=",", names=True, \ + dtype=None) """ logger.info(f"writing optimization stats") - fid = os.path.join(self.path.output, f"optim_stats.txt") - # First time, write header information - if not os.path.exists(fid): - with open(fid, "w") as f: - for header in [# "ITER", "FACTOR", - "GRAD_NORM_L1", "GRAD_NORM_L2", - "MISFIT", "RESTART", "SLOPE", "STEP", "LENGTH", - "THETA"]: - f.write(f"{header.upper()},") - f.write("\n") + if not os.path.exists(self.path._stats_file): + _head = ("step_count,step_length,gradient_norm_L1,gradient_norm_L2," + "misfit,if_restarted,slope,theta\n") + with open(self.path._stats_file, "w") as f: + f.write(_head) g = self.load_vector("g_new") p = self.load_vector("p_new") @@ -518,14 +539,14 @@ def _write_stats(self): step_length = x[f.argmin()] theta = 180. * np.pi ** -1 * angle(p.vector, -1 * g.vector) - with open(fid, "a") as f: + with open(self.path._stats_file, "a") as f: f.write(# f"{factor:6.3E}," + f"{step_count:0>2}," + f"{step_length:6.3E}," f"{grad_norm_L1:6.3E}," f"{grad_norm_L2:6.3E}," f"{misfit:6.3E}," - f"{self._restarted:6.3E}," + f"{int(self._restarted)}," f"{slope:6.3E}," - f"{step_count:0>2}," - f"{step_length:6.3E}," f"{theta:6.3E}\n" ) diff --git a/seisflows/plugins/line_search/backtrack.py b/seisflows/plugins/line_search/backtrack.py index a4a8cc71..b637b1fa 100644 --- a/seisflows/plugins/line_search/backtrack.py +++ b/seisflows/plugins/line_search/backtrack.py @@ -46,6 +46,16 @@ def calculate_step_length(self): n: number of model updates in optimization problem gtg: dot product of gradient with itself gtp: dot product of gradient and search direction + + .. note: + Available status returns are: + 'TRY': try/re-try the line search as conditions have not been met + 'PASS': line search was successful, you can terminate the search + 'FAIL': line search has failed for one or more reasons. + + :rtype: tuple (float, str) + :return: (alpha==calculated step length, + status==how to treat the next step count evaluation) """ # Determine the line search history x, f, gtg, gtp, step_count, update_count = self.get_search_history() @@ -64,13 +74,13 @@ def calculate_step_length(self): if step_count == 0: alpha = min(1., self.step_len_max) logger.info(f"try: attempt unit step length w/ alpha={alpha}") - status = 0 + status = "TRY" # Pass if misfit is reduced elif f.min() < f[0]: alpha = x[f.argmin()] logger.info(f"pass: misfit decreased, line search " f"successful w/ alpha={alpha}") - status = 1 + status = "PASS" # If misfit continually increases, decrease step length elif step_count <= self.step_count_max: slope = gtp[-1] / gtg[-1] @@ -78,14 +88,14 @@ def calculate_step_length(self): f1=f[1], b1=0.1, b2=0.5) logger.info(f"try: misfit increasing, attempting " f"to decrease step length to alpha={alpha}") - status = 0 + status = "TRY" # Failed because step_count_max exceeded else: logger.info(f"fail: backtracking line search has " f"failed because the maximum allowable step counts " f"({self.step_count_max}) has been exceeded") alpha = None - status = -1 + status = "FAIL" return alpha, status diff --git a/seisflows/plugins/line_search/bracket.py b/seisflows/plugins/line_search/bracket.py index 64f7744f..12262893 100644 --- a/seisflows/plugins/line_search/bracket.py +++ b/seisflows/plugins/line_search/bracket.py @@ -134,6 +134,16 @@ def calculate_step_length(self): """ Determines step length (alpha) and search status (status) using a bracketing line search. + + .. note: + Available status returns are: + 'TRY': try/re-try the line search as conditions have not been met + 'PASS': line search was successful, you can terminate the search + 'FAIL': line search has failed for one or more reasons. + + :rtype: tuple (float, str) + :return: (alpha==calculated step length, + status==how to treat the next step count evaluation) """ # Determine the line search history x, f, gtg, gtp, step_count, update_count = self.get_search_history() @@ -145,7 +155,7 @@ def calculate_step_length(self): alpha = gtg[-1] ** -1 logger.info(f"try: first evaluation, attempt guess step length, " f"alpha={alpha:.2E}") - status = 0 + status = "TRY" # For every iteration's initial step, set alpha manually elif step_count == 0: # Based on the first equation in sec 3.5 of Nocedal and Wright 2ed @@ -153,26 +163,26 @@ def calculate_step_length(self): alpha = self.step_lens[idx] * gtp[-2] / gtp[-1] logger.info(f"try: first step count of iteration, " f"setting scaled step length, alpha={alpha:.2E}") - status = 0 + status = "TRY" # If misfit is reduced and then increased, we've bracketed. Pass elif _check_bracket(x, f) and _good_enough(x, f): alpha = x[f.argmin()] logger.info(f"pass: bracket acceptable and step length " f"reasonable.") - status = 1 + status = "PASS" # If misfit is reduced but not close, set to quadratic fit elif _check_bracket(x, f): alpha = polynomial_fit(x, f) logger.info(f"try: bracket acceptable but step length unreasonable " f"attempting to re-adjust step length " f"alpha={alpha:.2E}") - status = 0 + status = "TRY" # If misfit continues to step down, increase step length elif step_count <= self.step_count_max and all(f <= f[0]): alpha = 1.618034 * x[-1] # 1.618034 is the 'golden ratio' logger.info(f"try: misfit not bracketed, increasing step length " f"using golden ratio, alpha={alpha:.2E}") - status = 0 + status = "TRY" # If misfit increases, reduce step length by backtracking elif step_count <= self.step_count_max: slope = gtp[-1] / gtg[-1] @@ -181,14 +191,14 @@ def calculate_step_length(self): logger.info(f"try: misfit increasing, attempting " f"to reduce step length using parabloic backtrack, " f"alpha={alpha:.2E}") - status = 0 + status = "TRY" # step_count_max exceeded, fail else: logger.info(f"fail: bracketing line search has failed " f"to reduce the misfit before exceeding " f"`step_count_max`={self.step_count_max}") alpha = None - status = -1 + status = "FAIL" # Apply optional step length safeguard if alpha is not None: @@ -197,14 +207,14 @@ def calculate_step_length(self): logger.info(f"try: applying initial step length " f"safegaurd as alpha has exceeded maximum step " f"length, alpha_new={alpha:.2E}") - status = 0 + status = "TRY" # Stop because safeguard prevents us from going further elif alpha > self.step_len_max: alpha = self.step_len_max logger.info(f"try: applying initial step length " f"safegaurd as alpha has exceeded maximum step " f"length, alpha_new={alpha:.2E}") - status = 1 # TODO shouldn't this be 0 or -1? + status = "PASS" # TODO shouldn't this be 0 or -1? return alpha, status diff --git a/seisflows/tests/test_data/test_optimize/g_new.npz b/seisflows/tests/test_data/test_optimize/g_new.npz new file mode 120000 index 00000000..397dba1f --- /dev/null +++ b/seisflows/tests/test_data/test_optimize/g_new.npz @@ -0,0 +1 @@ +gradient_rosenbrock.npz \ No newline at end of file diff --git a/seisflows/tests/test_data/test_optimize/gradient_rosenbrock.npz b/seisflows/tests/test_data/test_optimize/gradient_rosenbrock.npz new file mode 100644 index 0000000000000000000000000000000000000000..8d53d2f174c2f9a24cae47769bffc867f64a0e30 GIT binary patch literal 526 zcmWIWW@Zs#fB;1X7OyF1@*N2qLQM-y!ia0lvI$oTVhUeDp0&QBe5VA$k))+QK(g* z2yp2E-H-&tnLwNe!~rNCaXqp35zHg3K#x?A=8ZH9qP$_KqhLgtSGblL8}BgwoOM8f zfgyo`A?!eaHzSh>Gp-l``V$Ho7(onZ{Ge+>^*u}r149GjNgxLt76IO@Y#<3HAj|;L I6Ts#H0I$kkJOBUy literal 0 HcmV?d00001 diff --git a/seisflows/tests/test_data/test_optimize/m_init_rosenbrock.npz b/seisflows/tests/test_data/test_optimize/m_init_rosenbrock.npz new file mode 100644 index 0000000000000000000000000000000000000000..e46904d0122532177d723e22cbf71c62a3ae94f1 GIT binary patch literal 526 zcmWIWW@Zs#fB;1X7OyF1@*N2qLQM-y!ia0lvI$oTVhUeDp0&QBe5VA$k))+QK(g* z2yp2E-H-&tnLwNe!~rNC*Gp-ncr~;71@*N2qLQM-y!ia0lvI$oTVhUeDp0&QBe5VA$k))+QK(g* z2yp2E-H-&tnLwNe!~rNC**NV+2+Sj_K#x?A=8ZH9qP$_KqhLgtSGa&K2Z9gwFgn1S ykx7IZSByYZfyf3%5DASRbWNzf2PuSr2F8;>1~@DNyjj^mf=ob|0i-8@%>w|L(pr81 literal 0 HcmV?d00001 diff --git a/seisflows/tests/test_data/test_optimize/m_try.npz b/seisflows/tests/test_data/test_optimize/m_try.npz new file mode 120000 index 00000000..444a5bf5 --- /dev/null +++ b/seisflows/tests/test_data/test_optimize/m_try.npz @@ -0,0 +1 @@ +m_init_rosenbrock.npz \ No newline at end of file diff --git a/seisflows/tests/test_optimize.py b/seisflows/tests/test_optimize.py index 3a5c4387..8050d700 100644 --- a/seisflows/tests/test_optimize.py +++ b/seisflows/tests/test_optimize.py @@ -5,96 +5,364 @@ import os import pytest import numpy as np +from seisflows.tools.core import Dict from seisflows.tools.specfem import Model +from seisflows.tools.math import angle from seisflows.optimize.gradient import Gradient from seisflows.optimize.LBFGS import LBFGS from seisflows.optimize.NLCG import NLCG -@pytest.fixture -def rosenbrock(): +def rosenbrock_objective_function(x): """ - Rosenbrock test problem for optimization library testing + Rosenbrock objective function used to test the optimization module. + The Rosenbrock is defined mathematically as: + + f(x,y) = (a-x)^2 + b(y-x^2)^2 + + where the global minimum is at (x,y) == (a, a^2) + and typical constant values are: a==1, b==100 https://en.wikipedia.org/wiki/Rosenbrock_function + + :type x: np.array + :param x: input model [x, y] to feed into the objective function + :rtype: float + :return: misfit value for the given input model + """ + return (1 - x[0]) ** 2 + 100 * (-x[0] ** 2 + x[1]) ** 2 + + +def rosenbrock_gradient(x): """ - model_init = np.array([-1.2, 1]) # This is the guess for the global min - model_true = np.array([1, 1]) # This is the actual minimum + Define the gradient of the Rosenbrock function, i.e., the gradient of the + `rosenbrock_objective_function` - def objective_function(x): - """ - Rosenbrock objective function which is defined mathematically as: + :type x: np.array + :param x: input model [x, y] to feed into the gradient of the obj function + :rtype: np.array + :return: gradient vector of the Rosenbrock function + """ + return np.array([-2 * (1 - x[0]) - 400 * x[0] * (-x[0] ** 2 + x[1]), + 200 * (- x[0] ** 2 + x[1])]) - f(x,y) = (a-x)^2 + b(y-x^2)^2 - where the global minimum is at (x,y) == (a, a^2) - and typical constant values are: a==1, b==100 - """ - return np.array([((1 - x[0]) ** 2 + 100 * (-x[0] ** 2 + x[1]) ** 2)]) +@pytest.fixture +def setup_optimization_vectors(tmpdir): + """ + Create vectors required for a Rosenbrock optimization problem to be used + to test the line search and optimization algorithms. - def gradient(x): - """ - Gradient of the objective function for Rosenbrock test - """ - return np.array([-2*(1-x[0]) - 400*x[0]*(-x[0]**2+x[1]), - 200*(- x[0]**2+x[1])]) + .. note:: + The optimization module requires a model (m_new), corresponding + misfit of that model (f_new), the gradient of that misfit + function (g_new) and a search direction (p_new; calculated by each + individual optimization sub-class) + """ + m_init = Model() + m_init.model = Dict(x=[np.array([-1.2, 1])]) # Starting guess for Rosenbrock + m_init.save(path=os.path.join(tmpdir, "m_new.npz")) - return model_init, model_true, objective_function, gradient + # Calculate the misfit and save as a text file + misfit = rosenbrock_objective_function(x=m_init.vector) + np.savetxt(os.path.join(tmpdir, "f_new.txt"), [misfit]) + # Calcualte the gradient and save as a Model + gradient = rosenbrock_gradient(x=m_init.vector) + g_new = m_init.update(vector=gradient) + g_new.save(path=os.path.join(tmpdir, "g_new.npz")) -def rosenbrock_n(n=1E5): + +def test_gradient_compute_direction(tmpdir, setup_optimization_vectors): + """ + Ensure that gradient computes the correct search direction. The gradient + vector created by the Rosenbrock function is expected to be [-215.6, -88.] """ - N dimensional Rosenbrock test problem for optimization library testing + optimize = Gradient(path_optimize=tmpdir) - https://en.wikipedia.org/wiki/Rosenbrock_function + # Check that setup created the correct gradient vector + g_new = optimize.load_vector("g_new") + assert(g_new.vector.sum() == pytest.approx(-303.59, 3)) + + # Gradient descent search direction is just the negative gradient + p_new = optimize.compute_direction() + assert(p_new.vector.sum() == pytest.approx(303.59, 3)) + + +def test_gradient_initialize_search(tmpdir, setup_optimization_vectors): """ - model_init = 0.1 * np.ones(int(n)) # This is a guess for the global min - model_true = np.ones(int(n)) + Test that the optimization module properly initializes the line search by + providing a new model and misfit value. - def objective_function(x): - """ - Rosenbrock objective function - """ - return sum(100 * (x[:-1]**2. - x[1:])**2. + (x[:-1] - 1.)**2) + Initialize search requres 4 vectors to properly intialize the search, + they are 'm_new' (model), 'g_new' (gradient), 'p_new' (search direciton_ + and 'f_new' (misfit) + """ + optimize = Gradient(path_optimize=tmpdir) + p_new = optimize.compute_direction() + p_new.save(path=os.path.join(tmpdir, "p_new")) - def gradient(x): - """ - Gradient of the objective function for Rosenbrock test - """ - g = np.zeros(int(n)) - g[1:-1] = -200 * (x[:-2] ** 2. - x[1:-1]) + \ - 400. * x[1:-1] * (x[1:-1] ** 2. - x[2:]) + \ - 2. * (x[1:-1] - 1.) + m_try, alpha = optimize.initialize_search() - g[0] = 400. * x[0] * (x[0] ** 2. - x[1]) + \ - 2. * (x[0] - 1) + assert(m_try.vector[0] == pytest.approx(-1.14, 3)) + assert(m_try.vector[1] == pytest.approx(1.02, 3)) + assert(alpha == pytest.approx(2.789e-4, 4)) + + +def test_gradient_update_line_search(tmpdir, setup_optimization_vectors): + """ + Ensure that updating the line search works as advertised, i.e., we get a + status on how the line search should proceed + """ + optimize = Gradient(path_optimize=tmpdir) + p_new = optimize.compute_direction() + p_new.save(path=os.path.join(tmpdir, "p_new")) - g[-1] = -200. * (x[-2] ** 2. - x[-1]) + m_try, alpha = optimize.initialize_search() + optimize.save_vector("m_try", m_try) + optimize.save_vector("alpha", alpha) - return g + f_try = rosenbrock_objective_function(m_try.vector) + np.savetxt(os.path.join(tmpdir, "f_try.txt"), [f_try]) - return model_init, model_true, objective_function, gradient + m_try, status, status = optimize.update_line_search() + assert(status == "TRY") + assert(optimize._line_search.step_count == 1) + assert(optimize._line_search.func_vals[1] == f_try) -def test_gradient_descent_w_bracket(tmpdir, rosenbrock): +def test_bracket_line_search(tmpdir, setup_optimization_vectors): """ - Test Gradient class with Rosenbrock problem + Run a small optimization problem to try to reduce the Rosenbrock + objective function using the Bracket'ing line search method. Checks that the + line search only takes a few steps and that the reduced misfit value is + as expected. """ - m_new, m_true, evaluate_objective_function, evaluate_gradient = rosenbrock() + optimize = Gradient(path_optimize=tmpdir, path_output=tmpdir, + line_search_method="bracket", step_count_max=100) + + # Make sure the initial misfit is high + assert(optimize.load_vector("f_new") == pytest.approx(24.2, 1)) + + # Calculate the initial search direction which is just the inverse gradient + p_new = optimize.compute_direction() + p_new.save(path=os.path.join(tmpdir, "p_new")) + + # Saves trial model 'm_try' and corresponding step length 'alpha' + m_try, alpha = optimize.initialize_search() + optimize.save_vector("m_try", m_try) + optimize.save_vector("alpha", alpha) - optimize = Gradient(workdir=tmpdir, line_search_method="bracket") - optimize.setup() - optimize.check() + def line_search(): + """Each line search evaluation requires calculating a new model and + corresponding misfit value""" + m_try = optimize.load_vector("m_try") + f_try = rosenbrock_objective_function(m_try.vector) + np.savetxt(os.path.join(tmpdir, "f_try.txt"), [f_try]) - # Set up the optimization problem - optimize.save_vector(name="m_new", m=m_new) - optimize.initialize_search() + m_try, alpha, status = optimize.update_line_search() + optimize.save_vector("m_try", m_try) + optimize.save_vector("alpha", alpha) + return status + # Run a line search until acceptable misfit reduction while True: - f_try = evaluate_objective_function(x=m_new) - optimize.save_vector(name="f_try", m=f_try) + status = line_search() + if status == "PASS": + break + + assert(status == "PASS") # pass + assert(optimize.step_count == 5) # Took 5 steps to reduce misfit + # Make sure we have reduced the final misfit + assert(min(optimize._line_search.func_vals) == pytest.approx(4.22, 3)) + + # Change model names and reset line search + optimize.finalize_search() + + # Check that the final model + m_new = optimize.load_vector("m_new") + m_old = optimize.load_vector("m_old") + m_angle = angle(m_new.vector, m_old.vector) + assert(m_angle == pytest.approx(0.3, 2)) - g_new = evaluate_gradient(x=m_new) - optimize.save_vector(name="g_new", m=g_new) - optimize. +def test_optimize_checkpoint_reload(tmpdir): + """ + Checkpointing is used to store the status of the optimization module + in the case of a failed or re-started workflow. Test that this works as + advertised + """ + rand_val = 123 + optimize = Gradient(path_optimize=tmpdir) + optimize._line_search.step_count = rand_val + optimize.checkpoint() + + new_optimize = Gradient(path_optimize=tmpdir) + new_optimize.load_checkpoint() + assert(new_optimize.step_count == rand_val) + + +def test_optimize_attempt_line_search_restart(tmpdir, + setup_optimization_vectors): + """ + Make sure we can tell when we're supposed to attempt a line search restart, + i.e., when the gradient and search direction are the same + """ + optimize = Gradient(path_optimize=tmpdir) + p_new = optimize.compute_direction() + p_new.save(path=os.path.join(tmpdir, "p_new")) + + # this query requires 'g_new' and 'p_new' + assert(optimize.attempt_line_search_restart() == False) + + p_new.update(vector=np.array([-99.99, -99.99])) + p_new.save(path=os.path.join(tmpdir, "p_new")) + assert(optimize.attempt_line_search_restart() == True) + + +# def test_inversion_optimization_problem_with_gradient( +# tmpdir, setup_optimization_vectors): +# """ +# Rather than run a single line search evaluation, which all the previous +# tests have done, we want to run a inversion workflow to find a best fitting +# model. To do this we essentially have to mimic the inversion workflow, but +# with barebones functions. +# +# This takes bits and pieces from the previous tests +# +# .. note:: +# We do not save `m_try` to disk each time it is evaluated because it is +# small. However in real workflows, `m_try` must be saved to disk rather +# than passed in memory because it is likely to be a large vector. +# """ +# optimize = Gradient(path_optimize=tmpdir, path_output=tmpdir) +# +# m_init = Model() +# m_init.model = Dict(x=[np.array([-1.2, 1])]) # Starting guess for Rosenbrock +# m_init.save(path=os.path.join(tmpdir, "m_new.npz")) +# +# for iteration in range(100): +# # Step 1: Evaluate the objective function for given model 'm_new' +# m_new = optimize.load_vector("m_new") +# f_new = rosenbrock_objective_function(x=m_new.vector) +# optimize.save_vector("f_new", f_new) +# # Step 2: Evaluate the gradient of the objective function +# gradient = rosenbrock_gradient(x=m_new.vector) +# g_new = m_new.update(vector=gradient) +# optimize.save_vector("g_new", g_new) +# # Step 3: Compute the search direction using the gradient +# p_new = optimize.compute_direction() +# optimize.save_vector("p_new", p_new) +# # Step 4a: Set up the line search with a trial model `m_try` +# m_try, alpha = optimize.initialize_search() +# optimize.save_vector("alpha", alpha) +# # Step 4b: Run the line search w/ various trial models 'til lower misfit +# while True: +# f_try = rosenbrock_objective_function(m_try.vector) +# np.savetxt(os.path.join(tmpdir, "f_try.txt"), [f_try]) +# # Will look for the previously saved 'alpha' value +# m_try, alpha, status = optimize.update_line_search() +# if status == "PASS": +# break +# else: +# optimize.save_vector("alpha", alpha) +# optimize.save_vector("m_try", m_try) +# # Set up for the next iteration, most importantly `m_try` -> `m_new` +# optimize.finalize_search() +# +# # Just check a few of the stats file outputs to make sure this runs right +# assert(os.path.exists(optimize.path._stats_file)) +# stats = np.genfromtxt(optimize.path._stats_file, delimiter=",", names=True) +# assert(len(stats) == 100.) +# assert(stats["misfit"].min() == pytest.approx(0.9992, 3)) +# assert(stats["if_restarted"].sum() == 0.) + + +def _test_inversion_optimization_problem_general(optimize): + """ + Rather than run a single line search evaluation, which all the previous + tests have done, we want to run a inversion workflow to find a best fitting + model. To do this we essentially have to mimic the inversion workflow, but + with barebones functions. + + This function is written to be general, other tests should populate the + `optimize` input parameter with instantiated Optimization modules. + + .. note:: + We do not save `m_try` to disk each time it is evaluated because it is + small. However in real workflows, `m_try` must be saved to disk rather + than passed in memory because it is likely to be a large vector. + """ + m_init = Model() + m_init.model = Dict(x=[np.array([-1.2, 1])]) # Starting guess for Rosenbrock + optimize.save_vector("m_new", m_init) + + for iteration in range(100): + print(iteration) + # Step 1: Evaluate the objective function for given model 'm_new' + m_new = optimize.load_vector("m_new") + f_new = rosenbrock_objective_function(x=m_new.vector) + optimize.save_vector("f_new", f_new) + # Step 2: Evaluate the gradient of the objective function + gradient = rosenbrock_gradient(x=m_new.vector) + g_new = m_new.update(vector=gradient) + optimize.save_vector("g_new", g_new) + # Step 3: Compute the search direction using the gradient + p_new = optimize.compute_direction() + optimize.save_vector("p_new", p_new) + # Step 4a: Set up the line search with a trial model `m_try` + m_try, alpha = optimize.initialize_search() + optimize.save_vector("alpha", alpha) + # Step 4b: Run the line search w/ various trial models 'til lower misfit + while True: + f_try = rosenbrock_objective_function(m_try.vector) + optimize.save_vector("f_try", f_try) + # Will look for the previously saved 'alpha' value + m_try, alpha, status = optimize.update_line_search() + if status == "PASS": + break + else: + optimize.save_vector("alpha", alpha) + optimize.save_vector("m_try", m_try) + # Set up for the next iteration, most importantly `m_try` -> `m_new` + optimize.finalize_search() + + return optimize + + +def test_inversion_optimization_problem_with_gradient( + tmpdir, setup_optimization_vectors): + """Wrapper function to test the Gradient descent optimization problem""" + gradient = Gradient(path_optimize=tmpdir, path_output=tmpdir) + optimize = _test_inversion_optimization_problem_general(gradient) + # Just check a few of the stats file outputs to make sure this runs right + assert(os.path.exists(optimize.path._stats_file)) + stats = np.genfromtxt(optimize.path._stats_file, delimiter=",", names=True) + assert(len(stats) == 100.) + assert(stats["misfit"].min() == pytest.approx(0.9992, 3)) + assert(stats["if_restarted"].sum() == 0.) + + +def test_inversion_optimization_problem_with_LBFGS( # NOQA + tmpdir, setup_optimization_vectors): + """Wrapper function to test the L-BFGS descent optimization problem""" + lbfgs = LBFGS(path_optimize=tmpdir, path_output=tmpdir, + line_search_method="backtrack") + os.mkdir(lbfgs.path._LBFGS) + optimize = _test_inversion_optimization_problem_general(lbfgs) + + # Just check a few of the stats file outputs to make sure this runs right + assert(os.path.exists(optimize.path._stats_file)) + stats = np.genfromtxt(optimize.path._stats_file, delimiter=",", names=True) + assert(len(stats) == 100.) + assert(stats["misfit"].min() == pytest.approx(0.1468, 3)) + # assert(stats["if_restarted"].sum() == 0.) + + +def test_optimize_recover_from_failure(): + """ + We need to make sure we can recover an optimization from a job failure. + That means that checkpointing and re-loading from checkpoint works as + expected + """ + pass \ No newline at end of file diff --git a/seisflows/tools/math.py b/seisflows/tools/math.py index dabe8c53..d7aaf33a 100644 --- a/seisflows/tools/math.py +++ b/seisflows/tools/math.py @@ -2,9 +2,13 @@ """ Mathematical tools for Seisflows """ +import sys import numpy as np from scipy.signal import hilbert as analytic +from seisflows import logger +from seisflows.tools import msg + def angle(x, y): """ @@ -148,8 +152,9 @@ def polynomial_fit(x, f): p = np.polyfit(x[i-1:i+2], f[i-1:i+2], 2) if p[0] <= 0: - # TODO Figure out why this exit condition is here - print(msg.cli("Polynomial line fitting returned a negative p[0] value")) + logger.critical(msg.cli("Polynomial line fitting returned a negative " + "p[0] value which signifies a negative misfit " + "and is not allowed.")) sys.exit(-1) return -p[1] / (2 * p[0]) diff --git a/seisflows/tools/specfem.py b/seisflows/tools/specfem.py index 8f038fb1..81bec698 100644 --- a/seisflows/tools/specfem.py +++ b/seisflows/tools/specfem.py @@ -27,7 +27,7 @@ class Model: "vpv", "vph", "vsv", "vsh", "eta"] acceptable_parameters.extend([f"{_}_kernel" for _ in acceptable_parameters]) - def __init__(self, path=None, fmt=None, parameters=None): + def __init__(self, path=None, fmt="", parameters=None): """ Model only needs path to model to determine model parameters. Format `fmt` can be provided by the user or guessed based on available file diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index 0a2371fa..189d54b9 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -294,21 +294,35 @@ def perform_line_search(self): self._evaluate_line_search_misfit() # Increment step count, calculate new step length/model, check misfit - status = self.optimize.update_line_search() + m_try, alpha, status = self.optimize.update_line_search() self.optimize.checkpoint() # Proceed based on the outcome of the line search - if status == 1: + if status.upper() == "PASS": # Save outcome of line search to disk; reset step to 0 for next iter logger.info("trial step successful. finalizing line search") + + # Save new model (m_try) and step length (alpha) for records + self.optimize.save_vector("alpha", alpha) + self.optimize.save_vector("m_try", m_try) + del m_try # clear potentially large model vector from memory + self.optimize.finalize_search() self.optimize.checkpoint() return - elif status == 0: + elif status.upper() == "TRY": logger.info("trial step unsuccessful. re-attempting line search") + + # Save new model (m_try) and step length (alpha) for new trial step + self.optimize.save_vector("alpha", alpha) + self.optimize.save_vector("m_try", m_try) + del m_try # clear potentially large model vector from memory + + # Checkpoint and re-run line search evaluation self.optimize.checkpoint() self.perform_line_search() # RECURSIVE CALL - elif status == -1: + elif status.upper() == "FAIL": + # Check if we are able to restart line search w/ new parameters if self.optimize.attempt_line_search_restart(): logger.info("line search has failed. restarting " "optimization algorithm and line search.") @@ -316,6 +330,7 @@ def perform_line_search(self): self.optimize.restart() self.optimize.checkpoint() self.perform_line_search() # RECURSIVE CALL + # If we can't then line search has failed. Abort workflow else: logger.critical( msg.cli("Line search has failed to reduce the misfit and " diff --git a/seisflows/workflow/test.py b/seisflows/workflow/test.py index 42f18b61..6f354d83 100644 --- a/seisflows/workflow/test.py +++ b/seisflows/workflow/test.py @@ -261,7 +261,6 @@ def rosenbrock(): """ Rosenbrock test problem for optimization library testing - https://en.wikipedia.org/wiki/Rosenbrock_function """ model_init = np.array([-1.2, 1]) # This is the guess for the global min model_true = np.array([1, 1]) # This is the actual minimum From 7a4aecd3a2d5be1710a50c9f620c668a63a2f88c Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Tue, 26 Jul 2022 18:19:18 -0800 Subject: [PATCH 083/195] adding to-do tests and updated NLCG formatting for new Model approach rather than old nparray vector approach to defining models/gradients/kernels --- seisflows/optimize/NLCG.py | 52 ++++++++++++--------- seisflows/tests/test_optimize.py | 78 ++++++++------------------------ 2 files changed, 49 insertions(+), 81 deletions(-) diff --git a/seisflows/optimize/NLCG.py b/seisflows/optimize/NLCG.py index 762f7c5d..c9f18147 100644 --- a/seisflows/optimize/NLCG.py +++ b/seisflows/optimize/NLCG.py @@ -55,6 +55,17 @@ def __init__(self, nlcg_max=np.inf, nlcg_thresh=np.inf, self._NLCG_iter = 0 self._calc_beta = getattr(self, f"_{calc_beta}") + def checkpoint(self): + """ + Overwrite default checkpointing to store internal L-BFGS Attributes + """ + super().checkpoint() + + checkpoint_dict = np.load(self.path._checkpoint) + checkpoint_dict["NLCG_iter"] = self._NLCG_iter + + np.savez(file=self.path._checkpoint, **dict_out) # NOQA + def compute_direction(self): """ Compute search direction using the Nonlinear Conjugate Gradient method @@ -78,14 +89,14 @@ def compute_direction(self): self._NLCG_iter += 1 # Load the current gradient direction - g_new = self.load("g_new") + g_new = self.load_vector("g_new") # CASE 1: If first iteration, search direction is the current gradient if self._NLCG_iter == 1: logger.info("first NLCG iteration, setting search direction " "as inverse gradient") - p_new = -1 * g_new.vector - restarted = 0 + p_new = g_new.update(vector=-1 * g_new.vector) + restarted = False # CASE 2: Force restart if the iterations have surpassed the maximum # number of allowable iter elif self._NLCG_iter > self.NLCG_max: @@ -93,36 +104,34 @@ def compute_direction(self): "condition. setting search direction as inverse " "gradient") self.restart() - p_new = -1 * g_new.vector - restarted = 1 + p_new = g_new.update(vector=-1 * g_new.vector) + restarted = True # Normal NLCG direction compuitation else: # Compute search direction - g_old = self.load("g_old").vector - p_old = self.load("p_old").vector + g_old = self.load_vector("g_old") + p_old = self.load_vector("p_old") + beta = self._calc_beta(g_new.vector, g_old.vector) # Apply preconditioner and calc. scale factor for search dir. (beta) - if self.preconditioner is not None: - beta = self._calc_beta(g_new, g_old) - p_new = -1 * self._precondition(g_new) + beta * p_old - else: - beta = self._calc_beta(g_new, g_old) - p_new = -g_new + beta * p_old + _p_new_vec = (-1 * self._precondition(g_new.vector) + + beta * p_old.vector) + p_new = g_new.update(vector=_p_new_vec) # Check restart conditions, return search direction and status - if check_conjugacy(g_new, g_old) > self.NLCG_thresh: + if check_conjugacy(g_new.vector, g_old.vector) > self.NLCG_thresh: logger.info("restarting NLCG due to loss of conjugacy") self.restart() - p_new = -1 * g_new.vector - restarted = 1 + p_new = g_new.update(vector=-1 * g_new.vector) + restarted = True elif check_descent(p_new, g_new) > 0.: logger.info("restarting NLCG, not a descent direction") self.restart() - p_new = -1 * g_new.vector - restarted = 1 + p_new = g_new.update(vector=-1 * g_new.vector) + restarted = True else: p_new = p_new - restarted = 0 + restarted = False # Save values to disk and memory self._restarted = restarted @@ -135,8 +144,9 @@ def restart(self): """ logger.info("restarting NLCG optimization algorithm") - g = self.load("g_new") - self.save("p_new", -1 * g.vector) + g = self.load_vector("g_new") + p_new = g.update(vector=-1 * g.vector) + self.save_vector("p_new", p_new) self._line_search.clear_search_history() self._restarted = 1 diff --git a/seisflows/tests/test_optimize.py b/seisflows/tests/test_optimize.py index 8050d700..53206403 100644 --- a/seisflows/tests/test_optimize.py +++ b/seisflows/tests/test_optimize.py @@ -220,64 +220,6 @@ def test_optimize_attempt_line_search_restart(tmpdir, assert(optimize.attempt_line_search_restart() == True) -# def test_inversion_optimization_problem_with_gradient( -# tmpdir, setup_optimization_vectors): -# """ -# Rather than run a single line search evaluation, which all the previous -# tests have done, we want to run a inversion workflow to find a best fitting -# model. To do this we essentially have to mimic the inversion workflow, but -# with barebones functions. -# -# This takes bits and pieces from the previous tests -# -# .. note:: -# We do not save `m_try` to disk each time it is evaluated because it is -# small. However in real workflows, `m_try` must be saved to disk rather -# than passed in memory because it is likely to be a large vector. -# """ -# optimize = Gradient(path_optimize=tmpdir, path_output=tmpdir) -# -# m_init = Model() -# m_init.model = Dict(x=[np.array([-1.2, 1])]) # Starting guess for Rosenbrock -# m_init.save(path=os.path.join(tmpdir, "m_new.npz")) -# -# for iteration in range(100): -# # Step 1: Evaluate the objective function for given model 'm_new' -# m_new = optimize.load_vector("m_new") -# f_new = rosenbrock_objective_function(x=m_new.vector) -# optimize.save_vector("f_new", f_new) -# # Step 2: Evaluate the gradient of the objective function -# gradient = rosenbrock_gradient(x=m_new.vector) -# g_new = m_new.update(vector=gradient) -# optimize.save_vector("g_new", g_new) -# # Step 3: Compute the search direction using the gradient -# p_new = optimize.compute_direction() -# optimize.save_vector("p_new", p_new) -# # Step 4a: Set up the line search with a trial model `m_try` -# m_try, alpha = optimize.initialize_search() -# optimize.save_vector("alpha", alpha) -# # Step 4b: Run the line search w/ various trial models 'til lower misfit -# while True: -# f_try = rosenbrock_objective_function(m_try.vector) -# np.savetxt(os.path.join(tmpdir, "f_try.txt"), [f_try]) -# # Will look for the previously saved 'alpha' value -# m_try, alpha, status = optimize.update_line_search() -# if status == "PASS": -# break -# else: -# optimize.save_vector("alpha", alpha) -# optimize.save_vector("m_try", m_try) -# # Set up for the next iteration, most importantly `m_try` -> `m_new` -# optimize.finalize_search() -# -# # Just check a few of the stats file outputs to make sure this runs right -# assert(os.path.exists(optimize.path._stats_file)) -# stats = np.genfromtxt(optimize.path._stats_file, delimiter=",", names=True) -# assert(len(stats) == 100.) -# assert(stats["misfit"].min() == pytest.approx(0.9992, 3)) -# assert(stats["if_restarted"].sum() == 0.) - - def _test_inversion_optimization_problem_general(optimize): """ Rather than run a single line search evaluation, which all the previous @@ -356,7 +298,19 @@ def test_inversion_optimization_problem_with_LBFGS( # NOQA stats = np.genfromtxt(optimize.path._stats_file, delimiter=",", names=True) assert(len(stats) == 100.) assert(stats["misfit"].min() == pytest.approx(0.1468, 3)) - # assert(stats["if_restarted"].sum() == 0.) + # TODO finish assertion tests here, figure out why LBFGS is restarting + # at each iteration? + + +def test_inversion_optimization_problem_with_NLCG( # NOQA + tmpdir, setup_optimization_vectors): + nlcg = NLCG(path_optimize=tmpdir, path_output=tmpdir) + os.mkdir(nlcg.path._LBFGS) + optimize = _test_inversion_optimization_problem_general(nlcg) + + # Just check a few of the stats file outputs to make sure this runs right + assert(os.path.exists(optimize.path._stats_file)) + # TODO finish assertion tests here def test_optimize_recover_from_failure(): @@ -364,5 +318,9 @@ def test_optimize_recover_from_failure(): We need to make sure we can recover an optimization from a job failure. That means that checkpointing and re-loading from checkpoint works as expected + + TODO """ - pass \ No newline at end of file + pass + + From 640208430618840a9cabf1343ab16d627a13ba52 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Wed, 27 Jul 2022 11:54:34 -0800 Subject: [PATCH 084/195] rolled in 'core' utilities to the 'config' tool script. changed import statements throughout package to match --- seisflows/optimize/gradient.py | 2 +- seisflows/plugins/solver_io/ascii.py | 5 +- seisflows/plugins/solver_io/fortran_binary.py | 5 +- seisflows/preprocess/default.py | 2 +- seisflows/seisflows.py | 2 +- seisflows/solver/specfem.py | 2 +- seisflows/system/workstation.py | 4 +- seisflows/tests/test_optimize.py | 5 +- seisflows/tests/test_seisflows.py | 4 +- seisflows/tests/test_solver.py | 2 +- seisflows/tests/test_tools.py | 2 +- seisflows/tools/config.py | 175 +++++++++++++++-- seisflows/tools/core.py | 185 ------------------ seisflows/tools/specfem.py | 2 +- seisflows/tools/unix.py | 23 ++- seisflows/workflow/forward.py | 2 +- seisflows/workflow/test.py | 2 +- 17 files changed, 202 insertions(+), 222 deletions(-) delete mode 100644 seisflows/tools/core.py diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index 08e356ae..e2d6f952 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -31,7 +31,7 @@ from seisflows import logger from seisflows.tools import msg, unix -from seisflows.tools.core import Dict +from seisflows.tools.config import Dict from seisflows.tools.math import angle, dot from seisflows.tools.specfem import Model from seisflows.plugins import line_search as line_search_dir diff --git a/seisflows/plugins/solver_io/ascii.py b/seisflows/plugins/solver_io/ascii.py index ccc2bd6a..3ca6a0e7 100644 --- a/seisflows/plugins/solver_io/ascii.py +++ b/seisflows/plugins/solver_io/ascii.py @@ -5,7 +5,6 @@ import numpy as np from glob import glob from shutil import copyfile -from seisflows.tools.core import iterable def read_slice(path, parameters, iproc): @@ -26,7 +25,7 @@ def read_slice(path, parameters, iproc): model = np.loadtxt(filename).T vals = [] - for key in iterable(parameters): + for key in parameters: vals += [model[available_parameters.index(key)]] return vals @@ -48,7 +47,7 @@ def write_slice(data, path, parameters, iproc): :type iproc: int :param iproc: processor/slice number to write """ - for key in iterable(parameters): + for key in parameters: filename = os.path.join(path, f"proc{int(iproc):06d}_{key}.bin") _write(data, filename) diff --git a/seisflows/plugins/solver_io/fortran_binary.py b/seisflows/plugins/solver_io/fortran_binary.py index 9991b306..738dc66c 100644 --- a/seisflows/plugins/solver_io/fortran_binary.py +++ b/seisflows/plugins/solver_io/fortran_binary.py @@ -4,7 +4,6 @@ import os import numpy as np from shutil import copyfile -from seisflows.tools.core import iterable def read_slice(path, parameters, iproc): @@ -19,7 +18,7 @@ def read_slice(path, parameters, iproc): :param iproc: processor/slice number to read """ vals = [] - for key in iterable(parameters): + for key in parameters: filename = os.path.join(path, f"proc{int(iproc):06d}_{key}.bin") vals += [_read(filename)] return vals @@ -38,7 +37,7 @@ def write_slice(data, path, parameters, iproc): :type iproc: int :param iproc: processor/slice number to write """ - for key in iterable(parameters): + for key in parameters: filename = os.path.join(path, f"proc{int(iproc):06d}_{key}.bin") _write(data, filename) diff --git a/seisflows/preprocess/default.py b/seisflows/preprocess/default.py index f78f2eef..66ad82a3 100644 --- a/seisflows/preprocess/default.py +++ b/seisflows/preprocess/default.py @@ -12,7 +12,7 @@ from seisflows import logger from seisflows.tools import signal, unix -from seisflows.tools.core import Dict +from seisflows.tools.config import Dict from seisflows.plugins.preprocess import misfit as misfit_functions from seisflows.plugins.preprocess import adjoint as adjoint_sources diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 78862bfa..4125eb11 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -25,7 +25,7 @@ from seisflows import logger, ROOT_DIR, NAMES from seisflows.tools import unix, msg -from seisflows.tools.core import load_yaml, Dict +from seisflows.tools.config import load_yaml, Dict from seisflows.tools.config import custom_import, import_seisflows from seisflows.tools.specfem import (getpar, setpar, getpar_vel_model, setpar_vel_model) diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index bcac58d0..0a4d26cc 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -25,7 +25,7 @@ from seisflows import logger from seisflows.plugins import solver_io as solver_io_dir from seisflows.tools import msg, unix -from seisflows.tools.core import get_task_id, Dict +from seisflows.tools.config import get_task_id, Dict from seisflows.tools.specfem import getpar, setpar diff --git a/seisflows/system/workstation.py b/seisflows/system/workstation.py index 299aabd2..b70fadff 100644 --- a/seisflows/system/workstation.py +++ b/seisflows/system/workstation.py @@ -7,9 +7,9 @@ from contextlib import redirect_stdout from seisflows import logger -from seisflows.tools.core import Dict +from seisflows.tools.config import Dict from seisflows.tools import unix -from seisflows.tools.core import number_fid, get_task_id, set_task_id +from seisflows.tools.config import number_fid, get_task_id, set_task_id class Workstation: diff --git a/seisflows/tests/test_optimize.py b/seisflows/tests/test_optimize.py index 53206403..94dde895 100644 --- a/seisflows/tests/test_optimize.py +++ b/seisflows/tests/test_optimize.py @@ -5,7 +5,7 @@ import os import pytest import numpy as np -from seisflows.tools.core import Dict +from seisflows.tools.config import Dict from seisflows.tools.specfem import Model from seisflows.tools.math import angle from seisflows.optimize.gradient import Gradient @@ -298,8 +298,7 @@ def test_inversion_optimization_problem_with_LBFGS( # NOQA stats = np.genfromtxt(optimize.path._stats_file, delimiter=",", names=True) assert(len(stats) == 100.) assert(stats["misfit"].min() == pytest.approx(0.1468, 3)) - # TODO finish assertion tests here, figure out why LBFGS is restarting - # at each iteration? + pytest.set_trace() def test_inversion_optimization_problem_with_NLCG( # NOQA diff --git a/seisflows/tests/test_seisflows.py b/seisflows/tests/test_seisflows.py index 43c94079..cf39831d 100644 --- a/seisflows/tests/test_seisflows.py +++ b/seisflows/tests/test_seisflows.py @@ -11,10 +11,10 @@ import subprocess from unittest.mock import patch -from seisflows.tools.core import Dict +from seisflows.tools.config import Dict from seisflows.seisflows import SeisFlows from seisflows.tools.config import ROOT_DIR, NAMES, CFGPATHS -from seisflows.tools.core import load_yaml +from seisflows.tools.config import load_yaml TEST_DIR = os.path.join(ROOT_DIR, "tests") diff --git a/seisflows/tests/test_solver.py b/seisflows/tests/test_solver.py index 0ad93992..591fa0e2 100644 --- a/seisflows/tests/test_solver.py +++ b/seisflows/tests/test_solver.py @@ -6,7 +6,7 @@ import pytest from glob import glob from seisflows import ROOT_DIR -from seisflows.tools.core import set_task_id +from seisflows.tools.config import set_task_id from seisflows.solver.specfem import Specfem diff --git a/seisflows/tests/test_tools.py b/seisflows/tests/test_tools.py index 3e8c5ff0..f651b8e9 100644 --- a/seisflows/tests/test_tools.py +++ b/seisflows/tests/test_tools.py @@ -6,7 +6,7 @@ import numpy as np from glob import glob from seisflows import ROOT_DIR -from seisflows.tools.core import Dict +from seisflows.tools.config import Dict from seisflows.tools.specfem import Model from seisflows.tools.config import custom_import diff --git a/seisflows/tools/config.py b/seisflows/tools/config.py index 08f346b2..17605aef 100755 --- a/seisflows/tools/config.py +++ b/seisflows/tools/config.py @@ -1,26 +1,153 @@ #!/usr/bin/env python3 """ -This is the Seisflows Config script, it contains utilities that are called upon -throughout the Seisflows workflow. It also (re)defines some important functions -that are used extensively by the machinery of Seisflows. - - -Each corresponds simultaneously to a module in the SeisFlows source code, -a class that is instantiated and made accessible via sys.modules, and a -parameter in a global dictionary. Once in memory, these objects can be thought -of as comprising the complete 'state' of a SeisFlows session +Seisflows configuration tools, containing core utilities that are called upon +throughout the Seisflows workflow. """ import os import sys +import re +import yaml import logging +import numpy as np import traceback from pkgutil import find_loader from importlib import import_module from seisflows import logger, NAMES -from seisflows.tools.core import Dict, Null from seisflows.tools import msg -from seisflows.tools.core import load_yaml + +ENV_VARIABLES = ["SEISFLOWS_TASKID", "SLURM_ARRAY_TASK_ID"] + + +class Dict(dict): + """ + A dictionary replacement which allows for easier parameter access through + getting and setting attributes. Also has some functionality to make string + printing prettier + """ + def __str__(self): + """Pretty print dictionaries and first level nested dictionaries""" + str_ = "" + try: + longest_key = max([len(_) for _ in self.keys()]) + for key, val in self.items(): + str_ += f"{key:<{longest_key}}: {val}\n" + except ValueError: + pass + return str_ + + def __repr__(self): + """Pretty print when calling an instance of this object""" + return self.__str__() + + def __getattr__(self, key): + """Attribute-like access of the internal dictionary attributes""" + try: + return self[key] + except KeyError: + raise AttributeError(f"{key} not found in Dict") + + def __setattr__(self, key, val): + """Setting attributes can only be performed one time""" + self.__dict__[key] = val + + +class Null: + """ + A null object that always and reliably does nothing + """ + def __init__(self, *args, **kwargs): + pass + + def __call__(self, *args, **kwargs): + return self + + def __bool__(self): + return False + + def __nonzero__(self): + return False + + def __getattr__(self, key): + return self + + def __setattr__(self, key, val): + return self + + def __delattr__(self, key): + return self + + +def load_yaml(filename): + """ + Define how the PyYaml yaml loading function behaves. + Replaces None and inf strings with NoneType and numpy.inf respectively + + :type filename: str + :param filename: .yaml file to load in + :rtype: Dict + :return: Dictionary containing all parameters in a YAML file + """ + # work around PyYAML bugs + yaml.SafeLoader.add_implicit_resolver( + u'tag:yaml.org,2002:float', + re.compile(u'''^(?: + [-+]?(?:[0-9][0-9_]*)\\.[0-9_]*(?:[eE][-+]?[0-9]+)? + |[-+]?(?:[0-9][0-9_]*)(?:[eE][-+]?[0-9]+) + |\\.[0-9_]+(?:[eE][-+][0-9]+)? + |[-+]?[0-9][0-9_]*(?::[0-5]?[0-9])+\\.[0-9_]* + |[-+]?\\.(?:inf|Inf|INF) + |\\.(?:nan|NaN|NAN))$''', re.X), + list(u'-+0123456789.')) + + with open(filename, 'r') as f: + mydict = Dict(yaml.safe_load(f)) + + if mydict is None: + mydict = Dict() + + # Replace 'None' and 'inf' values to match expectations + for key, val in mydict.items(): + if val == "None": + mydict[key] = None + if val == "inf": + mydict[key] = np.inf + + return mydict + + +def get_task_id(): + """ + Task IDs are assigned to each child process spawned by the system module + during a SeisFlows workflow. SeisFlows modules use this Task ID to keep + track of embarassingly parallel process, e.g., solver uses the Task ID to + determine which source is being considered. + + :rtype: int + :return: task id for given solver + """ + for env_var in ENV_VARIABLES: + _taskid = os.getenv(env_var) + if _taskid is not None: + return int(_taskid) + else: + logger.warning("Environment Task ID variable not found. Assigning 0") + return 0 + + +def set_task_id(task_id): + """ + Set the SEISFLOWS_TASKID in os environs for local workflows. If running + on HPC systems, running array jobs will assign the Task ID + + .. note:: + Mostly used for debugging/testing purposes as a way of mimicing + system.run() assigning task ids to child processes + + :type task_id: int + :param task_id: integer task id to assign to the current working environment + """ + os.environ["SEISFLOWS_TASKID"] = str(task_id) def import_seisflows(workdir=os.getcwd(), parameter_file="parameters.yaml"): @@ -210,3 +337,29 @@ class 'Inversion'. print(msg.cli(f"The following method was not found in the imported " f"class: seisflows.{name}.{module}.{classname}")) sys.exit(-1) + + +def number_fid(fid, i=0): + """ + Number a filename. Used to store old log files without overwriting them. + Premise is, if you have a file e.g., called: output.txt + This function would return incrementing filenames: + output_000.txt, output_001.txt, output_002.txt, ouput_003.txt ... + + .. note:: + Replace statement is catch-all, so we assume that there is only one + instance of the file extension in the entire path. + + :type fid: str + :param fid: path to the file that you want to increment + :type i: int + :param i: number to append to file id + :rtype: str + :return: filename with appended number. filename ONLY, will strip away + the original path location + """ + fid_only = os.path.basename(fid) + ext = os.path.splitext(fid_only)[-1] # e.g., .txt + new_ext = f"_{i:0>3}{ext}" # e.g., _000.txt + new_fid = fid_only.replace(ext, new_ext) + return new_fid \ No newline at end of file diff --git a/seisflows/tools/core.py b/seisflows/tools/core.py deleted file mode 100644 index 7927200d..00000000 --- a/seisflows/tools/core.py +++ /dev/null @@ -1,185 +0,0 @@ -""" -Core utility functions and classes which help define the working structure of -SeisFlows pacakge. -""" -import os -import re -import yaml -import numpy as np -from seisflows import logger - -# Acceptable environment variables assigned to individually running tasks when -# running SeisFlows on a system -ENV_VARIABLES = ["SEISFLOWS_TASKID", "SLURM_ARRAY_TASK_ID"] - - -class Dict(dict): - """ - A dictionary replacement which allows for easier parameter access through - getting and setting attributes. Also has some functionality to make string - printing prettier - """ - def __str__(self): - """Pretty print dictionaries and first level nested dictionaries""" - str_ = "" - try: - longest_key = max([len(_) for _ in self.keys()]) - for key, val in self.items(): - str_ += f"{key:<{longest_key}}: {val}\n" - except ValueError: - pass - return str_ - - def __repr__(self): - """Pretty print when calling an instance of this object""" - return self.__str__() - - def __getattr__(self, key): - """Attribute-like access of the internal dictionary attributes""" - try: - return self[key] - except KeyError: - raise AttributeError(f"{key} not found in Dict") - - def __setattr__(self, key, val): - """Setting attributes can only be performed one time""" - self.__dict__[key] = val - - -class Null: - """ - A null object that always and reliably does nothing - """ - def __init__(self, *args, **kwargs): - pass - - def __call__(self, *args, **kwargs): - return self - - def __bool__(self): - return False - - def __nonzero__(self): - return False - - def __getattr__(self, key): - return self - - def __setattr__(self, key, val): - return self - - def __delattr__(self, key): - return self - - -def get_task_id(): - """ - Task IDs are assigned to each child process spawned by the system module - during a SeisFlows workflow. SeisFlows modules use this Task ID to keep - track of embarassingly parallel process, e.g., solver uses the Task ID to - determine which source is being considered. - - :rtype: int - :return: task id for given solver - """ - for env_var in ENV_VARIABLES: - _taskid = os.getenv(env_var) - if _taskid is not None: - return int(_taskid) - else: - logger.warning("Environment Task ID variable not found. Assigning 0") - return 0 - - -def set_task_id(task_id): - """ - Set the SEISFLOWS_TASKID in os environs for local workflows. If running - on HPC systems, running array jobs will assign the Task ID - - .. note:: - Mostly used for debugging/testing purposes as a way of mimicing - system.run() assigning task ids to child processes - - :type task_id: int - :param task_id: integer task id to assign to the current working environment - """ - os.environ["SEISFLOWS_TASKID"] = str(task_id) - - -def load_yaml(filename): - """ - Define how the PyYaml yaml loading function behaves. - Replaces None and inf strings with NoneType and numpy.inf respectively - - :type filename: str - :param filename: .yaml file to load in - :rtype: Dict - :return: Dictionary containing all parameters in a YAML file - """ - # work around PyYAML bugs - yaml.SafeLoader.add_implicit_resolver( - u'tag:yaml.org,2002:float', - re.compile(u'''^(?: - [-+]?(?:[0-9][0-9_]*)\\.[0-9_]*(?:[eE][-+]?[0-9]+)? - |[-+]?(?:[0-9][0-9_]*)(?:[eE][-+]?[0-9]+) - |\\.[0-9_]+(?:[eE][-+][0-9]+)? - |[-+]?[0-9][0-9_]*(?::[0-5]?[0-9])+\\.[0-9_]* - |[-+]?\\.(?:inf|Inf|INF) - |\\.(?:nan|NaN|NAN))$''', re.X), - list(u'-+0123456789.')) - - with open(filename, 'r') as f: - mydict = Dict(yaml.safe_load(f)) - - if mydict is None: - mydict = Dict() - - # Replace 'None' and 'inf' values to match expectations - for key, val in mydict.items(): - if val == "None": - mydict[key] = None - if val == "inf": - mydict[key] = np.inf - - return mydict - - -def iterable(arg): - """ - Make an argument iterable - - :param arg: an argument to make iterable - :type: list - :return: iterable argument - """ - if not isinstance(arg, (list, tuple)): - return [arg] - else: - return arg - - -def number_fid(fid, i=0): - """ - Number a filename. Used to store old log files without overwriting them. - Premise is, if you have a file e.g., called: output.txt - This function would return incrementing filenames: - output_000.txt, output_001.txt, output_002.txt, ouput_003.txt ... - - .. note:: - Replace statement is catch-all, so we assume that there is only one - instance of the file extension in the entire path. - - :type fid: str - :param fid: path to the file that you want to increment - :type i: int - :param i: number to append to file id - :rtype: str - :return: filename with appended number. filename ONLY, will strip away - the original path location - """ - fid_only = os.path.basename(fid) - ext = os.path.splitext(fid_only)[-1] # e.g., .txt - new_ext = f"_{i:0>3}{ext}" # e.g., _000.txt - new_fid = fid_only.replace(ext, new_ext) - return new_fid - diff --git a/seisflows/tools/specfem.py b/seisflows/tools/specfem.py index 81bec698..4454ad7b 100644 --- a/seisflows/tools/specfem.py +++ b/seisflows/tools/specfem.py @@ -6,7 +6,7 @@ import numpy as np from glob import glob from seisflows import logger -from seisflows.tools.core import Dict +from seisflows.tools.config import Dict from seisflows.tools import unix from seisflows.tools.math import poissons_ratio diff --git a/seisflows/tools/unix.py b/seisflows/tools/unix.py index dc1bccbf..0b07e3af 100644 --- a/seisflows/tools/unix.py +++ b/seisflows/tools/unix.py @@ -9,7 +9,22 @@ import shutil import socket import subprocess -from seisflows.tools.core import iterable + + +def _iterable(arg): + """ + Make an argument iterable. Allows for more generalized inputs to these + unix-style functions. + + :type arg: anything + :param arg: an argument to make iterable + :rtype: list + :return: iterable argument + """ + if not isinstance(arg, (list, tuple)): + return [arg] + else: + return arg def cat(src, dst=None): @@ -136,7 +151,7 @@ def mkdir(dirs): """ time.sleep(2 * random.random()) # interval [0, 2]s - for dir_ in iterable(dirs): + for dir_ in _iterable(dirs): if not os.path.isdir(dir_): os.makedirs(dir_) @@ -174,7 +189,7 @@ def rename(old, new, names): :type names: list :param names: files to replace expressions in """ - for name in iterable(names): + for name in _iterable(names): if name.find(old) >= 0: os.rename(name, name.replace(old, new)) @@ -183,7 +198,7 @@ def rm(path): """ Remove files or directories """ - for name in iterable(path): + for name in _iterable(path): if os.path.isfile(name) or os.path.islink(name): os.remove(name) elif os.path.isdir(name): diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index f3e6aba4..392e05fd 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -10,7 +10,7 @@ from seisflows import logger from seisflows.tools import msg, unix -from seisflows.tools.core import Dict +from seisflows.tools.config import Dict from seisflows.tools.specfem import Model diff --git a/seisflows/workflow/test.py b/seisflows/workflow/test.py index 6f354d83..e7fd0c43 100644 --- a/seisflows/workflow/test.py +++ b/seisflows/workflow/test.py @@ -12,7 +12,7 @@ import numpy as np from glob import glob -from seisflows.tools.core import Base +from seisflows.tools.config import Base from seisflows.tools.config import ROOT_DIR, CFGPATHS, save, config_logger From ad92c210ae6a7da899da8772e260dc22cd49f0d5 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Wed, 27 Jul 2022 11:57:56 -0800 Subject: [PATCH 085/195] removing solver_io plugin, this has been completely replaced by the Model class, detaching model interaction from the solver module --- seisflows/plugins/solver_io/__init__.py | 9 -- seisflows/plugins/solver_io/adios.py | 30 ----- seisflows/plugins/solver_io/ascii.py | 122 ------------------ seisflows/plugins/solver_io/fortran_binary.py | 100 -------------- seisflows/solver/specfem.py | 12 +- 5 files changed, 1 insertion(+), 272 deletions(-) delete mode 100644 seisflows/plugins/solver_io/__init__.py delete mode 100644 seisflows/plugins/solver_io/adios.py delete mode 100644 seisflows/plugins/solver_io/ascii.py delete mode 100644 seisflows/plugins/solver_io/fortran_binary.py diff --git a/seisflows/plugins/solver_io/__init__.py b/seisflows/plugins/solver_io/__init__.py deleted file mode 100644 index 01ef1e41..00000000 --- a/seisflows/plugins/solver_io/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -""" -Used by the SOLVER class and specified by the SOLVERIO parameter - -Note: - ADIOS IO format is currently implemented in SPECFEM3D, but not SeisFlows - import adios -""" -from . import fortran_binary - diff --git a/seisflows/plugins/solver_io/adios.py b/seisflows/plugins/solver_io/adios.py deleted file mode 100644 index 43278a8e..00000000 --- a/seisflows/plugins/solver_io/adios.py +++ /dev/null @@ -1,30 +0,0 @@ - -from os.path import abspath, join -from shutil import copyfile - -import numpy as np - - -def mread(path, parameters, iproc, prefix='', suffix=''): - """ Multiparameter read, callable by a single mpi process - """ - keys = [] - vals = [] - for key in sorted(parameters): - val = _read1(path, iproc, prefix+key+suffix) - keys += [key] - vals += [val] - return keys, vals - - -def read(path, parameter, iproc): - """ Reads from ADIOS container - """ - raise NotImplementedError - - -def write(v, path, parameter, iproc): - """ Writes to ADIOS container - """ - raise NotImplementedError - diff --git a/seisflows/plugins/solver_io/ascii.py b/seisflows/plugins/solver_io/ascii.py deleted file mode 100644 index 3ca6a0e7..00000000 --- a/seisflows/plugins/solver_io/ascii.py +++ /dev/null @@ -1,122 +0,0 @@ -""" -Functions to read and write ASCII model (.dat) files used by SPECFEM2D -""" -import os -import numpy as np -from glob import glob -from shutil import copyfile - - -def read_slice(path, parameters, iproc): - """ - Reads SPECFEM model slice(s) based on .dat ASCII files - - :type path: str - :param path: path to the database files - :type parameters: str - :param parameters: parameters to read, e.g. 'vs', 'vp' - :type iproc: int - :param iproc: processor/slice number to read - :rtype: list of np.array - :return: list of arrays corresponding to model parameters in given order - """ - filename = _get_filename(path, iproc) - available_parameters = _get_available_parameters(filename) - model = np.loadtxt(filename).T - - vals = [] - for key in parameters: - vals += [model[available_parameters.index(key)]] - - return vals - - -def write_slice(data, path, parameters, iproc): - """ - Writes SPECFEM model slice - - !!! This won't work because we need access to the spatial components that - !!! are only the model - - :type data: seisflows.Container - :param data: data to be written to a slice - :type path: str - :param path: path to the database files - :type parameters: str - :param parameters: parameters to write, e.g. 'vs', 'vp' - :type iproc: int - :param iproc: processor/slice number to write - """ - for key in parameters: - filename = os.path.join(path, f"proc{int(iproc):06d}_{key}.bin") - _write(data, filename) - - -def copy_slice(src, dst, iproc, parameter): - """ - Copies SPECFEM model slice - - :type src: str - :param src: source location to copy slice from - :type dst: str - :param dst: destination location to copy slice to - :type parameter: str - :param parameter: parameters to copy, e.g. 'vs', 'vp' - :type iproc: int - :param iproc: processor/slice number to copy - """ - filename = os.path.basename(_get_filename(src, iproc)) - copyfile(os.path.join(src, filename), - os.path.join(dst, filename)) - - -def _get_filename(path, iproc): - """ - ASCII .dat files list the available parameters in the fileid, meaning - there is no standard format for retrieving files. Use glob to search for - the file based on file extension. - - :type path: str - :param path: path to the database files - :type iproc: int - :param iproc: processor/slice number to read - :rtype: str - :return: filename of the model - """ - filename_glob = os.path.join(path, f"proc{int(iproc):06d}_*.dat") - filename = glob(filename_glob) - assert(len(filename) == 1), \ - f"Expected only one .dat file, found {len(filename)}" - - return filename[0] - - -def _get_available_parameters(filename): - """ - The available parameters are listed in the file name. Split off the - uncessary text and return the listend parameters. - - :type filename: str - :param filename: filename to check parameters from - :rtype: list - :return: list of parameters from the file id - """ - fid = os.path.splitext(os.path.basename(filename))[0] - _, *available_parameters = fid.split("_") - - return available_parameters - - -def _write(v, filename): - """ - Writes Fortran style binary files - Data are written as single precision floating point numbers - """ - n = np.array([4 * len(v)], dtype='int32') - v = np.array(v, dtype='float32') - - with open(filename, 'wb') as file: - n.tofile(file) - v.tofile(file) - n.tofile(file) - diff --git a/seisflows/plugins/solver_io/fortran_binary.py b/seisflows/plugins/solver_io/fortran_binary.py deleted file mode 100644 index 738dc66c..00000000 --- a/seisflows/plugins/solver_io/fortran_binary.py +++ /dev/null @@ -1,100 +0,0 @@ -""" -Functions to read and write FORTRAN binary files that are outputted by Specfem -""" -import os -import numpy as np -from shutil import copyfile - - -def read_slice(path, parameters, iproc): - """ - Reads SPECFEM model slice(s) - - :type path: str - :param path: path to the database files - :type parameters: str - :param parameters: parameters to read, e.g. 'vs', 'vp' - :type iproc: int - :param iproc: processor/slice number to read - """ - vals = [] - for key in parameters: - filename = os.path.join(path, f"proc{int(iproc):06d}_{key}.bin") - vals += [_read(filename)] - return vals - - -def write_slice(data, path, parameters, iproc): - """ - Writes SPECFEM model slice - - :type data: seisflows.Container - :param data: data to be written to a slice - :type path: str - :param path: path to the database files - :type parameters: str - :param parameters: parameters to write, e.g. 'vs', 'vp' - :type iproc: int - :param iproc: processor/slice number to write - """ - for key in parameters: - filename = os.path.join(path, f"proc{int(iproc):06d}_{key}.bin") - _write(data, filename) - - -def copy_slice(src, dst, iproc, parameter): - """ - Copies SPECFEM model slice - - :type src: str - :param src: source location to copy slice from - :type dst: str - :param dst: destination location to copy slice to - :type parameter: str - :param parameter: parameters to copy, e.g. 'vs', 'vp' - :type iproc: int - :param iproc: processor/slice number to copy - """ - filename = f"proc{int(iproc):06d}_{parameter}.bin" - copyfile(os.path.join(src, filename), - os.path.join(dst, filename)) - - -def _read(filename): - """ - Reads Fortran style binary data into numpy array - """ - nbytes = os.path.getsize(filename) - with open(filename, 'rb') as file: - # read size of record - file.seek(0) - n = np.fromfile(file, dtype='int32', count=1)[0] - - if n == nbytes-8: - file.seek(4) - data = np.fromfile(file, dtype='float32') - return data[:-1] - else: - file.seek(0) - data = np.fromfile(file, dtype='float32') - return data - - -def _write(v, filename): - """ - Writes Fortran style binary files - Data are written as single precision floating point numbers - - .. note:: - FORTRAN unformatted binaries are bounded by an INT*4 byte count. This - function mimics that behavior by tacking on the boundary data. - https://docs.oracle.com/cd/E19957-01/805-4939/6j4m0vnc4/index.html - """ - n = np.array([4 * len(v)], dtype='int32') - v = np.array(v, dtype='float32') - - with open(filename, 'wb') as file: - n.tofile(file) - v.tofile(file) - n.tofile(file) - diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 0a4d26cc..0ef010c9 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -23,7 +23,6 @@ from glob import glob from seisflows import logger -from seisflows.plugins import solver_io as solver_io_dir from seisflows.tools import msg, unix from seisflows.tools.config import get_task_id, Dict from seisflows.tools.specfem import getpar, setpar @@ -119,7 +118,7 @@ def __init__(self, data_format="ascii", materials="acoustic", self.smooth_h = smooth_h self.smooth_v = smooth_v self.components = components - self.solver_io = solver_io + # self.solver_io = solver_io # currently not used self.source_prefix = source_prefix or "SOURCE" # Define internally used directory structure @@ -144,7 +143,6 @@ def __init__(self, data_format="ascii", materials="acoustic", self._mpiexec = mpiexec self._source_names = None # for property source_names self._ext = None # for database file extensions - self._io = getattr(solver_io_dir, self.solver_io) # for database IO # Define available choices for check parameters self._available_model_types = ["gll"] @@ -169,14 +167,6 @@ def check(self): f"solver.data_format must be {self._available_data_formats}" ) - # Make sure we can read in the model/kernel/gradient files - # TODO is this even used? Can we remove? - assert hasattr(solver_io_dir, self.solver_io) - assert hasattr(self._io, "read_slice"), \ - "IO method has no attribute 'read'" - assert hasattr(self._io, "write_slice"), \ - "IO method has no attribute 'write'" - # Check that User has provided appropriate binary files to run SPECFEM assert(self.path.specfem_bin is not None and os.path.exists(self.path.specfem_bin)), ( From 43eb704dd28035ec98c5f42862e809d44d7fb741 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Wed, 27 Jul 2022 12:41:54 -0800 Subject: [PATCH 086/195] added a copy() function to Model class for specfem models, whenever new vectors are instantiated, they deepcopy off of old model files. This fixes a bug where two different vectors were pointing to the same part of memory --- seisflows/optimize/LBFGS.py | 31 +++++++++++++++--------------- seisflows/optimize/gradient.py | 9 ++++++--- seisflows/tests/test_optimize.py | 10 +++++++--- seisflows/tests/test_preprocess.py | 2 +- seisflows/tools/array.py | 9 +-------- seisflows/tools/specfem.py | 9 ++++++--- 6 files changed, 36 insertions(+), 34 deletions(-) diff --git a/seisflows/optimize/LBFGS.py b/seisflows/optimize/LBFGS.py index 1f2e158a..c6936f82 100644 --- a/seisflows/optimize/LBFGS.py +++ b/seisflows/optimize/LBFGS.py @@ -146,10 +146,11 @@ def compute_direction(self): # Load the current gradient direction, which is the L-BFGS search # direction if this is the first iteration g = self.load_vector("g_new") + p_new = g.copy() if self._LBFGS_iter == 1: logger.info("first L-BFGS iteration, default to 'Gradient' descent") - p_new = g.update(vector=-1 * g.vector) + p_new.update(vector=-1 * g.vector) restarted = False # Restart condition or first iteration lead to setting search direction # as the inverse gradient (i.e., default to steepest descent) @@ -157,7 +158,7 @@ def compute_direction(self): logger.info("restarting L-BFGS due to periodic restart condition. " "setting search direction as inverse gradient") self.restart() - p_new = g.update(vector=-1 * g.vector) + p_new.update(vector=-1 * g.vector) restarted = True # Normal LBFGS direction computation else: @@ -165,20 +166,20 @@ def compute_direction(self): # 'q' becomes the new search direction 'g' logger.info("applying inverse Hessian to gradient") s, y = self._update_search_history() - _q_vector = self._apply_inverse_hessian(g.vector, s, y) - q = g.update(vector=_q_vector) + q = g.copy() + q.update(vector=self._apply_inverse_hessian(g.vector, s, y)) # Determine if the new search direction is appropriate by checking # its angle to the previous search direction if self._check_status(g.vector, q.vector): logger.info("new L-BFGS search direction found") - p_new = q.update(vector=-1 * q.vector) + p_new.update(vector=-1 * q.vector) restarted = False else: logger.info("new search direction not appropriate, defaulting " "to gradient desceitn") self.restart() - p_new = g.update(vector=-1 * g.vector) + p_new.update(vector=-1 * g.vector) restarted = True # Assign restart condition to internal memory @@ -195,7 +196,8 @@ def restart(self): # Fall back to gradient descent for search direction g = self.load_vector("g_new") - p_new = g.update(vector=-1 * g.vector) + p_new = g.copy() + p_new.update(vector=-1 * g.vector) self.save_vector("p_new", p_new) # Clear internal memory @@ -286,10 +288,10 @@ def _apply_inverse_hessian(self, q, s=None, y=None): if s is None or y is None: m = len(q) n = self.LBFGS_mem - s = np.memmap(filename=self.path._s_file, mode="w+", dtype="float32", - shape=(m, n)) - y = np.memmap(filename=self.path._y_file, mode="w+", dtype="float32", - shape=(m, n)) + s = np.memmap(filename=self.path._s_file, mode="w+", + dtype="float32", shape=(m, n)) + y = np.memmap(filename=self.path._y_file, mode="w+", + dtype="float32", shape=(m, n)) # First matrix product # Recursion step 2 from appendix A of Modrak & Tromp 2016 @@ -301,11 +303,8 @@ def _apply_inverse_hessian(self, q, s=None, y=None): al[ii] = rh[ii] * np.dot(s[:, ii], q) q = q - al[ii] * y[:, ii] - # Apply a preconditioner if available - if self.preconditioner: - r = self._precondition(q) - else: - r = q + # Apply an optional preconditioner. Otherwise r==q + r = self._precondition(q) # Use scaling M3 proposed by Liu and Nocedal 1989 sty = np.dot(y[:, 0], s[:, 0]) diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index e2d6f952..59d81b72 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -302,7 +302,8 @@ def compute_direction(self): :return: search direction as a Model instance """ g_new = self.load_vector("g_new") - p_new = g_new.update(vector=-1 * self._precondition(g_new.vector)) + p_new = g_new.copy() + p_new.update(vector=-1 * self._precondition(g_new.vector)) return p_new @@ -349,7 +350,8 @@ def initialize_search(self): # The new model is the old model, scaled by the step direction and # gradient threshold to remove any outlier values - m_try = m.update(vector=m.vector + alpha * p.vector) + m_try = m.copy() + m_try.update(vector=m.vector + alpha * p.vector) logger.info("trial model 'm_try' parameters: ") m_try.check() @@ -401,7 +403,8 @@ def update_line_search(self): _p = self.load_vector("p_new") # Sets the latest trial model using the current `alpha` value - m_try = _m.update(vector=_m.vector + alpha * _p.vector) + m_try = _m.copy() + m_try.update(vector=_m.vector + alpha * _p.vector) logger.info("trial model 'm_try' parameters: ") m_try.check() diff --git a/seisflows/tests/test_optimize.py b/seisflows/tests/test_optimize.py index 94dde895..7364ab82 100644 --- a/seisflows/tests/test_optimize.py +++ b/seisflows/tests/test_optimize.py @@ -69,7 +69,8 @@ def setup_optimization_vectors(tmpdir): # Calcualte the gradient and save as a Model gradient = rosenbrock_gradient(x=m_init.vector) - g_new = m_init.update(vector=gradient) + g_new = m_init.copy() + g_new.update(vector=gradient) g_new.save(path=os.path.join(tmpdir, "g_new.npz")) @@ -240,14 +241,14 @@ def _test_inversion_optimization_problem_general(optimize): optimize.save_vector("m_new", m_init) for iteration in range(100): - print(iteration) # Step 1: Evaluate the objective function for given model 'm_new' m_new = optimize.load_vector("m_new") f_new = rosenbrock_objective_function(x=m_new.vector) optimize.save_vector("f_new", f_new) # Step 2: Evaluate the gradient of the objective function gradient = rosenbrock_gradient(x=m_new.vector) - g_new = m_new.update(vector=gradient) + g_new = m_new.copy() + g_new.update(vector=gradient) optimize.save_vector("g_new", g_new) # Step 3: Compute the search direction using the gradient p_new = optimize.compute_direction() @@ -288,6 +289,9 @@ def test_inversion_optimization_problem_with_gradient( def test_inversion_optimization_problem_with_LBFGS( # NOQA tmpdir, setup_optimization_vectors): """Wrapper function to test the L-BFGS descent optimization problem""" + # from seisflows.tools.config import config_logger + # config_logger(level="DEBUG", filename=os.path.join(tmpdir, "log.txt"), verbose=False) + lbfgs = LBFGS(path_optimize=tmpdir, path_output=tmpdir, line_search_method="backtrack") os.mkdir(lbfgs.path._LBFGS) diff --git a/seisflows/tests/test_preprocess.py b/seisflows/tests/test_preprocess.py index 9e09471e..364d7bc5 100644 --- a/seisflows/tests/test_preprocess.py +++ b/seisflows/tests/test_preprocess.py @@ -5,7 +5,7 @@ import os import pytest from glob import glob -from seisflows.tools.config import ROOT_DIR +from seisflows import ROOT_DIR from seisflows.preprocess.default import Default diff --git a/seisflows/tools/array.py b/seisflows/tools/array.py index 16291019..69352ac3 100644 --- a/seisflows/tools/array.py +++ b/seisflows/tools/array.py @@ -86,13 +86,6 @@ def uniquerows(a, sort_array=False, return_index=False): return ua -def stack(*args): - """ - Column-wise stack arrays - """ - return np.column_stack(args) - - def gridsmooth(Z, span): """ Smooths values on 2D rectangular grid @@ -178,7 +171,7 @@ def mesh2grid(v, mesh): x = np.linspace(x.min(), x.max(), nx) z = np.linspace(z.min(), z.max(), nz) X, Z = np.meshgrid(x, z) - grid = stack(X.flatten(), Z.flatten()) + grid = np.column_stack(X.flatten(), Z.flatten()) # Interpolate to structured grid V = _interp.griddata(mesh, v, grid, 'linear') diff --git a/seisflows/tools/specfem.py b/seisflows/tools/specfem.py index 4454ad7b..18a8fc36 100644 --- a/seisflows/tools/specfem.py +++ b/seisflows/tools/specfem.py @@ -4,6 +4,7 @@ """ import os import numpy as np +from copy import deepcopy from glob import glob from seisflows import logger from seisflows.tools.config import Dict @@ -61,7 +62,7 @@ def __init__(self, path=None, fmt="", parameters=None): self._nproc = len(self.model[_first_key]) # Read a SPECFEM model from its native output files else: - if self.fmt is None: + if not self.fmt: self.fmt = self._guess_file_format() self._nproc, self.available_parameters = \ self._get_nproc_parameters() @@ -155,6 +156,10 @@ def vector(self): raise TypeError("Model cannot merge files into continous " "vector") from e + def copy(self): + """Returns a deep copy of self so that models can be transferred""" + return deepcopy(self) + def read(self, parameters=None): """ Utility function to load in SPECFEM models/kernels/gradients saved in @@ -339,8 +344,6 @@ def update(self, model=None, vector=None): elif vector is not None: self.model = self.split(vector=vector) - return self - def _get_nproc_parameters(self): """ Get the number of processors and the available parameters from a list of From a4651b7701942572120ab717b279d085d7a29a4f Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Wed, 27 Jul 2022 17:21:46 -0800 Subject: [PATCH 087/195] finished optimization test suite, working on system test suite. Fixing up cluster system which now can run tasks in parallel on the login/direct compute node. Also cleaned up the 'seisflows submit' call which now just imports system to submit and doesnt import the whole workflow. Also moving test data around in preparation for removing some old tests. --- seisflows/optimize/NLCG.py | 31 ++-- seisflows/seisflows.py | 12 +- seisflows/system/cluster.py | 127 +++++++++------- .../system/runscripts/submit_workflow.py | 47 +++--- seisflows/system/slurm.py | 2 - seisflows/system/workstation.py | 56 ++++--- .../{ => specfem}/DATA/CMTSOLUTION_c46e1d99 | 0 .../{ => specfem}/DATA/FORCESOLUTION_c46e1d99 | 0 .../DATA/Par_file_SPECFEM2D_cf893667 | 0 .../DATA/Par_file_SPECFEM3D_c46e1d99 | 0 .../{ => specfem}/DATA/SOURCE_cf893667 | 0 .../OUTPUT_FILES/AA.S0001.BXY.semd | 0 .../OUTPUT_FILES/Uy_file_single_d.su | Bin .../tests/test_data/test_optimize/g_new.npz | 1 - .../test_optimize/gradient_rosenbrock.npz | Bin 526 -> 0 bytes .../test_optimize/m_init_rosenbrock.npz | Bin 526 -> 0 bytes .../test_optimize/m_true_rosenbrock.npz | Bin 526 -> 0 bytes .../tests/test_data/test_optimize/m_try.npz | 1 - seisflows/tests/test_optimize.py | 140 ++++++++++++++---- seisflows/tests/test_system.py | 82 ++++++++++ seisflows/tools/config.py | 42 +++++- 21 files changed, 389 insertions(+), 152 deletions(-) rename seisflows/tests/test_data/{ => specfem}/DATA/CMTSOLUTION_c46e1d99 (100%) rename seisflows/tests/test_data/{ => specfem}/DATA/FORCESOLUTION_c46e1d99 (100%) rename seisflows/tests/test_data/{ => specfem}/DATA/Par_file_SPECFEM2D_cf893667 (100%) rename seisflows/tests/test_data/{ => specfem}/DATA/Par_file_SPECFEM3D_c46e1d99 (100%) rename seisflows/tests/test_data/{ => specfem}/DATA/SOURCE_cf893667 (100%) rename seisflows/tests/test_data/{ => specfem}/OUTPUT_FILES/AA.S0001.BXY.semd (100%) rename seisflows/tests/test_data/{ => specfem}/OUTPUT_FILES/Uy_file_single_d.su (100%) delete mode 120000 seisflows/tests/test_data/test_optimize/g_new.npz delete mode 100644 seisflows/tests/test_data/test_optimize/gradient_rosenbrock.npz delete mode 100644 seisflows/tests/test_data/test_optimize/m_init_rosenbrock.npz delete mode 100644 seisflows/tests/test_data/test_optimize/m_true_rosenbrock.npz delete mode 120000 seisflows/tests/test_data/test_optimize/m_try.npz create mode 100644 seisflows/tests/test_system.py diff --git a/seisflows/optimize/NLCG.py b/seisflows/optimize/NLCG.py index c9f18147..8bc88fed 100644 --- a/seisflows/optimize/NLCG.py +++ b/seisflows/optimize/NLCG.py @@ -31,9 +31,9 @@ def __init__(self, nlcg_max=np.inf, nlcg_thresh=np.inf, super().__init__(**kwargs) # Overwrite user-chosen line search. L-BFGS requires 'Backtrack'ing LS - if self.line_search_method.title != "Bracket": + if self.line_search_method.title() != "Bracket": logger.warning(f"NLCG optimization requires 'bracket'ing line " - f"search. Overwritng {self.line_search_method}") + f"search. Overwritng '{self.line_search_method}'") self.line_search_method = "Bracket" self._line_search = getattr( line_search_dir, self.line_search_method)( @@ -90,12 +90,13 @@ def compute_direction(self): # Load the current gradient direction g_new = self.load_vector("g_new") + p_new = g_new.copy() # CASE 1: If first iteration, search direction is the current gradient if self._NLCG_iter == 1: logger.info("first NLCG iteration, setting search direction " "as inverse gradient") - p_new = g_new.update(vector=-1 * g_new.vector) + p_new.update(vector=-1 * g_new.vector) restarted = False # CASE 2: Force restart if the iterations have surpassed the maximum # number of allowable iter @@ -104,7 +105,7 @@ def compute_direction(self): "condition. setting search direction as inverse " "gradient") self.restart() - p_new = g_new.update(vector=-1 * g_new.vector) + p_new.update(vector=-1 * g_new.vector) restarted = True # Normal NLCG direction compuitation else: @@ -114,23 +115,24 @@ def compute_direction(self): beta = self._calc_beta(g_new.vector, g_old.vector) # Apply preconditioner and calc. scale factor for search dir. (beta) - _p_new_vec = (-1 * self._precondition(g_new.vector) + - beta * p_old.vector) - p_new = g_new.update(vector=_p_new_vec) + _p_new_vec = ( + -1 * self._precondition(g_new.vector) + beta * p_old.vector + ) + p_new.update(vector=_p_new_vec) - # Check restart conditions, return search direction and status + # Check restart conditions, return search direction and statusa if check_conjugacy(g_new.vector, g_old.vector) > self.NLCG_thresh: logger.info("restarting NLCG due to loss of conjugacy") self.restart() - p_new = g_new.update(vector=-1 * g_new.vector) + p_new.update(vector=-1 * g_new.vector) restarted = True - elif check_descent(p_new, g_new) > 0.: + elif check_descent(p_new.vector, g_new.vector) > 0.: logger.info("restarting NLCG, not a descent direction") self.restart() - p_new = g_new.update(vector=-1 * g_new.vector) + p_new.update(vector=-1 * g_new.vector) restarted = True else: - p_new = p_new + # p_new = p_new restarted = False # Save values to disk and memory @@ -144,8 +146,9 @@ def restart(self): """ logger.info("restarting NLCG optimization algorithm") - g = self.load_vector("g_new") - p_new = g.update(vector=-1 * g.vector) + g_new = self.load_vector("g_new") + p_new = g_new.copy() + p_new.update(vector=-1 * g_new.vector) self.save_vector("p_new", p_new) self._line_search.clear_search_history() diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 4125eb11..f6ac7ed6 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -25,8 +25,8 @@ from seisflows import logger, ROOT_DIR, NAMES from seisflows.tools import unix, msg -from seisflows.tools.config import load_yaml, Dict -from seisflows.tools.config import custom_import, import_seisflows +from seisflows.tools.config import (Dict, load_yaml, custom_import, + import_seisflows, config_logger) from seisflows.tools.specfem import (getpar, setpar, getpar_vel_model, setpar_vel_model) @@ -571,10 +571,10 @@ def submit(self, **kwargs): unix.mkdir(self._args.workdir) unix.cd(self._args.workdir) - workflow = import_seisflows(workdir=self._args.workdir, - parameter_file=self._args.parameter_file) - system = workflow._modules.system - system.submit(workflow) + parameters = load_yaml(self._args.parameter_file) + system = custom_import("system", parameters.system)(**parameters) + system.submit(workdir=self._args.workdir, + par_file=self._args.parameter_file) def clean(self, force=False, **kwargs): """ diff --git a/seisflows/system/cluster.py b/seisflows/system/cluster.py index 4d25be83..5f28e6d3 100644 --- a/seisflows/system/cluster.py +++ b/seisflows/system/cluster.py @@ -12,10 +12,11 @@ """ import os import sys -import dill import subprocess -from seisflows import logger -from seisflows.tools.config import ROOT_DIR +from concurrent.futures import ProcessPoolExecutor, wait +from seisflows import logger, ROOT_DIR +from seisflows.tools.unix import nproc +from seisflows.tools.config import pickle_function_list from seisflows.system.workstation import Workstation @@ -29,6 +30,8 @@ class Cluster(Workstation): :type mpiexec: str :param mpiexec: Function used to invoke executables on the system. For example 'mpirun', 'mpiexec', 'srun', 'ibrun' + :type ntask_max: int + :param ntask_max: limit the number of concurrent tasks in a given array job :type walltime: int :param walltime: maximum job time in minutes for the master SeisFlows job submitted to cluster @@ -44,8 +47,8 @@ class Cluster(Workstation): """ __doc__ = Workstation.__doc__ + __doc__ - def __init__(self, title=None, mpiexec="", walltime=10, tasktime=1, - environs="", **kwargs): + def __init__(self, title=None, mpiexec="", ntask_max=None, walltime=10, + tasktime=1, environs="", **kwargs): """Instantiate the Cluster System class""" super().__init__(**kwargs) @@ -54,40 +57,41 @@ def __init__(self, title=None, mpiexec="", walltime=10, tasktime=1, else: self.title = title self.mpiexec = mpiexec + self.ntask_max = ntask_max or nproc() - 1 # -1 because master job self.walltime = walltime self.tasktime = tasktime self.environs = environs or "" - def _pickle_func_list(self, funcs, **kwargs): + def submit(self, workdir=None, parameter_file="parameters.yaml", + submit_call=None): """ - Save a list of functions and their keyword arguments as pickle files. - Return the names of the files for the run() function. - - .. note:: - The idea here is that we need this list of functions to be - discoverable by a system separate to the one that defined them. To - do this we can pickle Python objects on disk, and have the new - system read in the pickle files and evaluate the objects. We use - 'dill' because Pickle can't serialize methods/functions - - :type funcs: list of methods - :param funcs: a list of functions that should be run in order. All - kwargs passed to run() will be passed into the functions. + Submits the main workflow job as a separate job submitted directly to + the system that is running the master job + + :type workdir: str + :param workdir: path to the current working directory + :type parameter_file: str + :param parameter_file: paramter file file name used to instantiate + the SeisFlows package + :type submit_call: str + :param submit_call: child classes may require a specific submit call + if the job should be submitted to another system (e.g., on cluster + submitting jobs on compute nodes and not running directly on the + login node) """ - # Save the instances that define the functions as a pickle object - func_names = "_".join([_.__name__ for _ in funcs]) # unique identifier - fid_funcs_pickle = os.path.join(self.path.scratch, f"{func_names}.p") - - with open(fid_funcs_pickle, "wb") as f: - dill.dump(obj=funcs, file=f) - - # Save the kwargs as a separate pickle object - fid_kwargs_pickle = os.path.join(self.path.scratch, - f"{func_names}_kwargs.p") - with open(fid_kwargs_pickle, "wb") as f: - dill.dump(obj=kwargs, file=f) - - return fid_funcs_pickle, fid_kwargs_pickle + if submit_call is None: + # e.g., submit -w ./ -p parameters.yaml + submit_call = " ".join([ + f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'submit')}", + f"--workdir {workdir}", + f"--parameter_file {parameter_file}", + ]) + logger.debug(submit_call) + try: + subprocess.run(submit_call, shell=True) + except subprocess.CalledProcessError as e: + logger.critical(f"SeisFlows master job has failed with: {e}") + sys.exit(-1) def run(self, funcs, single=False, run_call=None, **kwargs): """ @@ -108,18 +112,18 @@ def run(self, funcs, single=False, run_call=None, **kwargs): :type run_call: str :param run_call: the call used to submit the run script. If None, attempts default run call which should be suited for the given - system + system. Can be overwritten by child classes to involve other + arguments """ # Single tasks only need to be run one time, as `TASK_ID` === 0 - if single: - ntasks = 1 - else: - ntasks = self.ntask - - funcs_fid, kwargs_fid = self._pickle_func_list(funcs, **kwargs) + ntasks = {True: 1, False: self.ntask}[single] + funcs_fid, kwargs_fid = pickle_function_list(functions=funcs, + path=self.path.scratch, + **kwargs) logger.info(f"running functions {[_.__name__ for _ in funcs]} on " f"system {self.ntask} times") + # Create the run call which will simply call an external Python script if run_call is None: # e.g., run --funcs func.p --kwargs kwargs.p --environment ... run_call = " ".join([ @@ -130,13 +134,36 @@ def run(self, funcs, single=False, run_call=None, **kwargs): ]) logger.debug(run_call) - for task_id in range(ntasks): - logger.debug(f"running task id {task_id} " - f"(job {task_id + 1}/{self.ntask})") - # Subprocess waits for the process to end before running the next - try: - subprocess.run(run_call.format(task_id=task_id), shell=True) - except subprocess.CalledProcessError as e: - logger.critical(f"run task_id {task_id} has failed with error " - f"message {e}") - sys.exit(-1) + # Don't need to spin up concurrent.futures for a single run + if single: + self._run_task(run_call=run_call, task_id=0) + # Run tasks in parallel and wait for all of them to finish + else: + with ProcessPoolExecutor(max_workers=nproc() - 1) as executor: + futures = [executor.submit(self._run_task, run_call, task_id) + for task_id in range(ntasks)] + wait(futures) + + def _run_task(self, run_call, task_id): + """ + Convenience function to run a single Python job with subprocess.run + with some error catching and redirect of stdout to a log file. + + :type run_call: str + :param run_call: python call to run a task involving loading the + pickled function list and its kwargs, and then running them + :type task_id: int + :param task_id: given task id, used for log messages and to format + the run call + """ + logger.debug(f"running task id {task_id} ({task_id + 1}/{self.ntask})") + try: + f = open(self._get_log_file(task_id), "w") + subprocess.run(run_call.format(task_id=task_id), shell=True, + stdout=f) + except subprocess.CalledProcessError as e: + logger.critical(f"run task_id {task_id} has failed with error " + f"message {e}") + sys.exit(-1) + finally: + f.close() diff --git a/seisflows/system/runscripts/submit_workflow.py b/seisflows/system/runscripts/submit_workflow.py index 023a79e8..3bb85c47 100644 --- a/seisflows/system/runscripts/submit_workflow.py +++ b/seisflows/system/runscripts/submit_workflow.py @@ -1,9 +1,9 @@ #!/usr/bin/env python3 """ Only required when system==cluster (or any subclass of cluster) - -This script is executes a MASTER job through job scheduler -(e.g., PBS, LSF, or SLURM) by running workflow.main() on the compute system. +This script is used to execute a MASTER job on system. It is essentially the +same as `system.workstation.Workstation.submit()`, except it can be called as +a script using subprocess, allowing it to be submitted to e.g., a job scheduler .. note:: Not to be called by the user. This is called when the user runs @@ -11,15 +11,13 @@ be called by system.submit(). .. rubric:: - >> python submit --output ./OUTPUT + >> python submit -w ./ -p parameters.yaml OR - >> sbatch submit --output ./OUTPUT + >> sbatch submit -w ./ -p parameters.yaml """ -import sys +import os import argparse - -from seisflows.tools import unix -from seisflows.tools.config import load, config_logger +from seisflows.tools.config import import_seisflows def parse_args(): @@ -27,10 +25,11 @@ def parse_args(): Get command line arguments required for the submit script """ parser = argparse.ArgumentParser("Run arguments for system submitted tasks") - parser.add_argument("-o", "--output", type=str, nargs="?", required=True, - help="the SeisFlows output directory used to load the " - "active working state from inside the compute node" - ) + parser.add_argument("-w", "--workdir", type=str, nargs="?", required=True, + default=os.getcwd(), help="SeisFlows working directory") + parser.add_argument("-p", "--parameter_file", type=str, nargs="?", + required=True, default="parameters.yaml", + help="SeisFlows parameter file") return parser.parse_args() @@ -40,20 +39,8 @@ def parse_args(): Submit workflow.main() as a MASTER JOB on the compute system """ args = parse_args() - - # Load the currently active working state - unix.cd(args.output) - load(args.output) - - # Ensure that the two main modules are loaded - workflow = sys.modules["seisflows_workflow"] - system = sys.modules["seisflows_system"] - - # Set up logging on the compute system to print to stdout only - PAR = sys.modules["seisflows_parameters"] - PATH = sys.modules["seisflows_paths"] - config_logger(level=PAR.LOG_LEVEL, verbose=PAR.VERBOSE) - - # Execute MASTER JOB as workflow.main() - workflow.main() - + workflow = import_seisflows(workdir=args.workdir, + parameter_file=args.parameter_file) + workflow.check() + workflow.setup() + workflow.run() diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index ab9acaa6..be1cf4fe 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -30,8 +30,6 @@ class Slurm(Cluster): [system.slurm] Interface for submitting jobs to Simple Linux Utility for Resource Management (SLURM) system. - :type ntask_max: int - :param ntask_max: limit the number of concurrent tasks in a given array job :type slurm_args: str :param slurm_args: Any (optional) additional SLURM arguments that will be passed to the SBATCH scripts. Should be in the form: diff --git a/seisflows/system/workstation.py b/seisflows/system/workstation.py index b70fadff..74fd1441 100644 --- a/seisflows/system/workstation.py +++ b/seisflows/system/workstation.py @@ -7,9 +7,9 @@ from contextlib import redirect_stdout from seisflows import logger -from seisflows.tools.config import Dict from seisflows.tools import unix -from seisflows.tools.config import number_fid, get_task_id, set_task_id +from seisflows.tools.config import Dict, import_seisflows +from seisflows.tools.config import number_fid, set_task_id class Workstation: @@ -67,6 +67,7 @@ def __init__(self, ntask=1, nproc=1, log_level="DEBUG", verbose=False, # Define internal path system self.path = Dict( + workdir=workdir or os.getcwd(), scratch=path_system or os.path.join(workdir, "scratch", "system"), par_file=path_par_file or os.path.join(workdir, "parameters.yaml"), output=path_output or os.path.join(workdir, "output"), @@ -84,7 +85,7 @@ def check(self): assert(self.ntask > 0), f"number of events/tasks `ntask` cannot be neg'" assert(self.nproc == 1), f"system.workstation rqeuires `nproc`==1" - assert(self.log_level) in self._acceptable_log_levels, \ + assert(self.log_level in self._acceptable_log_levels), \ f"`system.log_level` must be in {self._acceptable_log_levels}" def setup(self): @@ -120,16 +121,25 @@ def setup(self): logger.debug(f"copying par/log file to: {dst}") unix.cp(src=src, dst=dst) - def submit(self, workflow, submit_call=None): + def submit(self, workdir=None, parameter_file="parameters.yaml", + submit_call=None): """ Submits the main workflow job as a serial job submitted directly to the system that is running the master job - :type submit_call: str or None - :param submit_call: the command line workload manager call to be run by - subprocess. This is only needed for overriding classes, it has no - effect on the Workstation class + :type workdir: str + :param workdir: path to the current working directory + :type parameter_file: str + :param parameter_file: paramter file file name used to instantiate + the SeisFlows package + :type submit_call: str + :param submit_call: child classes may require a specific submit call + if the job should be submitted to another system (e.g., on cluster + submitting jobs on compute nodes and not running directly on the + login node) """ + workflow = import_seisflows(workdir=workdir or self.path.workdir, + parameter_file=parameter_file) workflow.check() workflow.setup() workflow.run() @@ -156,19 +166,10 @@ def run(self, funcs, single=False, **kwargs): else: ntasks = self.ntask - for taskid in range(ntasks): + for task_id in range(ntasks): # Set Task ID for currently running process - set_task_id(taskid) - - # Make sure that we're creating new log files EACH time we run() - idx = 0 - while True: - log_file = os.path.join(self.path.log_files, - f"{idx:0>4}_{taskid:0>2}.log") - if os.path.exists(log_file): - idx += 1 - else: - break + set_task_id(task_id) + log_file = self._get_log_file(task_id) # Redirect output to a log file to mimic cluster runs where 'run' # task output logs are sent to different files @@ -176,3 +177,18 @@ def run(self, funcs, single=False, **kwargs): with redirect_stdout(f): for func in funcs: func(**kwargs) + + def _get_log_file(self, task_id): + """ + To mimic clusters which assign job numbers to spawned processes, our + on-system runs will also assign job numbers simply be incrementing the + number on the log files on system. + """ + idx = 1 + while True: + log_file = os.path.join(self.path.log_files, + f"{idx:0>4}_{task_id:0>2}.log") + if os.path.exists(log_file): + idx += 1 + else: + return log_file diff --git a/seisflows/tests/test_data/DATA/CMTSOLUTION_c46e1d99 b/seisflows/tests/test_data/specfem/DATA/CMTSOLUTION_c46e1d99 similarity index 100% rename from seisflows/tests/test_data/DATA/CMTSOLUTION_c46e1d99 rename to seisflows/tests/test_data/specfem/DATA/CMTSOLUTION_c46e1d99 diff --git a/seisflows/tests/test_data/DATA/FORCESOLUTION_c46e1d99 b/seisflows/tests/test_data/specfem/DATA/FORCESOLUTION_c46e1d99 similarity index 100% rename from seisflows/tests/test_data/DATA/FORCESOLUTION_c46e1d99 rename to seisflows/tests/test_data/specfem/DATA/FORCESOLUTION_c46e1d99 diff --git a/seisflows/tests/test_data/DATA/Par_file_SPECFEM2D_cf893667 b/seisflows/tests/test_data/specfem/DATA/Par_file_SPECFEM2D_cf893667 similarity index 100% rename from seisflows/tests/test_data/DATA/Par_file_SPECFEM2D_cf893667 rename to seisflows/tests/test_data/specfem/DATA/Par_file_SPECFEM2D_cf893667 diff --git a/seisflows/tests/test_data/DATA/Par_file_SPECFEM3D_c46e1d99 b/seisflows/tests/test_data/specfem/DATA/Par_file_SPECFEM3D_c46e1d99 similarity index 100% rename from seisflows/tests/test_data/DATA/Par_file_SPECFEM3D_c46e1d99 rename to seisflows/tests/test_data/specfem/DATA/Par_file_SPECFEM3D_c46e1d99 diff --git a/seisflows/tests/test_data/DATA/SOURCE_cf893667 b/seisflows/tests/test_data/specfem/DATA/SOURCE_cf893667 similarity index 100% rename from seisflows/tests/test_data/DATA/SOURCE_cf893667 rename to seisflows/tests/test_data/specfem/DATA/SOURCE_cf893667 diff --git a/seisflows/tests/test_data/OUTPUT_FILES/AA.S0001.BXY.semd b/seisflows/tests/test_data/specfem/OUTPUT_FILES/AA.S0001.BXY.semd similarity index 100% rename from seisflows/tests/test_data/OUTPUT_FILES/AA.S0001.BXY.semd rename to seisflows/tests/test_data/specfem/OUTPUT_FILES/AA.S0001.BXY.semd diff --git a/seisflows/tests/test_data/OUTPUT_FILES/Uy_file_single_d.su b/seisflows/tests/test_data/specfem/OUTPUT_FILES/Uy_file_single_d.su similarity index 100% rename from seisflows/tests/test_data/OUTPUT_FILES/Uy_file_single_d.su rename to seisflows/tests/test_data/specfem/OUTPUT_FILES/Uy_file_single_d.su diff --git a/seisflows/tests/test_data/test_optimize/g_new.npz b/seisflows/tests/test_data/test_optimize/g_new.npz deleted file mode 120000 index 397dba1f..00000000 --- a/seisflows/tests/test_data/test_optimize/g_new.npz +++ /dev/null @@ -1 +0,0 @@ -gradient_rosenbrock.npz \ No newline at end of file diff --git a/seisflows/tests/test_data/test_optimize/gradient_rosenbrock.npz b/seisflows/tests/test_data/test_optimize/gradient_rosenbrock.npz deleted file mode 100644 index 8d53d2f174c2f9a24cae47769bffc867f64a0e30..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 526 zcmWIWW@Zs#fB;1X7OyF1@*N2qLQM-y!ia0lvI$oTVhUeDp0&QBe5VA$k))+QK(g* z2yp2E-H-&tnLwNe!~rNCaXqp35zHg3K#x?A=8ZH9qP$_KqhLgtSGblL8}BgwoOM8f zfgyo`A?!eaHzSh>Gp-l``V$Ho7(onZ{Ge+>^*u}r149GjNgxLt76IO@Y#<3HAj|;L I6Ts#H0I$kkJOBUy diff --git a/seisflows/tests/test_data/test_optimize/m_init_rosenbrock.npz b/seisflows/tests/test_data/test_optimize/m_init_rosenbrock.npz deleted file mode 100644 index e46904d0122532177d723e22cbf71c62a3ae94f1..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 526 zcmWIWW@Zs#fB;1X7OyF1@*N2qLQM-y!ia0lvI$oTVhUeDp0&QBe5VA$k))+QK(g* z2yp2E-H-&tnLwNe!~rNC*Gp-ncr~;71@*N2qLQM-y!ia0lvI$oTVhUeDp0&QBe5VA$k))+QK(g* z2yp2E-H-&tnLwNe!~rNC**NV+2+Sj_K#x?A=8ZH9qP$_KqhLgtSGa&K2Z9gwFgn1S ykx7IZSByYZfyf3%5DASRbWNzf2PuSr2F8;>1~@DNyjj^mf=ob|0i-8@%>w|L(pr81 diff --git a/seisflows/tests/test_data/test_optimize/m_try.npz b/seisflows/tests/test_data/test_optimize/m_try.npz deleted file mode 120000 index 444a5bf5..00000000 --- a/seisflows/tests/test_data/test_optimize/m_try.npz +++ /dev/null @@ -1 +0,0 @@ -m_init_rosenbrock.npz \ No newline at end of file diff --git a/seisflows/tests/test_optimize.py b/seisflows/tests/test_optimize.py index 7364ab82..cc234cb3 100644 --- a/seisflows/tests/test_optimize.py +++ b/seisflows/tests/test_optimize.py @@ -221,7 +221,89 @@ def test_optimize_attempt_line_search_restart(tmpdir, assert(optimize.attempt_line_search_restart() == True) -def _test_inversion_optimization_problem_general(optimize): +def test_line_search_recover_from_failure(tmpdir, setup_optimization_vectors): + """ + Run a small optimization problem to try to reduce the Rosenbrock + objective function using the Bracket'ing line search method. Simulate a job + failure during the line search, and attempts to recover the line search + from a checkpointed state, mimicing a real life inversion where a line + search might fail a job (forward simulation), and we do not want to have to + run the line search from the beginning. + """ + optimize = Gradient(path_optimize=tmpdir, path_output=tmpdir, + line_search_method="bracket", step_count_max=100) + + # Make sure the initial misfit is high + assert(optimize.load_vector("f_new") == pytest.approx(24.2, 1)) + + # Calculate the initial search direction which is just the inverse gradient + p_new = optimize.compute_direction() + p_new.save(path=os.path.join(tmpdir, "p_new")) + + # Saves trial model 'm_try' and corresponding step length 'alpha' + m_try, alpha = optimize.initialize_search() + optimize.save_vector("m_try", m_try) + optimize.save_vector("alpha", alpha) + + def line_search(_optimize, allow_break=True): + """Each line search evaluation requires calculating a new model and + corresponding misfit value. Simulate a break at a given step count""" + m_try = _optimize.load_vector("m_try") + f_try = rosenbrock_objective_function(m_try.vector) + + # Line search is most likely to break at the function evaluation + # break mimics a job failure on cluster, before `f_try` has been saved + if allow_break and _optimize.step_count == 3: + return "BREAK" + + np.savetxt(os.path.join(tmpdir, "f_try.txt"), [f_try]) + + m_try, alpha, status = _optimize.update_line_search() + _optimize.save_vector("m_try", m_try) + _optimize.save_vector("alpha", alpha) + return status + + # Run a line search until the line search breaks + while True: + status = line_search(optimize, allow_break=True) + if status == "PASS": + break + elif status == "BREAK": + optimize.checkpoint() + break + + # Try to restart the line search by re-instantiating from a checkpoint with + # a newly instantiated optimization module + optimize_restarted = Gradient(path_optimize=tmpdir, path_output=tmpdir, + line_search_method="bracket", + step_count_max=100) + optimize_restarted.load_checkpoint() + assert(optimize_restarted.step_count == 3) + while True: + status = line_search(optimize_restarted, allow_break=False) + if status == "PASS": + break + + # The rest of this is copied from `test_bracket_line_search` which completes + # a successful line search. So if these values are the same then we know + # we have successfully restarted a line search + assert(status == "PASS") # pass + assert(optimize_restarted.step_count == 5) # Took 5 steps to reduce misfit + # Make sure we have reduced the final misfit + assert(min(optimize_restarted._line_search.func_vals) == + pytest.approx(4.22, 3)) + + # Change model names and reset line search + optimize_restarted.finalize_search() + + # Check that the final model + m_new = optimize_restarted.load_vector("m_new") + m_old = optimize_restarted.load_vector("m_old") + m_angle = angle(m_new.vector, m_old.vector) + assert(m_angle == pytest.approx(0.3, 2)) + + +def _test_inversion_optimization_problem_general(optimize, iterations=200): """ Rather than run a single line search evaluation, which all the previous tests have done, we want to run a inversion workflow to find a best fitting @@ -235,12 +317,21 @@ def _test_inversion_optimization_problem_general(optimize): We do not save `m_try` to disk each time it is evaluated because it is small. However in real workflows, `m_try` must be saved to disk rather than passed in memory because it is likely to be a large vector. + + :type optimize: module + :param optimize: specific SeisFlows optimization module to test + :type iterations: int + :param iterations: number of iterations to run. defaults to 200 """ m_init = Model() - m_init.model = Dict(x=[np.array([-1.2, 1])]) # Starting guess for Rosenbrock + m_init.model = Dict(x=[np.array([-1.2, 1])]) # Initial guess for Rosenbrock optimize.save_vector("m_new", m_init) - for iteration in range(100): + m_true = m_init.copy() + m_true.update(vector=np.array([1., 1.])) # Rosenbrock global minimum + + # 200 allowable iterations, but reaching global min. will stop inversion + for iteration in range(iterations): # Step 1: Evaluate the objective function for given model 'm_new' m_new = optimize.load_vector("m_new") f_new = rosenbrock_objective_function(x=m_new.vector) @@ -264,12 +355,20 @@ def _test_inversion_optimization_problem_general(optimize): m_try, alpha, status = optimize.update_line_search() if status == "PASS": break + elif status == "FAIL": + return optimize else: optimize.save_vector("alpha", alpha) optimize.save_vector("m_try", m_try) # Set up for the next iteration, most importantly `m_try` -> `m_new` optimize.finalize_search() + # Figure out how far the updated model is from global minimum + m_diff = np.linalg.norm(m_new.vector - m_true.vector) + m_diff /= np.linalg.norm(m_new.vector) + if m_diff < 1e-3: + break + return optimize @@ -281,17 +380,14 @@ def test_inversion_optimization_problem_with_gradient( # Just check a few of the stats file outputs to make sure this runs right assert(os.path.exists(optimize.path._stats_file)) stats = np.genfromtxt(optimize.path._stats_file, delimiter=",", names=True) - assert(len(stats) == 100.) - assert(stats["misfit"].min() == pytest.approx(0.9992, 3)) + assert(len(stats) == 200.) # Fails to reach global minimum + assert(stats["misfit"].min() == pytest.approx(0.2669, 3)) assert(stats["if_restarted"].sum() == 0.) def test_inversion_optimization_problem_with_LBFGS( # NOQA tmpdir, setup_optimization_vectors): """Wrapper function to test the L-BFGS descent optimization problem""" - # from seisflows.tools.config import config_logger - # config_logger(level="DEBUG", filename=os.path.join(tmpdir, "log.txt"), verbose=False) - lbfgs = LBFGS(path_optimize=tmpdir, path_output=tmpdir, line_search_method="backtrack") os.mkdir(lbfgs.path._LBFGS) @@ -300,30 +396,22 @@ def test_inversion_optimization_problem_with_LBFGS( # NOQA # Just check a few of the stats file outputs to make sure this runs right assert(os.path.exists(optimize.path._stats_file)) stats = np.genfromtxt(optimize.path._stats_file, delimiter=",", names=True) - assert(len(stats) == 100.) - assert(stats["misfit"].min() == pytest.approx(0.1468, 3)) - pytest.set_trace() + assert(len(stats) == 95.) # reaches global min. in 95 iterations + assert(stats["misfit"].min() == pytest.approx(1.07e-7, 3)) + assert(stats["if_restarted"].sum() == 0.) def test_inversion_optimization_problem_with_NLCG( # NOQA tmpdir, setup_optimization_vectors): - nlcg = NLCG(path_optimize=tmpdir, path_output=tmpdir) - os.mkdir(nlcg.path._LBFGS) + # NLCG will need more step counts + nlcg = NLCG(path_optimize=tmpdir, path_output=tmpdir, + step_count_max=20) optimize = _test_inversion_optimization_problem_general(nlcg) # Just check a few of the stats file outputs to make sure this runs right assert(os.path.exists(optimize.path._stats_file)) - # TODO finish assertion tests here - - -def test_optimize_recover_from_failure(): - """ - We need to make sure we can recover an optimization from a job failure. - That means that checkpointing and re-loading from checkpoint works as - expected - - TODO - """ - pass - + stats = np.genfromtxt(optimize.path._stats_file, delimiter=",", names=True) + assert(len(stats) == 200.) # Fails to reach global minimum + assert(stats["misfit"].min()) == pytest.approx(0.1013, 3) + assert(stats["if_restarted"].sum() == 91.) diff --git a/seisflows/tests/test_system.py b/seisflows/tests/test_system.py new file mode 100644 index 00000000..30d2b182 --- /dev/null +++ b/seisflows/tests/test_system.py @@ -0,0 +1,82 @@ +""" +Test the system modules ability to run jobs +""" +import os +import pytest +from glob import glob +from seisflows.tools import unix +from seisflows.tools.config import get_task_id +from seisflows.system.workstation import Workstation +from seisflows.system.cluster import Cluster + + +def _test_function_a(tmpdir): + """ + A test function that simply prints, used to check run + """ + test_dir = os.path.join(tmpdir, "test_dir") + if not os.path.exists(test_dir): + unix.mkdir(test_dir) + + +def _test_function_b(tmpdir): + """ + A test function that simply prints + """ + idx = get_task_id() + test_fid = f"test_file_{idx:0>2}.txt" + test_file = os.path.join(tmpdir, "test_dir", test_fid) + print(f"making test file {test_file}") + open(test_file, "w").close() + + +def test_workstation_run(tmpdir, ntask=26): + """ + Make sure that Workstation run simply calls the functions in question + """ + system = Workstation(workdir=tmpdir, path_system=tmpdir, ntask=ntask) + system.setup() # make required dir. structure + system.run(funcs=[_test_function_a, _test_function_b], tmpdir=tmpdir) + + assert(len(glob(os.path.join(system.path.log_files, "*"))) == ntask) + assert(len(glob(os.path.join(tmpdir, "test_dir", "*"))) == ntask) + + +def test_workstation_run_single(tmpdir, ntask=26): + """ + Make sure that Workstation run simply calls the functions only once + """ + system = Workstation(workdir=tmpdir, path_system=tmpdir, ntask=ntask) + system.setup() # make required dir. structure + system.run(funcs=[_test_function_a, _test_function_b], tmpdir=tmpdir, + single=True) + + assert(len(glob(os.path.join(system.path.log_files, "*"))) == 1) + assert(len(glob(os.path.join(tmpdir, "test_dir", "*"))) == 1) + + +def test_cluster_run(tmpdir, ntask=26): + """ + Make sure that Cluster run can pickle the tasks and run them with subprocess + """ + system = Cluster(workdir=tmpdir, path_system=tmpdir, ntask=ntask) + system.setup() # make required dir. structure + system.run(funcs=[_test_function_a, _test_function_b], tmpdir=tmpdir) + + assert(len(glob(os.path.join(system.path.log_files, "*"))) == ntask) + assert(len(glob(os.path.join(tmpdir, "test_dir", "*"))) == ntask) + + +def test_cluster_run_single(tmpdir, ntask=26): + """ + Make sure that Cluster runs single tasks accetpably + """ + system = Cluster(workdir=tmpdir, path_system=tmpdir, ntask=ntask) + system.setup() # make required dir. structure + system.run(funcs=[_test_function_a, _test_function_b], tmpdir=tmpdir, + single=True) + + assert(len(glob(os.path.join(system.path.log_files, "*"))) == 1) + assert(len(glob(os.path.join(tmpdir, "test_dir", "*"))) == 1) + + diff --git a/seisflows/tools/config.py b/seisflows/tools/config.py index 17605aef..36a91012 100755 --- a/seisflows/tools/config.py +++ b/seisflows/tools/config.py @@ -3,11 +3,12 @@ Seisflows configuration tools, containing core utilities that are called upon throughout the Seisflows workflow. """ +import dill +import logging import os import sys import re import yaml -import logging import numpy as np import traceback from pkgutil import find_loader @@ -185,7 +186,6 @@ def import_seisflows(workdir=os.getcwd(), parameter_file="parameters.yaml"): if name == "workflow": continue modules[name] = custom_import(name, parameters[name])(**parameters) - # parameters.pop(name) # drop name so workflow doesnt instantiate it # Import workflow separately by providing all the instantiated modules to it workflow = \ @@ -339,6 +339,44 @@ class 'Inversion'. sys.exit(-1) +def pickle_function_list(functions, path=os.getcwd(), **kwargs): + """ + Save a list of functions and their keyword arguments as pickle files. + Return the names of the files. Used for running functions from spawned + processes during cluster runs. + + .. note:: + The idea here is that we need this list of functions to be + discoverable by a system separate to the one that defined them. To + do this we can pickle Python objects on disk, and have the new + system read in the pickle files and evaluate the objects. We use + 'dill' because Pickle can't serialize methods/functions + + :type functions: list of methods + :param functions: a list of functions that should be run in order. All + kwargs passed to run() will be passed into the functions. + :type path: str + :param path: path to save the pickle files. Defaults to current working + directory + :rtype: tuple of str + :return: (name of the pickle file containing the function, + name of the pickle file containing keyword arguments) + """ + # Save the instances that define the functions as a pickle object + func_names = "_".join([_.__name__ for _ in functions]) # unique identifier + fid_funcs_pickle = os.path.join(path, f"{func_names}.p") + + with open(fid_funcs_pickle, "wb") as f: + dill.dump(obj=functions, file=f) + + # Save the kwargs as a separate pickle object + fid_kwargs_pickle = os.path.join(path, f"{func_names}_kwargs.p") + with open(fid_kwargs_pickle, "wb") as f: + dill.dump(obj=kwargs, file=f) + + return fid_funcs_pickle, fid_kwargs_pickle + + def number_fid(fid, i=0): """ Number a filename. Used to store old log files without overwriting them. From b95ade6385fb67ad55e41515fcf42724c2937339 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Wed, 27 Jul 2022 17:22:13 -0800 Subject: [PATCH 088/195] removing old test workflow, this is now replaced by unit tests --- seisflows/workflow/test.py | 321 ------------------------------------- 1 file changed, 321 deletions(-) delete mode 100644 seisflows/workflow/test.py diff --git a/seisflows/workflow/test.py b/seisflows/workflow/test.py deleted file mode 100644 index e7fd0c43..00000000 --- a/seisflows/workflow/test.py +++ /dev/null @@ -1,321 +0,0 @@ -#!/usr/bin/env python3 -""" -This is the SeisFlows Test class which is used to test out the underlying -machinery before running an actual workflow. Contains simple functions used to -make sure that all parts of the package are working as expected. Creates -its own directory structure and acts as a standalone workflow tool -""" -import os -import sys -import time -import subprocess -import numpy as np -from glob import glob - -from seisflows.tools.config import Base -from seisflows.tools.config import ROOT_DIR, CFGPATHS, save, config_logger - - -class Test(Base): - """ - The Test workflow class provides a base parameter and directory structure - as well as test functions which can be run to ensure that the chosen - modules are working as expected - """ - def __init__(self): - """ - Initiate the TEST workflow - """ - super().__init__() - - self.required.path( - "TEST_DATA", required=False, - default=os.path.join(ROOT_DIR, "tests", "test_data"), - docstr="Example data for test system which is shipped with the " - "SeisFlows repository" - ) - - def check(self, validate=True): - """ - Checks parameters and paths. The validate function ensures that all - required paths and parameters are accounted for, and that all - optional paths and parameters are set to user-defined or default values. - - :type validate: bool - :param validate: set required paths and parameters into sys.modules - """ - self.required.validate() - - def checkpoint(self): - """ - Saves active SeisFlows working state to disk as Pickle files such that - the workflow can be resumed following a crash, pause or termination of - workflow. - """ - save(path=self.path.OUTPUT) - - def main(self, return_flow=False): - """ - This controls the main testing workflow - """ - FLOW = [self.test_system, - # self.test_preprocess, - # self.test_solver, - # self.test_optimize - ] - if return_flow: - return FLOW - - for func in FLOW: - func() - - def _test_function_print(self, check_value): - """ - A simple function that can be called by system.run() - """ - system = self.module("system") - - print(f"Hello world, from taskid {system.taskid()}. " - f"Check: {check_value}") - - config_logger(level="DEBUG", filemode="a", verbose=False) - - system.logger.info(f"Hello world, from taskid {system.taskid()}. " - f"Logger 'info' message. Check: {check_value}") - - system.logger.debug(f"Hello world, from taskid {system.taskid()}. " - f"Logger 'debug' message. Check: {check_value}") - - system.logger.warning(f"Hello world, from taskid {system.taskid()}. " - f"Logger 'warning' message. Check: {check_value}") - - config_logger(level="DEBUG", filemode="a", verbose=True) - - system.logger.info(f"Hello world, from taskid {system.taskid()}. " - f"Logger 'info' message. Check: {check_value}") - - system.logger.debug(f"Hello world, from taskid {system.taskid()}. " - f"Logger 'debug' message. Check: {check_value}") - - system.logger.warning(f"Hello world, from taskid {system.taskid()}. " - f"Logger 'warning' message. Check: {check_value}") - - def test_system(self): - """ - Test the system by submitting a simple print statement using the - run() and run(single) functions. - - Check that these functions perform as expected by passing in a random - value and checking that this value gets logged back - """ - system = self.module("system") - system.setup() - - # Run a very simple test function using system.run() - check_value_1 = 1234.5 - system.run(classname="workflow", method="_test_function_print", - check_value=check_value_1) - - time.sleep(3) # wait a bit for system to catch up - - check_value_2 = 5432.1 - system.run(classname="workflow", method="_test_function_print", - single=True, check_value=check_value_2) - - # Check the output log files to match the check values - for fid, check in zip( - sorted(glob(os.path.join(CFGPATHS.LOGDIR, "*.log"))), - [check_value_1, check_value_2] - ): - with open(fid, "r") as f: - line = f.readlines()[0] - assert(float(line.strip().split(" ")[-1]) == check) - - # Check that MPI Exec works - assert("MPIEXEC" in self.par), f"MPIEXEC is not defined for this system" - if self.par.MPIEXEC: - stdout = subprocess.run(self.par.MPIEXEC, shell=True, check=True, - stdout=subprocess.PIPE) - - def test_preprocess(self): - """ - Test the exposed 'prepare_eval_grad()' preprocessing function - """ - preprocess = self.module("preprocess") - preprocess.setup() - - cwd = os.path.join(self.path.TEST_DATA, "test_solver") - taskid = 0 - filenames = ["AA.S0001.BXY.semd"] - source_name = "001" - preprocess.prepare_eval_grad(cwd=cwd, taskid=taskid, - filenames=filenames, - source_name=source_name - ) - - def test_solver(self): - """ - Simply test that the solver binaries can be called, which is what the - solver module is ultimately responsible for - """ - solver = self.module("solver") - - assert(self.path.SPECFEM_BIN is not None and - os.path.exists(self.path.SPECFEM_BIN)), ( - f"SPECFEM_BIN {self.path.SPECFEM_BIN} directory does not exist" - ) - try: - solver.call_solver( - executable=f"{self.path.SPECFEM_BIN}/xcombine_sem", - output=os.path.join(self.path.TEST_DATA, "test_solver.log") - ) - # We expect this to throw a system exit because we are not running with - # MPI - except SystemExit: - pass - - def test_optimize(self): - """ - Test optimization module with a simple Rosenbrock function - """ - optimize = self.module("optimize") - - self.par.log_level = "CRITICAL" - m_new, m_true, objective_function, gradient = rosenbrock() - - optimize.setup() - optimize.save("m_new", m_new) - - def evaluate_function(): - """ - Evalaute the misfit function of a given model - """ - self.logger.info("evaluating objective function") - m_try = optimize.load("m_try") - f_try = objective_function(m_try) - optimize.save("f_try", f_try) - - def evaluate_gradient(): - """ - Evaluate the gradient of a given model - """ - self.logger.info("evaluating gradient") - m_new = optimize.load("m_new") - f_new = objective_function(m_new) - g_new = gradient(m_new) - optimize.save("f_new", f_new) - optimize.save("g_new", g_new) - - def line_search(): - """ - Run a line search until a suitable model has been found - Note this is almost the same as workflow.inversion.line_search - """ - optimize.initialize_search() - while True: - evaluate_function() - optimize.line_search.step_count += 1 - status = optimize.update_search() - if status == 1: - self.logger.info("finalizing line search") - optimize.finalize_search() - return - elif status == 0: - self.logger.info("continuing line search") - continue - elif status == -1: - if optimize.retry_status(): - self.logger.info("restarting line search") - optimize.restart() - # Recursively run the line search after restart - line_search() - else: - sys.exit(-1) - - def finalize(thresh=5e-3): - """ - Finish off one iteration, check the distance between old and new - vectors to see if model stops changing - """ - m_new = optimize.load("m_new") - m_diff = np.linalg.norm(m_new - m_true) / np.linalg.norm(m_new) - if m_diff < thresh: - self.logger.info(f"successful inversion after {optimize.iter} " - f"iterations") - sys.exit(0) - else: - self.logger.info(f"model difference: {m_diff:.2E}") - return - - self.logger.info("testing optimization library with Rosenbrock problem") - for iteration in range(1, 200): - self.logger.info(f"iteration {iteration}") - evaluate_gradient() - optimize.compute_direction() - line_search() - optimize.iter += 1 - finalize() - - -def rosenbrock(): - """ - Rosenbrock test problem for optimization library testing - - """ - model_init = np.array([-1.2, 1]) # This is the guess for the global min - model_true = np.array([1, 1]) # This is the actual minimum - - def objective_function(x): - """ - Rosenbrock objective function which is defined mathematically as: - - f(x,y) = (a-x)^2 + b(y-x^2)^2 - - where the global minimum is at (x,y) == (a, a^2) - and typical constant values are: a==1, b==100 - """ - return np.array([((1 - x[0]) ** 2 + 100 * (-x[0] ** 2 + x[1]) ** 2)]) - - def gradient(x): - """ - Gradient of the objective function for Rosenbrock test - """ - return np.array([-2*(1-x[0]) - 400*x[0]*(-x[0]**2+x[1]), - 200*(- x[0]**2+x[1])]) - - return model_init, model_true, objective_function, gradient - - -def rosenbrock_n(n=1E5): - """ - N dimensional Rosenbrock test problem for optimization library testing - - https://en.wikipedia.org/wiki/Rosenbrock_function - """ - model_init = 0.1 * np.ones(int(n)) # This is a guess for the global min - model_true = np.ones(int(n)) - - def objective_function(x): - """ - Rosenbrock objective function - """ - return sum(100 * (x[:-1]**2. - x[1:])**2. + (x[:-1] - 1.)**2) - - def gradient(x): - """ - Gradient of the objective function for Rosenbrock test - """ - g = np.zeros(int(n)) - g[1:-1] = -200 * (x[:-2] ** 2. - x[1:-1]) + \ - 400. * x[1:-1] * (x[1:-1] ** 2. - x[2:]) + \ - 2. * (x[1:-1] - 1.) - - g[0] = 400. * x[0] * (x[0] ** 2. - x[1]) + \ - 2. * (x[0] - 1) - - g[-1] = -200. * (x[-2] ** 2. - x[-1]) - - return g - - return model_init, model_true, objective_function, gradient - From ca7c33a39e279063a2f3d15a96d55f5ff85acf6e Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Wed, 27 Jul 2022 17:25:45 -0800 Subject: [PATCH 089/195] removing out of date seisflows cmd line tool tests --- seisflows/tests/test_seisflows.py | 222 ++++++++---------------------- 1 file changed, 59 insertions(+), 163 deletions(-) diff --git a/seisflows/tests/test_seisflows.py b/seisflows/tests/test_seisflows.py index cf39831d..539d96cb 100644 --- a/seisflows/tests/test_seisflows.py +++ b/seisflows/tests/test_seisflows.py @@ -12,8 +12,9 @@ from unittest.mock import patch from seisflows.tools.config import Dict +from seisflows import ROOT_DIR from seisflows.seisflows import SeisFlows -from seisflows.tools.config import ROOT_DIR, NAMES, CFGPATHS +from seisflows.tools.config import NAMES from seisflows.tools.config import load_yaml TEST_DIR = os.path.join(ROOT_DIR, "tests") @@ -111,35 +112,6 @@ def test_edited_parameter_file_name(tmpdir, par_file_dict, filled_par_file): assert(out.stdout.strip() == f"{par_name.upper()}: {check_val}") -def test_register(tmpdir, par_file_dict, copy_par_file): - """ - Test that the register function, which reads in PATHS and PARAMETERS - works as expected, returning paths and parameters that we can read - """ - copy_par_file - os.chdir(tmpdir) - - with patch.object(sys, "argv", ["seisflows"]): - sf = SeisFlows() - assert(sf._paths is None) - assert(sf._parameters is None) - sf._register_parameters() - - # Check that paths and parameters have been set in sys.modules - paths = sys.modules["seisflows_paths"] - parameters = sys.modules["seisflows_parameters"] - - # Check one or two parameters have been set correctly - assert("PATHS" not in parameters) - for key, val in par_file_dict.items(): - if key == "PATHS": - continue - assert(parameters[key] == val) - - path_check_full = os.path.abspath(par_file_dict.PATHS["SCRATCH"]) - assert(path_check_full == paths.SCRATCH) - - def test_cmd_setup(tmpdir): """ Test setting up the SeisFlows working directory @@ -175,142 +147,66 @@ def test_cmd_setup(tmpdir): assert(test_phrase not in text) -def test_cmd_init(tmpdir, copy_par_file): - """ - Test 'seisflows init' command which instantiates a working directory and - saves the active working state as pickle files - :return: - """ - os.chdir(tmpdir) - copy_par_file - - # Create necessary paths to get past some assertion errors - parameters = load_yaml("parameters.yaml") - paths = parameters.pop("PATHS") - - for key in ["MODEL_INIT", "MODEL_TRUE"]: - os.mkdir(paths[key]) - - with patch.object(sys, "argv", ["seisflows"]): - sf = SeisFlows() - sf.init() - - for name in NAMES: - assert(os.path.exists(os.path.join(paths["OUTPUT"], - f"seisflows_{name}.p")) - ) - - -def test_cmd_submit(tmpdir): - """ - Test submit, also test the functionality of resume and restart which - are essentially wrappers for this call - :param tmpdir: - :return: - """ +# def test_cmd_submit(tmpdir): +# """ +# Test submit, also test the functionality of resume and restart which +# are essentially wrappers for this call +# :param tmpdir: +# :return: +# """ +# pass +# +# +# def test_cmd_clean(tmpdir): +# """ +# +# :param tmpdir: +# :return: +# """ pass -def test_cmd_clean(tmpdir): - """ - - :param tmpdir: - :return: - """ - os.chdir(tmpdir) - - # Create a bunch of files that match what should be deleted. Make them as - # directories even though some should be files because we just want to see - # if they get deleted or not - for path in CFGPATHS.values(): - os.mkdir(path) - - # Symlink the last file to make sure it still exists even if it matches - shutil.rmtree(path) - os.symlink(src=CFGPATHS.PAR_FILE, dst=path) - - with patch.object(sys, "argv", ["seisflows"]): - sf = SeisFlows() - sf.clean(force=True) - - for fid in [path, "parameters.yaml"]: - assert(os.path.exists(fid)) - - -def test_load_modules(tmpdir, copy_par_file): - """ - Test if module loading from sys.modules works - - :param tmpdir: - :return: - """ - # Run init first to create a working state - os.chdir(tmpdir) - copy_par_file - - # Create necessary paths to get past some assertion errors - parameters = load_yaml("parameters.yaml") - paths = parameters.pop("PATHS") - - for key in ["MODEL_INIT", "MODEL_TRUE"]: - os.mkdir(paths[key]) - - with patch.object(sys, "argv", ["seisflows"]): - sf = SeisFlows() - sf.init() - - # Check a random parameter and then set it to something different - preprocess = sys.modules["seisflows_preprocess"] - assert(preprocess.misfit is None) - preprocess.misfit = 1 - - # See if we can load modules and restore previous working state which - # overwrites the previous operation - sf._load_modules() - assert(sys.modules["seisflows_preprocess"].misfit != 1) - - -def test_cmd_configure(tmpdir, setup_par_file, conf_par_file): - """ - Test configuring a parameter file from a template par file - - .. note:: - I don't know exactly why, but this test needs to be run AFTER any other - test which runs seisflows.init(), otherwise the parameters are not - instantiated properly (you will hit a KeyError when trying to access - PAR). I think this is because of how seisflows.configure() registers - a relatively empty parameter file (only modules are defined), and this - gets saved into sys modules, affecting subsequent tests which end up - accessing sys.modules. I tried flushing sys.modules but it didn't work. - This behavior shouldn't get encountered in a real run because we - won't need to run init() and configure() in the same python - runtime environment, but I leave this warning here - wondering if I'll have to fix it at some point... -B - """ - os.chdir(tmpdir) - - # Copy in the setup par file so we can configure it - src = setup_par_file - dst = os.path.join(tmpdir, "parameters.yaml") - shutil.copy(src, dst) - - # run seisflows init - with patch.object(sys, "argv", ["seisflows"]): - sf = SeisFlows() - sf.configure(relative_paths=False) - - # Simple check that the configuration parameter file has the same number - # of lines as the one that has been created by configure - lines_conf = open(conf_par_file, "r").readlines() - lines_fill = open("parameters.yaml", "r").readlines() - assert (len(lines_conf) == len(lines_fill)) - - # My attempt to flush sys.modules which did NOT work - # from seisflows.tools.config import NAMES, PAR, PATH - # for name in NAMES: - # del sys.modules[f"seisflows_{name}"] - # del sys.modules[PAR] - # del sys.modules[PATH] +# def test_cmd_configure(tmpdir, setup_par_file, conf_par_file): +# """ +# Test configuring a parameter file from a template par file +# +# .. note:: +# I don't know exactly why, but this test needs to be run AFTER any other +# test which runs seisflows.init(), otherwise the parameters are not +# instantiated properly (you will hit a KeyError when trying to access +# PAR). I think this is because of how seisflows.configure() registers +# a relatively empty parameter file (only modules are defined), and this +# gets saved into sys modules, affecting subsequent tests which end up +# accessing sys.modules. I tried flushing sys.modules but it didn't work. +# This behavior shouldn't get encountered in a real run because we +# won't need to run init() and configure() in the same python +# runtime environment, but I leave this warning here +# wondering if I'll have to fix it at some point... -B +# """ +# os.chdir(tmpdir) +# +# # Copy in the setup par file so we can configure it +# src = setup_par_file +# dst = os.path.join(tmpdir, "parameters.yaml") +# shutil.copy(src, dst) +# +# # run seisflows init +# with patch.object(sys, "argv", ["seisflows"]): +# sf = SeisFlows() +# sf.configure(relative_paths=False) +# +# # Simple check that the configuration parameter file has the same number +# # of lines as the one that has been created by configure +# lines_conf = open(conf_par_file, "r").readlines() +# lines_fill = open("parameters.yaml", "r").readlines() +# assert (len(lines_conf) == len(lines_fill)) +# +# # My attempt to flush sys.modules which did NOT work +# # from seisflows.tools.config import NAMES, PAR, PATH +# # for name in NAMES: +# # del sys.modules[f"seisflows_{name}"] +# # del sys.modules[PAR] +# # del sys.modules[PATH] def test_cmd_par(tmpdir, copy_par_file): From eeb4b56de6384ce9aa43b12e105e5265ebf45696 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Thu, 28 Jul 2022 14:36:54 -0800 Subject: [PATCH 090/195] trying to incorporate pyatoa preprocessing. shifting workload from pyaflowa back into pyatoa class to make things more explicit. having trouble with data gathering and implicitely crafted paths which are making things confusing --- seisflows/preprocess/default.py | 7 + seisflows/preprocess/pyatoa.py | 752 ++++++++++++++++++++------------ seisflows/seisflows.py | 2 +- seisflows/workflow/forward.py | 42 +- seisflows/workflow/inversion.py | 35 +- 5 files changed, 541 insertions(+), 297 deletions(-) diff --git a/seisflows/preprocess/default.py b/seisflows/preprocess/default.py index 66ad82a3..1d34c52c 100644 --- a/seisflows/preprocess/default.py +++ b/seisflows/preprocess/default.py @@ -125,6 +125,11 @@ def __init__(self, data_format="ascii", misfit="waveform", self._acceptable_adjsrcs = [_ for _ in dir(adjoint_sources) if not _.startswith("_")] + # Internal attributes used to keep track of inversion workflows + self._iteration = None + self._step_count = None + self._source_names = None + def check(self): """ Checks parameters and paths @@ -332,6 +337,8 @@ def quantify_misfit(self, observed, synthetic, preprocessing, assesses misfit, and writes out adjoint sources and STATIONS_ADJOINT file. + TODO use concurrent futures to parallelize this + .. note:: Meant to be called by solver.eval_func(), may have unused arguments to keep functions general across subclasses. diff --git a/seisflows/preprocess/pyatoa.py b/seisflows/preprocess/pyatoa.py index f938be87..7410b17d 100644 --- a/seisflows/preprocess/pyatoa.py +++ b/seisflows/preprocess/pyatoa.py @@ -1,124 +1,120 @@ #!/usr/bin/env python3 """ -The Pyatoa preprocessing module abstracts all preprocessing functionality -onto Pyatoa (https://github.com/adjtomo/pyatoa/). The module defined below is -meant to set up and execute Pyatoa within a running SeisFlows workflow. - -Pyatoa itself aggregates all of its connection with SeisFlows in the Pyaflowa -class, a purpose built object used to simplify calling Pyatoa from within -a SeisFlows workflow. +The Pyatoa preprocessing module for waveform gathering, preprocessing and +misfit quantification. """ import os -import sys import numpy as np -from glob import glob -from pyatoa import Pyaflowa, Inspector +from pyasdf import ASDFDataSet +from pyatoa import Config, Manager, ManagerError +from pyatoa.utils.read import read_station_codes from seisflows import logger -from seisflows.tools import unix, msg -from seisflows.tools.config import CFGPATHS +from seisflows.tools import unix +from seisflows.tools.config import Dict class Pyatoa: """ - Data preprocessing class using the Pyaflowa class within the Pyatoa package. - In charge of data discovery, preprocessing, filtering, misfiti - quantification and data storage. The User does not need to implement Pyatoa, - but rather interacts with it via the parameters and paths of SeisFlows. + [preprocess.pyatoa] preprocessing and misfit quantification using Pyatoa + + :type data_format: str + :param data_format: data format for reading traces into memory. Pyatoa + only works with 'ASCII' currently. + :type components: str + :param components: components to consider and tag data with. Should be + string of letters such as 'RTZ' + :type min_period: float + :param min_period: Minimum filter corner in unit seconds. Bandpass + filter if set with `max_period`, highpass filter if set without + `max_period`, no filtering if not set and `max_period also not set + :type max_period: float + :param max_period: Maximum filter corner in unit seconds. Bandpass + filter if set with `min_period`, lowpass filter if set without + `min_period`, no filtering if not set and `min_period also not set + :type filter_corners: int + :param filter_corners: number of filter corners applied to filtering + :type client: str + :param client: Client name for ObsPy FDSN data gathering. Pyatoa will + attempt to collect waveform and metadata based on network and + station codes provided in the SPECFEM STATIONS file. If set None, + no FDSN gathering will be attempted + :type rotate: bool + :param rotate: Attempt to rotate waveform components from NEZ -> RTZ + :type pyflex_preset: str + :param pyflex_preset: Parameter map for misfit window configuration + defined by Pyflex. IF None, misfit and adjoint sources will be + calculated on whole traces. For available choices, see Pyatoa docs + page (pyatoa.rtfd.io) + :type fix_windows: bool or str + :param fix_windows: How to address misfit window evaluation at each + evaluation. Options to re-use misfit windows collected during an + inversion, available options: + [True, False, 'ITER', 'ONCE'] + True: Re-use windows after first evaluation (i01s00); + False: Calculate new windows each evaluation; + 'ITER': Calculate new windows at first evaluation of + each iteration (e.g., i01s00... i02s00... + 'ONCE': Calculate new windows at first evaluation of + the workflow, i.e., at self.par.BEGIN + :type adj_src_type: str + :param adj_src_type: Adjoint source type to evaluate misfit, defined by + Pyadjoint. Currently available options: ['cc': cross-correlation, + 'mt': multitaper, 'wav': waveform'] + :type plot: bool + :param plot: plot waveform figures and source receiver maps during + the preprocessing stage + :type pyatoa_log_level: str + :param pyatoa_log_level: Log level to set Pyatoa, Pyflex, Pyadjoint. + Available: ['null': no logging, 'warning': warnings only, + 'info': task tracking, 'debug': log all small details (recommended)] + :type start_pad_s: int + :param start_pad_s: seconds BEFORE origin time to gather data. Must be + >= T_0 specificed in SPECFEM constants.h. Positive values only + :type end_pad_s: int + :param end_pad_s: seconds AFTER origin time to gather data. Must be + >= NT * DT (from SPECFEM Par_file) postive values only. + :type unit_output: str + :param unit_output: Data units. Must match the synthetic output of + external solver. Available: ['DISP': displacement, 'VEL': velocity, + 'ACC': acceleration] + :type export_datasets: bool + :param export_datasets: periodically save the output ASDFDataSets which + contain data, metadata and results collected during the + preprocessing procedure + :type export_figures: bool + :param export_figures: periodically save the output basemaps and + data-synthetic waveform comparison figures + :type export_log_files: bool + :param export_log_files: periodically save log files created by Pyatoa + :type path_preprocess: str + :param path_preprocess: scratch path for preprocessing related steps + :type path_data: str + :param path_data: optional path for preprocessing module to discover + waveform and meta-data. """ - def __init__(self, data_format="ascii", components=None, ntask=1, nproc=1, - min_period=None, max_period=None, filter_corners=4, - client=None, rotate=False, pyflex_preset="default", - fix_windows=False, adj_src_type="cc", plot=True, - pyatoa_log_level="DEBUG", unit_output="VEL", - start_pad_s=0., end_pad_s=None, path_preprocess=None, - path_data=None, path_output=None, save_datasets=True, - save_figures=True, save_log_files=True, **kwargs): + def __init__(self, data_format="ascii", components=None, start=None, + ntask=1, nproc=1, source_prefix=None, min_period=None, + max_period=None, filter_corners=4, client=None, rotate=False, + pyflex_preset="default", fix_windows=False, adj_src_type="cc", + plot=True, pyatoa_log_level="DEBUG", unit_output="VEL", + start_pad_s=0., end_pad_s=None, workdir=os.getcwd(), + path_preprocess=None, path_specfem_data=None, path_data=None, + path_output=None, export_datasets=True, export_figures=True, + export_log_files=True, + **kwargs): """ Pyatoa preprocessing parameters that will be passed to Pyaflowa - - :type data_format: str - :param data_format: data format for reading traces into memory. Pyatoa - only works with 'ASCII' currently. - :type components: str - :param components: components to consider and tag data with. Should be - string of letters such as 'RTZ' - :type min_period: float - :param min_period: Minimum filter corner in unit seconds. Bandpass - filter if set with `max_period`, highpass filter if set without - `max_period`, no filtering if not set and `max_period also not set - :type max_period: float - :param max_period: Maximum filter corner in unit seconds. Bandpass - filter if set with `min_period`, lowpass filter if set without - `min_period`, no filtering if not set and `min_period also not set - :type filter_corners: int - :param filter_corners: number of filter corners applied to filtering - :type client: str - :param client: Client name for ObsPy FDSN data gathering. Pyatoa will - attempt to collect waveform and metadata based on network and - station codes provided in the SPECFEM STATIONS file. If set None, - no FDSN gathering will be attempted - :type rotate: bool - :param rotate: Attempt to rotate waveform components from NEZ -> RTZ - :type pyflex_preset: str - :param pyflex_preset: Parameter map for misfit window configuration - defined by Pyflex. IF None, misfit and adjoint sources will be - calculated on whole traces. For available choices, see Pyatoa docs - page (pyatoa.rtfd.io) - :type fix_windows: bool or str - :param fix_windows: How to address misfit window evaluation at each - evaluation. Options to re-use misfit windows collected during an - inversion, available options: - [True, False, 'ITER', 'ONCE'] - True: Re-use windows after first evaluation (i01s00); - False: Calculate new windows each evaluation; - 'ITER': Calculate new windows at first evaluation of - each iteration (e.g., i01s00... i02s00... - 'ONCE': Calculate new windows at first evaluation of - the workflow, i.e., at self.par.BEGIN - :type adj_src_type: str - :param adj_src_type: Adjoint source type to evaluate misfit, defined by - Pyadjoint. Currently available options: ['cc': cross-correlation, - 'mt': multitaper, 'wav': waveform'] - :type plot: bool - :param plot: plot waveform figures and source receiver maps during - the preprocessing stage - :type pyatoa_log_level: str - :param pyatoa_log_level: Log level to set Pyatoa, Pyflex, Pyadjoint. - Available: ['null': no logging, 'warning': warnings only, - 'info': task tracking, 'debug': log all small details (recommended)] - :type start_pad_s: int - :param start_pad_s: seconds BEFORE origin time to gather data. Must be - >= T_0 specificed in SPECFEM constants.h. Positive values only - :type end_pad_s: int - :param end_pad_s: seconds AFTER origin time to gather data. Must be - >= NT * DT (from SPECFEM Par_file) postive values only. - :type unit_output: str - :param unit_output: Data units. Must match the synthetic output of - external solver. Available: ['DISP': displacement, 'VEL': velocity, - 'ACC': acceleration] - :type save_datasets: bool - :param save_datasets: periodically save the output ASDFDataSets which - contain data, metadata and results collected during the - preprocessing procedure - :type save_figures: bool - :param save_figures: periodically save the output basemaps and - data-synthetic waveform comparison figures - :type save_log_files: bool - :param save_log_files: periodically save log files created by Pyatoa - :type path_preprocess: str - :param path_preprocess: scratch path for preprocessing related steps - :type path_data: str - :param path_data: optional path for preprocessing module to discover - waveform and meta-data. """ # Shared SeisFlows parameters - self.data_format = data_format + self.data_format = data_format.upper() self.components = components + self.start = start self.ntask = ntask self.nproc = nproc + self.source_prefix = source_prefix - # Pyatoa specific parameters + # Pyatoa-specific parameters that are provided to the Config class self.min_period = min_period self.max_period = max_period self.filter_corners = filter_corners @@ -133,14 +129,40 @@ def __init__(self, data_format="ascii", components=None, ntask=1, nproc=1, self.start_pad_s = start_pad_s self.end_pad_s = end_pad_s - self.path = path_preprocess or \ - os.path.join(os.getcwd(), "scratch", "preprocess") - self.path_output = path_output or os.path.join(os.getcwd(), "output") - self.path_data = path_data - - self.save_datasets = save_datasets - self.save_figures = save_figures - self.save_log_files = save_log_files + self.path = Dict( + scratch=path_preprocess or os.path.join(workdir, "scratch", + "preprocess"), + output=path_output or os.path.join(workdir, "output"), + specfem_data=path_specfem_data, + data=path_data, + ) + + # How to handle saving output data to disk + self.export_datasets = export_datasets + self.export_figures = export_figures + self.export_log_files = export_log_files + + # Pyatoa-specific internal path structure for storing data etc. + self.path["_logs"] = os.path.join(self.path.scratch, "logs") + self.path["_datasets"] = os.path.join(self.path.scratch, "datasets") + self.path["_figures"] = os.path.join(self.path.scratch, "figures") + + # Where to look for externally stored waveform data and response files + if self.path.data: + self.path["_waveforms"] = os.path.join(self.path.data, "mseed") + self.path["_responses"] = os.path.join(self.path.data, "seed") + else: + self.path["_waveforms"] = None + self.path["_responses"] = None + + # Internal parameters for workflow + self._acceptable_data_formats = ["ASCII"] + self._acceptable_source_prefixes = ["SOURCE", "FORCESOLUTION", + "CMTSOLUTION"] + self._config = None # to be created by setup() + self._fix_windows = False + self._station_codes = [] + self._source_names = [] def check(self): """ @@ -150,61 +172,58 @@ def check(self): assert(self.data_format.upper() == "ASCII"), \ "Pyatoa preprocess requires `data_format`=='ASCII'" + assert(self.path.specfem_data is not None and + os.path.exists(self.path.specfem_data)), ( + f"Pyatoa requires `path_specfem_data` to exist" + ) + + assert(os.path.exists(os.path.join(self.path.specfem_data, + "STATIONS"))), \ + f"Pyatoa preprocessing requires that the `STATIONS` file exists " \ + f"within `path_specfem_data`" + + assert(self.source_prefix in self._acceptable_source_prefixes), ( + f"Pyatoa can only accept `source_prefix` in " + f"{self._acceptable_source_prefixes}, not '{self.source_prefix}'" + ) + def setup(self): """ Sets up data preprocessing machinery by establishing an internally defined directory structure that will be used to store the outputs of the preprocessing workflow """ - unix.mkdir(self.path) - - def finalize(self): - """ - Run some serial finalization tasks specific to Pyatoa, which will help - aggregate the collection of output information. - - .. note:: - This finalize function performs the following tasks: - * Generate .csv files using the Inspector - * Aggregate event-specific PDFs into a single evaluation PDF - * Save scratch/ data into output/ if requested - """ - # Initiate Pyaflowa to get access to path structure - pyaflowa = Pyaflowa(sfpar=self.par, sfpath=self.path) - unix.cd(pyaflowa.paths.datasets) - - # Generate the Inspector from existing datasets and save to disk - # Allow this is fail, which might happen if we don't have enough data - # or the Dataset is not formatted as expected - insp = Inspector(self.par.TITLE, verbose=False) # !!! TODO - try: - insp.discover() - insp.save() - except Exception as e: - logger.warning(f"Uncontrolled exception in Inspector creation " - f"will not create inspector:\n{e}") - - # Make the final PDF for easier User ingestion of waveform/map figures - pyaflowa.make_evaluation_composite_pdf() - - # Move scratch/ directory results into more permanent storage - if self.save_datasets: - datasets = glob(os.path.join(pyaflowa.paths.datasets, "*.h5")) - self._save_quantity(datasets, tag="datasets") - - if self.save_figures: - figures = glob(os.path.join(pyaflowa.paths.figures, "*.pdf")) - self._save_quantity(figures, tag="figures") - - if self.save_log_files: - logs = glob(os.path.join(pyaflowa.paths.logs, "*.txt")) - path_out = os.path.join(self.path_output, CFGPATHS.LOGDIR) - self._save_quantity(logs, path_out=path_out) - - def prepare_eval_grad(self, cwd, taskid, source_name, **kwargs): + for pathname in ["scratch", "_logs", "_datasets", "_figures"]: + unix.mkdir(self.path[pathname]) + + # These values should be constant for a given workflow. The Config class + # will run its own internal checks when instantiated + self._config = Config( + min_period=self.min_period, max_period=self.max_period, + filter_corners=self.filter_corners, client=self.client, + rotate=self.rotate, pyflex_preset=self.pyflex_preset, + fix_windows=self.fix_windows, adj_src_type=self.adj_src_type, + log_level=self.pyatoa_log_level, unit_output=self.unit_output, + start_pad_s=self.start_pad_s, end_pad_s=self.end_pad_s, + paths={"waveforms": self.path["_waveforms"] or [], + "responses": self.path["_responses"] or [], + "events": [self.path.specfem_data] + } + ) + + # Generate a list of station codes that will be used to search for data + self._station_codes = read_station_codes( + path_to_stations=os.path.join(self.path.specfem_data, "STATIONS"), + loc="*", cha="*" + ) + + def quantify_misfit(self, source_name=None, save_residuals=None, + save_adjsrcs=None, iteration=1, step_count=0, + **kwargs): """ - Prepare the gradient evaluation by gathering, preprocessing waveforms, - and measuring misfit between observations and synthetics using Pyatoa. + Prepares solver for gradient evaluation by writing residuals and + adjoint traces. Meant to be called by + `workflow.evaluate_objective_function` Reads in observed and synthetic waveforms, applies optional preprocessing, assesses misfit, and writes out adjoint sources and @@ -212,127 +231,306 @@ def prepare_eval_grad(self, cwd, taskid, source_name, **kwargs): .. note:: Meant to be called by solver.eval_func(), may have unused arguments - to keep functions general across preprocessing subclasses. - - :type cwd: str - :param cwd: current specfem working directory containing observed and - synthetic seismic data to be read and processed. Should be defined - by solver.cwd - :type source_name: str - :param source_name: the event id to be used for tagging and data lookup. - Should be defined by solver.source_name - :type taskid: int - :param taskid: identifier of the currently running solver instance. - Should be defined by solver.taskid - :type filenames: list of str - :param filenames: [not used] list of filenames defining the files in - traces - """ - if taskid == 0: - logger.debug("preparing files for gradient evaluation with " - "Pyaflowa") - - # Process all the stations for a given event using Pyaflowa - pyaflowa = self._setup_event_pyaflowa(source_name) - scaled_misfit = pyaflowa.process(nproc=self.nproc) - - if scaled_misfit is None: - print(msg.cli(f"Event {source_name} returned no misfit, you may " - f"want to check logs and waveform figures, " - f"or consider discarding this event from your " - f"workflow", - items=[pyaflowa.paths.logs, pyaflowa.paths.figures], - header="pyatoa preprocessing error", border="=")) - sys.exit(-1) - - # Event misfit defined by Tape et al. (2010) written to solver dir. - self._write_residuals(path=cwd, scaled_misfit=scaled_misfit) - - def _setup_event_pyaflowa(self, source_name, iteration, step_count=""): - """ - A convenience function to set up a Pyaflowa processing instance for - a specific event. - - .. note:: - This is meant to be called by preprocess.prepare_eval_grad() but its - also useful for debugging and manual processing where you can simply - return a formatted Pyaflowa object and debug it directly. + to keep functions general across subclasses. - :type source_name: str - :param source_name: solver source name to evaluate setup for. Must - match from list defined by: solver.source_names + :type save_residuals: str + :param save_residuals: if not None, path to write misfit/residuls to + :type save_adjsrcs: str + :param save_adjsrcs: if not None, path to write adjoint sources to """ - # Outsource data processing to an event-specfic Pyaflowa instance - pyaflowa = Pyaflowa(sfpar=self.par, sfpath=self.path) - pyaflowa.setup(source_name=source_name, iteration=iteration, - step_count=step_count, loc="*", cha="*") - - return pyaflowa - - def _save_quantity(self, filepaths, tag="", path_out=""): + config = self._config.copy() + config.event_id = source_name + config.iteration = iteration + config.step_count = step_count + + ds = ASDFDataSet(os.path.join(self.path["_datasets"], source_name)) + mgmt = Manager(config=config, ds=ds) + + # Will look for events in `path_specfem_data` matching {prefix}_{name} + # e.g., 'CMTSOLUTION_2018p130600' + import pdb;pdb.set_trace() + mgmt.gather(choice=["event"], source_name=source_name, + prefix=self.source_prefix) + misfit, nwin = 0, 0 + for station_code in self._station_codes: + net, sta, loc, cha = station_code.split(".") + _processed = False + + try: + # Will gather data and metadata based on the station codes and + # input paths from the Configuration object + mgmt.gather(choice=["inv", "st_obs", "st_syn"], + code=station_code) + except ManagerError as e: + continue + # If any part of the processing fails, move on to plotting + try: + mgmt.standardize() + mgmt.preprocess() + mgmt.window( + fix_windows=self._check_fixed_windows(iteration, step_count) + ) + mgmt.measure() + _processed = True + except ManagerError as e: + pass + + if self.plot: + plot_fid = ( + f"{source_name}_{config.iter_tag}_{config.step_tag}_" + f"{net}_{sta}.png" + ) + save = os.path.join(self.path["_figures"], plot_fid) + mgmt.plot(choice="both", show=False, save=save) + + # Write out the .adj adjoint source files + if _processed and save_adjsrcs: + mgmt.write_adjsrcs(path=save_adjsrcs, write_blanks=True) + + misfit += mgmt.stats.misfit + nwin += mgmt.stats.nwin + + if save_residuals: + try: + residuals = 0.5 * misfit / nwin + except ZeroDivisionError: + # Dealing with the case where nwin==0 (signifying either no + # windows found, or calc'ing misfit on whole trace) + residuals = misfit + np.savetxt(save_residuals, [residuals], fmt="%11.6e") + + def _check_fixed_windows(self, iteration, step_count): """ - Repeatable convenience function to save quantities from the scratch/ - directory to the output/ directory - - :type filepaths: list - :param filepaths: full path to files that should be saved to output/ - :type tag: str - :param tag: tag for saving the files in self.path.OUTPUT. If not given, will - save directly into the output/ directory - :type path_out: str - :param path_out: overwrite the default output path file naming - """ - if not path_out: - path_out = os.path.join(self.path_output, tag) - - if not os.path.exists(path_out): - unix.mkdir(path_out) - - for src in filepaths: - dst = os.path.join(path_out, os.path.basename(src)) - unix.cp(src, dst) - - @staticmethod - def _write_residuals(path, scaled_misfit): - """ - Computes residuals and saves them to a text file in the appropriate path - - :type path: str - :param path: scratch directory path, e.g. self.path.GRAD or self.path.FUNC - :type scaled_misfit: float - :param scaled_misfit: the summation of misfit from each - source-receiver pair calculated by prepare_eval_grad() - :type source_name: str - :param source_name: name of the source related to the misfit, used - for file naming - """ - residuals_file = os.path.join(path, "residuals") - np.savetxt(residuals_file, [scaled_misfit], fmt="%11.6e") - - def sum_residuals(self, files): + Determine how to address re-using misfit windows during an inversion + workflow. Throw some log messages out to let the User know whether or + not misfit windows will be re used throughout an inversion. + + True: Always fix windows except for i01s00 because we don't have any + windows for the first function evaluation + False: Don't fix windows, always choose a new set of windows + Iter: Pick windows only on the initial step count (0th) for each + iteration. WARNING - does not work well with Thrifty Inversion + because the 0th step count is usually skipped + Once: Pick new windows on the first function evaluation and then fix + windows. Useful for when parameters have changed, e.g. filter + bounds + + :type iteration: int + :param iteration: The current iteration of the SeisFlows3 workflow, + within SeisFlows3 this is defined by `optimize.iter` + :type step_count: int + :param step_count: Current line search step count within the SeisFlows3 + workflow. Within SeisFlows3 this is defined by + `optimize.line_search.step_count` + :rtype: bool + :return: bool on whether to use windows from the previous step """ - Averages the event misfits and returns the total misfit. - Total misfit defined by Tape et al. (2010) - - :type files: str - :param files: list of single-column text files containing residuals - that will have been generated using prepare_eval_grad() - :rtype: float - :return: average misfit - """ - if len(files) != self.ntask: - print(msg.cli(f"Pyatoa preprocessing module did not recover the " - f"correct number of residual files " - f"({len(files)}/{self.ntask}). Please check that " - f"the preprocessing logs", header="error") - ) - sys.exit(-1) - - total_misfit = 0 - for filename in files: - total_misfit += np.sum(np.loadtxt(filename)) - - total_misfit /= self.ntask - - return total_misfit - + fix_windows = False + # First function evaluation never fixes windows + if iteration == 1 and step_count == 0: + fix_windows = False + logger.info("new windows; first evaluation") + elif isinstance(self.fix_windows, str): + # By 'iter'ation only pick new windows on the first step count + if self.fix_windows.upper() == "ITER": + if step_count == 0: + fix_windows = False + logger.info("new windows; first step count") + else: + fix_windows = True + logger.info("fix windows; mid line search") + # 'Once' picks windows only for the first function evaluation of + # the current set of iterations. + elif self.fix_windows.upper() == "ONCE": + if iteration == self.start and step_count == 0: + fix_windows = False + logger.info("new windows; first workflow evaluation") + else: + fix_windows = True + logger.info("fix windows; mid workflow") + # Bool fix windows simply sets the parameter + elif isinstance(self.fix_windows, bool): + fix_windows = self.fix_windows + logger.info(f"fixed windows flag set: {self.fix_windows}") + + return fix_windows + + # + # def finalize(self): + # """ + # Run some serial finalization tasks specific to Pyatoa, which will help + # aggregate the collection of output information. + # + # .. note:: + # This finalize function performs the following tasks: + # * Generate .csv files using the Inspector + # * Aggregate event-specific PDFs into a single evaluation PDF + # * Save scratch/ data into output/ if requested + # """ + # # Initiate Pyaflowa to get access to path structure + # pyaflowa = Pyaflowa(sfpar=self.par, sfpath=self.path) + # unix.cd(pyaflowa.paths.datasets) + # + # # Generate the Inspector from existing datasets and save to disk + # # Allow this is fail, which might happen if we don't have enough data + # # or the Dataset is not formatted as expected + # insp = Inspector(self.par.TITLE, verbose=False) # !!! TODO + # try: + # insp.discover() + # insp.save() + # except Exception as e: + # logger.warning(f"Uncontrolled exception in Inspector creation " + # f"will not create inspector:\n{e}") + # + # # Make the final PDF for easier User ingestion of waveform/map figures + # pyaflowa.make_evaluation_composite_pdf() + # + # # Move scratch/ directory results into more permanent storage + # if self.export_datasets: + # datasets = glob(os.path.join(pyaflowa.paths.datasets, "*.h5")) + # self._save_quantity(datasets, tag="datasets") + # + # if self.export_figures: + # figures = glob(os.path.join(pyaflowa.paths.figures, "*.pdf")) + # self._save_quantity(figures, tag="figures") + # + # if self.export_log_files: + # logs = glob(os.path.join(pyaflowa.paths.logs, "*.txt")) + # path_out = os.path.join(self.path_output, CFGPATHS.LOGDIR) + # self._save_quantity(logs, path_out=path_out) + # + # def prepare_eval_grad(self, cwd, taskid, source_name, **kwargs): + # """ + # Prepare the gradient evaluation by gathering, preprocessing waveforms, + # and measuring misfit between observations and synthetics using Pyatoa. + # + # Reads in observed and synthetic waveforms, applies optional + # preprocessing, assesses misfit, and writes out adjoint sources and + # STATIONS_ADJOINT file. + # + # .. note:: + # Meant to be called by solver.eval_func(), may have unused arguments + # to keep functions general across preprocessing subclasses. + # + # :type cwd: str + # :param cwd: current specfem working directory containing observed and + # synthetic seismic data to be read and processed. Should be defined + # by solver.cwd + # :type source_name: str + # :param source_name: the event id to be used for tagging and data lookup. + # Should be defined by solver.source_name + # :type taskid: int + # :param taskid: identifier of the currently running solver instance. + # Should be defined by solver.taskid + # :type filenames: list of str + # :param filenames: [not used] list of filenames defining the files in + # traces + # """ + # if taskid == 0: + # logger.debug("preparing files for gradient evaluation with " + # "Pyaflowa") + # + # # Process all the stations for a given event using Pyaflowa + # pyaflowa = self._setup_event_pyaflowa(source_name) + # scaled_misfit = pyaflowa.process(nproc=self.nproc) + # + # if scaled_misfit is None: + # print(msg.cli(f"Event {source_name} returned no misfit, you may " + # f"want to check logs and waveform figures, " + # f"or consider discarding this event from your " + # f"workflow", + # items=[pyaflowa.paths.logs, pyaflowa.paths.figures], + # header="pyatoa preprocessing error", border="=")) + # sys.exit(-1) + # + # # Event misfit defined by Tape et al. (2010) written to solver dir. + # self._write_residuals(path=cwd, scaled_misfit=scaled_misfit) + # + # def _setup_event_pyaflowa(self, source_name, iteration, step_count=""): + # """ + # A convenience function to set up a Pyaflowa processing instance for + # a specific event. + # + # .. note:: + # This is meant to be called by preprocess.prepare_eval_grad() but its + # also useful for debugging and manual processing where you can simply + # return a formatted Pyaflowa object and debug it directly. + # + # :type source_name: str + # :param source_name: solver source name to evaluate setup for. Must + # match from list defined by: solver.source_names + # """ + # # Outsource data processing to an event-specfic Pyaflowa instance + # pyaflowa = Pyaflowa(sfpar=self.par, sfpath=self.path) + # pyaflowa.setup(source_name=source_name, iteration=iteration, + # step_count=step_count, loc="*", cha="*") + # + # return pyaflowa + # + # def _save_quantity(self, filepaths, tag="", path_out=""): + # """ + # Repeatable convenience function to save quantities from the scratch/ + # directory to the output/ directory + # + # :type filepaths: list + # :param filepaths: full path to files that should be saved to output/ + # :type tag: str + # :param tag: tag for saving the files in self.path.OUTPUT. If not given, will + # save directly into the output/ directory + # :type path_out: str + # :param path_out: overwrite the default output path file naming + # """ + # if not path_out: + # path_out = os.path.join(self.path_output, tag) + # + # if not os.path.exists(path_out): + # unix.mkdir(path_out) + # + # for src in filepaths: + # dst = os.path.join(path_out, os.path.basename(src)) + # unix.cp(src, dst) + # + # @staticmethod + # def _write_residuals(path, scaled_misfit): + # """ + # Computes residuals and saves them to a text file in the appropriate path + # + # :type path: str + # :param path: scratch directory path, e.g. self.path.GRAD or self.path.FUNC + # :type scaled_misfit: float + # :param scaled_misfit: the summation of misfit from each + # source-receiver pair calculated by prepare_eval_grad() + # :type source_name: str + # :param source_name: name of the source related to the misfit, used + # for file naming + # """ + # residuals_file = os.path.join(path, "residuals") + # np.savetxt(residuals_file, [scaled_misfit], fmt="%11.6e") + # + # def sum_residuals(self, files): + # """ + # Averages the event misfits and returns the total misfit. + # Total misfit defined by Tape et al. (2010) + # + # :type files: str + # :param files: list of single-column text files containing residuals + # that will have been generated using prepare_eval_grad() + # :rtype: float + # :return: average misfit + # """ + # if len(files) != self.ntask: + # print(msg.cli(f"Pyatoa preprocessing module did not recover the " + # f"correct number of residual files " + # f"({len(files)}/{self.ntask}). Please check that " + # f"the preprocessing logs", header="error") + # ) + # sys.exit(-1) + # + # total_misfit = 0 + # for filename in files: + # total_misfit += np.sum(np.loadtxt(filename)) + # + # total_misfit /= self.ntask + # + # return total_misfit + # diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index f6ac7ed6..a8fb0030 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -574,7 +574,7 @@ def submit(self, **kwargs): parameters = load_yaml(self._args.parameter_file) system = custom_import("system", parameters.system)(**parameters) system.submit(workdir=self._args.workdir, - par_file=self._args.parameter_file) + parameter_file=self._args.parameter_file) def clean(self, force=False, **kwargs): """ diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index 392e05fd..1d4228e5 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -293,8 +293,10 @@ def evaluate_initial_misfit(self): because we are potentially running two simulations back to back. """ logger.info(msg.mnr("EVALUATING MISFIT FOR INITIAL MODEL")) + self.system.run( [self.prepare_data_for_solver, + self.run_forward_simulations, self.evaluate_objective_function], path_model=self.path.model_init, save_residuals=os.path.join(self.path.eval_grad, "residuals") @@ -335,12 +337,9 @@ def prepare_data_for_solver(self, **kwargs): export_traces=export_traces ) - def evaluate_objective_function(self, path_model, save_residuals=False, - **kwargs): + def run_forward_simulations(self, path_model, **kwargs): """ - Performs forward simulation for a single given event. Also evaluates the - objective function and writes residuals and adjoint sources for later - tasks. + Performs forward simulation for a single given event. .. note:: if PAR.PREPROCESS == None, will not perform misfit quantification @@ -371,13 +370,26 @@ def evaluate_objective_function(self, path_model, save_residuals=False, export_traces=export_traces ) - # (optional) Perform data-synthetic misfit quantification - if self.preprocess: - logger.debug(f"quantifying misfit with " - f"'{self.preprocess.__class__.__name__}'") - self.preprocess.quantify_misfit( - observed=self.solver.data_filenames(choice="obs"), - synthetic=self.solver.data_filenames(choice="syn"), - save_adjsrcs=os.path.join(self.solver.cwd, "traces", "adj"), - save_residuals=save_residuals - ) + def evaluate_objective_function(self, save_residuals=False, **kwargs): + """ + Uses the preprocess module to evaluate the misfit/objective function + given synthetics generated during forward simulations + + .. note:: + Must be run by system.run() so that solvers are assigned individual + task ids/ working directories. + """ + if self.preprocess is None: + logger.debug("no preprocessing module selected, will not evaluate " + "objective function") + return + + logger.debug(f"quantifying misfit with " + f"'{self.preprocess.__class__.__name__}'") + self.preprocess.quantify_misfit( + # observed=self.solver.data_filenames(choice="obs"), + # synthetic=self.solver.data_filenames(choice="syn"), + source_name=self.solver.source_name, + save_adjsrcs=os.path.join(self.solver.cwd, "traces", "adj"), + save_residuals=save_residuals + ) diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index 189d54b9..95a207f3 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -167,7 +167,7 @@ def setup(self): unix.mkdir(self.path.eval_func) - self.optimize = self._modules.optimize + self.optimize = self._modules.optimize # NOQA # If optimization has been run before, re-load from checkpoint self.optimize.load_checkpoint() @@ -189,6 +189,29 @@ def checkpoint(self): with open(self.path.state_file, "a") as f: f.write(f"iteration: {self.iteration}") + def evaluate_objective_function(self, save_residuals=False, **kwargs): + """ + Overwrite evaluate objective function to include MORE input parameters + specifying which evaluation in the inversion we are at. Also removes + the check for a preprocessing module because it is assumed we have a + preprocsesing module for an inversion workflow. + + .. note:: + Must be run by system.run() so that solvers are assigned individual + task ids/ working directories. + """ + logger.debug(f"quantifying misfit with " + f"'{self.preprocess.__class__.__name__}'") + + self.preprocess.quantify_misfit( + observed=self.solver.data_filenames(choice="obs"), + synthetic=self.solver.data_filenames(choice="syn"), + save_adjsrcs=os.path.join(self.solver.cwd, "traces", "adj"), + save_residuals=save_residuals, + iteration=self.iteration, + step_count=self.optimize.step_count, + ) + def evaluate_initial_misfit(self): """ Overwrite `workflow.forward` to skip over initial misfit evaluation @@ -211,11 +234,14 @@ def evaluate_initial_misfit(self): path_model = os.path.join(self.path.eval_grad, "model") m_new = self.optimize.load_vector("m_new") m_new.write(path=path_model) + # Run forward simulation/misfit quantification with previous model self.system.run( - [self.evaluate_objective_function], + [self.run_forward_simulations, + self.evaluate_objective_function], path_model=path_model, - save_residuals=os.path.join(self.path.eval_grad, "residuals") + save_residuals=os.path.join(self.path.eval_grad, + "residuals") ) # Override function to sum residuals into the optimization library @@ -343,7 +369,8 @@ def perform_line_search(self): def _evaluate_line_search_misfit(self): """Convenience fuinction to wrap forward solver and misfit calc""" self.system.run( - [self.evaluate_objective_function], + [self.run_forward_simulations, + self.evaluate_objective_function], path_model=os.path.join(self.path.eval_func, "model"), save_residuals=os.path.join(self.path.eval_func, "residuals") ) From 3d4189c88a0c11068ffa574e7853e2f0aa803cf5 Mon Sep 17 00:00:00 2001 From: bch0w Date: Fri, 29 Jul 2022 16:27:40 -0800 Subject: [PATCH 091/195] working on preprocessing pyatoa tests and fixing up pyatoa class --- seisflows/preprocess/pyatoa.py | 114 +- seisflows/solver/specfem.py | 48 +- seisflows/tests/test_data/test_solver/001 | 1 + .../mainsolver/DATA/CMTSOLUTION_001 | 14 +- .../mainsolver/DATA/CMTSOLUTION_002 | 14 +- .../mainsolver/DATA/CMTSOLUTION_003 | 14 +- .../mainsolver/DATA/CMTSOLUTION_004 | 14 +- .../mainsolver/DATA/CMTSOLUTION_005 | 14 +- .../mainsolver/DATA/CMTSOLUTION_006 | 14 +- .../test_solver/mainsolver/DATA/STATIONS | 3 +- .../traces/obs/AA.S0001.BXY.semd | 0 .../traces/obs/AA.S0002.BXY.semd} | 0 .../mainsolver/traces/syn/AA.S0001.BXY.semd | 5000 +++++++++++++++++ .../mainsolver/traces/syn/AA.S0002.BXY.semd | 5000 +++++++++++++++++ seisflows/tests/test_preprocess.py | 43 + seisflows/tests/test_solver.py | 4 +- seisflows/tools/specfem.py | 47 +- 17 files changed, 10250 insertions(+), 94 deletions(-) create mode 120000 seisflows/tests/test_data/test_solver/001 mode change 100644 => 100755 seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_001 mode change 100644 => 100755 seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_002 mode change 100644 => 100755 seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_003 mode change 100644 => 100755 seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_004 mode change 100644 => 100755 seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_005 mode change 100644 => 100755 seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_006 rename seisflows/tests/test_data/test_solver/{ => mainsolver}/traces/obs/AA.S0001.BXY.semd (100%) rename seisflows/tests/test_data/test_solver/{traces/syn/AA.S0001.BXY.semd => mainsolver/traces/obs/AA.S0002.BXY.semd} (100%) create mode 100644 seisflows/tests/test_data/test_solver/mainsolver/traces/syn/AA.S0001.BXY.semd create mode 100644 seisflows/tests/test_data/test_solver/mainsolver/traces/syn/AA.S0002.BXY.semd diff --git a/seisflows/preprocess/pyatoa.py b/seisflows/preprocess/pyatoa.py index 7410b17d..d545b71f 100644 --- a/seisflows/preprocess/pyatoa.py +++ b/seisflows/preprocess/pyatoa.py @@ -1,7 +1,8 @@ #!/usr/bin/env python3 """ -The Pyatoa preprocessing module for waveform gathering, preprocessing and -misfit quantification. +The Pyaflowa preprocessing module for waveform gathering, preprocessing and +misfit quantification. We use the name 'Pyaflowa' to avoid any potential +name overlaps with the actual pyatoa package. """ import os import numpy as np @@ -11,12 +12,13 @@ from seisflows import logger from seisflows.tools import unix -from seisflows.tools.config import Dict +from seisflows.tools.config import Dict, get_task_id +from seisflows.tools.specfem import check_source_names -class Pyatoa: +class Pyaflowa: """ - [preprocess.pyatoa] preprocessing and misfit quantification using Pyatoa + [preprocess.pyaflowa] preprocessing and misfit quantification using Pyatoa :type data_format: str :param data_format: data format for reading traces into memory. Pyatoa @@ -93,28 +95,18 @@ class Pyatoa: :param path_data: optional path for preprocessing module to discover waveform and meta-data. """ - def __init__(self, data_format="ascii", components=None, start=None, - ntask=1, nproc=1, source_prefix=None, min_period=None, - max_period=None, filter_corners=4, client=None, rotate=False, - pyflex_preset="default", fix_windows=False, adj_src_type="cc", - plot=True, pyatoa_log_level="DEBUG", unit_output="VEL", - start_pad_s=0., end_pad_s=None, workdir=os.getcwd(), - path_preprocess=None, path_specfem_data=None, path_data=None, + def __init__(self, min_period=1., max_period=10., filter_corners=4, + client=None, rotate=False, pyflex_preset="default", + fix_windows=False, adj_src_type="cc", plot=True, + pyatoa_log_level="DEBUG", unit_output="VEL", start_pad_s=0., + end_pad_s=None, workdir=os.getcwd(), path_preprocess=None, + path_solver=None, path_specfem_data=None, path_data=None, path_output=None, export_datasets=True, export_figures=True, - export_log_files=True, + export_log_files=True, data_format="ascii", + data_case="data", components=None, + start=None, ntask=1, nproc=1, source_prefix=None, **kwargs): - """ - Pyatoa preprocessing parameters that will be passed to Pyaflowa - """ - # Shared SeisFlows parameters - self.data_format = data_format.upper() - self.components = components - self.start = start - self.ntask = ntask - self.nproc = nproc - self.source_prefix = source_prefix - - # Pyatoa-specific parameters that are provided to the Config class + """Pyatoa preprocessing parameters""" self.min_period = min_period self.max_period = max_period self.filter_corners = filter_corners @@ -132,6 +124,7 @@ def __init__(self, data_format="ascii", components=None, start=None, self.path = Dict( scratch=path_preprocess or os.path.join(workdir, "scratch", "preprocess"), + solver=path_solver or os.path.join(workdir, "scratch", "solver"), output=path_output or os.path.join(workdir, "output"), specfem_data=path_specfem_data, data=path_data, @@ -155,11 +148,23 @@ def __init__(self, data_format="ascii", components=None, start=None, self.path["_waveforms"] = None self.path["_responses"] = None - # Internal parameters for workflow + # SeisFlows parameters that should be set by other modules. Keep hidden + # so `seisflows configure` doesn't attribute these to preprocess. + self._data_format = data_format.upper() + self._data_case = data_case.lower() + self._components = components + self._start = start + self._ntask = ntask + self._nproc = nproc + self._source_prefix = source_prefix + + # Internal parameters to check against user-set parameters self._acceptable_data_formats = ["ASCII"] self._acceptable_source_prefixes = ["SOURCE", "FORCESOLUTION", "CMTSOLUTION"] - self._config = None # to be created by setup() + + # Internal attributes to be filled in by setup() + self._config = None self._fix_windows = False self._station_codes = [] self._source_names = [] @@ -169,7 +174,7 @@ def check(self): Checks Parameter and Path files, will be run at the start of a Seisflows workflow to ensure that things are set appropriately. """ - assert(self.data_format.upper() == "ASCII"), \ + assert(self._data_format.upper() == "ASCII"), \ "Pyatoa preprocess requires `data_format`=='ASCII'" assert(self.path.specfem_data is not None and @@ -182,9 +187,9 @@ def check(self): f"Pyatoa preprocessing requires that the `STATIONS` file exists " \ f"within `path_specfem_data`" - assert(self.source_prefix in self._acceptable_source_prefixes), ( + assert(self._source_prefix in self._acceptable_source_prefixes), ( f"Pyatoa can only accept `source_prefix` in " - f"{self._acceptable_source_prefixes}, not '{self.source_prefix}'" + f"{self._acceptable_source_prefixes}, not '{self._source_prefix}'" ) def setup(self): @@ -196,8 +201,7 @@ def setup(self): for pathname in ["scratch", "_logs", "_datasets", "_figures"]: unix.mkdir(self.path[pathname]) - # These values should be constant for a given workflow. The Config class - # will run its own internal checks when instantiated + # Generalized Config object that can be shared among all child processes self._config = Config( min_period=self.min_period, max_period=self.max_period, filter_corners=self.filter_corners, client=self.client, @@ -205,19 +209,26 @@ def setup(self): fix_windows=self.fix_windows, adj_src_type=self.adj_src_type, log_level=self.pyatoa_log_level, unit_output=self.unit_output, start_pad_s=self.start_pad_s, end_pad_s=self.end_pad_s, + component_list=list(self._components), + synthetics_only=bool(self._data_case == "synthetic"), paths={"waveforms": self.path["_waveforms"] or [], "responses": self.path["_responses"] or [], "events": [self.path.specfem_data] } ) - # Generate a list of station codes that will be used to search for data self._station_codes = read_station_codes( path_to_stations=os.path.join(self.path.specfem_data, "STATIONS"), loc="*", cha="*" ) - def quantify_misfit(self, source_name=None, save_residuals=None, + # Get an internal list of source names. Will be the same as solver + self._source_names = check_source_names( + path_specfem_data=self.path.specfem_data, + source_prefix=self._source_prefix, ntask=self._ntask + ) + + def quantify_misfit(self, source_name, save_residuals=None, save_adjsrcs=None, iteration=1, step_count=0, **kwargs): """ @@ -238,27 +249,37 @@ def quantify_misfit(self, source_name=None, save_residuals=None, :type save_adjsrcs: str :param save_adjsrcs: if not None, path to write adjoint sources to """ + # Set the individual Config class for our given event and evaluation config = self._config.copy() config.event_id = source_name config.iteration = iteration config.step_count = step_count - ds = ASDFDataSet(os.path.join(self.path["_datasets"], source_name)) + # Force the Manager to look in the solver directory for data + # Note: we are assuming the SeisFlows solver directory structure here + config.paths["waveforms"].append( + os.path.join(self.path.solver, source_name, "traces", "obs") + ) + config.paths["synthetics"].append( + os.path.join(self.path.solver, source_name, "traces", "syn") + ) + + # Set up the Pyatoa workflow, attempt to gather event metadata + ds = ASDFDataSet( + os.path.join(self.path["_datasets"], f"{source_name}.h5") + ) mgmt = Manager(config=config, ds=ds) + mgmt.gather(choice=["event"], event_id=source_name, + prefix=f"{self._source_prefix}_") - # Will look for events in `path_specfem_data` matching {prefix}_{name} - # e.g., 'CMTSOLUTION_2018p130600' - import pdb;pdb.set_trace() - mgmt.gather(choice=["event"], source_name=source_name, - prefix=self.source_prefix) + # Run data/metadata gathering, processing and misfit quantification misfit, nwin = 0, 0 for station_code in self._station_codes: net, sta, loc, cha = station_code.split(".") _processed = False - + # Will gather data and metadata based on the station codes and + # input paths from the Configuration object try: - # Will gather data and metadata based on the station codes and - # input paths from the Configuration object mgmt.gather(choice=["inv", "st_obs", "st_syn"], code=station_code) except ManagerError as e: @@ -281,7 +302,10 @@ def quantify_misfit(self, source_name=None, save_residuals=None, f"{net}_{sta}.png" ) save = os.path.join(self.path["_figures"], plot_fid) - mgmt.plot(choice="both", show=False, save=save) + try: + mgmt.plot(choice="both", show=False, save=save) + except ManagerError: + mgmt.plot(choice="wav", show=False, save=save) # Write out the .adj adjoint source files if _processed and save_adjsrcs: @@ -342,7 +366,7 @@ def _check_fixed_windows(self, iteration, step_count): # 'Once' picks windows only for the first function evaluation of # the current set of iterations. elif self.fix_windows.upper() == "ONCE": - if iteration == self.start and step_count == 0: + if iteration == self._start and step_count == 0: fix_windows = False logger.info("new windows; first workflow evaluation") else: diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 0ef010c9..030749e2 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -25,7 +25,7 @@ from seisflows import logger from seisflows.tools import msg, unix from seisflows.tools.config import get_task_id, Dict -from seisflows.tools.specfem import getpar, setpar +from seisflows.tools.specfem import getpar, setpar, check_source_names class Specfem: @@ -222,7 +222,10 @@ def check(self): f"`path_model_true` is empty but should have model files" # Check that the number of tasks/events matches the number of events - self._source_names = self._check_source_names() + self._source_names = check_source_names( + path_specfem_data=self.path.specfem_data, + source_prefix=self.source_prefix, ntask=self.ntask + ) @property def source_names(self): @@ -239,7 +242,10 @@ def source_names(self): :return: list of source names """ if self._source_names is None: - self._source_names = self._check_source_names() + self._source_names = check_source_names( + path_specfem_data=self.path.specfem_data, + source_prefix=self.source_prefix, ntask=self.ntask + ) return self._source_names @property @@ -796,39 +802,3 @@ def _initialize_working_directory(self, cwd=None): logger.debug(f"linking source '{source_name}' as 'mainsolver'") unix.ln(cwd, self.path.mainsolver) - def _check_source_names(self): - """ - Determines names of sources by applying wildcard rule to user-supplied - input files. Source names are only provided up to PAR.NTASK and are - returned in alphabetical order. - - :rtype: list - :return: alphabetically ordered list of source names up to PAR.NTASK - """ - assert(self.path.specfem_data is not None), \ - f"solver source names requires 'solver.path.specfem_data' to exist" - assert(os.path.exists(self.path.specfem_data)), \ - f"solver source names requires 'solver.path.specfem_data' to exist" - - # Apply wildcard rule and check for available sources, exit if no - # sources found because then we can't proceed - wildcard = f"{self.source_prefix}_*" - fids = sorted(glob(os.path.join(self.path.specfem_data, wildcard))) - if not fids: - print(msg.cli("No matching source files when searching PATH for " - "the given WILDCARD", - items=[f"PATH: {self.path.specfem_data}", - f"WILDCARD: {wildcard}"], header="error" - ) - ) - sys.exit(-1) - else: - assert(len(fids) >= self.ntask), ( - f"Number of requested tasks/events {self.ntask} exceeds number " - f"of available sources {len(fids)}" - ) - - # Create internal definition of sources names by stripping prefixes - names = [os.path.basename(fid).split("_")[-1] for fid in fids] - - return names[:self.ntask] diff --git a/seisflows/tests/test_data/test_solver/001 b/seisflows/tests/test_data/test_solver/001 new file mode 120000 index 00000000..cbb9ee10 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001 @@ -0,0 +1 @@ +mainsolver/ \ No newline at end of file diff --git a/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_001 b/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_001 old mode 100644 new mode 100755 index 2850366a..cacf1f81 --- a/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_001 +++ b/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_001 @@ -1 +1,13 @@ -EVENT 1 +XXXX 2012 03 30 18 47 38.66 -40.9316 175.4123 17.6 0.0 0.0 NORTH ISLAND, NEW ZEALAND +event name: 001 +time shift: 0.0000 +half duration: 0.0000 +latitude: -40.9316 +longitude: 175.4123 +depth: 17.5977 +Mrr: -1.451500E+22 +Mtt: 2.777100E+22 +Mpp: -1.325600E+22 +Mrt: 1.085300E+22 +Mrp: -2.075000E+21 +Mtp: -1.439600E+22 diff --git a/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_002 b/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_002 old mode 100644 new mode 100755 index 528a18ac..53709cf7 --- a/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_002 +++ b/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_002 @@ -1 +1,13 @@ -EVENT 2 +XXXX 2012 03 30 18 47 38.66 -40.9316 175.4123 17.6 0.0 0.0 NORTH ISLAND, NEW ZEALAND +event name: 002 +time shift: 0.0000 +half duration: 0.0000 +latitude: -40.9316 +longitude: 175.4123 +depth: 17.5977 +Mrr: -1.451500E+22 +Mtt: 2.777100E+22 +Mpp: -1.325600E+22 +Mrt: 1.085300E+22 +Mrp: -2.075000E+21 +Mtp: -1.439600E+22 diff --git a/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_003 b/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_003 old mode 100644 new mode 100755 index e41bec6c..7d46df94 --- a/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_003 +++ b/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_003 @@ -1 +1,13 @@ -EVENT 3 +XXXX 2012 03 30 18 47 38.66 -40.9316 175.4123 17.6 0.0 0.0 NORTH ISLAND, NEW ZEALAND +event name: 003 +time shift: 0.0000 +half duration: 0.0000 +latitude: -40.9316 +longitude: 175.4123 +depth: 17.5977 +Mrr: -1.451500E+22 +Mtt: 2.777100E+22 +Mpp: -1.325600E+22 +Mrt: 1.085300E+22 +Mrp: -2.075000E+21 +Mtp: -1.439600E+22 diff --git a/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_004 b/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_004 old mode 100644 new mode 100755 index ccb69325..51d51497 --- a/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_004 +++ b/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_004 @@ -1 +1,13 @@ -EVENT 4 +XXXX 2012 03 30 18 47 38.66 -40.9316 175.4123 17.6 0.0 0.0 NORTH ISLAND, NEW ZEALAND +event name: 004 +time shift: 0.0000 +half duration: 0.0000 +latitude: -40.9316 +longitude: 175.4123 +depth: 17.5977 +Mrr: -1.451500E+22 +Mtt: 2.777100E+22 +Mpp: -1.325600E+22 +Mrt: 1.085300E+22 +Mrp: -2.075000E+21 +Mtp: -1.439600E+22 diff --git a/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_005 b/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_005 old mode 100644 new mode 100755 index 0cea6274..50b4aeb0 --- a/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_005 +++ b/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_005 @@ -1 +1,13 @@ -EVENT 5 +XXXX 2012 03 30 18 47 38.66 -40.9316 175.4123 17.6 0.0 0.0 NORTH ISLAND, NEW ZEALAND +event name: 005 +time shift: 0.0000 +half duration: 0.0000 +latitude: -40.9316 +longitude: 175.4123 +depth: 17.5977 +Mrr: -1.451500E+22 +Mtt: 2.777100E+22 +Mpp: -1.325600E+22 +Mrt: 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1.0308193721441524E-006 + 251.69999999999999 7.9707533310012337E-007 + 251.75999999999999 5.5701156701066452E-007 + 251.81999999999999 3.1065398409177109E-007 + 251.88000000000000 5.8084079838092886E-008 + 251.94000000000000 -2.0056016975898630E-007 diff --git a/seisflows/tests/test_preprocess.py b/seisflows/tests/test_preprocess.py index 364d7bc5..614f1906 100644 --- a/seisflows/tests/test_preprocess.py +++ b/seisflows/tests/test_preprocess.py @@ -7,9 +7,11 @@ from glob import glob from seisflows import ROOT_DIR from seisflows.preprocess.default import Default +from seisflows.preprocess.pyatoa import Pyaflowa TEST_DATA = os.path.join(ROOT_DIR, "tests", "test_data", "test_preprocess") +TEST_SOLVER = os.path.join(ROOT_DIR, "tests", "test_data", "test_solver") def test_read(): @@ -93,3 +95,44 @@ def test_quantify_misfit(tmpdir): residuals = open(os.path.join(tmpdir, "residuals_ascii")).readlines() assert(len(residuals) == 1) assert(float(residuals[0]) == 0) + + +def test_pyaflowa_setup(tmpdir): + """ + Test setup procedure for SeisFlows which internalizes some workflow + information that is crucial for later tasks + """ + pyaflowa = Pyaflowa( + workdir=tmpdir, + path_specfem_data=os.path.join(TEST_SOLVER, "mainsolver", "DATA"), + path_solver=os.path.join(TEST_SOLVER, "mainsolver"), + source_prefix="CMTSOLUTION", + ntask=2, + ) + + assert(pyaflowa._station_codes == []) + assert(pyaflowa._source_names == []) + + pyaflowa.setup() + + assert(len(pyaflowa._station_codes) == 2) + assert(pyaflowa._station_codes[0] == "AA.S0001.*.*") + assert(len(pyaflowa._source_names) == pyaflowa._ntask) + assert(pyaflowa._source_names[0] == "001") + + +def test_pyaflowa_quantify_misfit(tmpdir): + """ + Test misfit quantification for Pyatoa including data gathering + """ + pyaflowa = Pyaflowa( + workdir=tmpdir, + path_specfem_data=os.path.join(TEST_SOLVER, "mainsolver", "DATA"), + path_solver=TEST_SOLVER, source_prefix="CMTSOLUTION", ntask=1, + data_case="synthetic", components="Y", + ) + pyaflowa.setup() + save_residuals = os.path.join(tmpdir, "residuals.txt") + pyaflowa.quantify_misfit(source_name=pyaflowa._source_names[0], + save_residuals=save_residuals) + pytest.set_trace() diff --git a/seisflows/tests/test_solver.py b/seisflows/tests/test_solver.py index 591fa0e2..76b5f48e 100644 --- a/seisflows/tests/test_solver.py +++ b/seisflows/tests/test_solver.py @@ -59,8 +59,8 @@ def test_initialize_working_directory(tmpdir): assert(glob(os.path.join(solver.cwd, "*"))) event_fid = os.path.join(solver.cwd, "DATA", "CMTSOLUTION") assert(os.path.islink(event_fid)) - event_line = open(event_fid).readlines()[0].strip() - assert(event_line == "EVENT 1") + event_line = open(event_fid).readlines()[1].strip() + assert(event_line.split(":")[1].strip() == "001") def test_run_binary(tmpdir): diff --git a/seisflows/tools/specfem.py b/seisflows/tools/specfem.py index 18a8fc36..992571ff 100644 --- a/seisflows/tools/specfem.py +++ b/seisflows/tools/specfem.py @@ -8,7 +8,7 @@ from glob import glob from seisflows import logger from seisflows.tools.config import Dict -from seisflows.tools import unix +from seisflows.tools import unix, msg from seisflows.tools.math import poissons_ratio @@ -494,6 +494,51 @@ def _write_model_fortran_binary(self, path): buffer.tofile(f) +def check_source_names(path_specfem_data, source_prefix, ntask=None): + """ + Determines names of sources by applying wildcard rule to user-supplied + input files. Source names are only provided up to PAR.NTASK and are + returned in alphabetical order. + + .. note:: + SeisFlows expects sources to be stored in the DATA/ directory with a + prefix and a source name, e.g., {source_prefix}_{source_name} which + would evaluate to something like CMTSOLUTION_001 + + :type path_specfem_data: str + :param path_specfem_data: path to a + :type source_prefix: str + :param source_prefix: type of SPECFEM input source, e.g., CMTSOLUTION + :type ntask: int + :parma ntask: if provided, curtails the list of sources up to `ntask`. If + None, returns all files found matching the wildcard + :rtype: list + :return: alphabetically ordered list of source names up to PAR.NTASK + """ + wildcard = f"{source_prefix}_*" + fids = sorted(glob(os.path.join(path_specfem_data, wildcard))) + if not fids: + logger.warning( + msg.cli("No matching source files when searching PATH for the " + "given WILDCARD", + items=[f"PATH: {path_specfem_data}", + f"WILDCARD: {wildcard}"], + header="error") + ) + return + if ntask is not None: + assert(len(fids) >= ntask), ( + f"Number of requested tasks/events {ntask} exceeds number " + f"of available sources {len(fids)}" + ) + fids = fids[:ntask] + + # Create internal definition of sources names by stripping prefixes + names = [os.path.basename(fid).split("_")[-1] for fid in fids] + + return names + + def getpar(key, file, delim="=", match_partial=False): """ Reads and returns parameters from a SPECFEM or SeisFlows parameter file From a6d46325ea91b28f0fdd7d6cf952ca34edb40244 Mon Sep 17 00:00:00 2001 From: bch0w Date: Mon, 1 Aug 2022 14:01:19 -0800 Subject: [PATCH 092/195] pyatoa preprocessing splitting off sion misfit quantification as a separate function so that it can be run in parallel using concurrent.futures --- seisflows/preprocess/pyatoa.py | 109 ++++++++++++++++++++------------- 1 file changed, 67 insertions(+), 42 deletions(-) diff --git a/seisflows/preprocess/pyatoa.py b/seisflows/preprocess/pyatoa.py index d545b71f..48ccf006 100644 --- a/seisflows/preprocess/pyatoa.py +++ b/seisflows/preprocess/pyatoa.py @@ -6,13 +6,14 @@ """ import os import numpy as np +from concurrent.futures import ProcessPoolExecutor from pyasdf import ASDFDataSet from pyatoa import Config, Manager, ManagerError from pyatoa.utils.read import read_station_codes from seisflows import logger from seisflows.tools import unix -from seisflows.tools.config import Dict, get_task_id +from seisflows.tools.config import Dict from seisflows.tools.specfem import check_source_names @@ -268,51 +269,24 @@ def quantify_misfit(self, source_name, save_residuals=None, ds = ASDFDataSet( os.path.join(self.path["_datasets"], f"{source_name}.h5") ) - mgmt = Manager(config=config, ds=ds) + mgmt = Manager(config=config) mgmt.gather(choice=["event"], event_id=source_name, prefix=f"{self._source_prefix}_") - # Run data/metadata gathering, processing and misfit quantification + # Run misfit quantification for each station concurrently misfit, nwin = 0, 0 - for station_code in self._station_codes: - net, sta, loc, cha = station_code.split(".") - _processed = False - # Will gather data and metadata based on the station codes and - # input paths from the Configuration object - try: - mgmt.gather(choice=["inv", "st_obs", "st_syn"], - code=station_code) - except ManagerError as e: - continue - # If any part of the processing fails, move on to plotting - try: - mgmt.standardize() - mgmt.preprocess() - mgmt.window( - fix_windows=self._check_fixed_windows(iteration, step_count) - ) - mgmt.measure() - _processed = True - except ManagerError as e: - pass - - if self.plot: - plot_fid = ( - f"{source_name}_{config.iter_tag}_{config.step_tag}_" - f"{net}_{sta}.png" - ) - save = os.path.join(self.path["_figures"], plot_fid) - try: - mgmt.plot(choice="both", show=False, save=save) - except ManagerError: - mgmt.plot(choice="wav", show=False, save=save) - - # Write out the .adj adjoint source files - if _processed and save_adjsrcs: - mgmt.write_adjsrcs(path=save_adjsrcs, write_blanks=True) - - misfit += mgmt.stats.misfit - nwin += mgmt.stats.nwin + with ProcessPoolExecutor(max_workers=unix.nproc() - 1) as executor: + futures = [ + executor.submit( + self._quantify_misfit_station, mgmt, code, save_adjsrcs) + for code in self._station_codes + ] + # Collect misfit values from function as they complete + for future in futures: + _misfit, _nwin = future.result() + if _misfit is not None: + misfit += _misfit + nwin += _nwin if save_residuals: try: @@ -323,6 +297,57 @@ def quantify_misfit(self, source_name, save_residuals=None, residuals = misfit np.savetxt(save_residuals, [residuals], fmt="%11.6e") + def _quantify_misfit_station(self, mgmt, station_code, save_adjsrcs): + """ + Run misfit quantification for a single event-station pair. Gathers, + preprocesses, windows and measures data, saves adjoint source if + requested, and then returns the total misfit and the collected + windows for the station. + """ + _processed = False + net, sta, loc, cha = station_code.split(".") + try: + mgmt.gather(choice=["inv", "st_obs", "st_syn"], code=station_code) + except ManagerError as e: + return None, None + + # If any part of the processing fails, move on to plotting because we + # will have gathered waveform data so a figure is still useful. + try: + _fix_windows = self._check_fixed_windows( + iteration=mgmt.config.iteration, + step_count=mgmt.config.step_count + ) + mgmt.standardize() + mgmt.preprocess() + mgmt.window(fix_windows=_fix_windows) + mgmt.measure() + _processed = True + except ManagerError as e: + pass + + # Plot waveform + map figure. Map may fail if we don't have appropriate + # metdata, in which case we fall back to plotting waveform only + if self.plot: + # e.g., 001_i01_s00_XX_ABC.png + plot_fid = ( + f"{mgmt.config.event_id}_" + f"{mgmt.config.iter_tag}_{mgmt.config.step_tag}_" + f"{net}_{sta}.png" + ) + save = os.path.join(self.path["_figures"], plot_fid) + try: + mgmt.plot(choice="both", show=False, save=save) + except ManagerError: + mgmt.plot(choice="wav", show=False, save=save) + + # Write out the .adj adjoint source files for solver to discover. + # Write empty adjoint sources for components with no adjoint sources + if _processed and save_adjsrcs: + mgmt.write_adjsrcs(path=save_adjsrcs, write_blanks=True) + + return mgmt.stats.misfit, mgmt.stats.nwin + def _check_fixed_windows(self, iteration, step_count): """ Determine how to address re-using misfit windows during an inversion From 082496d497d6bdaa36873be11d2239821ecfd043 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Mon, 1 Aug 2022 13:09:43 -0800 Subject: [PATCH 093/195] bug fix: during line search wrong alpha was being used to calculate the trial model causing the wrong model to be used for the next line search evaluation. fixed this and confirmed with updated Rosenbrock test suite. Test inversions now available for LBFGS, NLCG and gradient descent --- seisflows/optimize/gradient.py | 34 ++++++----- seisflows/plugins/line_search/bracket.py | 8 ++- seisflows/tests/test_optimize.py | 74 ++++++++++++++++++------ 3 files changed, 79 insertions(+), 37 deletions(-) diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index 59d81b72..8365e652 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -365,8 +365,10 @@ def update_line_search(self): .. note:: This is a bit confusing as it calculates the step length `alpha` for - the NEXT line search step, while working with the `alpha` value that - was calculated from the LAST line search step. + the NEXT line search step, while storing the `alpha` value that + was calculated from the LAST line search step. This is because we + need a corresponding misfit `f_try` from the value of `alpha`, which + happens externally with the solver module If line search returns a passing exit code (0 or 1), sets up for a subsequent line search evaluation by saving a new step length (alpha), @@ -383,20 +385,21 @@ def update_line_search(self): status==how to proceed with line search) """ # Collect information on a forward evaluation that just took place - alpha = self.load_vector("alpha") # step length + alpha_try = self.load_vector("alpha") # step length f_try = self.load_vector("f_try") # misfit for the trial model # Update the line search with a new step length and misfit value self._line_search.step_count += 1 - self._line_search.update_search_history(step_len=alpha, func_val=f_try) + self._line_search.update_search_history(step_len=alpha_try, + func_val=f_try) - # Calculate a new step length based on the step length and corresponding - # misfit that we should have just calculated - alpha_try, status = self._line_search.calculate_step_length() + # Calculate a new step length based on the current step length and its + # corresponding misfit. + alpha, status = self._line_search.calculate_step_length() - # Vectors are saved to disk immediately to avoid passing them in memory - # Status == 0: Retry line search // Status == 1: Line search passed - if status.upper() in ["TRY", "PASS"]: + # Note: if status is 'PASS' then `alpha` represents the step length of + # the lowest misfit in the line search and we reconstruct `m_try` w/ it + if status.upper() in ["PASS", "TRY"]: # Create a new trial model based on search direction, step length # and the initial model vector _m = self.load_vector("m_new") @@ -405,12 +408,10 @@ def update_line_search(self): # Sets the latest trial model using the current `alpha` value m_try = _m.copy() m_try.update(vector=_m.vector + alpha * _p.vector) - logger.info("trial model 'm_try' parameters: ") + logger.info("line search model 'm_try' parameters: ") m_try.check() - - # Newly calculated `alpha` value overwrites original `alpha` - alpha = alpha_try - else: + elif status.upper() == "FAIL": + # Failed line search skips over costly vector manipulations m_try = None return m_try, alpha, status @@ -446,6 +447,9 @@ def finalize_search(self): dst = src.replace("_new.", "_old.") unix.mv(src, dst) + # Reconstruct + x, f, *_ = self._line_search.get_search_history() + logger.info("setting accepted trial model (try) as current model (new)") unix.mv(src=os.path.join(self.path.scratch, "m_try.npz"), dst=os.path.join(self.path.scratch, "m_new.npz")) diff --git a/seisflows/plugins/line_search/bracket.py b/seisflows/plugins/line_search/bracket.py index 12262893..c4fc6f02 100644 --- a/seisflows/plugins/line_search/bracket.py +++ b/seisflows/plugins/line_search/bracket.py @@ -133,7 +133,8 @@ def _print_stats(self, x, f): def calculate_step_length(self): """ Determines step length (alpha) and search status (status) using a - bracketing line search. + bracketing line search. Evaluates Wolfe conditions to determine if + a step length is acceptable. .. note: Available status returns are: @@ -168,7 +169,7 @@ def calculate_step_length(self): elif _check_bracket(x, f) and _good_enough(x, f): alpha = x[f.argmin()] logger.info(f"pass: bracket acceptable and step length " - f"reasonable.") + f"reasonable. returning minimum line search misfit.") status = "PASS" # If misfit is reduced but not close, set to quadratic fit elif _check_bracket(x, f): @@ -209,12 +210,13 @@ def calculate_step_length(self): f"length, alpha_new={alpha:.2E}") status = "TRY" # Stop because safeguard prevents us from going further + # TODO Why is this passing? should status not be carried over? elif alpha > self.step_len_max: alpha = self.step_len_max logger.info(f"try: applying initial step length " f"safegaurd as alpha has exceeded maximum step " f"length, alpha_new={alpha:.2E}") - status = "PASS" # TODO shouldn't this be 0 or -1? + status = "PASS" return alpha, status diff --git a/seisflows/tests/test_optimize.py b/seisflows/tests/test_optimize.py index cc234cb3..17a94826 100644 --- a/seisflows/tests/test_optimize.py +++ b/seisflows/tests/test_optimize.py @@ -5,6 +5,7 @@ import os import pytest import numpy as np +import matplotlib.pyplot as plt from seisflows.tools.config import Dict from seisflows.tools.specfem import Model from seisflows.tools.math import angle @@ -303,7 +304,7 @@ def line_search(_optimize, allow_break=True): assert(m_angle == pytest.approx(0.3, 2)) -def _test_inversion_optimization_problem_general(optimize, iterations=200): +def _test_inversion_optimization_problem_general(optimize, iterations=250): """ Rather than run a single line search evaluation, which all the previous tests have done, we want to run a inversion workflow to find a best fitting @@ -318,10 +319,13 @@ def _test_inversion_optimization_problem_general(optimize, iterations=200): small. However in real workflows, `m_try` must be saved to disk rather than passed in memory because it is likely to be a large vector. + .. note:: + This replaces `workflow.test_optimize` from original code + :type optimize: module :param optimize: specific SeisFlows optimization module to test :type iterations: int - :param iterations: number of iterations to run. defaults to 200 + :param iterations: number of iterations to run. defaults to 250 """ m_init = Model() m_init.model = Dict(x=[np.array([-1.2, 1])]) # Initial guess for Rosenbrock @@ -330,7 +334,8 @@ def _test_inversion_optimization_problem_general(optimize, iterations=200): m_true = m_init.copy() m_true.update(vector=np.array([1., 1.])) # Rosenbrock global minimum - # 200 allowable iterations, but reaching global min. will stop inversion + # N allowable iterations, but reaching global min. will stop inversion + inversion_status = "DNF" # finished for iteration in range(iterations): # Step 1: Evaluate the objective function for given model 'm_new' m_new = optimize.load_vector("m_new") @@ -356,7 +361,7 @@ def _test_inversion_optimization_problem_general(optimize, iterations=200): if status == "PASS": break elif status == "FAIL": - return optimize + return optimize, status else: optimize.save_vector("alpha", alpha) optimize.save_vector("m_try", m_try) @@ -367,23 +372,36 @@ def _test_inversion_optimization_problem_general(optimize, iterations=200): m_diff = np.linalg.norm(m_new.vector - m_true.vector) m_diff /= np.linalg.norm(m_new.vector) if m_diff < 1e-3: + inversion_status = "FIN" # finished break - return optimize + return optimize, inversion_status def test_inversion_optimization_problem_with_gradient( tmpdir, setup_optimization_vectors): """Wrapper function to test the Gradient descent optimization problem""" - gradient = Gradient(path_optimize=tmpdir, path_output=tmpdir) - optimize = _test_inversion_optimization_problem_general(gradient) + gradient = Gradient(path_optimize=tmpdir, path_output=tmpdir, + step_count_max=40) + optimize, status = _test_inversion_optimization_problem_general(gradient) + # Just check a few of the stats file outputs to make sure this runs right assert(os.path.exists(optimize.path._stats_file)) stats = np.genfromtxt(optimize.path._stats_file, delimiter=",", names=True) - assert(len(stats) == 200.) # Fails to reach global minimum - assert(stats["misfit"].min() == pytest.approx(0.2669, 3)) + assert(status == "DNF") # Did Not Finish + assert(len(stats) == 250.) # Fails to reach global minimum + assert(stats["misfit"].min() == pytest.approx(0.1638, 3)) assert(stats["if_restarted"].sum() == 0.) + # Make plot in the tmpdir incase we need to debug the misfit reduction + if True: + plt.plot(stats["misfit"], "go-", markersize=2) + plt.title("Gradient descent misfit; Rosenbrock problem") + plt.xlabel("Iteration") + plt.ylabel("Misfit") + plt.axhline(1e-3, c="k") + plt.savefig(os.path.join(tmpdir, "gradient_misfit.png")) + def test_inversion_optimization_problem_with_LBFGS( # NOQA tmpdir, setup_optimization_vectors): @@ -391,27 +409,45 @@ def test_inversion_optimization_problem_with_LBFGS( # NOQA lbfgs = LBFGS(path_optimize=tmpdir, path_output=tmpdir, line_search_method="backtrack") os.mkdir(lbfgs.path._LBFGS) - optimize = _test_inversion_optimization_problem_general(lbfgs) + optimize, status = _test_inversion_optimization_problem_general(lbfgs) # Just check a few of the stats file outputs to make sure this runs right assert(os.path.exists(optimize.path._stats_file)) stats = np.genfromtxt(optimize.path._stats_file, delimiter=",", names=True) - assert(len(stats) == 95.) # reaches global min. in 95 iterations - assert(stats["misfit"].min() == pytest.approx(1.07e-7, 3)) - assert(stats["if_restarted"].sum() == 0.) + assert(status == "FIN") + assert(len(stats) == 53.) # reaches global min. in 95 iterations + assert(stats["misfit"].min() == pytest.approx(1.043e-7, 3)) + assert(stats["if_restarted"].sum() == 1.) # 1 restart + + if True: + plt.plot(stats["misfit"], "ro-", markersize=2) + plt.title("L-BFGS misfit; Rosenbrock problem") + plt.xlabel("Iteration") + plt.ylabel("Misfit") + plt.axhline(1e-3, c="k") + plt.savefig(os.path.join(tmpdir, "lbfgs_misfit.png")) def test_inversion_optimization_problem_with_NLCG( # NOQA tmpdir, setup_optimization_vectors): # NLCG will need more step counts nlcg = NLCG(path_optimize=tmpdir, path_output=tmpdir, - step_count_max=20) - optimize = _test_inversion_optimization_problem_general(nlcg) + step_count_max=40) + optimize, status = _test_inversion_optimization_problem_general(nlcg) # Just check a few of the stats file outputs to make sure this runs right assert(os.path.exists(optimize.path._stats_file)) stats = np.genfromtxt(optimize.path._stats_file, delimiter=",", names=True) - assert(len(stats) == 200.) # Fails to reach global minimum - assert(stats["misfit"].min()) == pytest.approx(0.1013, 3) - assert(stats["if_restarted"].sum() == 91.) - + assert(status == "FIN") + assert(len(stats) == 46.) # Fails to reach global minimum + assert(stats["misfit"].min()) == pytest.approx(3.693E-5, 3) + assert(stats["if_restarted"].sum() == 1.) + + if True: + plt.plot(stats["misfit"], "bo-", markersize=2) + plt.title("NLCG misfit; Rosenbrock problem") + plt.xlabel("Iteration") + plt.ylabel("Misfit") + plt.axhline(1e-3, c="k") + plt.savefig(os.path.join(tmpdir, "nlcg_misfit.png")) + \ No newline at end of file From c557275a843224224022bb5472c45a1106784f42 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Mon, 1 Aug 2022 14:12:32 -0800 Subject: [PATCH 094/195] small optimize test update --- seisflows/tests/test_optimize.py | 1 - 1 file changed, 1 deletion(-) diff --git a/seisflows/tests/test_optimize.py b/seisflows/tests/test_optimize.py index 17a94826..8524594d 100644 --- a/seisflows/tests/test_optimize.py +++ b/seisflows/tests/test_optimize.py @@ -450,4 +450,3 @@ def test_inversion_optimization_problem_with_NLCG( # NOQA plt.ylabel("Misfit") plt.axhline(1e-3, c="k") plt.savefig(os.path.join(tmpdir, "nlcg_misfit.png")) - \ No newline at end of file From 43078f18a3dba246b7523c620bd7a7d5dd7d21c8 Mon Sep 17 00:00:00 2001 From: bch0w Date: Mon, 1 Aug 2022 14:18:53 -0800 Subject: [PATCH 095/195] fixing some test assertions that were coming up False --- seisflows/tests/test_optimize.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/seisflows/tests/test_optimize.py b/seisflows/tests/test_optimize.py index 8524594d..9f1c7078 100644 --- a/seisflows/tests/test_optimize.py +++ b/seisflows/tests/test_optimize.py @@ -174,7 +174,7 @@ def line_search(): break assert(status == "PASS") # pass - assert(optimize.step_count == 5) # Took 5 steps to reduce misfit + assert(optimize.step_count == 4) # Took 4 steps to reduce misfit # Make sure we have reduced the final misfit assert(min(optimize._line_search.func_vals) == pytest.approx(4.22, 3)) @@ -289,7 +289,7 @@ def line_search(_optimize, allow_break=True): # a successful line search. So if these values are the same then we know # we have successfully restarted a line search assert(status == "PASS") # pass - assert(optimize_restarted.step_count == 5) # Took 5 steps to reduce misfit + assert(optimize_restarted.step_count == 4) # Took 4 steps to reduce misfit # Make sure we have reduced the final misfit assert(min(optimize_restarted._line_search.func_vals) == pytest.approx(4.22, 3)) From 0990b8faf4a9da7a7cc43c7922b05b8326bf9699 Mon Sep 17 00:00:00 2001 From: bch0w Date: Mon, 1 Aug 2022 18:33:14 -0800 Subject: [PATCH 096/195] pyaflowa preprocessing module now working with example data and problem, replaces the 'pyaflowa' class within Pyatoa so that seisflows users are more exposed to what's happening under the hood. Some basic tests so far but need to cover more test cases --- seisflows/preprocess/default.py | 4 + .../preprocess/{pyatoa.py => pyaflowa.py} | 524 ++++++++++-------- seisflows/tests/test_data/test_solver/002 | 1 + seisflows/tests/test_preprocess.py | 37 +- seisflows/workflow/inversion.py | 5 +- 5 files changed, 346 insertions(+), 225 deletions(-) rename seisflows/preprocess/{pyatoa.py => pyaflowa.py} (53%) create mode 120000 seisflows/tests/test_data/test_solver/002 diff --git a/seisflows/preprocess/default.py b/seisflows/preprocess/default.py index 1d34c52c..e0380a5c 100644 --- a/seisflows/preprocess/default.py +++ b/seisflows/preprocess/default.py @@ -391,6 +391,10 @@ def quantify_misfit(self, observed, synthetic, fid = self._rename_as_adjoint_source(fid) self.write(st=adjsrc, fid=os.path.join(save_adjsrcs, fid)) + def finalize(self): + """Teardown procedures for the default preprocessing class""" + pass + @staticmethod def sum_residuals(residuals): """ diff --git a/seisflows/preprocess/pyatoa.py b/seisflows/preprocess/pyaflowa.py similarity index 53% rename from seisflows/preprocess/pyatoa.py rename to seisflows/preprocess/pyaflowa.py index 48ccf006..158fc786 100644 --- a/seisflows/preprocess/pyatoa.py +++ b/seisflows/preprocess/pyaflowa.py @@ -5,15 +5,20 @@ name overlaps with the actual pyatoa package. """ import os +import logging +import time +import random import numpy as np from concurrent.futures import ProcessPoolExecutor +from glob import glob from pyasdf import ASDFDataSet -from pyatoa import Config, Manager, ManagerError +from pyatoa import Config, Manager, Inspector, ManagerError from pyatoa.utils.read import read_station_codes +from pyatoa.utils.images import imgs_to_pdf, merge_pdfs from seisflows import logger from seisflows.tools import unix -from seisflows.tools.config import Dict +from seisflows.tools.config import Null, Dict, get_task_id from seisflows.tools.specfem import check_source_names @@ -71,7 +76,7 @@ class Pyaflowa: :param pyatoa_log_level: Log level to set Pyatoa, Pyflex, Pyadjoint. Available: ['null': no logging, 'warning': warnings only, 'info': task tracking, 'debug': log all small details (recommended)] - :type start_pad_s: int + :type start_pad_s: float :param start_pad_s: seconds BEFORE origin time to gather data. Must be >= T_0 specificed in SPECFEM constants.h. Positive values only :type end_pad_s: int @@ -138,6 +143,7 @@ def __init__(self, min_period=1., max_period=10., filter_corners=4, # Pyatoa-specific internal path structure for storing data etc. self.path["_logs"] = os.path.join(self.path.scratch, "logs") + self.path["_tmplogs"] = os.path.join(self.path._logs, "tmp") self.path["_datasets"] = os.path.join(self.path.scratch, "datasets") self.path["_figures"] = os.path.join(self.path.scratch, "figures") @@ -199,10 +205,12 @@ def setup(self): defined directory structure that will be used to store the outputs of the preprocessing workflow """ - for pathname in ["scratch", "_logs", "_datasets", "_figures"]: + for pathname in ["scratch", "_logs", "_tmplogs", "_datasets", + "_figures"]: unix.mkdir(self.path[pathname]) # Generalized Config object that can be shared among all child processes + # Contains paths to look for data and metadata self._config = Config( min_period=self.min_period, max_period=self.max_period, filter_corners=self.filter_corners, client=self.client, @@ -222,42 +230,50 @@ def setup(self): path_to_stations=os.path.join(self.path.specfem_data, "STATIONS"), loc="*", cha="*" ) - # Get an internal list of source names. Will be the same as solver self._source_names = check_source_names( path_specfem_data=self.path.specfem_data, source_prefix=self._source_prefix, ntask=self._ntask ) - def quantify_misfit(self, source_name, save_residuals=None, + def quantify_misfit(self, source_name=None, save_residuals=None, save_adjsrcs=None, iteration=1, step_count=0, **kwargs): """ - Prepares solver for gradient evaluation by writing residuals and - adjoint traces. Meant to be called by - `workflow.evaluate_objective_function` - - Reads in observed and synthetic waveforms, applies optional - preprocessing, assesses misfit, and writes out adjoint sources and - STATIONS_ADJOINT file. + Prepares solver for gradient evaluation by evaluating data-synthetic + misfit and writing residuals and adjoint traces. Meant to be called by + `workflow.evaluate_objective_function`. .. note:: - Meant to be called by solver.eval_func(), may have unused arguments - to keep functions general across subclasses. + meant to be run on system using system.run() with access to solver + :type source_name: str + :param source_name: name of the event to quantify misfit for. If not + given, will attempt to gather event id from the given task id which + is assigned by system.run() :type save_residuals: str :param save_residuals: if not None, path to write misfit/residuls to :type save_adjsrcs: str :param save_adjsrcs: if not None, path to write adjoint sources to + :type iteration: int + :param iteration: current iteration of the workflow, information should + be provided by `workflow` module if we are running an inversion. + Defaults to 1 if not given (1st iteration) + :type step_count: int + :param step_count: current step count of the line search. Information + should be provided by the `optimize` module if we are running an + inversion. Defaults to 0 if not given (1st evaluation) """ # Set the individual Config class for our given event and evaluation config = self._config.copy() - config.event_id = source_name + config.event_id = source_name or self._source_names[get_task_id()] config.iteration = iteration config.step_count = step_count # Force the Manager to look in the solver directory for data - # Note: we are assuming the SeisFlows solver directory structure here + # note: we are assuming the SeisFlows `solver` directory structure here. + # If we change how the default `solver` directory is named (defined by + # `solver.initialize_solver_directories()`), then this will break config.paths["waveforms"].append( os.path.join(self.path.solver, source_name, "traces", "obs") ) @@ -265,50 +281,109 @@ def quantify_misfit(self, source_name, save_residuals=None, os.path.join(self.path.solver, source_name, "traces", "syn") ) - # Set up the Pyatoa workflow, attempt to gather event metadata - ds = ASDFDataSet( - os.path.join(self.path["_datasets"], f"{source_name}.h5") - ) - mgmt = Manager(config=config) - mgmt.gather(choice=["event"], event_id=source_name, - prefix=f"{self._source_prefix}_") - # Run misfit quantification for each station concurrently misfit, nwin = 0, 0 with ProcessPoolExecutor(max_workers=unix.nproc() - 1) as executor: futures = [ executor.submit( - self._quantify_misfit_station, mgmt, code, save_adjsrcs) + self._quantify_misfit_station, config, code, save_adjsrcs) for code in self._station_codes ] - # Collect misfit values from function as they complete + # We only need to return misfit information. All data/results are + # saved to the ASDFDataSet and status is logged to separate log file for future in futures: _misfit, _nwin = future.result() if _misfit is not None: misfit += _misfit nwin += _nwin + # Calculate misfit based on the raw misfit and total number of windows if save_residuals: + # Calculate the misfit based on the number of windows. Equation from + # Tape et al. (2010). If no windows, misfit is simply raw misfit try: residuals = 0.5 * misfit / nwin except ZeroDivisionError: # Dealing with the case where nwin==0 (signifying either no # windows found, or calc'ing misfit on whole trace) residuals = misfit - np.savetxt(save_residuals, [residuals], fmt="%11.6e") + with open(save_residuals, "a") as f: + f.write(f"{residuals:.2E}\n") + + # Combine all the individual .png files created into a single PDF + if self.plot: + output_fid = os.path.join(self.path._figures, self._ftag(config)) + self._make_event_figure_pdf(source_name, output_fid) + + # Finally, collect all the temporary log files and write a main log file + pyatoa_logger = self._config_pyatoa_logger( + fid=os.path.join(self.path._logs, f"{self._ftag(config)}.log") + ) + pyatoa_logger.info( + f"\n{'=' * 80}\n{'SUMMARY':^80}\n{'=' * 80}\n" + f"SOURCE NAME: {config.event_id}\n" + f"WINDOWS: {nwin}\n" + f"RAW MISFIT: {misfit:.4f}\n" + f"\n{'=' * 80}\n{'RAW LOGS':^80}\n{'=' * 80}" + ) + self._collect_tmp_log_files(pyatoa_logger, config.event_id) - def _quantify_misfit_station(self, mgmt, station_code, save_adjsrcs): + @staticmethod + def _ftag(config): + """ + Create a re-usable file tag from the Config object as multiple functions + will use this tag for file naming and file discovery. + + :type config: pyatoa.core.config.Config + :param config: Configuration object that must contain the 'event_id', + iteration and step count + """ + return f"{config.event_id}_{config.iter_tag}_{config.step_tag}" + + def _quantify_misfit_station(self, config, station_code, + save_adjsrcs=False): """ Run misfit quantification for a single event-station pair. Gathers, preprocesses, windows and measures data, saves adjoint source if requested, and then returns the total misfit and the collected windows for the station. + + :type config: pyatoa.core.config.Config + :param config: Config object that defines all the processing parameters + required by the Pyatoa workflow + :type station_code: str + :param station_code: chosen station to quantify misfit for. Should be + in the format 'NN.SSS.LL.CCC' + :type save_adjsrcs: str + :param save_adjsrcs: path to directory where adjoint sources should be + saved. Filenames will be generated automatically by Pyatoa to fit + the naming schema required by SPECFEM. If False, no adjoint sources + will be saved. They of course can be saved manually later using + Pyatoa + PyASDF """ - _processed = False + # Unique identifier for the given source-receiver pair for file naming + # Something like 001_i01_s00_XX_XYZ net, sta, loc, cha = station_code.split(".") + tag = f"{self._ftag(config)}_{net}_{sta}" + + # Configure a single source-receiver pair logger which will be collected + # later by the main function + log_file = os.path.join(self.path._tmplogs, f"{tag}.log") + station_logger = self._config_pyatoa_logger(fid=log_file) + station_logger.info(f"\n{'/' * 80}\n{station_code:^80}\n{'/' * 80}") + + # Begin data gathering/processing and misfit quantification + mgmt = Manager(config=config) + mgmt.gather(choice=["event"], event_id=config.event_id, + prefix=f"{self._source_prefix}_") + + # Attempt to gather data. If fail, return because theres nothing else + # we can do without data + _processed = False try: mgmt.gather(choice=["inv", "st_obs", "st_syn"], code=station_code) except ManagerError as e: + station_logger.critical(e) return None, None # If any part of the processing fails, move on to plotting because we @@ -316,7 +391,8 @@ def _quantify_misfit_station(self, mgmt, station_code, save_adjsrcs): try: _fix_windows = self._check_fixed_windows( iteration=mgmt.config.iteration, - step_count=mgmt.config.step_count + step_count=mgmt.config.step_count, + logger=station_logger, ) mgmt.standardize() mgmt.preprocess() @@ -324,21 +400,18 @@ def _quantify_misfit_station(self, mgmt, station_code, save_adjsrcs): mgmt.measure() _processed = True except ManagerError as e: + station_logger.warning(e) pass # Plot waveform + map figure. Map may fail if we don't have appropriate # metdata, in which case we fall back to plotting waveform only if self.plot: # e.g., 001_i01_s00_XX_ABC.png - plot_fid = ( - f"{mgmt.config.event_id}_" - f"{mgmt.config.iter_tag}_{mgmt.config.step_tag}_" - f"{net}_{sta}.png" - ) - save = os.path.join(self.path["_figures"], plot_fid) + save = os.path.join(self.path["_figures"], f"{tag}.png") try: mgmt.plot(choice="both", show=False, save=save) - except ManagerError: + except ManagerError as e: + station_logger.warning(e) mgmt.plot(choice="wav", show=False, save=save) # Write out the .adj adjoint source files for solver to discover. @@ -346,9 +419,82 @@ def _quantify_misfit_station(self, mgmt, station_code, save_adjsrcs): if _processed and save_adjsrcs: mgmt.write_adjsrcs(path=save_adjsrcs, write_blanks=True) + # Wait until the very end to write to the HDF5 file, then do it + # pseudo-serially to get around trying to parallel write to HDF5 file + while True: + try: + with ASDFDataSet(os.path.join(self.path["_datasets"], + f"{config.event_id}.h5")) as ds: + mgmt.write(ds=ds) + break + except (BlockingIOError, FileExistsError): + # Random sleep time [0,1]s to decrease chances of two processes + # attempting to access at exactly the same time + time.sleep(random.random()) + return mgmt.stats.misfit, mgmt.stats.nwin - def _check_fixed_windows(self, iteration, step_count): + def sum_residuals(self, residuals): + """ + Return summed residuals devided by number of events following equation + in Tape et al. 2010 + + :type residuals: np.array + :param residuals: list of residuals from each NTASK event + :rtype: float + :return: sum of squares of residuals + """ + assert(len(residuals) == self._ntask), \ + f"recovered an incorrect number of residual values" + summed_residuals = np.sum(residuals) + return summed_residuals / self._ntask + + def finalize(self): + """ + Run some serial finalization tasks specific to Pyatoa, which will help + aggregate the collection of output information. + + .. note:: + This finalize function performs the following tasks: + * Generate .csv files using the Inspector + * Aggregate event-specific PDFs into a single evaluation PDF + * Save scratch/ data into output/ if requested + """ + # Generate the Inspector from existing datasets and save to disk + # Allow this is fail, which might happen if we don't have enough data + # or the Dataset is not formatted as expected + unix.cd(self.path._datasets) + insp = Inspector("inspector", verbose=False) + try: + insp.discover() + insp.save() + except Exception as e: + logger.warning(f"Uncontrolled exception in Pyatoa Inspector " + f"creation -- will not create inspector:\n{e}") + + # Make the final PDF for easier User ingestion of waveform/map figures + self._make_evaluation_composite_pdf() + + # Move scratch/ directory results into more permanent storage + if self.export_datasets: + src = glob(os.path.join(self.path._datasets, "*.h5")) + dst = os.path.join(self.path.output, "datasets", "") + unix.mkdir(dst) + unix.cp(src, dst) + + if self.export_figures: + src = glob(os.path.join(self.path._figures, "*.pdf")) + dst = os.path.join(self.path.output, "figures", "") + unix.mkdir(dst) + unix.cp(src, dst) + + if self.export_log_files: + src = glob(os.path.join(self.path._logs, "*.txt")) + dst = os.path.join(self.path.output, "logs", "") + unix.mkdir(dst) + unix.cp(src, dst) + + def _check_fixed_windows(self, iteration, step_count, logger=Null()): """ Determine how to address re-using misfit windows during an inversion workflow. Throw some log messages out to let the User know whether or @@ -371,6 +517,10 @@ def _check_fixed_windows(self, iteration, step_count): :param step_count: Current line search step count within the SeisFlows3 workflow. Within SeisFlows3 this is defined by `optimize.line_search.step_count` + :type logger: logging.Logger + :param logger: The main logger for a given event, should be + defined by `pyaflowa.quantify_misfit()`. If not provided, logs will + get sent to DevNull :rtype: bool :return: bool on whether to use windows from the previous step """ @@ -404,182 +554,120 @@ def _check_fixed_windows(self, iteration, step_count): return fix_windows - # - # def finalize(self): - # """ - # Run some serial finalization tasks specific to Pyatoa, which will help - # aggregate the collection of output information. - # - # .. note:: - # This finalize function performs the following tasks: - # * Generate .csv files using the Inspector - # * Aggregate event-specific PDFs into a single evaluation PDF - # * Save scratch/ data into output/ if requested - # """ - # # Initiate Pyaflowa to get access to path structure - # pyaflowa = Pyaflowa(sfpar=self.par, sfpath=self.path) - # unix.cd(pyaflowa.paths.datasets) - # - # # Generate the Inspector from existing datasets and save to disk - # # Allow this is fail, which might happen if we don't have enough data - # # or the Dataset is not formatted as expected - # insp = Inspector(self.par.TITLE, verbose=False) # !!! TODO - # try: - # insp.discover() - # insp.save() - # except Exception as e: - # logger.warning(f"Uncontrolled exception in Inspector creation " - # f"will not create inspector:\n{e}") - # - # # Make the final PDF for easier User ingestion of waveform/map figures - # pyaflowa.make_evaluation_composite_pdf() - # - # # Move scratch/ directory results into more permanent storage - # if self.export_datasets: - # datasets = glob(os.path.join(pyaflowa.paths.datasets, "*.h5")) - # self._save_quantity(datasets, tag="datasets") - # - # if self.export_figures: - # figures = glob(os.path.join(pyaflowa.paths.figures, "*.pdf")) - # self._save_quantity(figures, tag="figures") - # - # if self.export_log_files: - # logs = glob(os.path.join(pyaflowa.paths.logs, "*.txt")) - # path_out = os.path.join(self.path_output, CFGPATHS.LOGDIR) - # self._save_quantity(logs, path_out=path_out) - # - # def prepare_eval_grad(self, cwd, taskid, source_name, **kwargs): - # """ - # Prepare the gradient evaluation by gathering, preprocessing waveforms, - # and measuring misfit between observations and synthetics using Pyatoa. - # - # Reads in observed and synthetic waveforms, applies optional - # preprocessing, assesses misfit, and writes out adjoint sources and - # STATIONS_ADJOINT file. - # - # .. note:: - # Meant to be called by solver.eval_func(), may have unused arguments - # to keep functions general across preprocessing subclasses. - # - # :type cwd: str - # :param cwd: current specfem working directory containing observed and - # synthetic seismic data to be read and processed. Should be defined - # by solver.cwd - # :type source_name: str - # :param source_name: the event id to be used for tagging and data lookup. - # Should be defined by solver.source_name - # :type taskid: int - # :param taskid: identifier of the currently running solver instance. - # Should be defined by solver.taskid - # :type filenames: list of str - # :param filenames: [not used] list of filenames defining the files in - # traces - # """ - # if taskid == 0: - # logger.debug("preparing files for gradient evaluation with " - # "Pyaflowa") - # - # # Process all the stations for a given event using Pyaflowa - # pyaflowa = self._setup_event_pyaflowa(source_name) - # scaled_misfit = pyaflowa.process(nproc=self.nproc) - # - # if scaled_misfit is None: - # print(msg.cli(f"Event {source_name} returned no misfit, you may " - # f"want to check logs and waveform figures, " - # f"or consider discarding this event from your " - # f"workflow", - # items=[pyaflowa.paths.logs, pyaflowa.paths.figures], - # header="pyatoa preprocessing error", border="=")) - # sys.exit(-1) - # - # # Event misfit defined by Tape et al. (2010) written to solver dir. - # self._write_residuals(path=cwd, scaled_misfit=scaled_misfit) - # - # def _setup_event_pyaflowa(self, source_name, iteration, step_count=""): - # """ - # A convenience function to set up a Pyaflowa processing instance for - # a specific event. - # - # .. note:: - # This is meant to be called by preprocess.prepare_eval_grad() but its - # also useful for debugging and manual processing where you can simply - # return a formatted Pyaflowa object and debug it directly. - # - # :type source_name: str - # :param source_name: solver source name to evaluate setup for. Must - # match from list defined by: solver.source_names - # """ - # # Outsource data processing to an event-specfic Pyaflowa instance - # pyaflowa = Pyaflowa(sfpar=self.par, sfpath=self.path) - # pyaflowa.setup(source_name=source_name, iteration=iteration, - # step_count=step_count, loc="*", cha="*") - # - # return pyaflowa - # - # def _save_quantity(self, filepaths, tag="", path_out=""): - # """ - # Repeatable convenience function to save quantities from the scratch/ - # directory to the output/ directory - # - # :type filepaths: list - # :param filepaths: full path to files that should be saved to output/ - # :type tag: str - # :param tag: tag for saving the files in self.path.OUTPUT. If not given, will - # save directly into the output/ directory - # :type path_out: str - # :param path_out: overwrite the default output path file naming - # """ - # if not path_out: - # path_out = os.path.join(self.path_output, tag) - # - # if not os.path.exists(path_out): - # unix.mkdir(path_out) - # - # for src in filepaths: - # dst = os.path.join(path_out, os.path.basename(src)) - # unix.cp(src, dst) - # - # @staticmethod - # def _write_residuals(path, scaled_misfit): - # """ - # Computes residuals and saves them to a text file in the appropriate path - # - # :type path: str - # :param path: scratch directory path, e.g. self.path.GRAD or self.path.FUNC - # :type scaled_misfit: float - # :param scaled_misfit: the summation of misfit from each - # source-receiver pair calculated by prepare_eval_grad() - # :type source_name: str - # :param source_name: name of the source related to the misfit, used - # for file naming - # """ - # residuals_file = os.path.join(path, "residuals") - # np.savetxt(residuals_file, [scaled_misfit], fmt="%11.6e") - # - # def sum_residuals(self, files): - # """ - # Averages the event misfits and returns the total misfit. - # Total misfit defined by Tape et al. (2010) - # - # :type files: str - # :param files: list of single-column text files containing residuals - # that will have been generated using prepare_eval_grad() - # :rtype: float - # :return: average misfit - # """ - # if len(files) != self.ntask: - # print(msg.cli(f"Pyatoa preprocessing module did not recover the " - # f"correct number of residual files " - # f"({len(files)}/{self.ntask}). Please check that " - # f"the preprocessing logs", header="error") - # ) - # sys.exit(-1) - # - # total_misfit = 0 - # for filename in files: - # total_misfit += np.sum(np.loadtxt(filename)) - # - # total_misfit /= self.ntask - # - # return total_misfit - # + def _config_pyatoa_logger(self, fid): + """ + Create a log file to track processing of a given source-receiver pair. + Because each station is processed asynchronously, we don't want them to + log to the main file at the same time, otherwise we get a random mixing + of log messages. Instead we have them log to temporary files, which + are combined at the end of the processing script in serial. + + :type fid: str + :param fid: full path and filename for logger that will be configured + :rtype: logging.Logger + :return: a logger which does NOT log to stdout and only logs to + the given file defined by `fid` + """ + handler = logging.FileHandler(fid, mode="w") + logfmt = "[%(asctime)s] - %(name)s - %(levelname)s: %(message)s" + formatter = logging.Formatter(logfmt, datefmt="%Y-%m-%d %H:%M:%S") + handler.setFormatter(formatter) + for log in ["pyflex", "pyadjoint", "pyatoa"]: + # Set the overall log level + logger = logging.getLogger(log) + # Turn off any existing handlers (stream and file) + while logger.hasHandlers(): + logger.removeHandler(logger.handlers[0]) + # Log to new temporary file + logger.setLevel(self.pyatoa_log_level) + logger.addHandler(handler) + + return logger + + def _collect_tmp_log_files(self, pyatoa_logger, event_id): + """ + Each source-receiver pair has made its own log file. This function + collects these files and writes their content back into the main log. + This is a lot of IO but should be okay since the files are small. + + .. note:: + This was the most foolproof method for having multiple parallel + processes write to the same file. I played around with StringIO + buffers and file locks, but they became overly complicated and + ultimately did not work how I wanted them to. This function trades + filecount and IO overhead for simplicity. + + .. warning:: + The assumption here is that the number of source-receiver pairs + is manageable (in the thousands). If we start reaching file count + limits on the cluster then this method for logging may have to be + re-thought. See link for example: + https://stackless.readthedocs.io/en/3.7-slp/howto/ + logging-cookbook.html#using-concurrent-futures-processpoolexecutor + + :type pyatoa_logger: logging.Logger + :param pyatoa_logger: The main logger for a given event, should be + defined by `pyaflowa.quantify_misfit()` + :type event_id: str + :param event_id: given event id that we are concerned with. Used to + search for matching log files in the temporary log file directory + """ + tmp_logs = sorted(glob(os.path.join(self.path._tmplogs, + f"*{event_id}_*.log"))) + with open(pyatoa_logger.handlers[0].baseFilename, "a") as fw: + for tmp_log in tmp_logs: + with open(tmp_log, "r") as fr: + fw.writelines(fr.readlines()) + unix.rm(tmp_log) # delete after writing + + def _make_event_figure_pdf(self, source_name, output_fid): + """ + Combine a list of single source-receiver PNGs into a single PDF file + for the given event. Mostly a convenience function to make it easier + to ingest waveform figures during a workflow. + + """ + # Sorrted by network and station name + input_fids = sorted(glob(os.path.join(self.path._figures, + f"{source_name}*.png"))) + if not input_fids: + logger.warning(f"Pyatoa found no event figures for {source_name} " + f"to combine") + return + + # Assuming the file naming format defined by `_quantify_misfit_station` + source_name, iteration, step_count, *_ = input_fids[0].split("_") + + # e.g. i01s00_2018p130600.pdf + output_fid = (f"{source_name}_{iteration}_{step_count}.pdf") + + # Merge all output pdfs into a single pdf, delete originals + save = os.path.join(self.path._figures, output_fid) + imgs_to_pdf(fids=sorted(input_fids), fid_out=save) + for fid in input_fids: + os.remove(fid) + + def _make_evaluation_composite_pdf(self): + """ + Utility function to combine all PDFs generated by + make_event_figure_pdf() into a single PDF tagged by the current + evaluation (iteration, step count). This is meant to make it easier + for the User to scroll through figures. Option to delete the original + event-specific PDFs which are now redundant + .. note:: + This can be run without running Pyaflowa.format() + :type delete_originals: bool + :param delete_originals: delete original pdf files after mergin + """ + event_figures = glob(os.path.join(self.path._figures, "*", "*.pdf")) + if not event_figures: + logger.warning("Pyatoa could not find event PDFs to merge") + return + # Collecting evaluation tags, e.g., ['i01s00', 'i01s01'] + tags = set([os.path.basename(_).split("_")[0] for _ in event_figures]) + for tag in tags: + fids = [fid for fid in event_figures if tag in fid] + fid_out = os.path.join(self.path._figures, f"{tag}.pdf") + merge_pdfs(fids=sorted(fids), fid_out=fid_out) + for fid in fids: + os.remove(fid) diff --git a/seisflows/tests/test_data/test_solver/002 b/seisflows/tests/test_data/test_solver/002 new file mode 120000 index 00000000..cbb9ee10 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/002 @@ -0,0 +1 @@ +mainsolver/ \ No newline at end of file diff --git a/seisflows/tests/test_preprocess.py b/seisflows/tests/test_preprocess.py index 614f1906..09ba5f66 100644 --- a/seisflows/tests/test_preprocess.py +++ b/seisflows/tests/test_preprocess.py @@ -4,7 +4,9 @@ """ import os import pytest +import numpy as np from glob import glob +from pyasdf import ASDFDataSet from seisflows import ROOT_DIR from seisflows.preprocess.default import Default from seisflows.preprocess.pyatoa import Pyaflowa @@ -108,6 +110,7 @@ def test_pyaflowa_setup(tmpdir): path_solver=os.path.join(TEST_SOLVER, "mainsolver"), source_prefix="CMTSOLUTION", ntask=2, + components="Y", ) assert(pyaflowa._station_codes == []) @@ -119,20 +122,44 @@ def test_pyaflowa_setup(tmpdir): assert(pyaflowa._station_codes[0] == "AA.S0001.*.*") assert(len(pyaflowa._source_names) == pyaflowa._ntask) assert(pyaflowa._source_names[0] == "001") + assert(pyaflowa._config.component_list == ["Y"]) def test_pyaflowa_quantify_misfit(tmpdir): """ - Test misfit quantification for Pyatoa including data gathering + Test misfit quantification for Pyatoa including data gathering. Waveform + data and source and receiver metadata is exposed from the test data + directory. Data and synthetics are the same so residuals will be 0. Want + to check that we can process in parallel and that Pyatoa outputs figures, + and data """ pyaflowa = Pyaflowa( workdir=tmpdir, path_specfem_data=os.path.join(TEST_SOLVER, "mainsolver", "DATA"), - path_solver=TEST_SOLVER, source_prefix="CMTSOLUTION", ntask=1, + path_solver=TEST_SOLVER, source_prefix="CMTSOLUTION", ntask=2, data_case="synthetic", components="Y", ) pyaflowa.setup() save_residuals = os.path.join(tmpdir, "residuals.txt") - pyaflowa.quantify_misfit(source_name=pyaflowa._source_names[0], - save_residuals=save_residuals) - pytest.set_trace() + for source_name in pyaflowa._source_names: + save_residuals = os.path.join(tmpdir, f"residuals_{source_name}.txt") + pyaflowa.quantify_misfit(source_name=source_name, + save_residuals=save_residuals, + save_adjsrcs=tmpdir) + + residuals = np.loadtxt(save_residuals) # just check one of the file + assert(residuals == 0.) # data and synthetics are the same + + # Check that windows and adjoint sources were saved to dataset + for source_name in pyaflowa._source_names: + with ASDFDataSet(os.path.join(pyaflowa.path._datasets, + f"{source_name}.h5")) as ds: + # Pyatoa selects 18 windows for 2 events and 2 stations + assert(len(ds.auxiliary_data.MisfitWindows.i01.s00.list()) == 18) + assert(len(ds.auxiliary_data.AdjointSources.i01.s00.list()) == 2) + + # Check that adjoint sources are all zero + adjsrcs = glob(os.path.join(tmpdir, "*.adj")) + for adjsrc in adjsrcs: + data = np.loadtxt(adjsrc) + assert(not data[:,1].any()) # assert all zeros diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index 95a207f3..897c435d 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -204,8 +204,7 @@ def evaluate_objective_function(self, save_residuals=False, **kwargs): f"'{self.preprocess.__class__.__name__}'") self.preprocess.quantify_misfit( - observed=self.solver.data_filenames(choice="obs"), - synthetic=self.solver.data_filenames(choice="syn"), + source_name=self.solver.source_name, save_adjsrcs=os.path.join(self.solver.cwd, "traces", "adj"), save_residuals=save_residuals, iteration=self.iteration, @@ -414,6 +413,8 @@ def finalize_iteration(self): unix.mkdir(self.path.eval_grad) unix.mkdir(self.path.eval_func) + self.preprocess.finalize() + def _update_thrifty_status(self): """ Determine if line search forward simulation can be carried over to the From 021abacb0fd8942d8f3fb0bae43c11aa8af36f16 Mon Sep 17 00:00:00 2001 From: bch0w Date: Mon, 1 Aug 2022 20:56:06 -0800 Subject: [PATCH 097/195] fixed a small nuisance where user had to input T0 for Pyatoa themselves, and if not properly set, this would cause the adjoint sources to be incorrect length. As a replacement, Pyatoa now just reads T0 and length of seismogram from a synthetic before quantifying misfit. --- seisflows/preprocess/pyaflowa.py | 106 ++++++++++++++++++++++--------- seisflows/workflow/forward.py | 2 +- seisflows/workflow/inversion.py | 7 +- 3 files changed, 82 insertions(+), 33 deletions(-) diff --git a/seisflows/preprocess/pyaflowa.py b/seisflows/preprocess/pyaflowa.py index 158fc786..1eecbdfb 100644 --- a/seisflows/preprocess/pyaflowa.py +++ b/seisflows/preprocess/pyaflowa.py @@ -13,7 +13,7 @@ from glob import glob from pyasdf import ASDFDataSet from pyatoa import Config, Manager, Inspector, ManagerError -from pyatoa.utils.read import read_station_codes +from pyatoa.utils.read import read_station_codes, read_sem from pyatoa.utils.images import imgs_to_pdf, merge_pdfs from seisflows import logger @@ -76,11 +76,11 @@ class Pyaflowa: :param pyatoa_log_level: Log level to set Pyatoa, Pyflex, Pyadjoint. Available: ['null': no logging, 'warning': warnings only, 'info': task tracking, 'debug': log all small details (recommended)] - :type start_pad_s: float - :param start_pad_s: seconds BEFORE origin time to gather data. Must be + :type start_pad: float + :param start_pad: seconds BEFORE origin time to gather data. Must be >= T_0 specificed in SPECFEM constants.h. Positive values only - :type end_pad_s: int - :param end_pad_s: seconds AFTER origin time to gather data. Must be + :type end_pad: int + :param end_pad: seconds AFTER origin time to gather data. Must be >= NT * DT (from SPECFEM Par_file) postive values only. :type unit_output: str :param unit_output: Data units. Must match the synthetic output of @@ -104,8 +104,8 @@ class Pyaflowa: def __init__(self, min_period=1., max_period=10., filter_corners=4, client=None, rotate=False, pyflex_preset="default", fix_windows=False, adj_src_type="cc", plot=True, - pyatoa_log_level="DEBUG", unit_output="VEL", start_pad_s=0., - end_pad_s=None, workdir=os.getcwd(), path_preprocess=None, + pyatoa_log_level="DEBUG", unit_output="VEL", start_pad=None, + end_pad=None, workdir=os.getcwd(), path_preprocess=None, path_solver=None, path_specfem_data=None, path_data=None, path_output=None, export_datasets=True, export_figures=True, export_log_files=True, data_format="ascii", @@ -124,8 +124,8 @@ def __init__(self, min_period=1., max_period=10., filter_corners=4, self.plot = plot self.pyatoa_log_level = pyatoa_log_level self.unit_output = unit_output - self.start_pad_s = start_pad_s - self.end_pad_s = end_pad_s + self.start_pad = start_pad + self.end_pad = end_pad self.path = Dict( scratch=path_preprocess or os.path.join(workdir, "scratch", @@ -180,6 +180,8 @@ def check(self): """ Checks Parameter and Path files, will be run at the start of a Seisflows workflow to ensure that things are set appropriately. + + TODO some type of check for the time offset value """ assert(self._data_format.upper() == "ASCII"), \ "Pyatoa preprocess requires `data_format`=='ASCII'" @@ -199,6 +201,21 @@ def check(self): f"{self._acceptable_source_prefixes}, not '{self._source_prefix}'" ) + if self.start_pad is None: + logger.warning("Pyatoa `start_pad` is not set, setting to T0=0. " + "This value should be set to the T0 value of " + "SPECFEM, otherwise its output adjoint sources will " + "be incorrect length.") + self.start_pad = 0. + + if self.end_pad is None: + logger.warning("Pyatoa `end_pad` is not set, setting to T=0. " + "This value should be set to the total length of " + "your synthetic seismograms (i.e., DT * NT) from " + "SPECFEM, IFF you want Pyatoa to gather observed " + "data from disk/webservice.") + self.end_pad = 0. + def setup(self): """ Sets up data preprocessing machinery by establishing an internally @@ -209,7 +226,7 @@ def setup(self): "_figures"]: unix.mkdir(self.path[pathname]) - # Generalized Config object that can be shared among all child processes + # Convert SeisFlows user parameters into Pyatoa config parameters # Contains paths to look for data and metadata self._config = Config( min_period=self.min_period, max_period=self.max_period, @@ -217,7 +234,6 @@ def setup(self): rotate=self.rotate, pyflex_preset=self.pyflex_preset, fix_windows=self.fix_windows, adj_src_type=self.adj_src_type, log_level=self.pyatoa_log_level, unit_output=self.unit_output, - start_pad_s=self.start_pad_s, end_pad_s=self.end_pad_s, component_list=list(self._components), synthetics_only=bool(self._data_case == "synthetic"), paths={"waveforms": self.path["_waveforms"] or [], @@ -236,6 +252,55 @@ def setup(self): source_prefix=self._source_prefix, ntask=self._ntask ) + def _setup_quantify_misfit(self, source_name, iteration, step_count): + """ + Create an event-specific Config object which contains information about + the current event, and position in the workflow evaluation. Also + provides specific information on event paths and timing to be used by + the Manager + + :type source_name: str + :param source_name: name of the event to quantify misfit for. If not + given, will attempt to gather event id from the given task id which + is assigned by system.run() + :type iteration: int + :param iteration: current iteration of the workflow, information should + be provided by `workflow` module if we are running an inversion. + Defaults to 1 if not given (1st iteration) + :type step_count: int + :param step_count: current step count of the line search. Information + should be provided by the `optimize` module if we are running an + inversion. Defaults to 0 if not given (1st evaluation) + :rtype: pyatoa.core.config.Config + :return: Config object that is specifically crafted for a given event + that can be directly fed to the Manager for misfit quantification + """ + config = self._config.copy() + config.event_id = source_name or self._source_names[get_task_id()] + config.iteration = iteration + config.step_count = step_count + + # Force the Manager to look in the solver directory for data + # note: we are assuming the SeisFlows `solver` directory structure here. + # If we change how the default `solver` directory is named (defined by + # `solver.initialize_solver_directories()`), then this will break + obs_path = os.path.join(self.path.solver, source_name, "traces", "obs") + config.paths["waveforms"].append(obs_path) + + syn_path = os.path.join(self.path.solver, source_name, "traces", "syn") + config.paths["synthetics"].append(syn_path) + + # Extract start and end times from one of the synthetic traces so that + # Pyatoa knows how long seismograms are and when to start them + # NOTE: assuming all synthetic time axes are the same for this source + synthetics = glob(os.path.join(syn_path, "*")) + assert(synthetics), f"Pyatoa found no synthetics in: {syn_path}" + tr_syn = read_sem(synthetics[0])[0] + config.start_pad = abs(tr_syn.stats.time_offset) # needs to be positive + config.end_pad = tr_syn.stats.endtime - tr_syn.stats.starttime + + return config + def quantify_misfit(self, source_name=None, save_residuals=None, save_adjsrcs=None, iteration=1, step_count=0, **kwargs): @@ -264,22 +329,7 @@ def quantify_misfit(self, source_name=None, save_residuals=None, should be provided by the `optimize` module if we are running an inversion. Defaults to 0 if not given (1st evaluation) """ - # Set the individual Config class for our given event and evaluation - config = self._config.copy() - config.event_id = source_name or self._source_names[get_task_id()] - config.iteration = iteration - config.step_count = step_count - - # Force the Manager to look in the solver directory for data - # note: we are assuming the SeisFlows `solver` directory structure here. - # If we change how the default `solver` directory is named (defined by - # `solver.initialize_solver_directories()`), then this will break - config.paths["waveforms"].append( - os.path.join(self.path.solver, source_name, "traces", "obs") - ) - config.paths["synthetics"].append( - os.path.join(self.path.solver, source_name, "traces", "syn") - ) + config = self._setup_quantify_misfit(source_name, iteration, step_count) # Run misfit quantification for each station concurrently misfit, nwin = 0, 0 @@ -444,8 +494,6 @@ def sum_residuals(self, residuals): :rtype: float :return: sum of squares of residuals """ - assert(len(residuals) == self._ntask), \ - f"recovered an incorrect number of residual values" summed_residuals = np.sum(residuals) return summed_residuals / self._ntask diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index 1d4228e5..bee3527e 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -299,7 +299,7 @@ def evaluate_initial_misfit(self): self.run_forward_simulations, self.evaluate_objective_function], path_model=self.path.model_init, - save_residuals=os.path.join(self.path.eval_grad, "residuals") + save_residuals=os.path.join(self.path.eval_grad, "residuals.txt") ) def prepare_data_for_solver(self, **kwargs): diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index 897c435d..6072847c 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -144,7 +144,7 @@ def check(self): f"scratch path `eval_grad` does not exist but should for a " \ f"workflow with `iteration` >= 1" - if self.iteration >= self.end: + if self.iteration >= self.end + 1: logger.warning(f"current `iteration` is >= chosen `end` point. " f"Inversion workflow will not `run`") @@ -173,7 +173,7 @@ def setup(self): def run(self): """Call the forward.run() function iteratively, from `start` to `end`""" - while self.iteration < self.end: + while self.iteration < self.end + 1: logger.info(msg.mnr(f"RUNNING ITERATION {self.iteration:0>2}")) super().run() # Runs task list logger.info(msg.mnr(f"COMPLETED ITERATION {self.iteration:0>2}")) @@ -244,7 +244,8 @@ def evaluate_initial_misfit(self): ) # Override function to sum residuals into the optimization library - residuals = np.loadtxt(os.path.join(self.path.eval_grad, "residuals")) + residuals = np.loadtxt(os.path.join(self.path.eval_grad, + "residuals.txt")) total_misfit = self.preprocess.sum_residuals(residuals) self.optimize.save_vector(name="f_new", m=total_misfit) From 3f4870627034a52076d0378796426fc6e22405e0 Mon Sep 17 00:00:00 2001 From: bch0w Date: Mon, 1 Aug 2022 23:35:48 -0800 Subject: [PATCH 098/195] small typo fix pyaflowa --- seisflows/preprocess/pyaflowa.py | 41 ++++++++++++++++---------------- 1 file changed, 20 insertions(+), 21 deletions(-) diff --git a/seisflows/preprocess/pyaflowa.py b/seisflows/preprocess/pyaflowa.py index 1eecbdfb..6905a3d1 100644 --- a/seisflows/preprocess/pyaflowa.py +++ b/seisflows/preprocess/pyaflowa.py @@ -104,8 +104,9 @@ class Pyaflowa: def __init__(self, min_period=1., max_period=10., filter_corners=4, client=None, rotate=False, pyflex_preset="default", fix_windows=False, adj_src_type="cc", plot=True, - pyatoa_log_level="DEBUG", unit_output="VEL", start_pad=None, - end_pad=None, workdir=os.getcwd(), path_preprocess=None, + pyatoa_log_level="DEBUG", unit_output="VEL", + # start_pad=None, end_pad=None, + workdir=os.getcwd(), path_preprocess=None, path_solver=None, path_specfem_data=None, path_data=None, path_output=None, export_datasets=True, export_figures=True, export_log_files=True, data_format="ascii", @@ -124,8 +125,8 @@ def __init__(self, min_period=1., max_period=10., filter_corners=4, self.plot = plot self.pyatoa_log_level = pyatoa_log_level self.unit_output = unit_output - self.start_pad = start_pad - self.end_pad = end_pad + # self.start_pad = start_pad + # self.end_pad = end_pad self.path = Dict( scratch=path_preprocess or os.path.join(workdir, "scratch", @@ -180,8 +181,6 @@ def check(self): """ Checks Parameter and Path files, will be run at the start of a Seisflows workflow to ensure that things are set appropriately. - - TODO some type of check for the time offset value """ assert(self._data_format.upper() == "ASCII"), \ "Pyatoa preprocess requires `data_format`=='ASCII'" @@ -200,21 +199,21 @@ def check(self): f"Pyatoa can only accept `source_prefix` in " f"{self._acceptable_source_prefixes}, not '{self._source_prefix}'" ) - - if self.start_pad is None: - logger.warning("Pyatoa `start_pad` is not set, setting to T0=0. " - "This value should be set to the T0 value of " - "SPECFEM, otherwise its output adjoint sources will " - "be incorrect length.") - self.start_pad = 0. - - if self.end_pad is None: - logger.warning("Pyatoa `end_pad` is not set, setting to T=0. " - "This value should be set to the total length of " - "your synthetic seismograms (i.e., DT * NT) from " - "SPECFEM, IFF you want Pyatoa to gather observed " - "data from disk/webservice.") - self.end_pad = 0. + # + # if self.start_pad is None: + # logger.warning("Pyatoa `start_pad` is not set, setting to T0=0. " + # "This value should be set to the T0 value of " + # "SPECFEM, otherwise its output adjoint sources will " + # "be incorrect length.") + # self.start_pad = 0. + # + # if self.end_pad is None: + # logger.warning("Pyatoa `end_pad` is not set, setting to T=0. " + # "This value should be set to the total length of " + # "your synthetic seismograms (i.e., DT * NT) from " + # "SPECFEM, IFF you want Pyatoa to gather observed " + # "data from disk/webservice.") + # self.end_pad = 0. def setup(self): """ From ad4c7c2ba5cf84bd603b0bd8d4c5537508550203 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Tue, 2 Aug 2022 17:01:25 -0800 Subject: [PATCH 099/195] new test data for testing Pyaflowa preprocessing workflow, matches Tape2007 specfem2d example bug: pyaflowa and pytest hangs computer, probably because the parallel processes are memory intensive? trying to optimize --- seisflows/preprocess/pyaflowa.py | 318 +- .../scripts/make_parameter_files.sh | 0 .../scripts/make_test_directory_structure.py | 0 .../specfem/DATA/CMTSOLUTION_c46e1d99 | 0 .../specfem/DATA/FORCESOLUTION_c46e1d99 | 0 .../specfem/DATA/Par_file_SPECFEM2D_cf893667 | 0 .../specfem/DATA/Par_file_SPECFEM3D_c46e1d99 | 0 .../{ => hold}/specfem/DATA/SOURCE_cf893667 | 0 .../specfem/OUTPUT_FILES/AA.S0001.BXY.semd | 0 .../specfem/OUTPUT_FILES/Uy_file_single_d.su | Bin .../{ => hold}/test_conf_parameters.yaml | 0 .../{ => hold}/test_filled_parameters.yaml | 0 .../{ => hold}/test_setup_parameters.yaml | 0 .../001}/DATA/CMTSOLUTION_001 | 0 .../001}/DATA/CMTSOLUTION_002 | 0 .../001}/DATA/CMTSOLUTION_003 | 0 .../001}/DATA/CMTSOLUTION_004 | 0 .../001}/DATA/CMTSOLUTION_005 | 0 .../001}/DATA/CMTSOLUTION_006 | 0 .../001}/DATA/Par_file | 0 .../001}/DATA/STATIONS | 0 .../001}/bin/xcombine_sem | 0 .../001}/bin/xmeshfem2D | 0 .../001}/bin/xsmooth_sem | 0 .../001}/bin/xspecfem2D | 0 .../001}/traces/obs/AA.S0001.BXY.semd | 0 .../001}/traces/obs/AA.S0002.BXY.semd | 0 .../001}/traces/syn/AA.S0001.BXY.semd | 0 .../001}/traces/syn/AA.S0002.BXY.semd | 0 .../old_test_solver/002/DATA/CMTSOLUTION_001 | 13 + .../old_test_solver/002/DATA/CMTSOLUTION_002 | 13 + .../old_test_solver/002/DATA/CMTSOLUTION_003 | 13 + .../old_test_solver/002/DATA/CMTSOLUTION_004 | 13 + .../old_test_solver/002/DATA/CMTSOLUTION_005 | 13 + .../old_test_solver/002/DATA/CMTSOLUTION_006 | 13 + .../old_test_solver/002/DATA/Par_file | 1 + .../old_test_solver/002/DATA/STATIONS | 2 + .../old_test_solver/002/bin/xcombine_sem | 3 + .../old_test_solver/002/bin/xmeshfem2D | 2 + .../old_test_solver/002/bin/xsmooth_sem | 3 + .../old_test_solver/002/bin/xspecfem2D | 3 + .../002/traces/obs/AA.S0001.BXY.semd | 5000 +++++++++++++++++ .../002/traces/obs/AA.S0002.BXY.semd | 5000 +++++++++++++++++ .../002/traces/syn/AA.S0001.BXY.semd | 5000 +++++++++++++++++ .../002/traces/syn/AA.S0002.BXY.semd | 5000 +++++++++++++++++ .../old_test_solver/sources/CMTSOLUTION_001 | 1 + .../old_test_solver/sources/CMTSOLUTION_002 | 1 + .../old_test_solver/sources/SOURCE_001 | 1 + .../old_test_solver/sources/SOURCE_002 | 1 + seisflows/tests/test_data/test_solver/001 | 1 - .../test_data/test_solver/001/DATA/Par_file | 1 + .../test_data/test_solver/001/DATA/SOURCE | 1 + .../test_data/test_solver/001/DATA/SOURCE_001 | 57 + .../test_data/test_solver/001/DATA/SOURCE_002 | 57 + .../test_data/test_solver/001/DATA/SOURCE_003 | 57 + .../test_data/test_solver/001/DATA/SOURCE_004 | 57 + .../test_data/test_solver/001/DATA/SOURCE_005 | 57 + .../test_data/test_solver/001/DATA/SOURCE_006 | 57 + .../test_data/test_solver/001/DATA/SOURCE_007 | 57 + .../test_data/test_solver/001/DATA/SOURCE_008 | 57 + .../test_data/test_solver/001/DATA/SOURCE_009 | 57 + .../test_data/test_solver/001/DATA/SOURCE_010 | 57 + .../test_data/test_solver/001/DATA/SOURCE_011 | 57 + .../test_data/test_solver/001/DATA/SOURCE_012 | 57 + .../test_data/test_solver/001/DATA/SOURCE_013 | 57 + .../test_data/test_solver/001/DATA/SOURCE_014 | 57 + .../test_data/test_solver/001/DATA/SOURCE_015 | 57 + .../test_data/test_solver/001/DATA/SOURCE_016 | 57 + .../test_data/test_solver/001/DATA/SOURCE_017 | 57 + .../test_data/test_solver/001/DATA/SOURCE_018 | 57 + .../test_data/test_solver/001/DATA/SOURCE_019 | 57 + .../test_data/test_solver/001/DATA/SOURCE_020 | 57 + .../test_data/test_solver/001/DATA/SOURCE_021 | 57 + .../test_data/test_solver/001/DATA/SOURCE_022 | 57 + .../test_data/test_solver/001/DATA/SOURCE_023 | 57 + .../test_data/test_solver/001/DATA/SOURCE_024 | 57 + .../test_data/test_solver/001/DATA/SOURCE_025 | 57 + .../test_data/test_solver/001/DATA/STATIONS | 5 + .../test_solver/001/bin/xcombine_sem | 3 + .../test_data/test_solver/001/bin/xmeshfem2D | 2 + .../test_data/test_solver/001/bin/xsmooth_sem | 3 + .../test_data/test_solver/001/bin/xspecfem2D | 3 + .../001/traces/obs/AA.S000000.BXY.semd | 5000 +++++++++++++++++ .../001/traces/obs/AA.S000001.BXY.semd | 5000 +++++++++++++++++ .../001/traces/obs/AA.S000002.BXY.semd | 5000 +++++++++++++++++ .../001/traces/obs/AA.S000003.BXY.semd | 5000 +++++++++++++++++ .../001/traces/obs/AA.S000004.BXY.semd | 5000 +++++++++++++++++ .../001/traces/syn/AA.S000000.BXY.semd | 5000 +++++++++++++++++ .../001/traces/syn/AA.S000001.BXY.semd | 5000 +++++++++++++++++ .../001/traces/syn/AA.S000002.BXY.semd | 5000 +++++++++++++++++ .../001/traces/syn/AA.S000003.BXY.semd | 5000 +++++++++++++++++ .../001/traces/syn/AA.S000004.BXY.semd | 5000 +++++++++++++++++ seisflows/tests/test_data/test_solver/002 | 1 - .../test_data/test_solver/002/DATA/Par_file | 1 + .../test_data/test_solver/002/DATA/SOURCE | 1 + .../test_data/test_solver/002/DATA/SOURCE_002 | 57 + .../test_data/test_solver/002/DATA/STATIONS | 5 + .../test_solver/002/bin/xcombine_sem | 3 + .../test_data/test_solver/002/bin/xmeshfem2D | 2 + .../test_data/test_solver/002/bin/xsmooth_sem | 3 + .../test_data/test_solver/002/bin/xspecfem2D | 3 + .../002/traces/obs/AA.S000000.BXY.semd | 5000 +++++++++++++++++ .../002/traces/obs/AA.S000001.BXY.semd | 5000 +++++++++++++++++ .../002/traces/obs/AA.S000002.BXY.semd | 5000 +++++++++++++++++ .../002/traces/obs/AA.S000003.BXY.semd | 5000 +++++++++++++++++ .../002/traces/obs/AA.S000004.BXY.semd | 5000 +++++++++++++++++ .../002/traces/syn/AA.S000000.BXY.semd | 5000 +++++++++++++++++ .../002/traces/syn/AA.S000001.BXY.semd | 5000 +++++++++++++++++ .../002/traces/syn/AA.S000002.BXY.semd | 5000 +++++++++++++++++ .../002/traces/syn/AA.S000003.BXY.semd | 5000 +++++++++++++++++ .../002/traces/syn/AA.S000004.BXY.semd | 5000 +++++++++++++++++ .../tests/test_data/test_solver/mainsolver | 1 + seisflows/tests/test_preprocess.py | 160 +- seisflows/tests/test_solver.py | 8 +- 114 files changed, 121939 insertions(+), 164 deletions(-) rename seisflows/tests/test_data/{ => hold}/scripts/make_parameter_files.sh (100%) rename seisflows/tests/test_data/{ => hold}/scripts/make_test_directory_structure.py (100%) rename seisflows/tests/test_data/{ => hold}/specfem/DATA/CMTSOLUTION_c46e1d99 (100%) rename seisflows/tests/test_data/{ => hold}/specfem/DATA/FORCESOLUTION_c46e1d99 (100%) rename seisflows/tests/test_data/{ => hold}/specfem/DATA/Par_file_SPECFEM2D_cf893667 (100%) rename seisflows/tests/test_data/{ => hold}/specfem/DATA/Par_file_SPECFEM3D_c46e1d99 (100%) rename seisflows/tests/test_data/{ => hold}/specfem/DATA/SOURCE_cf893667 (100%) rename seisflows/tests/test_data/{ => hold}/specfem/OUTPUT_FILES/AA.S0001.BXY.semd (100%) rename seisflows/tests/test_data/{ => hold}/specfem/OUTPUT_FILES/Uy_file_single_d.su (100%) rename seisflows/tests/test_data/{ => hold}/test_conf_parameters.yaml (100%) rename seisflows/tests/test_data/{ => hold}/test_filled_parameters.yaml (100%) rename seisflows/tests/test_data/{ => hold}/test_setup_parameters.yaml (100%) rename seisflows/tests/test_data/{test_solver/mainsolver => old_test_solver/001}/DATA/CMTSOLUTION_001 (100%) rename seisflows/tests/test_data/{test_solver/mainsolver => old_test_solver/001}/DATA/CMTSOLUTION_002 (100%) rename seisflows/tests/test_data/{test_solver/mainsolver => old_test_solver/001}/DATA/CMTSOLUTION_003 (100%) rename seisflows/tests/test_data/{test_solver/mainsolver => old_test_solver/001}/DATA/CMTSOLUTION_004 (100%) rename seisflows/tests/test_data/{test_solver/mainsolver => old_test_solver/001}/DATA/CMTSOLUTION_005 (100%) rename seisflows/tests/test_data/{test_solver/mainsolver => old_test_solver/001}/DATA/CMTSOLUTION_006 (100%) rename seisflows/tests/test_data/{test_solver/mainsolver => old_test_solver/001}/DATA/Par_file (100%) rename seisflows/tests/test_data/{test_solver/mainsolver => old_test_solver/001}/DATA/STATIONS (100%) rename seisflows/tests/test_data/{test_solver/mainsolver => old_test_solver/001}/bin/xcombine_sem (100%) rename seisflows/tests/test_data/{test_solver/mainsolver => old_test_solver/001}/bin/xmeshfem2D (100%) rename seisflows/tests/test_data/{test_solver/mainsolver => old_test_solver/001}/bin/xsmooth_sem (100%) rename seisflows/tests/test_data/{test_solver/mainsolver => old_test_solver/001}/bin/xspecfem2D (100%) rename seisflows/tests/test_data/{test_solver/mainsolver => old_test_solver/001}/traces/obs/AA.S0001.BXY.semd (100%) rename 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seisflows/tests/test_data/old_test_solver/002/DATA/STATIONS create mode 100755 seisflows/tests/test_data/old_test_solver/002/bin/xcombine_sem create mode 100755 seisflows/tests/test_data/old_test_solver/002/bin/xmeshfem2D create mode 100755 seisflows/tests/test_data/old_test_solver/002/bin/xsmooth_sem create mode 100755 seisflows/tests/test_data/old_test_solver/002/bin/xspecfem2D create mode 100644 seisflows/tests/test_data/old_test_solver/002/traces/obs/AA.S0001.BXY.semd create mode 100644 seisflows/tests/test_data/old_test_solver/002/traces/obs/AA.S0002.BXY.semd create mode 100644 seisflows/tests/test_data/old_test_solver/002/traces/syn/AA.S0001.BXY.semd create mode 100644 seisflows/tests/test_data/old_test_solver/002/traces/syn/AA.S0002.BXY.semd create mode 100644 seisflows/tests/test_data/old_test_solver/sources/CMTSOLUTION_001 create mode 100644 seisflows/tests/test_data/old_test_solver/sources/CMTSOLUTION_002 create mode 100644 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100755 seisflows/tests/test_data/test_solver/002/bin/xcombine_sem create mode 100755 seisflows/tests/test_data/test_solver/002/bin/xmeshfem2D create mode 100755 seisflows/tests/test_data/test_solver/002/bin/xsmooth_sem create mode 100755 seisflows/tests/test_data/test_solver/002/bin/xspecfem2D create mode 100644 seisflows/tests/test_data/test_solver/002/traces/obs/AA.S000000.BXY.semd create mode 100644 seisflows/tests/test_data/test_solver/002/traces/obs/AA.S000001.BXY.semd create mode 100644 seisflows/tests/test_data/test_solver/002/traces/obs/AA.S000002.BXY.semd create mode 100644 seisflows/tests/test_data/test_solver/002/traces/obs/AA.S000003.BXY.semd create mode 100644 seisflows/tests/test_data/test_solver/002/traces/obs/AA.S000004.BXY.semd create mode 100644 seisflows/tests/test_data/test_solver/002/traces/syn/AA.S000000.BXY.semd create mode 100644 seisflows/tests/test_data/test_solver/002/traces/syn/AA.S000001.BXY.semd create mode 100644 seisflows/tests/test_data/test_solver/002/traces/syn/AA.S000002.BXY.semd create mode 100644 seisflows/tests/test_data/test_solver/002/traces/syn/AA.S000003.BXY.semd create mode 100644 seisflows/tests/test_data/test_solver/002/traces/syn/AA.S000004.BXY.semd create mode 120000 seisflows/tests/test_data/test_solver/mainsolver diff --git a/seisflows/preprocess/pyaflowa.py b/seisflows/preprocess/pyaflowa.py index 6905a3d1..a0807397 100644 --- a/seisflows/preprocess/pyaflowa.py +++ b/seisflows/preprocess/pyaflowa.py @@ -9,7 +9,7 @@ import time import random import numpy as np -from concurrent.futures import ProcessPoolExecutor +from concurrent.futures import ProcessPoolExecutor, as_completed from glob import glob from pyasdf import ASDFDataSet from pyatoa import Config, Manager, Inspector, ManagerError @@ -26,12 +26,6 @@ class Pyaflowa: """ [preprocess.pyaflowa] preprocessing and misfit quantification using Pyatoa - :type data_format: str - :param data_format: data format for reading traces into memory. Pyatoa - only works with 'ASCII' currently. - :type components: str - :param components: components to consider and tag data with. Should be - string of letters such as 'RTZ' :type min_period: float :param min_period: Minimum filter corner in unit seconds. Bandpass filter if set with `max_period`, highpass filter if set without @@ -40,7 +34,7 @@ class Pyaflowa: :param max_period: Maximum filter corner in unit seconds. Bandpass filter if set with `min_period`, lowpass filter if set without `min_period`, no filtering if not set and `min_period also not set - :type filter_corners: int + :type filter_corners: int :param filter_corners: number of filter corners applied to filtering :type client: str :param client: Client name for ObsPy FDSN data gathering. Pyatoa will @@ -76,12 +70,6 @@ class Pyaflowa: :param pyatoa_log_level: Log level to set Pyatoa, Pyflex, Pyadjoint. Available: ['null': no logging, 'warning': warnings only, 'info': task tracking, 'debug': log all small details (recommended)] - :type start_pad: float - :param start_pad: seconds BEFORE origin time to gather data. Must be - >= T_0 specificed in SPECFEM constants.h. Positive values only - :type end_pad: int - :param end_pad: seconds AFTER origin time to gather data. Must be - >= NT * DT (from SPECFEM Par_file) postive values only. :type unit_output: str :param unit_output: Data units. Must match the synthetic output of external solver. Available: ['DISP': displacement, 'VEL': velocity, @@ -95,25 +83,53 @@ class Pyaflowa: data-synthetic waveform comparison figures :type export_log_files: bool :param export_log_files: periodically save log files created by Pyatoa + + [path structure] :type path_preprocess: str :param path_preprocess: scratch path for preprocessing related steps - :type path_data: str - :param path_data: optional path for preprocessing module to discover - waveform and meta-data. """ def __init__(self, min_period=1., max_period=10., filter_corners=4, client=None, rotate=False, pyflex_preset="default", fix_windows=False, adj_src_type="cc", plot=True, - pyatoa_log_level="DEBUG", unit_output="VEL", - # start_pad=None, end_pad=None, + pyatoa_log_level="DEBUG", unit_output="VEL", max_workers=None, + export_datasets=True, export_figures=True, + export_log_files=True, workdir=os.getcwd(), path_preprocess=None, path_solver=None, path_specfem_data=None, path_data=None, - path_output=None, export_datasets=True, export_figures=True, - export_log_files=True, data_format="ascii", + path_output=None, data_format="ascii", data_case="data", components=None, start=None, ntask=1, nproc=1, source_prefix=None, **kwargs): - """Pyatoa preprocessing parameters""" + """ + Pyatoa preprocessing parameters + + .. note:: + Paths and parameters listed here are shared with other modules and + so are not included in the main class docstring. + + :type data_format: str + :param data_format: data format for reading traces into memory. Pyatoa + only works with 'ASCII' currently. + :type data_case: str + :param data_case: How to address 'data' in the workflow, options: + 'data': real data will be provided by the user in + `path_data/{source_name}` in the same format that the solver will + produce synthetics (controlled by `solver.format`) OR + synthetic': 'data' will be generated as synthetic seismograms using + a target model provided in `path_model_true`. If None, workflow will + not attempt to generate data. + :type components: str + :param components: components to consider and tag data with. Should be + string of letters such as 'RTZ' + :type workdir: str + :param workdir: working directory in which to look for data and store + results. Defaults to current working directory + :type path_solver: str + :param path_solver: scratch path for all solver related tasks + :type path_data: str + :param path_data: path to any externally stored data required by the + solver + """ self.min_period = min_period self.max_period = max_period self.filter_corners = filter_corners @@ -125,8 +141,8 @@ def __init__(self, min_period=1., max_period=10., filter_corners=4, self.plot = plot self.pyatoa_log_level = pyatoa_log_level self.unit_output = unit_output - # self.start_pad = start_pad - # self.end_pad = end_pad + + self.max_workers = max_workers self.path = Dict( scratch=path_preprocess or os.path.join(workdir, "scratch", @@ -142,7 +158,7 @@ def __init__(self, min_period=1., max_period=10., filter_corners=4, self.export_figures = export_figures self.export_log_files = export_log_files - # Pyatoa-specific internal path structure for storing data etc. + # Pyatoa-specific internal directory structure for storing data etc. self.path["_logs"] = os.path.join(self.path.scratch, "logs") self.path["_tmplogs"] = os.path.join(self.path._logs, "tmp") self.path["_datasets"] = os.path.join(self.path.scratch, "datasets") @@ -170,6 +186,8 @@ def __init__(self, min_period=1., max_period=10., filter_corners=4, self._acceptable_data_formats = ["ASCII"] self._acceptable_source_prefixes = ["SOURCE", "FORCESOLUTION", "CMTSOLUTION"] + self._acceptable_fix_windows = ["ITER", "ONCE", True, False] + self._acceptable_unit_outputs = ["VEL", "DISP", "ACC"] # Internal attributes to be filled in by setup() self._config = None @@ -199,27 +217,21 @@ def check(self): f"Pyatoa can only accept `source_prefix` in " f"{self._acceptable_source_prefixes}, not '{self._source_prefix}'" ) - # - # if self.start_pad is None: - # logger.warning("Pyatoa `start_pad` is not set, setting to T0=0. " - # "This value should be set to the T0 value of " - # "SPECFEM, otherwise its output adjoint sources will " - # "be incorrect length.") - # self.start_pad = 0. - # - # if self.end_pad is None: - # logger.warning("Pyatoa `end_pad` is not set, setting to T=0. " - # "This value should be set to the total length of " - # "your synthetic seismograms (i.e., DT * NT) from " - # "SPECFEM, IFF you want Pyatoa to gather observed " - # "data from disk/webservice.") - # self.end_pad = 0. + assert(self._fix_windows in self._acceptable_fix_windows), \ + f"Pyatoa `fix_windows` must be in {self._acceptable_fix_windows}" + + assert(self.unit_output in self._acceptable_unit_outputs), \ + f"Pyatoa `unit_output` must be in {self._acceptable_unit_outputs}" def setup(self): """ Sets up data preprocessing machinery by establishing an internally defined directory structure that will be used to store the outputs of the preprocessing workflow + + .. note:: + `config.save_to_ds` must be set False, otherwise Pyatoa will try to + write to a read-only ASDFDataSet causing preprocessing to fail. """ for pathname in ["scratch", "_logs", "_tmplogs", "_datasets", "_figures"]: @@ -233,7 +245,7 @@ def setup(self): rotate=self.rotate, pyflex_preset=self.pyflex_preset, fix_windows=self.fix_windows, adj_src_type=self.adj_src_type, log_level=self.pyatoa_log_level, unit_output=self.unit_output, - component_list=list(self._components), + component_list=list(self._components), save_to_ds=False, synthetics_only=bool(self._data_case == "synthetic"), paths={"waveforms": self.path["_waveforms"] or [], "responses": self.path["_responses"] or [], @@ -293,13 +305,25 @@ def _setup_quantify_misfit(self, source_name, iteration, step_count): # Pyatoa knows how long seismograms are and when to start them # NOTE: assuming all synthetic time axes are the same for this source synthetics = glob(os.path.join(syn_path, "*")) - assert(synthetics), f"Pyatoa found no synthetics in: {syn_path}" + assert synthetics, f"Pyatoa found no synthetics in: {syn_path}" tr_syn = read_sem(synthetics[0])[0] - config.start_pad = abs(tr_syn.stats.time_offset) # needs to be positive - config.end_pad = tr_syn.stats.endtime - tr_syn.stats.starttime + config.start_pad = abs(tr_syn.stats.time_offset) # [s] must be positive + config.end_pad = tr_syn.stats.endtime - tr_syn.stats.starttime # [s] return config + @staticmethod + def _ftag(config): + """ + Create a re-usable file tag from the Config object as multiple functions + will use this tag for file naming and file discovery. + + :type config: pyatoa.core.config.Config + :param config: Configuration object that must contain the 'event_id', + iteration and step count + """ + return f"{config.event_id}_{config.iter_tag}_{config.step_tag}" + def quantify_misfit(self, source_name=None, save_residuals=None, save_adjsrcs=None, iteration=1, step_count=0, **kwargs): @@ -329,22 +353,7 @@ def quantify_misfit(self, source_name=None, save_residuals=None, inversion. Defaults to 0 if not given (1st evaluation) """ config = self._setup_quantify_misfit(source_name, iteration, step_count) - - # Run misfit quantification for each station concurrently - misfit, nwin = 0, 0 - with ProcessPoolExecutor(max_workers=unix.nproc() - 1) as executor: - futures = [ - executor.submit( - self._quantify_misfit_station, config, code, save_adjsrcs) - for code in self._station_codes - ] - # We only need to return misfit information. All data/results are - # saved to the ASDFDataSet and status is logged to separate log file - for future in futures: - _misfit, _nwin = future.result() - if _misfit is not None: - misfit += _misfit - nwin += _nwin + misfit, nwin = self._run_quantify_misfit(config, save_adjsrcs, False) # Calculate misfit based on the raw misfit and total number of windows if save_residuals: @@ -361,8 +370,8 @@ def quantify_misfit(self, source_name=None, save_residuals=None, # Combine all the individual .png files created into a single PDF if self.plot: - output_fid = os.path.join(self.path._figures, self._ftag(config)) - self._make_event_figure_pdf(source_name, output_fid) + fid = os.path.join(self.path._figures, f"{self._ftag(config)}.pdf") + self._make_event_figure_pdf(source_name=source_name, output_fid=fid) # Finally, collect all the temporary log files and write a main log file pyatoa_logger = self._config_pyatoa_logger( @@ -377,17 +386,40 @@ def quantify_misfit(self, source_name=None, save_residuals=None, ) self._collect_tmp_log_files(pyatoa_logger, config.event_id) - @staticmethod - def _ftag(config): + def _run_quantify_misfit(self, config, save_adjsrcs, parallel=False): """ - Create a re-usable file tag from the Config object as multiple functions - will use this tag for file naming and file discovery. - - :type config: pyatoa.core.config.Config - :param config: Configuration object that must contain the 'event_id', - iteration and step count + Run misfit quantification for each station concurrently or in serial. + If concurrent, play it safe and cap the max parallel workers at + half the processors otherwise you may run out of memory and crash the + CPU """ - return f"{config.event_id}_{config.iter_tag}_{config.step_tag}" + misfit, nwin = 0, 0 + # Run processing in parallel + if parallel: + with ProcessPoolExecutor(max_workers=2) as executor: + futures = ( + executor.submit(self._quantify_misfit_station, config, + code, save_adjsrcs) + for code in self._station_codes + ) + # We only need to return misfit information. All data/results are + # saved to the ASDFDataSet and status is logged to separate log file + for future in as_completed(futures): + _misfit, _nwin = future.result() + del future # Free up memory otherwise ram lock + if _misfit is not None: + misfit += _misfit + nwin += _nwin + # Run processing in serial + else: + for code in self._station_codes: + _misfit, _nwin = self._quantify_misfit_station(config, code, + save_adjsrcs) + if _misfit is not None: + misfit += _misfit + nwin += _nwin + + return misfit, nwin def _quantify_misfit_station(self, config, station_code, save_adjsrcs=False): @@ -411,43 +443,45 @@ def _quantify_misfit_station(self, config, station_code, Pyatoa + PyASDF """ # Unique identifier for the given source-receiver pair for file naming - # Something like 001_i01_s00_XX_XYZ + # Something like: 001_i01_s00_XX_XYZ net, sta, loc, cha = station_code.split(".") tag = f"{self._ftag(config)}_{net}_{sta}" - # Configure a single source-receiver pair logger which will be collected - # later by the main function + # Configure a single source-receiver pair temporary logger log_file = os.path.join(self.path._tmplogs, f"{tag}.log") station_logger = self._config_pyatoa_logger(fid=log_file) station_logger.info(f"\n{'/' * 80}\n{station_code:^80}\n{'/' * 80}") - # Begin data gathering/processing and misfit quantification - mgmt = Manager(config=config) - mgmt.gather(choice=["event"], event_id=config.event_id, - prefix=f"{self._source_prefix}_") + # Check whether or not we want to use misfit windows from last eval. + _fix_win, _msg = self._check_fixed_windows(iteration=config.iteration, + step_count=config.step_count) + station_logger.info(_msg) - # Attempt to gather data. If fail, return because theres nothing else - # we can do without data - _processed = False + # Setup ASDFDataSet in read only so we can pull data/windows in parallel + ds_fid = os.path.join(self.path["_datasets"], f"{config.event_id}.h5") + if os.path.exists(ds_fid): + ds = ASDFDataSet(ds_fid, mode="r") # NOTE: read only mode + else: + ds = None + mgmt = Manager(config=config, ds=ds) + # If data gather fails, return because theres nothing else we can do try: - mgmt.gather(choice=["inv", "st_obs", "st_syn"], code=station_code) + # `gather` function uses Config path structure and Client attribute + # to search for data on disk or via webservices (if requested). + mgmt.gather(event_id=config.event_id, + prefix=f"{self._source_prefix}_", + code=station_code) except ManagerError as e: - station_logger.critical(e) + station_logger.warning(e) return None, None - # If any part of the processing fails, move on to plotting because we + # If any part of this processing fails, move on to plotting because we # will have gathered waveform data so a figure is still useful. try: - _fix_windows = self._check_fixed_windows( - iteration=mgmt.config.iteration, - step_count=mgmt.config.step_count, - logger=station_logger, - ) mgmt.standardize() mgmt.preprocess() - mgmt.window(fix_windows=_fix_windows) + mgmt.window(fix_windows=_fix_win) mgmt.measure() - _processed = True except ManagerError as e: station_logger.warning(e) pass @@ -465,15 +499,17 @@ def _quantify_misfit_station(self, config, station_code, # Write out the .adj adjoint source files for solver to discover. # Write empty adjoint sources for components with no adjoint sources - if _processed and save_adjsrcs: + if mgmt.stats.misfit and save_adjsrcs: mgmt.write_adjsrcs(path=save_adjsrcs, write_blanks=True) # Wait until the very end to write to the HDF5 file, then do it # pseudo-serially to get around trying to parallel write to HDF5 file + # WARNING: This is the biggest potential bottleneck of preprocessing + if ds is not None: + ds._close() # close the read-only version so we can open in write while True: try: - with ASDFDataSet(os.path.join(self.path["_datasets"], - f"{config.event_id}.h5")) as ds: + with ASDFDataSet(ds_fid, mode="a") as ds: mgmt.write(ds=ds) break except (BlockingIOError, FileExistsError): @@ -525,6 +561,7 @@ def finalize(self): # Move scratch/ directory results into more permanent storage if self.export_datasets: src = glob(os.path.join(self.path._datasets, "*.h5")) + src += glob(os.path.join(self.path._datasets, "*.csv")) # inspector dst = os.path.join(self.path.output, "datasets", "") unix.mkdir(dst) unix.cp(src, dst) @@ -536,12 +573,12 @@ def finalize(self): unix.cp(src, dst) if self.export_log_files: - src = glob(os.path.join(self.path._logs, "*.txt")) + src = glob(os.path.join(self.path._logs, "*.log")) dst = os.path.join(self.path.output, "logs", "") unix.mkdir(dst) unix.cp(src, dst) - def _check_fixed_windows(self, iteration, step_count, logger=Null()): + def _check_fixed_windows(self, iteration, step_count): """ Determine how to address re-using misfit windows during an inversion workflow. Throw some log messages out to let the User know whether or @@ -564,42 +601,41 @@ def _check_fixed_windows(self, iteration, step_count, logger=Null()): :param step_count: Current line search step count within the SeisFlows3 workflow. Within SeisFlows3 this is defined by `optimize.line_search.step_count` - :type logger: logging.Logger - :param logger: The main logger for a given event, should be - defined by `pyaflowa.quantify_misfit()`. If not provided, logs will - get sent to DevNull - :rtype: bool - :return: bool on whether to use windows from the previous step + :rtype: tuple (bool, str) + :return: (bool on whether to use windows from the previous step, + and a message that can be sent to the logger) """ fix_windows = False + msg = "" + # First function evaluation never fixes windows if iteration == 1 and step_count == 0: fix_windows = False - logger.info("new windows; first evaluation") + msg = "first evaluation of workflow, selecting new windows" elif isinstance(self.fix_windows, str): # By 'iter'ation only pick new windows on the first step count if self.fix_windows.upper() == "ITER": if step_count == 0: fix_windows = False - logger.info("new windows; first step count") + msg = "first step of line search, will select new windows" else: fix_windows = True - logger.info("fix windows; mid line search") + msg = "mid line search, fix windows from last evaluation" # 'Once' picks windows only for the first function evaluation of # the current set of iterations. elif self.fix_windows.upper() == "ONCE": if iteration == self._start and step_count == 0: fix_windows = False - logger.info("new windows; first workflow evaluation") + msg = "first evaluation of workflow, selecting new windows" else: fix_windows = True - logger.info("fix windows; mid workflow") + msg = "mid workflow, fix windows from last evaluation" # Bool fix windows simply sets the parameter elif isinstance(self.fix_windows, bool): fix_windows = self.fix_windows - logger.info(f"fixed windows flag set: {self.fix_windows}") + msg = f"fixed windows flag set constant: {self.fix_windows}" - return fix_windows + return fix_windows, msg def _config_pyatoa_logger(self, fid): """ @@ -669,10 +705,15 @@ def _collect_tmp_log_files(self, pyatoa_logger, event_id): def _make_event_figure_pdf(self, source_name, output_fid): """ - Combine a list of single source-receiver PNGs into a single PDF file - for the given event. Mostly a convenience function to make it easier - to ingest waveform figures during a workflow. + Combines source-receiver output PNGS into a single event-specific PDF. + Mostly a convenience function to make it easier to ingest waveform + figures during a workflow. + :type source_name: str + :param source_name: name of event to search for input files + :type output_fid: str + :param output_fid: full path and filename for output PDF which will be + a combination of all the PNG files created for each station """ # Sorrted by network and station name input_fids = sorted(glob(os.path.join(self.path._figures, @@ -681,40 +722,31 @@ def _make_event_figure_pdf(self, source_name, output_fid): logger.warning(f"Pyatoa found no event figures for {source_name} " f"to combine") return - - # Assuming the file naming format defined by `_quantify_misfit_station` - source_name, iteration, step_count, *_ = input_fids[0].split("_") - - # e.g. i01s00_2018p130600.pdf - output_fid = (f"{source_name}_{iteration}_{step_count}.pdf") - - # Merge all output pdfs into a single pdf, delete originals - save = os.path.join(self.path._figures, output_fid) - imgs_to_pdf(fids=sorted(input_fids), fid_out=save) - for fid in input_fids: - os.remove(fid) + # Merge all output pdfs into a single pdf, delete originals if okay + imgs_to_pdf(fids=sorted(input_fids), fid_out=output_fid) + if os.path.exists(output_fid): + for fid in input_fids: + os.remove(fid) def _make_evaluation_composite_pdf(self): """ - Utility function to combine all PDFs generated by - make_event_figure_pdf() into a single PDF tagged by the current - evaluation (iteration, step count). This is meant to make it easier - for the User to scroll through figures. Option to delete the original - event-specific PDFs which are now redundant - .. note:: - This can be run without running Pyaflowa.format() - :type delete_originals: bool - :param delete_originals: delete original pdf files after mergin + Combines event-specific PDFs to make an evaluation-specific PDF. + By evaluation we mean any given set of foward simulations, e.g., i01s00 + + This is meant to make it easier for the User to scroll through figures. + Deletes the original event-specific PDFs to keep filecount down """ - event_figures = glob(os.path.join(self.path._figures, "*", "*.pdf")) - if not event_figures: + # Event PDFs named like: 001_i01_s00.pdf + event_pdfs = sorted(glob(os.path.join(self.path._figures, "*_*_*.pdf"))) + if not event_pdfs: logger.warning("Pyatoa could not find event PDFs to merge") return - # Collecting evaluation tags, e.g., ['i01s00', 'i01s01'] - tags = set([os.path.basename(_).split("_")[0] for _ in event_figures]) - for tag in tags: - fids = [fid for fid in event_figures if tag in fid] - fid_out = os.path.join(self.path._figures, f"{tag}.pdf") - merge_pdfs(fids=sorted(fids), fid_out=fid_out) - for fid in fids: - os.remove(fid) + # Strip off event name to get evaluation tag for fid, i.e.: i01_s00.pdf + fid_out = "_".join(os.path.basename(event_pdfs[0]).split("_")[1:]) + path_out = os.path.join(self.path._figures, f"{fid_out}.pdf") + # Merge PDFs into a single PDF, delete originals + merge_pdfs(fids=event_pdfs, fid_out=path_out) + if os.path.exists(path_out): + for event_pdf in event_pdfs: + os.remove(event_pdf) + diff --git a/seisflows/tests/test_data/scripts/make_parameter_files.sh b/seisflows/tests/test_data/hold/scripts/make_parameter_files.sh similarity index 100% rename from seisflows/tests/test_data/scripts/make_parameter_files.sh rename to seisflows/tests/test_data/hold/scripts/make_parameter_files.sh diff --git a/seisflows/tests/test_data/scripts/make_test_directory_structure.py b/seisflows/tests/test_data/hold/scripts/make_test_directory_structure.py similarity index 100% rename from seisflows/tests/test_data/scripts/make_test_directory_structure.py rename to seisflows/tests/test_data/hold/scripts/make_test_directory_structure.py diff --git a/seisflows/tests/test_data/specfem/DATA/CMTSOLUTION_c46e1d99 b/seisflows/tests/test_data/hold/specfem/DATA/CMTSOLUTION_c46e1d99 similarity index 100% rename from seisflows/tests/test_data/specfem/DATA/CMTSOLUTION_c46e1d99 rename to seisflows/tests/test_data/hold/specfem/DATA/CMTSOLUTION_c46e1d99 diff --git a/seisflows/tests/test_data/specfem/DATA/FORCESOLUTION_c46e1d99 b/seisflows/tests/test_data/hold/specfem/DATA/FORCESOLUTION_c46e1d99 similarity index 100% rename from seisflows/tests/test_data/specfem/DATA/FORCESOLUTION_c46e1d99 rename to seisflows/tests/test_data/hold/specfem/DATA/FORCESOLUTION_c46e1d99 diff --git a/seisflows/tests/test_data/specfem/DATA/Par_file_SPECFEM2D_cf893667 b/seisflows/tests/test_data/hold/specfem/DATA/Par_file_SPECFEM2D_cf893667 similarity index 100% rename from seisflows/tests/test_data/specfem/DATA/Par_file_SPECFEM2D_cf893667 rename to seisflows/tests/test_data/hold/specfem/DATA/Par_file_SPECFEM2D_cf893667 diff --git a/seisflows/tests/test_data/specfem/DATA/Par_file_SPECFEM3D_c46e1d99 b/seisflows/tests/test_data/hold/specfem/DATA/Par_file_SPECFEM3D_c46e1d99 similarity index 100% rename from seisflows/tests/test_data/specfem/DATA/Par_file_SPECFEM3D_c46e1d99 rename to seisflows/tests/test_data/hold/specfem/DATA/Par_file_SPECFEM3D_c46e1d99 diff --git a/seisflows/tests/test_data/specfem/DATA/SOURCE_cf893667 b/seisflows/tests/test_data/hold/specfem/DATA/SOURCE_cf893667 similarity index 100% rename from seisflows/tests/test_data/specfem/DATA/SOURCE_cf893667 rename to seisflows/tests/test_data/hold/specfem/DATA/SOURCE_cf893667 diff --git a/seisflows/tests/test_data/specfem/OUTPUT_FILES/AA.S0001.BXY.semd b/seisflows/tests/test_data/hold/specfem/OUTPUT_FILES/AA.S0001.BXY.semd similarity index 100% rename from seisflows/tests/test_data/specfem/OUTPUT_FILES/AA.S0001.BXY.semd rename to seisflows/tests/test_data/hold/specfem/OUTPUT_FILES/AA.S0001.BXY.semd diff --git a/seisflows/tests/test_data/specfem/OUTPUT_FILES/Uy_file_single_d.su b/seisflows/tests/test_data/hold/specfem/OUTPUT_FILES/Uy_file_single_d.su similarity index 100% rename from seisflows/tests/test_data/specfem/OUTPUT_FILES/Uy_file_single_d.su rename to seisflows/tests/test_data/hold/specfem/OUTPUT_FILES/Uy_file_single_d.su diff --git a/seisflows/tests/test_data/test_conf_parameters.yaml b/seisflows/tests/test_data/hold/test_conf_parameters.yaml similarity index 100% rename from seisflows/tests/test_data/test_conf_parameters.yaml rename to seisflows/tests/test_data/hold/test_conf_parameters.yaml diff --git a/seisflows/tests/test_data/test_filled_parameters.yaml b/seisflows/tests/test_data/hold/test_filled_parameters.yaml similarity index 100% rename from seisflows/tests/test_data/test_filled_parameters.yaml rename to seisflows/tests/test_data/hold/test_filled_parameters.yaml diff --git a/seisflows/tests/test_data/test_setup_parameters.yaml b/seisflows/tests/test_data/hold/test_setup_parameters.yaml similarity index 100% rename from seisflows/tests/test_data/test_setup_parameters.yaml rename to seisflows/tests/test_data/hold/test_setup_parameters.yaml diff --git a/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_001 b/seisflows/tests/test_data/old_test_solver/001/DATA/CMTSOLUTION_001 similarity index 100% rename from seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_001 rename to seisflows/tests/test_data/old_test_solver/001/DATA/CMTSOLUTION_001 diff --git a/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_002 b/seisflows/tests/test_data/old_test_solver/001/DATA/CMTSOLUTION_002 similarity index 100% rename from seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_002 rename to seisflows/tests/test_data/old_test_solver/001/DATA/CMTSOLUTION_002 diff --git a/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_003 b/seisflows/tests/test_data/old_test_solver/001/DATA/CMTSOLUTION_003 similarity index 100% rename from seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_003 rename to seisflows/tests/test_data/old_test_solver/001/DATA/CMTSOLUTION_003 diff --git a/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_004 b/seisflows/tests/test_data/old_test_solver/001/DATA/CMTSOLUTION_004 similarity index 100% rename from seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_004 rename to seisflows/tests/test_data/old_test_solver/001/DATA/CMTSOLUTION_004 diff --git a/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_005 b/seisflows/tests/test_data/old_test_solver/001/DATA/CMTSOLUTION_005 similarity index 100% rename from seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_005 rename to seisflows/tests/test_data/old_test_solver/001/DATA/CMTSOLUTION_005 diff --git a/seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_006 b/seisflows/tests/test_data/old_test_solver/001/DATA/CMTSOLUTION_006 similarity index 100% rename from seisflows/tests/test_data/test_solver/mainsolver/DATA/CMTSOLUTION_006 rename to seisflows/tests/test_data/old_test_solver/001/DATA/CMTSOLUTION_006 diff --git a/seisflows/tests/test_data/test_solver/mainsolver/DATA/Par_file b/seisflows/tests/test_data/old_test_solver/001/DATA/Par_file similarity index 100% rename from seisflows/tests/test_data/test_solver/mainsolver/DATA/Par_file rename to seisflows/tests/test_data/old_test_solver/001/DATA/Par_file diff --git a/seisflows/tests/test_data/test_solver/mainsolver/DATA/STATIONS b/seisflows/tests/test_data/old_test_solver/001/DATA/STATIONS similarity index 100% rename from seisflows/tests/test_data/test_solver/mainsolver/DATA/STATIONS rename to seisflows/tests/test_data/old_test_solver/001/DATA/STATIONS diff --git a/seisflows/tests/test_data/test_solver/mainsolver/bin/xcombine_sem b/seisflows/tests/test_data/old_test_solver/001/bin/xcombine_sem similarity index 100% rename from seisflows/tests/test_data/test_solver/mainsolver/bin/xcombine_sem rename to seisflows/tests/test_data/old_test_solver/001/bin/xcombine_sem diff --git a/seisflows/tests/test_data/test_solver/mainsolver/bin/xmeshfem2D b/seisflows/tests/test_data/old_test_solver/001/bin/xmeshfem2D similarity index 100% rename from seisflows/tests/test_data/test_solver/mainsolver/bin/xmeshfem2D rename to seisflows/tests/test_data/old_test_solver/001/bin/xmeshfem2D diff --git a/seisflows/tests/test_data/test_solver/mainsolver/bin/xsmooth_sem b/seisflows/tests/test_data/old_test_solver/001/bin/xsmooth_sem similarity index 100% rename from seisflows/tests/test_data/test_solver/mainsolver/bin/xsmooth_sem rename to seisflows/tests/test_data/old_test_solver/001/bin/xsmooth_sem diff --git a/seisflows/tests/test_data/test_solver/mainsolver/bin/xspecfem2D b/seisflows/tests/test_data/old_test_solver/001/bin/xspecfem2D similarity index 100% rename from seisflows/tests/test_data/test_solver/mainsolver/bin/xspecfem2D rename to seisflows/tests/test_data/old_test_solver/001/bin/xspecfem2D diff --git a/seisflows/tests/test_data/test_solver/mainsolver/traces/obs/AA.S0001.BXY.semd b/seisflows/tests/test_data/old_test_solver/001/traces/obs/AA.S0001.BXY.semd similarity index 100% rename from seisflows/tests/test_data/test_solver/mainsolver/traces/obs/AA.S0001.BXY.semd rename to seisflows/tests/test_data/old_test_solver/001/traces/obs/AA.S0001.BXY.semd diff --git a/seisflows/tests/test_data/test_solver/mainsolver/traces/obs/AA.S0002.BXY.semd b/seisflows/tests/test_data/old_test_solver/001/traces/obs/AA.S0002.BXY.semd similarity index 100% rename from seisflows/tests/test_data/test_solver/mainsolver/traces/obs/AA.S0002.BXY.semd rename to seisflows/tests/test_data/old_test_solver/001/traces/obs/AA.S0002.BXY.semd diff --git a/seisflows/tests/test_data/test_solver/mainsolver/traces/syn/AA.S0001.BXY.semd b/seisflows/tests/test_data/old_test_solver/001/traces/syn/AA.S0001.BXY.semd similarity index 100% rename from seisflows/tests/test_data/test_solver/mainsolver/traces/syn/AA.S0001.BXY.semd rename to seisflows/tests/test_data/old_test_solver/001/traces/syn/AA.S0001.BXY.semd diff --git a/seisflows/tests/test_data/test_solver/mainsolver/traces/syn/AA.S0002.BXY.semd b/seisflows/tests/test_data/old_test_solver/001/traces/syn/AA.S0002.BXY.semd similarity index 100% rename from seisflows/tests/test_data/test_solver/mainsolver/traces/syn/AA.S0002.BXY.semd rename to seisflows/tests/test_data/old_test_solver/001/traces/syn/AA.S0002.BXY.semd diff --git a/seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_001 b/seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_001 new file mode 100755 index 00000000..cacf1f81 --- /dev/null +++ b/seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_001 @@ -0,0 +1,13 @@ +XXXX 2012 03 30 18 47 38.66 -40.9316 175.4123 17.6 0.0 0.0 NORTH ISLAND, NEW ZEALAND +event name: 001 +time shift: 0.0000 +half duration: 0.0000 +latitude: -40.9316 +longitude: 175.4123 +depth: 17.5977 +Mrr: -1.451500E+22 +Mtt: 2.777100E+22 +Mpp: -1.325600E+22 +Mrt: 1.085300E+22 +Mrp: -2.075000E+21 +Mtp: -1.439600E+22 diff --git a/seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_002 b/seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_002 new file mode 100755 index 00000000..53709cf7 --- /dev/null +++ b/seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_002 @@ -0,0 +1,13 @@ +XXXX 2012 03 30 18 47 38.66 -40.9316 175.4123 17.6 0.0 0.0 NORTH ISLAND, NEW ZEALAND +event name: 002 +time shift: 0.0000 +half duration: 0.0000 +latitude: -40.9316 +longitude: 175.4123 +depth: 17.5977 +Mrr: -1.451500E+22 +Mtt: 2.777100E+22 +Mpp: -1.325600E+22 +Mrt: 1.085300E+22 +Mrp: -2.075000E+21 +Mtp: -1.439600E+22 diff --git a/seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_003 b/seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_003 new file mode 100755 index 00000000..7d46df94 --- /dev/null +++ b/seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_003 @@ -0,0 +1,13 @@ +XXXX 2012 03 30 18 47 38.66 -40.9316 175.4123 17.6 0.0 0.0 NORTH ISLAND, NEW ZEALAND +event name: 003 +time shift: 0.0000 +half duration: 0.0000 +latitude: -40.9316 +longitude: 175.4123 +depth: 17.5977 +Mrr: -1.451500E+22 +Mtt: 2.777100E+22 +Mpp: -1.325600E+22 +Mrt: 1.085300E+22 +Mrp: -2.075000E+21 +Mtp: -1.439600E+22 diff --git a/seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_004 b/seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_004 new file mode 100755 index 00000000..51d51497 --- /dev/null +++ b/seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_004 @@ -0,0 +1,13 @@ +XXXX 2012 03 30 18 47 38.66 -40.9316 175.4123 17.6 0.0 0.0 NORTH ISLAND, NEW ZEALAND +event name: 004 +time shift: 0.0000 +half duration: 0.0000 +latitude: -40.9316 +longitude: 175.4123 +depth: 17.5977 +Mrr: -1.451500E+22 +Mtt: 2.777100E+22 +Mpp: -1.325600E+22 +Mrt: 1.085300E+22 +Mrp: -2.075000E+21 +Mtp: -1.439600E+22 diff --git a/seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_005 b/seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_005 new file mode 100755 index 00000000..50b4aeb0 --- /dev/null +++ b/seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_005 @@ -0,0 +1,13 @@ +XXXX 2012 03 30 18 47 38.66 -40.9316 175.4123 17.6 0.0 0.0 NORTH ISLAND, NEW ZEALAND +event name: 005 +time shift: 0.0000 +half duration: 0.0000 +latitude: -40.9316 +longitude: 175.4123 +depth: 17.5977 +Mrr: -1.451500E+22 +Mtt: 2.777100E+22 +Mpp: -1.325600E+22 +Mrt: 1.085300E+22 +Mrp: -2.075000E+21 +Mtp: -1.439600E+22 diff --git a/seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_006 b/seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_006 new file mode 100755 index 00000000..8d034437 --- /dev/null +++ b/seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_006 @@ -0,0 +1,13 @@ +XXXX 2012 03 30 18 47 38.66 -40.9316 175.4123 17.6 0.0 0.0 NORTH ISLAND, NEW ZEALAND +event name: 006 +time shift: 0.0000 +half duration: 0.0000 +latitude: -40.9316 +longitude: 175.4123 +depth: 17.5977 +Mrr: -1.451500E+22 +Mtt: 2.777100E+22 +Mpp: -1.325600E+22 +Mrt: 1.085300E+22 +Mrp: -2.075000E+21 +Mtp: -1.439600E+22 diff --git a/seisflows/tests/test_data/old_test_solver/002/DATA/Par_file b/seisflows/tests/test_data/old_test_solver/002/DATA/Par_file new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/seisflows/tests/test_data/old_test_solver/002/DATA/Par_file @@ -0,0 +1 @@ + diff --git a/seisflows/tests/test_data/old_test_solver/002/DATA/STATIONS b/seisflows/tests/test_data/old_test_solver/002/DATA/STATIONS new file mode 100644 index 00000000..d2383d2e --- /dev/null +++ b/seisflows/tests/test_data/old_test_solver/002/DATA/STATIONS @@ -0,0 +1,2 @@ + S0001 AA -99.999 -66.666 0.0 0.0 + S0002 AA -88.888 -55.555 0.0 0.0 diff --git a/seisflows/tests/test_data/old_test_solver/002/bin/xcombine_sem b/seisflows/tests/test_data/old_test_solver/002/bin/xcombine_sem new file mode 100755 index 00000000..969a4d93 --- /dev/null +++ b/seisflows/tests/test_data/old_test_solver/002/bin/xcombine_sem @@ -0,0 +1,3 @@ +#!/bin/bash -e +echo "xcombine_sem" + diff --git a/seisflows/tests/test_data/old_test_solver/002/bin/xmeshfem2D b/seisflows/tests/test_data/old_test_solver/002/bin/xmeshfem2D new file mode 100755 index 00000000..149ca704 --- /dev/null +++ b/seisflows/tests/test_data/old_test_solver/002/bin/xmeshfem2D @@ -0,0 +1,2 @@ +#!/bin/bash -e +echo "xmeshfem2D" diff --git a/seisflows/tests/test_data/old_test_solver/002/bin/xsmooth_sem b/seisflows/tests/test_data/old_test_solver/002/bin/xsmooth_sem new file mode 100755 index 00000000..376b4aa5 --- /dev/null +++ b/seisflows/tests/test_data/old_test_solver/002/bin/xsmooth_sem @@ -0,0 +1,3 @@ +#!/bin/bash -e +echo "xsmooth_sem" + diff --git a/seisflows/tests/test_data/old_test_solver/002/bin/xspecfem2D b/seisflows/tests/test_data/old_test_solver/002/bin/xspecfem2D new file mode 100755 index 00000000..e50c2b0b --- /dev/null +++ b/seisflows/tests/test_data/old_test_solver/002/bin/xspecfem2D @@ -0,0 +1,3 @@ +#!/bin/bash -e +echo "xspecfem2D" + diff --git a/seisflows/tests/test_data/old_test_solver/002/traces/obs/AA.S0001.BXY.semd b/seisflows/tests/test_data/old_test_solver/002/traces/obs/AA.S0001.BXY.semd new file mode 100644 index 00000000..082a0be7 --- /dev/null +++ b/seisflows/tests/test_data/old_test_solver/002/traces/obs/AA.S0001.BXY.semd @@ -0,0 +1,5000 @@ + -48.000000000000000 0.0000000000000000 + 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index 00000000..8b137891 --- /dev/null +++ b/seisflows/tests/test_data/old_test_solver/sources/SOURCE_001 @@ -0,0 +1 @@ + diff --git a/seisflows/tests/test_data/old_test_solver/sources/SOURCE_002 b/seisflows/tests/test_data/old_test_solver/sources/SOURCE_002 new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/seisflows/tests/test_data/old_test_solver/sources/SOURCE_002 @@ -0,0 +1 @@ + diff --git a/seisflows/tests/test_data/test_solver/001 b/seisflows/tests/test_data/test_solver/001 deleted file mode 120000 index cbb9ee10..00000000 --- a/seisflows/tests/test_data/test_solver/001 +++ /dev/null @@ -1 +0,0 @@ -mainsolver/ \ No newline at end of file diff --git a/seisflows/tests/test_data/test_solver/001/DATA/Par_file b/seisflows/tests/test_data/test_solver/001/DATA/Par_file new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/Par_file @@ -0,0 +1 @@ + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE new file mode 120000 index 00000000..93be0227 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE @@ -0,0 +1 @@ +SOURCE_001 \ No newline at end of file diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_001 b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_001 new file mode 100644 index 00000000..29f9b7f3 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_001 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 362079.06 # source location x in meters +zs = 61219.23 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_002 b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_002 new file mode 100644 index 00000000..8371bd1f --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_002 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 116059.49 # source location x in meters +zs = 105566.07 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_003 b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_003 new file mode 100644 index 00000000..1e470574 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_003 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 326286.97 # source location x in meters +zs = 411609.09 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_004 b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_004 new file mode 100644 index 00000000..88c63c31 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_004 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 84727.42 # source location x in meters +zs = 333825.53 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_005 b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_005 new file mode 100644 index 00000000..9cc4bdb0 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_005 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 192015.27 # source location x in meters +zs = 248162.11 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_006 b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_006 new file mode 100644 index 00000000..9c75ff61 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_006 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 335300.42 # source location x in meters +zs = 240097.73 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_007 b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_007 new file mode 100644 index 00000000..ce16fcf6 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_007 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 204670.83 # source location x in meters +zs = 441693.44 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_008 b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_008 new file mode 100644 index 00000000..3c42bd9b --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_008 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 223849.60 # source location x in meters +zs = 259437.84 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_009 b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_009 new file mode 100644 index 00000000..e307422a --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_009 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 197026.87 # source location x in meters +zs = 32902.82 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_010 b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_010 new file mode 100644 index 00000000..6d975236 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_010 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 67392.40 # source location x in meters +zs = 183183.89 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_011 b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_011 new file mode 100644 index 00000000..9c225ebe --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_011 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 20482.04 # source location x in meters +zs = 443390.92 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_012 b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_012 new file mode 100644 index 00000000..7779d01f --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_012 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 350280.81 # source location x in meters +zs = 283883.49 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_013 b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_013 new file mode 100644 index 00000000..7b415bab --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_013 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 449852.28 # source location x in meters +zs = 27585.00 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_014 b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_014 new file mode 100644 index 00000000..313e804d --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_014 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 141836.41 # source location x in meters +zs = 242463.91 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_015 b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_015 new file mode 100644 index 00000000..8bf6ea62 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_015 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 206999.46 # source location x in meters +zs = 105547.63 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_016 b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_016 new file mode 100644 index 00000000..fe50abde --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_016 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 423539.80 # source location x in meters +zs = 96379.35 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_017 b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_017 new file mode 100644 index 00000000..e00db1f3 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_017 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 135933.15 # source location x in meters +zs = 373123.66 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_018 b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_018 new file mode 100644 index 00000000..a2a56c91 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_018 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 299952.04 # source location x in meters +zs = 325462.20 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_019 b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_019 new file mode 100644 index 00000000..bcc9bb15 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_019 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 328568.63 # source location x in meters +zs = 163336.65 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_020 b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_020 new file mode 100644 index 00000000..7924ad90 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_020 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 140568.70 # source location x in meters +zs = 273985.02 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_021 b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_021 new file mode 100644 index 00000000..bd6a9361 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_021 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 186309.01 # source location x in meters +zs = 352472.55 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_022 b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_022 new file mode 100644 index 00000000..6b29767f --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_022 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 309676.50 # source location x in meters +zs = 177014.37 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_023 b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_023 new file mode 100644 index 00000000..6a3d7b27 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_023 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 301818.07 # source location x in meters +zs = 114690.70 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_024 b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_024 new file mode 100644 index 00000000..c22c7771 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_024 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 404440.55 # source location x in meters +zs = 142810.05 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_025 b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_025 new file mode 100644 index 00000000..fbfa72e2 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/SOURCE_025 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 299367.72 # source location x in meters +zs = 240300.65 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/001/DATA/STATIONS b/seisflows/tests/test_data/test_solver/001/DATA/STATIONS new file mode 100644 index 00000000..8f979fe1 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/DATA/STATIONS @@ -0,0 +1,5 @@ +S000000 AA 2.43610e+05 2.78904e+05 0.0 0.0 +S000001 AA 3.38981e+05 1.77849e+05 0.0 0.0 +S000002 AA 1.64438e+05 2.94733e+05 0.0 0.0 +S000003 AA 9.22250e+04 3.68887e+05 0.0 0.0 +S000004 AA 2.90702e+05 2.46865e+05 0.0 0.0 diff --git a/seisflows/tests/test_data/test_solver/001/bin/xcombine_sem b/seisflows/tests/test_data/test_solver/001/bin/xcombine_sem new file mode 100755 index 00000000..969a4d93 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/bin/xcombine_sem @@ -0,0 +1,3 @@ +#!/bin/bash -e +echo "xcombine_sem" + diff --git a/seisflows/tests/test_data/test_solver/001/bin/xmeshfem2D b/seisflows/tests/test_data/test_solver/001/bin/xmeshfem2D new file mode 100755 index 00000000..149ca704 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/bin/xmeshfem2D @@ -0,0 +1,2 @@ +#!/bin/bash -e +echo "xmeshfem2D" diff --git a/seisflows/tests/test_data/test_solver/001/bin/xsmooth_sem b/seisflows/tests/test_data/test_solver/001/bin/xsmooth_sem new file mode 100755 index 00000000..376b4aa5 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/bin/xsmooth_sem @@ -0,0 +1,3 @@ +#!/bin/bash -e +echo "xsmooth_sem" + diff --git a/seisflows/tests/test_data/test_solver/001/bin/xspecfem2D b/seisflows/tests/test_data/test_solver/001/bin/xspecfem2D new file mode 100755 index 00000000..e50c2b0b --- /dev/null +++ b/seisflows/tests/test_data/test_solver/001/bin/xspecfem2D @@ -0,0 +1,3 @@ +#!/bin/bash -e +echo "xspecfem2D" + diff --git 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@@ -0,0 +1 @@ + diff --git a/seisflows/tests/test_data/test_solver/002/DATA/SOURCE b/seisflows/tests/test_data/test_solver/002/DATA/SOURCE new file mode 120000 index 00000000..b0d2e8d8 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/002/DATA/SOURCE @@ -0,0 +1 @@ +SOURCE_002 \ No newline at end of file diff --git a/seisflows/tests/test_data/test_solver/002/DATA/SOURCE_002 b/seisflows/tests/test_data/test_solver/002/DATA/SOURCE_002 new file mode 100644 index 00000000..8371bd1f --- /dev/null +++ b/seisflows/tests/test_data/test_solver/002/DATA/SOURCE_002 @@ -0,0 +1,57 @@ +## Source 1 +source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver +xs = 116059.49 # source location x in meters +zs = 105566.07 # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) +## Source type parameters: +# 1 = elastic force or acoustic pressure +# 2 = moment tensor +# or Initial field type (when initialfield set in Par_file): +# For a plane wave including converted and reflected waves at the free surface: +# 1 = P wave, +# 2 = S wave, +# 3 = Rayleigh wave +# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: +# 4 = P wave, +# 5 = S wave +# For initial mode displacement: +# 6 = mode (2,3) of a rectangular membrane +source_type = 1 +# Source time function: +# In the case of a source located in an acoustic medium, +# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi +# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. +# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. +# Options: +# 1 = second derivative of a Gaussian (a.k.a. Ricker), +# 2 = first derivative of a Gaussian, +# 3 = Gaussian, +# 4 = Dirac, +# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), +# 6 = ocean acoustics type I, +# 7 = ocean acoustics type II, +# 8 = external source time function = 8 (source read from file), +# 9 = burst, +# 10 = Sinus source time function, +# 11 = Marmousi Ormsby wavelet +time_function_type = 2 +# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : +# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) +# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop +name_of_source_file = "" # Only for option 8 : file containing the source wavelet +burst_band_width = 0. # Only for option 9 : band width of the burst +f0 = 8.400e-02 # dominant source frequency (Hz) if not Dirac or Heaviside +tshift = 0.000e+00 # time shift when multi sources (if one source, must be zero) +## Force source +# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused +anglesource = 0.00 +## Moment tensor +# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. +Mxx = 1.000000 # Mxx component (for a moment tensor source only) +Mzz = -1.000000 # Mzz component (for a moment tensor source only) +Mxz = 0.000000 # Mxz component (for a moment tensor source only) +## Amplification (factor to amplify source time function) +factor = 1.000e+10 # amplification factor +## Moving source parameters +vx = 0.0 # Horizontal source velocity (m/s) +vz = 0.0 # Vertical source velocity (m/s) + diff --git a/seisflows/tests/test_data/test_solver/002/DATA/STATIONS b/seisflows/tests/test_data/test_solver/002/DATA/STATIONS new file mode 100644 index 00000000..8f979fe1 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/002/DATA/STATIONS @@ -0,0 +1,5 @@ +S000000 AA 2.43610e+05 2.78904e+05 0.0 0.0 +S000001 AA 3.38981e+05 1.77849e+05 0.0 0.0 +S000002 AA 1.64438e+05 2.94733e+05 0.0 0.0 +S000003 AA 9.22250e+04 3.68887e+05 0.0 0.0 +S000004 AA 2.90702e+05 2.46865e+05 0.0 0.0 diff --git a/seisflows/tests/test_data/test_solver/002/bin/xcombine_sem b/seisflows/tests/test_data/test_solver/002/bin/xcombine_sem new file mode 100755 index 00000000..969a4d93 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/002/bin/xcombine_sem @@ -0,0 +1,3 @@ +#!/bin/bash -e +echo "xcombine_sem" + diff --git a/seisflows/tests/test_data/test_solver/002/bin/xmeshfem2D b/seisflows/tests/test_data/test_solver/002/bin/xmeshfem2D new file mode 100755 index 00000000..149ca704 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/002/bin/xmeshfem2D @@ -0,0 +1,2 @@ +#!/bin/bash -e +echo "xmeshfem2D" diff --git a/seisflows/tests/test_data/test_solver/002/bin/xsmooth_sem b/seisflows/tests/test_data/test_solver/002/bin/xsmooth_sem new file mode 100755 index 00000000..376b4aa5 --- /dev/null +++ b/seisflows/tests/test_data/test_solver/002/bin/xsmooth_sem @@ -0,0 +1,3 @@ +#!/bin/bash -e +echo "xsmooth_sem" + diff --git a/seisflows/tests/test_data/test_solver/002/bin/xspecfem2D b/seisflows/tests/test_data/test_solver/002/bin/xspecfem2D new file mode 100755 index 00000000..e50c2b0b --- /dev/null +++ b/seisflows/tests/test_data/test_solver/002/bin/xspecfem2D @@ -0,0 +1,3 @@ +#!/bin/bash -e +echo "xspecfem2D" + diff --git 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seisflows.preprocess.pyatoa import Pyaflowa +from seisflows.preprocess.pyaflowa import Pyaflowa TEST_DATA = os.path.join(ROOT_DIR, "tests", "test_data", "test_preprocess") @@ -108,7 +109,7 @@ def test_pyaflowa_setup(tmpdir): workdir=tmpdir, path_specfem_data=os.path.join(TEST_SOLVER, "mainsolver", "DATA"), path_solver=os.path.join(TEST_SOLVER, "mainsolver"), - source_prefix="CMTSOLUTION", + source_prefix="SOURCE", ntask=2, components="Y", ) @@ -118,29 +119,69 @@ def test_pyaflowa_setup(tmpdir): pyaflowa.setup() - assert(len(pyaflowa._station_codes) == 2) - assert(pyaflowa._station_codes[0] == "AA.S0001.*.*") + assert(len(pyaflowa._station_codes) == 5) + assert(pyaflowa._station_codes[0] == "AA.S000000.*.*") assert(len(pyaflowa._source_names) == pyaflowa._ntask) assert(pyaflowa._source_names[0] == "001") assert(pyaflowa._config.component_list == ["Y"]) -def test_pyaflowa_quantify_misfit(tmpdir): +def test_pyaflowa_setup_quantify_misfit(tmpdir): """ - Test misfit quantification for Pyatoa including data gathering. Waveform - data and source and receiver metadata is exposed from the test data + Test Config setup that is used to control `quantify_misfit` function + """ + pyaflowa = Pyaflowa( + workdir=tmpdir, + path_specfem_data=os.path.join(TEST_SOLVER, "mainsolver", "DATA"), + path_solver=TEST_SOLVER, source_prefix="SOURCE", ntask=1, + data_case="synthetic", components="Y", fix_windows="ITER", + ) + pyaflowa.setup() + config = pyaflowa._setup_quantify_misfit(source_name="001", iteration=1, + step_count=1) + assert(config.eval_tag == "i01s01") + # Data specific time series values calculated by function + assert(config.start_pad == 48) + assert(config.end_pad == 299.94) + + +def test_pyaflowa_quantify_misfit_station(tmpdir): + """ + Check that the function to quantify misfit that should be run in parallel + works as a serial job + """ + pyaflowa = Pyaflowa( + workdir=tmpdir, + path_specfem_data=os.path.join(TEST_SOLVER, "mainsolver", "DATA"), + path_solver=TEST_SOLVER, source_prefix="SOURCE", ntask=2, + data_case="synthetic", components="Y", + ) + pyaflowa.setup() + config = pyaflowa._setup_quantify_misfit(source_name="001", iteration=1, + step_count=1) + misfit, nwin = pyaflowa._quantify_misfit_station( + config=config, station_code=pyaflowa._station_codes[0], + save_adjsrcs=False + ) + assert(misfit == 33.5304) + assert(nwin == 8.) + + +def test_pyaflowa_quantify_misfit_single(tmpdir): + """ + Test misfit quantification for Pyatoa during a single misfit evaluation. + Waveform data and source and receiver metadata is exposed from the test data directory. Data and synthetics are the same so residuals will be 0. Want to check that we can process in parallel and that Pyatoa outputs figures, - and data + and data. """ pyaflowa = Pyaflowa( workdir=tmpdir, path_specfem_data=os.path.join(TEST_SOLVER, "mainsolver", "DATA"), - path_solver=TEST_SOLVER, source_prefix="CMTSOLUTION", ntask=2, + path_solver=TEST_SOLVER, source_prefix="SOURCE", ntask=2, data_case="synthetic", components="Y", ) pyaflowa.setup() - save_residuals = os.path.join(tmpdir, "residuals.txt") for source_name in pyaflowa._source_names: save_residuals = os.path.join(tmpdir, f"residuals_{source_name}.txt") pyaflowa.quantify_misfit(source_name=source_name, @@ -148,18 +189,107 @@ def test_pyaflowa_quantify_misfit(tmpdir): save_adjsrcs=tmpdir) residuals = np.loadtxt(save_residuals) # just check one of the file - assert(residuals == 0.) # data and synthetics are the same + assert(residuals == 0.919) # Check that windows and adjoint sources were saved to dataset + nwin = {"001": 45, "002": 48} for source_name in pyaflowa._source_names: with ASDFDataSet(os.path.join(pyaflowa.path._datasets, f"{source_name}.h5")) as ds: - # Pyatoa selects 18 windows for 2 events and 2 stations - assert(len(ds.auxiliary_data.MisfitWindows.i01.s00.list()) == 18) - assert(len(ds.auxiliary_data.AdjointSources.i01.s00.list()) == 2) + # Pyatoa selects N number windows for each source + assert(len(ds.auxiliary_data.MisfitWindows.i01.s00.list()) == + nwin[source_name]) + assert(len(ds.auxiliary_data.AdjointSources.i01.s00.list()) == 5) # Check that adjoint sources are all zero adjsrcs = glob(os.path.join(tmpdir, "*.adj")) for adjsrc in adjsrcs: data = np.loadtxt(adjsrc) - assert(not data[:,1].any()) # assert all zeros + assert(data[:, 1].any()) # assert that adjoint sourcse are not zero + + +def test_pyaflowa_check_fixed_windows(tmpdir): + """ + Test that misfit window bool returner always returns how we want it to. + """ + pf = Pyaflowa(fix_windows=True) + assert(pf._check_fixed_windows(iteration=99, step_count=99)[0]) + pf = Pyaflowa(fix_windows="ITER") + assert(not pf._check_fixed_windows(iteration=1, step_count=0)[0]) + assert(pf._check_fixed_windows(iteration=1, step_count=1)[0]) + pf = Pyaflowa(fix_windows="ONCE", start=5) + assert(not pf._check_fixed_windows(iteration=5, step_count=0)[0]) + assert(pf._check_fixed_windows(iteration=5, step_count=1)[0]) + assert(pf._check_fixed_windows(iteration=6, step_count=0)[0]) + + +def test_pyaflowa_finalize(tmpdir): + """ + Test teardown procedures for the Pyaflowa preprocessing module which + includes creating an Inspector, condensing PDF files, and exporting + files to disk. + """ + pyaflowa = Pyaflowa( + workdir=tmpdir, + path_specfem_data=os.path.join(TEST_SOLVER, "mainsolver", "DATA"), + path_output=os.path.join(tmpdir, "output"), + path_solver=TEST_SOLVER, source_prefix="SOURCE", ntask=2, + data_case="synthetic", components="Y", fix_windows="ITER", + export_datasets=True, export_figures=True, export_log_files=True, + ) + pyaflowa.setup() + unix.mkdir(pyaflowa.path.output) # usually done by other modules setup + for source_name in pyaflowa._source_names: + for step_count in range(3): + # Ignore any outputs, just want to run misfit quantification + # misfit will not be reducing but thats okay + pyaflowa.quantify_misfit(source_name=source_name, + iteration=1, + step_count=step_count) + + pyaflowa.finalize() + # Just check file count to see that finalize did what it's supposed to do + # since finalize just moves and collects files + assert(len(glob(os.path.join(pyaflowa.path.output, "figures", "*"))) == 1) + assert(len(glob(os.path.join(pyaflowa.path.output, "logs", "*"))) == 6) + assert(len(glob(os.path.join(pyaflowa.path.output, + "datasets", "*.csv"))) == 2) + + +# def test_pyaflowa_quantify_misfit_inversion(tmpdir): +# """ +# Test misfit quantification for Pyatoa but simulating multiple back-to-back +# evaluations as one would encounter during an inversion This would involve +# re-using misfit windows throughout the evaluation, and reading in already +# gathered data from an ASDFDataSet +# """ +# pyaflowa = Pyaflowa( +# workdir=tmpdir, +# path_specfem_data=os.path.join(TEST_SOLVER, "mainsolver", "DATA"), +# path_solver=TEST_SOLVER, source_prefix="SOURCE", ntask=1, +# data_case="synthetic", components="Y", fix_windows="ITER", +# ) +# pyaflowa.setup() +# source_name = pyaflowa._source_names[0] +# for step_count in range(2): +# # Ignore any outputs, just want to run misfit quantification +# # misfit will not be reducing but thats okay +# pyaflowa.quantify_misfit(source_name=source_name, +# iteration=1, +# step_count=step_count) +# +# # Check that correct number of PDFs have been made +# assert(len(glob(os.path.join(pyaflowa.path._figures, "*pdf"))) == 4) +# +# # Check datasets for correct formatting of auxiliary data and rand vals +# fid = os.path.join(pyaflowa.path._datasets, f"{source_name}.h5") +# with ASDFDataSet(fid, mode="r") as ds: +# assert(len(ds.waveforms.list()) == 5) +# sta_0 = ds.waveforms[ds.waveforms.list()[0]] +# assert(len(sta_0.list()) == 5) # 1 observed, 4 synthetics +# assert(len(ds.auxiliary_data.AdjointSources.i01) == 2) +# assert(len(ds.auxiliary_data.MisfitWindows.i01) == 2) +# assert(len(ds.auxiliary_data.MisfitWindows.i01.s01.list()) == 45) +# adjsrc = ds.auxiliary_data.AdjointSources.i01.s00.AA_S000004_BXY +# misfit = adjsrc.parameters["misfit"] +# assert(misfit == pytest.approx(18.3167, 3)) diff --git a/seisflows/tests/test_solver.py b/seisflows/tests/test_solver.py index 76b5f48e..854435d7 100644 --- a/seisflows/tests/test_solver.py +++ b/seisflows/tests/test_solver.py @@ -40,7 +40,7 @@ def test_initialize_working_directory(tmpdir): solver = Specfem(path_specfem_data=specfem_data, path_specfem_bin=specfem_bin, - source_prefix="CMTSOLUTION", workdir=tmpdir + source_prefix="SOURCE", workdir=tmpdir ) assert(not os.path.exists(solver.path.mainsolver)) @@ -57,10 +57,10 @@ def test_initialize_working_directory(tmpdir): assert(os.path.islink(solver.path.mainsolver)) assert(os.path.exists(solver.cwd)) assert(glob(os.path.join(solver.cwd, "*"))) - event_fid = os.path.join(solver.cwd, "DATA", "CMTSOLUTION") + event_fid = os.path.join(solver.cwd, "DATA", "SOURCE") assert(os.path.islink(event_fid)) - event_line = open(event_fid).readlines()[1].strip() - assert(event_line.split(":")[1].strip() == "001") + event_line = open(event_fid).readlines()[0].strip() + assert(event_line == "## Source 1") def test_run_binary(tmpdir): From 11b45062060be015f7b0f4e89b4352192b20ecf8 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Tue, 2 Aug 2022 18:03:23 -0800 Subject: [PATCH 100/195] removing test data and slimming down preprocess integration test because tests were taking too long. also condensed pyaflowa test to a single integration line search test that tests all functionality at once --- seisflows/preprocess/pyaflowa.py | 128 +- .../old_test_solver/001/DATA/CMTSOLUTION_001 | 0 .../old_test_solver/001/DATA/CMTSOLUTION_002 | 0 .../old_test_solver/001/DATA/CMTSOLUTION_003 | 0 .../old_test_solver/001/DATA/CMTSOLUTION_004 | 0 .../old_test_solver/001/DATA/CMTSOLUTION_005 | 0 .../old_test_solver/001/DATA/CMTSOLUTION_006 | 0 .../old_test_solver/001/DATA/Par_file | 0 .../old_test_solver/001/DATA/STATIONS | 0 .../old_test_solver/001/bin/xcombine_sem | 0 .../old_test_solver/001/bin/xmeshfem2D | 0 .../old_test_solver/001/bin/xsmooth_sem | 0 .../old_test_solver/001/bin/xspecfem2D | 0 .../001/traces/obs/AA.S0001.BXY.semd | 0 .../001/traces/obs/AA.S0002.BXY.semd | 0 .../001/traces/syn/AA.S0001.BXY.semd | 0 .../001/traces/syn/AA.S0002.BXY.semd | 0 .../old_test_solver/002/DATA/CMTSOLUTION_001 | 0 .../old_test_solver/002/DATA/CMTSOLUTION_002 | 0 .../old_test_solver/002/DATA/CMTSOLUTION_003 | 0 .../old_test_solver/002/DATA/CMTSOLUTION_004 | 0 .../old_test_solver/002/DATA/CMTSOLUTION_005 | 0 .../old_test_solver/002/DATA/CMTSOLUTION_006 | 0 .../old_test_solver/002/DATA/Par_file | 0 .../old_test_solver/002/DATA/STATIONS | 0 .../old_test_solver/002/bin/xcombine_sem | 0 .../old_test_solver/002/bin/xmeshfem2D | 0 .../old_test_solver/002/bin/xsmooth_sem | 0 .../old_test_solver/002/bin/xspecfem2D | 0 .../002/traces/obs/AA.S0001.BXY.semd | 0 .../002/traces/obs/AA.S0002.BXY.semd | 0 .../002/traces/syn/AA.S0001.BXY.semd | 0 .../002/traces/syn/AA.S0002.BXY.semd | 0 .../old_test_solver/sources/CMTSOLUTION_001 | 0 .../old_test_solver/sources/CMTSOLUTION_002 | 0 .../old_test_solver/sources/SOURCE_001 | 0 .../old_test_solver/sources/SOURCE_002 | 0 .../test_data/test_solver/001/DATA/STATIONS | 3 - .../001/traces/obs/AA.S000002.BXY.semd | 5000 ----------------- .../001/traces/obs/AA.S000003.BXY.semd | 5000 ----------------- .../001/traces/obs/AA.S000004.BXY.semd | 5000 ----------------- .../001/traces/syn/AA.S000002.BXY.semd | 5000 ----------------- .../001/traces/syn/AA.S000003.BXY.semd | 5000 ----------------- .../001/traces/syn/AA.S000004.BXY.semd | 5000 ----------------- .../test_data/test_solver/002/DATA/STATIONS | 3 - .../002/traces/obs/AA.S000002.BXY.semd | 5000 ----------------- .../002/traces/obs/AA.S000003.BXY.semd | 5000 ----------------- .../002/traces/obs/AA.S000004.BXY.semd | 5000 ----------------- .../002/traces/syn/AA.S000002.BXY.semd | 5000 ----------------- .../002/traces/syn/AA.S000003.BXY.semd | 5000 ----------------- .../002/traces/syn/AA.S000004.BXY.semd | 5000 ----------------- seisflows/tests/test_preprocess.py | 153 +- 52 files changed, 96 insertions(+), 60191 deletions(-) rename seisflows/tests/test_data/{ => hold}/old_test_solver/001/DATA/CMTSOLUTION_001 (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/001/DATA/CMTSOLUTION_002 (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/001/DATA/CMTSOLUTION_003 (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/001/DATA/CMTSOLUTION_004 (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/001/DATA/CMTSOLUTION_005 (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/001/DATA/CMTSOLUTION_006 (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/001/DATA/Par_file (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/001/DATA/STATIONS (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/001/bin/xcombine_sem (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/001/bin/xmeshfem2D (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/001/bin/xsmooth_sem (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/001/bin/xspecfem2D (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/001/traces/obs/AA.S0001.BXY.semd (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/001/traces/obs/AA.S0002.BXY.semd (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/001/traces/syn/AA.S0001.BXY.semd (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/001/traces/syn/AA.S0002.BXY.semd (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/002/DATA/CMTSOLUTION_001 (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/002/DATA/CMTSOLUTION_002 (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/002/DATA/CMTSOLUTION_003 (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/002/DATA/CMTSOLUTION_004 (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/002/DATA/CMTSOLUTION_005 (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/002/DATA/CMTSOLUTION_006 (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/002/DATA/Par_file (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/002/DATA/STATIONS (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/002/bin/xcombine_sem (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/002/bin/xmeshfem2D (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/002/bin/xsmooth_sem (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/002/bin/xspecfem2D (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/002/traces/obs/AA.S0001.BXY.semd (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/002/traces/obs/AA.S0002.BXY.semd (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/002/traces/syn/AA.S0001.BXY.semd (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/002/traces/syn/AA.S0002.BXY.semd (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/sources/CMTSOLUTION_001 (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/sources/CMTSOLUTION_002 (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/sources/SOURCE_001 (100%) rename seisflows/tests/test_data/{ => hold}/old_test_solver/sources/SOURCE_002 (100%) delete mode 100644 seisflows/tests/test_data/test_solver/001/traces/obs/AA.S000002.BXY.semd delete mode 100644 seisflows/tests/test_data/test_solver/001/traces/obs/AA.S000003.BXY.semd delete mode 100644 seisflows/tests/test_data/test_solver/001/traces/obs/AA.S000004.BXY.semd delete mode 100644 seisflows/tests/test_data/test_solver/001/traces/syn/AA.S000002.BXY.semd delete mode 100644 seisflows/tests/test_data/test_solver/001/traces/syn/AA.S000003.BXY.semd delete mode 100644 seisflows/tests/test_data/test_solver/001/traces/syn/AA.S000004.BXY.semd delete mode 100644 seisflows/tests/test_data/test_solver/002/traces/obs/AA.S000002.BXY.semd delete mode 100644 seisflows/tests/test_data/test_solver/002/traces/obs/AA.S000003.BXY.semd delete mode 100644 seisflows/tests/test_data/test_solver/002/traces/obs/AA.S000004.BXY.semd delete mode 100644 seisflows/tests/test_data/test_solver/002/traces/syn/AA.S000002.BXY.semd delete mode 100644 seisflows/tests/test_data/test_solver/002/traces/syn/AA.S000003.BXY.semd delete mode 100644 seisflows/tests/test_data/test_solver/002/traces/syn/AA.S000004.BXY.semd diff --git a/seisflows/preprocess/pyaflowa.py b/seisflows/preprocess/pyaflowa.py index a0807397..e6aa4307 100644 --- a/seisflows/preprocess/pyaflowa.py +++ b/seisflows/preprocess/pyaflowa.py @@ -97,7 +97,7 @@ def __init__(self, min_period=1., max_period=10., filter_corners=4, workdir=os.getcwd(), path_preprocess=None, path_solver=None, path_specfem_data=None, path_data=None, path_output=None, data_format="ascii", - data_case="data", components=None, + data_case="data", components="ZNE", start=None, ntask=1, nproc=1, source_prefix=None, **kwargs): """ @@ -263,55 +263,6 @@ def setup(self): source_prefix=self._source_prefix, ntask=self._ntask ) - def _setup_quantify_misfit(self, source_name, iteration, step_count): - """ - Create an event-specific Config object which contains information about - the current event, and position in the workflow evaluation. Also - provides specific information on event paths and timing to be used by - the Manager - - :type source_name: str - :param source_name: name of the event to quantify misfit for. If not - given, will attempt to gather event id from the given task id which - is assigned by system.run() - :type iteration: int - :param iteration: current iteration of the workflow, information should - be provided by `workflow` module if we are running an inversion. - Defaults to 1 if not given (1st iteration) - :type step_count: int - :param step_count: current step count of the line search. Information - should be provided by the `optimize` module if we are running an - inversion. Defaults to 0 if not given (1st evaluation) - :rtype: pyatoa.core.config.Config - :return: Config object that is specifically crafted for a given event - that can be directly fed to the Manager for misfit quantification - """ - config = self._config.copy() - config.event_id = source_name or self._source_names[get_task_id()] - config.iteration = iteration - config.step_count = step_count - - # Force the Manager to look in the solver directory for data - # note: we are assuming the SeisFlows `solver` directory structure here. - # If we change how the default `solver` directory is named (defined by - # `solver.initialize_solver_directories()`), then this will break - obs_path = os.path.join(self.path.solver, source_name, "traces", "obs") - config.paths["waveforms"].append(obs_path) - - syn_path = os.path.join(self.path.solver, source_name, "traces", "syn") - config.paths["synthetics"].append(syn_path) - - # Extract start and end times from one of the synthetic traces so that - # Pyatoa knows how long seismograms are and when to start them - # NOTE: assuming all synthetic time axes are the same for this source - synthetics = glob(os.path.join(syn_path, "*")) - assert synthetics, f"Pyatoa found no synthetics in: {syn_path}" - tr_syn = read_sem(synthetics[0])[0] - config.start_pad = abs(tr_syn.stats.time_offset) # [s] must be positive - config.end_pad = tr_syn.stats.endtime - tr_syn.stats.starttime # [s] - - return config - @staticmethod def _ftag(config): """ @@ -352,9 +303,11 @@ def quantify_misfit(self, source_name=None, save_residuals=None, should be provided by the `optimize` module if we are running an inversion. Defaults to 0 if not given (1st evaluation) """ + # Generate an event/evaluation specific config object to control Pyatoa config = self._setup_quantify_misfit(source_name, iteration, step_count) - misfit, nwin = self._run_quantify_misfit(config, save_adjsrcs, False) - + # Run misfit quantification for ALL stations and this given event + misfit, nwin = self._run_quantify_misfit(config, save_adjsrcs, + parallel=True) # Calculate misfit based on the raw misfit and total number of windows if save_residuals: # Calculate the misfit based on the number of windows. Equation from @@ -386,6 +339,55 @@ def quantify_misfit(self, source_name=None, save_residuals=None, ) self._collect_tmp_log_files(pyatoa_logger, config.event_id) + def _setup_quantify_misfit(self, source_name, iteration, step_count): + """ + Create an event-specific Config object which contains information about + the current event, and position in the workflow evaluation. Also + provides specific information on event paths and timing to be used by + the Manager + + :type source_name: str + :param source_name: name of the event to quantify misfit for. If not + given, will attempt to gather event id from the given task id which + is assigned by system.run() + :type iteration: int + :param iteration: current iteration of the workflow, information should + be provided by `workflow` module if we are running an inversion. + Defaults to 1 if not given (1st iteration) + :type step_count: int + :param step_count: current step count of the line search. Information + should be provided by the `optimize` module if we are running an + inversion. Defaults to 0 if not given (1st evaluation) + :rtype: pyatoa.core.config.Config + :return: Config object that is specifically crafted for a given event + that can be directly fed to the Manager for misfit quantification + """ + config = self._config.copy() + config.event_id = source_name or self._source_names[get_task_id()] + config.iteration = iteration + config.step_count = step_count + + # Force the Manager to look in the solver directory for data + # note: we are assuming the SeisFlows `solver` directory structure here. + # If we change how the default `solver` directory is named (defined by + # `solver.initialize_solver_directories()`), then this will break + obs_path = os.path.join(self.path.solver, source_name, "traces", "obs") + config.paths["waveforms"].append(obs_path) + + syn_path = os.path.join(self.path.solver, source_name, "traces", "syn") + config.paths["synthetics"].append(syn_path) + + # Extract start and end times from one of the synthetic traces so that + # Pyatoa knows how long seismograms are and when to start them + # NOTE: assuming all synthetic time axes are the same for this source + synthetics = glob(os.path.join(syn_path, "*")) + assert synthetics, f"Pyatoa found no synthetics in: {syn_path}" + tr_syn = read_sem(synthetics[0])[0] + config.start_pad = abs(tr_syn.stats.time_offset) # [s] must be positive + config.end_pad = tr_syn.stats.endtime - tr_syn.stats.starttime # [s] + + return config + def _run_quantify_misfit(self, config, save_adjsrcs, parallel=False): """ Run misfit quantification for each station concurrently or in serial. @@ -396,25 +398,25 @@ def _run_quantify_misfit(self, config, save_adjsrcs, parallel=False): misfit, nwin = 0, 0 # Run processing in parallel if parallel: - with ProcessPoolExecutor(max_workers=2) as executor: - futures = ( - executor.submit(self._quantify_misfit_station, config, - code, save_adjsrcs) - for code in self._station_codes - ) - # We only need to return misfit information. All data/results are - # saved to the ASDFDataSet and status is logged to separate log file + with ProcessPoolExecutor(max_workers=1) as executor: + futures = (executor.submit(self._quantify_misfit_station, + config, code, save_adjsrcs) + for code in self._station_codes) + # We only need to return misfit information. All data/results + # are saved to the ASDFDataSet and status is logged to separate + # log file for future in as_completed(futures): _misfit, _nwin = future.result() - del future # Free up memory otherwise ram lock + del future # Free up memory once future is completed if _misfit is not None: misfit += _misfit nwin += _nwin # Run processing in serial else: for code in self._station_codes: - _misfit, _nwin = self._quantify_misfit_station(config, code, - save_adjsrcs) + _misfit, _nwin = self._quantify_misfit_station( + config=config, station_code=code, save_adjsrcs=save_adjsrcs + ) if _misfit is not None: misfit += _misfit nwin += _nwin diff --git a/seisflows/tests/test_data/old_test_solver/001/DATA/CMTSOLUTION_001 b/seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_001 similarity index 100% rename from seisflows/tests/test_data/old_test_solver/001/DATA/CMTSOLUTION_001 rename to seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_001 diff --git a/seisflows/tests/test_data/old_test_solver/001/DATA/CMTSOLUTION_002 b/seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_002 similarity index 100% rename from seisflows/tests/test_data/old_test_solver/001/DATA/CMTSOLUTION_002 rename to seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_002 diff --git a/seisflows/tests/test_data/old_test_solver/001/DATA/CMTSOLUTION_003 b/seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_003 similarity index 100% rename from seisflows/tests/test_data/old_test_solver/001/DATA/CMTSOLUTION_003 rename to seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_003 diff --git a/seisflows/tests/test_data/old_test_solver/001/DATA/CMTSOLUTION_004 b/seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_004 similarity index 100% rename from seisflows/tests/test_data/old_test_solver/001/DATA/CMTSOLUTION_004 rename to seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_004 diff --git a/seisflows/tests/test_data/old_test_solver/001/DATA/CMTSOLUTION_005 b/seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_005 similarity index 100% rename from seisflows/tests/test_data/old_test_solver/001/DATA/CMTSOLUTION_005 rename to seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_005 diff --git a/seisflows/tests/test_data/old_test_solver/001/DATA/CMTSOLUTION_006 b/seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_006 similarity index 100% rename from seisflows/tests/test_data/old_test_solver/001/DATA/CMTSOLUTION_006 rename to seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_006 diff --git a/seisflows/tests/test_data/old_test_solver/001/DATA/Par_file b/seisflows/tests/test_data/hold/old_test_solver/001/DATA/Par_file similarity index 100% rename from seisflows/tests/test_data/old_test_solver/001/DATA/Par_file rename to seisflows/tests/test_data/hold/old_test_solver/001/DATA/Par_file diff --git a/seisflows/tests/test_data/old_test_solver/001/DATA/STATIONS b/seisflows/tests/test_data/hold/old_test_solver/001/DATA/STATIONS similarity index 100% rename from seisflows/tests/test_data/old_test_solver/001/DATA/STATIONS rename to seisflows/tests/test_data/hold/old_test_solver/001/DATA/STATIONS diff --git a/seisflows/tests/test_data/old_test_solver/001/bin/xcombine_sem b/seisflows/tests/test_data/hold/old_test_solver/001/bin/xcombine_sem similarity index 100% rename from seisflows/tests/test_data/old_test_solver/001/bin/xcombine_sem rename to seisflows/tests/test_data/hold/old_test_solver/001/bin/xcombine_sem diff --git a/seisflows/tests/test_data/old_test_solver/001/bin/xmeshfem2D b/seisflows/tests/test_data/hold/old_test_solver/001/bin/xmeshfem2D similarity index 100% rename from seisflows/tests/test_data/old_test_solver/001/bin/xmeshfem2D rename to seisflows/tests/test_data/hold/old_test_solver/001/bin/xmeshfem2D diff --git a/seisflows/tests/test_data/old_test_solver/001/bin/xsmooth_sem b/seisflows/tests/test_data/hold/old_test_solver/001/bin/xsmooth_sem similarity index 100% rename from seisflows/tests/test_data/old_test_solver/001/bin/xsmooth_sem rename to seisflows/tests/test_data/hold/old_test_solver/001/bin/xsmooth_sem diff --git a/seisflows/tests/test_data/old_test_solver/001/bin/xspecfem2D b/seisflows/tests/test_data/hold/old_test_solver/001/bin/xspecfem2D similarity index 100% rename from seisflows/tests/test_data/old_test_solver/001/bin/xspecfem2D rename to seisflows/tests/test_data/hold/old_test_solver/001/bin/xspecfem2D diff --git a/seisflows/tests/test_data/old_test_solver/001/traces/obs/AA.S0001.BXY.semd b/seisflows/tests/test_data/hold/old_test_solver/001/traces/obs/AA.S0001.BXY.semd similarity index 100% rename from seisflows/tests/test_data/old_test_solver/001/traces/obs/AA.S0001.BXY.semd rename to seisflows/tests/test_data/hold/old_test_solver/001/traces/obs/AA.S0001.BXY.semd diff --git a/seisflows/tests/test_data/old_test_solver/001/traces/obs/AA.S0002.BXY.semd b/seisflows/tests/test_data/hold/old_test_solver/001/traces/obs/AA.S0002.BXY.semd similarity index 100% rename from seisflows/tests/test_data/old_test_solver/001/traces/obs/AA.S0002.BXY.semd rename to seisflows/tests/test_data/hold/old_test_solver/001/traces/obs/AA.S0002.BXY.semd diff --git a/seisflows/tests/test_data/old_test_solver/001/traces/syn/AA.S0001.BXY.semd b/seisflows/tests/test_data/hold/old_test_solver/001/traces/syn/AA.S0001.BXY.semd similarity index 100% rename from seisflows/tests/test_data/old_test_solver/001/traces/syn/AA.S0001.BXY.semd rename to seisflows/tests/test_data/hold/old_test_solver/001/traces/syn/AA.S0001.BXY.semd diff --git a/seisflows/tests/test_data/old_test_solver/001/traces/syn/AA.S0002.BXY.semd b/seisflows/tests/test_data/hold/old_test_solver/001/traces/syn/AA.S0002.BXY.semd similarity index 100% rename from seisflows/tests/test_data/old_test_solver/001/traces/syn/AA.S0002.BXY.semd rename to seisflows/tests/test_data/hold/old_test_solver/001/traces/syn/AA.S0002.BXY.semd diff --git a/seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_001 b/seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_001 similarity index 100% rename from seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_001 rename to seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_001 diff --git a/seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_002 b/seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_002 similarity index 100% rename from seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_002 rename to seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_002 diff --git a/seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_003 b/seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_003 similarity index 100% rename from seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_003 rename to seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_003 diff --git a/seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_004 b/seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_004 similarity index 100% rename from seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_004 rename to seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_004 diff --git a/seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_005 b/seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_005 similarity index 100% rename from seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_005 rename to seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_005 diff --git a/seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_006 b/seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_006 similarity index 100% rename from seisflows/tests/test_data/old_test_solver/002/DATA/CMTSOLUTION_006 rename to seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_006 diff --git a/seisflows/tests/test_data/old_test_solver/002/DATA/Par_file b/seisflows/tests/test_data/hold/old_test_solver/002/DATA/Par_file similarity index 100% rename from seisflows/tests/test_data/old_test_solver/002/DATA/Par_file rename to seisflows/tests/test_data/hold/old_test_solver/002/DATA/Par_file diff --git a/seisflows/tests/test_data/old_test_solver/002/DATA/STATIONS b/seisflows/tests/test_data/hold/old_test_solver/002/DATA/STATIONS similarity index 100% rename from seisflows/tests/test_data/old_test_solver/002/DATA/STATIONS rename to seisflows/tests/test_data/hold/old_test_solver/002/DATA/STATIONS diff --git a/seisflows/tests/test_data/old_test_solver/002/bin/xcombine_sem b/seisflows/tests/test_data/hold/old_test_solver/002/bin/xcombine_sem similarity index 100% rename from seisflows/tests/test_data/old_test_solver/002/bin/xcombine_sem rename to seisflows/tests/test_data/hold/old_test_solver/002/bin/xcombine_sem diff --git a/seisflows/tests/test_data/old_test_solver/002/bin/xmeshfem2D b/seisflows/tests/test_data/hold/old_test_solver/002/bin/xmeshfem2D similarity index 100% rename from seisflows/tests/test_data/old_test_solver/002/bin/xmeshfem2D rename to seisflows/tests/test_data/hold/old_test_solver/002/bin/xmeshfem2D diff --git a/seisflows/tests/test_data/old_test_solver/002/bin/xsmooth_sem b/seisflows/tests/test_data/hold/old_test_solver/002/bin/xsmooth_sem similarity index 100% rename from seisflows/tests/test_data/old_test_solver/002/bin/xsmooth_sem rename to seisflows/tests/test_data/hold/old_test_solver/002/bin/xsmooth_sem diff --git a/seisflows/tests/test_data/old_test_solver/002/bin/xspecfem2D b/seisflows/tests/test_data/hold/old_test_solver/002/bin/xspecfem2D similarity index 100% rename from seisflows/tests/test_data/old_test_solver/002/bin/xspecfem2D rename to seisflows/tests/test_data/hold/old_test_solver/002/bin/xspecfem2D diff --git a/seisflows/tests/test_data/old_test_solver/002/traces/obs/AA.S0001.BXY.semd b/seisflows/tests/test_data/hold/old_test_solver/002/traces/obs/AA.S0001.BXY.semd similarity index 100% rename from seisflows/tests/test_data/old_test_solver/002/traces/obs/AA.S0001.BXY.semd rename to seisflows/tests/test_data/hold/old_test_solver/002/traces/obs/AA.S0001.BXY.semd diff --git a/seisflows/tests/test_data/old_test_solver/002/traces/obs/AA.S0002.BXY.semd b/seisflows/tests/test_data/hold/old_test_solver/002/traces/obs/AA.S0002.BXY.semd similarity index 100% rename from seisflows/tests/test_data/old_test_solver/002/traces/obs/AA.S0002.BXY.semd rename to seisflows/tests/test_data/hold/old_test_solver/002/traces/obs/AA.S0002.BXY.semd diff --git 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import ASDFDataSet from seisflows import ROOT_DIR from seisflows.tools import unix from seisflows.preprocess.default import Default @@ -17,7 +15,7 @@ TEST_SOLVER = os.path.join(ROOT_DIR, "tests", "test_data", "test_solver") -def test_read(): +def test_default_read(): """ Test that we can read SPECFEM generated synthetics with the preprocess mod. """ @@ -35,7 +33,7 @@ def test_read(): assert(st1[0].stats.npts == st2[0].stats.npts) -def test_write(tmpdir): +def test_default_write(tmpdir): """ Make sure we can write both data formats """ @@ -52,7 +50,7 @@ def test_write(tmpdir): preprocess.write(st1, fid=os.path.join(tmpdir, "test_stream_su")) -def test_initialize_adjoint_traces(tmpdir): +def test_default_initialize_adjoint_traces(tmpdir): """ Make sure we can write empty adjoint sources expected by SPECFEM """ @@ -71,7 +69,7 @@ def test_initialize_adjoint_traces(tmpdir): assert(fid.endswith(".adj")) -def test_quantify_misfit(tmpdir): +def test_default_quantify_misfit(tmpdir): """ Quantify misfit with some example data """ @@ -119,7 +117,7 @@ def test_pyaflowa_setup(tmpdir): pyaflowa.setup() - assert(len(pyaflowa._station_codes) == 5) + assert(len(pyaflowa._station_codes) == 2) assert(pyaflowa._station_codes[0] == "AA.S000000.*.*") assert(len(pyaflowa._source_names) == pyaflowa._ntask) assert(pyaflowa._source_names[0] == "001") @@ -145,89 +143,27 @@ def test_pyaflowa_setup_quantify_misfit(tmpdir): assert(config.end_pad == 299.94) -def test_pyaflowa_quantify_misfit_station(tmpdir): - """ - Check that the function to quantify misfit that should be run in parallel - works as a serial job - """ - pyaflowa = Pyaflowa( - workdir=tmpdir, - path_specfem_data=os.path.join(TEST_SOLVER, "mainsolver", "DATA"), - path_solver=TEST_SOLVER, source_prefix="SOURCE", ntask=2, - data_case="synthetic", components="Y", - ) - pyaflowa.setup() - config = pyaflowa._setup_quantify_misfit(source_name="001", iteration=1, - step_count=1) - misfit, nwin = pyaflowa._quantify_misfit_station( - config=config, station_code=pyaflowa._station_codes[0], - save_adjsrcs=False - ) - assert(misfit == 33.5304) - assert(nwin == 8.) - - -def test_pyaflowa_quantify_misfit_single(tmpdir): - """ - Test misfit quantification for Pyatoa during a single misfit evaluation. - Waveform data and source and receiver metadata is exposed from the test data - directory. Data and synthetics are the same so residuals will be 0. Want - to check that we can process in parallel and that Pyatoa outputs figures, - and data. - """ - pyaflowa = Pyaflowa( - workdir=tmpdir, - path_specfem_data=os.path.join(TEST_SOLVER, "mainsolver", "DATA"), - path_solver=TEST_SOLVER, source_prefix="SOURCE", ntask=2, - data_case="synthetic", components="Y", - ) - pyaflowa.setup() - for source_name in pyaflowa._source_names: - save_residuals = os.path.join(tmpdir, f"residuals_{source_name}.txt") - pyaflowa.quantify_misfit(source_name=source_name, - save_residuals=save_residuals, - save_adjsrcs=tmpdir) - - residuals = np.loadtxt(save_residuals) # just check one of the file - assert(residuals == 0.919) - - # Check that windows and adjoint sources were saved to dataset - nwin = {"001": 45, "002": 48} - for source_name in pyaflowa._source_names: - with ASDFDataSet(os.path.join(pyaflowa.path._datasets, - f"{source_name}.h5")) as ds: - # Pyatoa selects N number windows for each source - assert(len(ds.auxiliary_data.MisfitWindows.i01.s00.list()) == - nwin[source_name]) - assert(len(ds.auxiliary_data.AdjointSources.i01.s00.list()) == 5) - - # Check that adjoint sources are all zero - adjsrcs = glob(os.path.join(tmpdir, "*.adj")) - for adjsrc in adjsrcs: - data = np.loadtxt(adjsrc) - assert(data[:, 1].any()) # assert that adjoint sourcse are not zero - - -def test_pyaflowa_check_fixed_windows(tmpdir): +def test_pyaflowa_check_fixed_windows(): """ Test that misfit window bool returner always returns how we want it to. """ pf = Pyaflowa(fix_windows=True) - assert(pf._check_fixed_windows(iteration=99, step_count=99)[0]) + assert (pf._check_fixed_windows(iteration=99, step_count=99)[0]) pf = Pyaflowa(fix_windows="ITER") - assert(not pf._check_fixed_windows(iteration=1, step_count=0)[0]) - assert(pf._check_fixed_windows(iteration=1, step_count=1)[0]) + assert (not pf._check_fixed_windows(iteration=1, step_count=0)[0]) + assert (pf._check_fixed_windows(iteration=1, step_count=1)[0]) pf = Pyaflowa(fix_windows="ONCE", start=5) - assert(not pf._check_fixed_windows(iteration=5, step_count=0)[0]) - assert(pf._check_fixed_windows(iteration=5, step_count=1)[0]) - assert(pf._check_fixed_windows(iteration=6, step_count=0)[0]) + assert (not pf._check_fixed_windows(iteration=5, step_count=0)[0]) + assert (pf._check_fixed_windows(iteration=5, step_count=1)[0]) + assert (pf._check_fixed_windows(iteration=6, step_count=0)[0]) -def test_pyaflowa_finalize(tmpdir): +def test_pyaflowa_line_search(tmpdir): """ - Test teardown procedures for the Pyaflowa preprocessing module which - includes creating an Inspector, condensing PDF files, and exporting - files to disk. + Test that the Pyaflowa preprocess class can quantify misfit over the course + of a few evaluations (a line search) and run its finalization task + Essentially an integration test testing the entire preprocessing module + works as a whole """ pyaflowa = Pyaflowa( workdir=tmpdir, @@ -239,57 +175,30 @@ def test_pyaflowa_finalize(tmpdir): ) pyaflowa.setup() unix.mkdir(pyaflowa.path.output) # usually done by other modules setup + save_residuals = os.path.join(tmpdir, f"residuals.txt") for source_name in pyaflowa._source_names: for step_count in range(3): # Ignore any outputs, just want to run misfit quantification # misfit will not be reducing but thats okay pyaflowa.quantify_misfit(source_name=source_name, - iteration=1, - step_count=step_count) + iteration=1, step_count=step_count, + save_residuals=save_residuals, + save_adjsrcs=tmpdir) pyaflowa.finalize() + + # Check that final residuals file is the same + residuals = np.loadtxt(save_residuals) + assert(pyaflowa.sum_residuals(residuals) == 6.045) + + # Check that atleast one adjoint sources are not zero + adjsrcs = glob(os.path.join(tmpdir, "*.adj")) + data = np.loadtxt(adjsrcs[0]) + assert(data[:, 1].any()) # assert that adjoint sourcse are not zero + # Just check file count to see that finalize did what it's supposed to do # since finalize just moves and collects files assert(len(glob(os.path.join(pyaflowa.path.output, "figures", "*"))) == 1) assert(len(glob(os.path.join(pyaflowa.path.output, "logs", "*"))) == 6) assert(len(glob(os.path.join(pyaflowa.path.output, "datasets", "*.csv"))) == 2) - - -# def test_pyaflowa_quantify_misfit_inversion(tmpdir): -# """ -# Test misfit quantification for Pyatoa but simulating multiple back-to-back -# evaluations as one would encounter during an inversion This would involve -# re-using misfit windows throughout the evaluation, and reading in already -# gathered data from an ASDFDataSet -# """ -# pyaflowa = Pyaflowa( -# workdir=tmpdir, -# path_specfem_data=os.path.join(TEST_SOLVER, "mainsolver", "DATA"), -# path_solver=TEST_SOLVER, source_prefix="SOURCE", ntask=1, -# data_case="synthetic", components="Y", fix_windows="ITER", -# ) -# pyaflowa.setup() -# source_name = pyaflowa._source_names[0] -# for step_count in range(2): -# # Ignore any outputs, just want to run misfit quantification -# # misfit will not be reducing but thats okay -# pyaflowa.quantify_misfit(source_name=source_name, -# iteration=1, -# step_count=step_count) -# -# # Check that correct number of PDFs have been made -# assert(len(glob(os.path.join(pyaflowa.path._figures, "*pdf"))) == 4) -# -# # Check datasets for correct formatting of auxiliary data and rand vals -# fid = os.path.join(pyaflowa.path._datasets, f"{source_name}.h5") -# with ASDFDataSet(fid, mode="r") as ds: -# assert(len(ds.waveforms.list()) == 5) -# sta_0 = ds.waveforms[ds.waveforms.list()[0]] -# assert(len(sta_0.list()) == 5) # 1 observed, 4 synthetics -# assert(len(ds.auxiliary_data.AdjointSources.i01) == 2) -# assert(len(ds.auxiliary_data.MisfitWindows.i01) == 2) -# assert(len(ds.auxiliary_data.MisfitWindows.i01.s01.list()) == 45) -# adjsrc = ds.auxiliary_data.AdjointSources.i01.s00.AA_S000004_BXY -# misfit = adjsrc.parameters["misfit"] -# assert(misfit == pytest.approx(18.3167, 3)) From 4d62b1bb7d6d74203929a131fb21591058a60600 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Tue, 2 Aug 2022 19:59:45 -0800 Subject: [PATCH 101/195] seisflows configure now works thanks to more strict docstring formatting. All docstrings that get put into the parameter file will need some special formatting for seisflows configure to work properly, but now all inherited docstrings are honored and paths are written separately --- seisflows/optimize/LBFGS.py | 10 ++- seisflows/optimize/NLCG.py | 10 ++- seisflows/optimize/gradient.py | 10 ++- seisflows/preprocess/default.py | 15 +++- seisflows/preprocess/pyaflowa.py | 21 +++++- seisflows/seisflows.py | 55 ++++++++++---- seisflows/solver/specfem.py | 11 ++- seisflows/solver/specfem2d.py | 10 ++- seisflows/solver/specfem3d.py | 10 ++- seisflows/solver/specfem3d_globe.py | 12 ++- seisflows/system/cluster.py | 10 ++- seisflows/system/slurm.py | 13 +++- seisflows/system/workstation.py | 10 ++- seisflows/workflow/forward.py | 13 +++- seisflows/workflow/inversion.py | 21 ++++-- seisflows/workflow/migration.py | 17 +++-- seisflows/workflow/thrifty_inversion.py | 97 ------------------------- 17 files changed, 196 insertions(+), 149 deletions(-) delete mode 100644 seisflows/workflow/thrifty_inversion.py diff --git a/seisflows/optimize/LBFGS.py b/seisflows/optimize/LBFGS.py index c6936f82..13219ad2 100644 --- a/seisflows/optimize/LBFGS.py +++ b/seisflows/optimize/LBFGS.py @@ -41,8 +41,12 @@ class LBFGS(Gradient): """ - [optimize.lbfgs] Limited memory BFGS nonlienar optimization algorithm + L-BFGS Optimization + ------------------- + Limited memory BFGS nonlienar optimization algorithm + Parameters + ---------- :type lbfgs_mem: int :param lbfgs_mem: L-BFGS memory. Max number of previous gradients to retain in local memory for approximating the objective function. @@ -51,6 +55,10 @@ class LBFGS(Gradient): :type lbfgs_thresh: L-BFGS angle restart threshold. If the angle between the current and previous search direction exceeds this value, optimization algorithm will be restarted. + + Paths + ----- + *** """ __doc__ = Gradient.__doc__ + __doc__ diff --git a/seisflows/optimize/NLCG.py b/seisflows/optimize/NLCG.py index 8bc88fed..cccce683 100644 --- a/seisflows/optimize/NLCG.py +++ b/seisflows/optimize/NLCG.py @@ -12,8 +12,12 @@ class NLCG(Gradient): """ - [optimize.NLCG] Nonlinear conjugate gradient method + NLCG Optimization + ----------------- + Nonlinear conjugate gradient method + Parameters + ---------- :type nlcg_max: int :param nlcg_max: NLCG periodic restart interval, should be between 1 and infinity @@ -22,6 +26,10 @@ class NLCG(Gradient): :type calc_beta: str :param calc_beta: method to calculate the parameter 'beta' in the NLCG algorithm. Available: 'pollak_ribere', 'fletcher_reeves' + + Paths + ----- + *** """ __doc__ = Gradient.__doc__ + __doc__ diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index 8365e652..917c9d9a 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -39,8 +39,12 @@ class Gradient: """ - [optimization.gradient] gradient/steepest descent optimization algorithm. + Gradient Optimization + --------------------- + Gradient/steepest descent optimization algorithm. + Parameters + ---------- :type line_search_method: str :param line_search_method: chosen line_search algorithm. Currently available are 'bracket' and 'backtrack'. See seisflows.plugins.line_search @@ -61,11 +65,13 @@ class Gradient: :param step_len_max: maximum allowable step length during the line search. Set as a fraction of the current model parameters - [path structure] + Paths + ----- :type path_preconditioner: str :param path_preconditioner: optional path to a set of preconditioner files formatted the same as the input model (or output model of solver). Required to exist and contain files if `preconditioner`==True + *** """ def __init__(self, line_search_method="bracket", preconditioner=None, step_count_max=10, step_len_init=0.05, diff --git a/seisflows/preprocess/default.py b/seisflows/preprocess/default.py index e0380a5c..a26fc323 100644 --- a/seisflows/preprocess/default.py +++ b/seisflows/preprocess/default.py @@ -20,9 +20,13 @@ class Default: """ - [preprocess.default] Data processing for seismic traces, with options for - data misfit, filtering, normalization and muting. + Default Preprocess + ------------------ + Data processing for seismic traces, with options for data misfit, + filtering, normalization and muting. + Parameters + ---------- :type data_format: str :param data_format: data format for reading traces into memory. For available see: seisflows.plugins.preprocess.readers @@ -69,6 +73,13 @@ class Default: LATE: mute late arrivals; SHORT: mute short source-receiver distances; LONG: mute long source-receiver distances + + Paths + ----- + :type path_preprocess: str + :param path_preprocess: scratch path for all preprocessing processes, + including saving files + *** """ def __init__(self, data_format="ascii", misfit="waveform", adjoint="waveform", normalize=None, filter=None, diff --git a/seisflows/preprocess/pyaflowa.py b/seisflows/preprocess/pyaflowa.py index e6aa4307..33b45216 100644 --- a/seisflows/preprocess/pyaflowa.py +++ b/seisflows/preprocess/pyaflowa.py @@ -24,8 +24,13 @@ class Pyaflowa: """ - [preprocess.pyaflowa] preprocessing and misfit quantification using Pyatoa + Preprocess Pyaflowa + ------------------- + Preprocessing and misfit quantification using Python's Adjoint Tomography + Operations Assistance (Pyatoa) + Parameters + ---------- :type min_period: float :param min_period: Minimum filter corner in unit seconds. Bandpass filter if set with `max_period`, highpass filter if set without @@ -84,9 +89,11 @@ class Pyaflowa: :type export_log_files: bool :param export_log_files: periodically save log files created by Pyatoa - [path structure] + Paths + ----- :type path_preprocess: str :param path_preprocess: scratch path for preprocessing related steps + *** """ def __init__(self, min_period=1., max_period=10., filter_corners=4, client=None, rotate=False, pyflex_preset="default", @@ -286,6 +293,12 @@ def quantify_misfit(self, source_name=None, save_residuals=None, .. note:: meant to be run on system using system.run() with access to solver + .. warning:: + parallel processing with concurrent futures currently leads to + computer crashes and I have not been able to figure out why or how + to deal with it. So for the moment misfit quantification on a + per-event basis is run serially + :type source_name: str :param source_name: name of the event to quantify misfit for. If not given, will attempt to gather event id from the given task id which @@ -305,9 +318,11 @@ def quantify_misfit(self, source_name=None, save_residuals=None, """ # Generate an event/evaluation specific config object to control Pyatoa config = self._setup_quantify_misfit(source_name, iteration, step_count) + # Run misfit quantification for ALL stations and this given event + # !!! Do not set parallel == True, see note in docstring !!! misfit, nwin = self._run_quantify_misfit(config, save_adjsrcs, - parallel=True) + parallel=False) # Calculate misfit based on the raw misfit and total number of windows if save_residuals: # Calculate the misfit based on the number of windows. Equation from diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index a8fb0030..7e831444 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -423,11 +423,46 @@ def configure(self, absolute_paths=False, **kwargs): default values for each of the SeisFlows module parameters. This function writes files manually, consistent with the .yaml format. + .. note:: + This function relies on docstrings being formatted the same way + throughout the package. Note the trailing '***' character at the end + of the docstring. This is required for `configure` to know where one + docstring ends and another beings. The formatting looks like: + + Title + ----- + some description + + Parameters + ---------- + :type a: int + :param a: parameter a + + Paths + ----- + :type path_a: str + :param path_a: path for a + *** + :type absolute_paths: bool :param absolute_paths: if True, expand pathnames to absolute paths, else if False, use path names relative to the working directory. Defaults to False, uses relative paths. """ + def split_module_docstring(mod, idx): + """ + Since our docstrings are concatenated, we need to break them + and remove the path docstrings, those come later. + + :type idx: int + :param idx: 0 returns parameter docstrings, 1 returns path docstring + """ + docstring = mod.__doc__.replace("\n", "\n#") + docssplit = docstring.split("***\n#") + docfinal = "".join([_.split("Paths\n# -----\n#")[idx] for _ in + docssplit]) + return docfinal + # Load in a barebones parameter file and instantiate specific classes parameters = load_yaml(os.path.join(self._args.workdir, self._args.parameter_file)) @@ -443,10 +478,8 @@ def configure(self, absolute_paths=False, **kwargs): f = open(self._args.parameter_file, "a") # Write all module parameters and corresponding docstrings for module in modules: - docstring = module.__doc__.replace("\n", "\n#") - docstring = docstring.split("[path structure]")[0] + docstring = split_module_docstring(module, 0) f.write(f"# {'=' * 77}\n#{docstring}\n# {'=' * 77}\n") - # Write the parameters, make sure to not have the same one twice for key, val in vars(module).items(): # Skip already written, hidden vars, and paths @@ -457,20 +490,16 @@ def configure(self, absolute_paths=False, **kwargs): val = "null" f.write(f"{key}: {val}\n") written.append(key) + # Write docstrings for publically accesible path structure f.write(f"# {'=' * 77}\n") f.write("#\n") - f.write("#\t [path structure] SeisFlows internal/external paths") + f.write("#\t Paths\n") + f.write("#\t -----\n") for module in modules: - docstring = module.__doc__.strip().replace("\n", "\n#") - docstring = docstring.split("[path structure]") - try: - # The extra split is to catch any inherited docstrings - f.write(docstring[1].split("[")[0]) - # IndexError means no path docstring to write out - except IndexError as e: - continue - f.write(f"\n# {'=' * 77}\n") + docstring = split_module_docstring(module, -1) + f.write(f"#{docstring}\n") + f.write(f"# {'=' * 77}\n") # Write values for publically accessible path structure written = [] diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 030749e2..a5fefecd 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -30,9 +30,12 @@ class Specfem: """ - [solver.specfem] Generalized SPECFEM interface to manipulate - SPECFEM2D/3D/3D_GLOBE via Python. + Solver SPECFEM + -------------- + Generalized SPECFEM interface to manipulate SPECFEM2D/3D/3D_GLOBE w/ Python + Parameters + ---------- :type data_format: str :param data_format: data format for reading traces into memory. Available: ['SU': seismic unix format, 'ASCII': human-readable ascii] @@ -67,7 +70,8 @@ class Specfem: :param mpiexec: MPI executable used to run parallel processes. Should also be defined for the system module - [path structure] + Paths + ----- :type path_data: str :param path_data: path to any externally stored data required by the solver :type path_specfem_bin: str @@ -77,6 +81,7 @@ class Specfem: :param path_specfem_data: path to SPECFEM DATA/ directory which must contain the CMTSOLUTION, STATIONS and Par_file files used for running SPECFEM + *** """ def __init__(self, data_format="ascii", materials="acoustic", density=False, nproc=1, ntask=1, attenuation=False, diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index e60e902e..b7d98c55 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -13,13 +13,21 @@ class Specfem2D(Specfem): """ - [solver.specfem2d] SPECFEM2D-specific alterations to the base SPECFEM module + Solver SPECFEM2D + ---------------- + SPECFEM2D-specific alterations to the base SPECFEM module + Parameters + ---------- :type source_prefix: str :param source_prefix: Prefix of source files in path SPECFEM_DATA. Defaults to 'SOURCE' :type multiples: bool :param multiples: set an absorbing top-boundary condition + + Paths + ----- + *** """ __doc__ = Specfem.__doc__ + __doc__ diff --git a/seisflows/solver/specfem3d.py b/seisflows/solver/specfem3d.py index 1715e03f..da449720 100644 --- a/seisflows/solver/specfem3d.py +++ b/seisflows/solver/specfem3d.py @@ -12,13 +12,21 @@ class Specfem3D(Specfem): """ - [solver.specfem3d] SPECFEM3D-specific alterations to the base SPECFEM module + Solver SPECFEM3D + ---------------- + SPECFEM3D-specific alterations to the base SPECFEM module + Parameters + ---------- :type source_prefix: str :param source_prefix: Prefix of source files in path SPECFEM_DATA. Defaults to 'CMTSOLUTION' :type multiples: bool :param multiples: set an absorbing top-boundary condition + + Paths + ----- + *** """ __doc__ = Specfem.__doc__ + __doc__ diff --git a/seisflows/solver/specfem3d_globe.py b/seisflows/solver/specfem3d_globe.py index 7e8a5561..f1205f86 100644 --- a/seisflows/solver/specfem3d_globe.py +++ b/seisflows/solver/specfem3d_globe.py @@ -11,8 +11,16 @@ class Specfem3DGlobe(Specfem3D): """ - [solver.specfem3d_globe] SPECFEM3D_Globe-specific alterations to the - solver.specfem3d (cartesian) module + Solver SPECFEM3D_GLOBE + ---------------------- + SPECFEM3D_Globe-specific alterations to the solver.specfem3d module + + Parameters + ---------- + + Paths + ----- + *** """ __doc__ = Specfem3D.__doc__ + __doc__ diff --git a/seisflows/system/cluster.py b/seisflows/system/cluster.py index 5f28e6d3..b1a73b9c 100644 --- a/seisflows/system/cluster.py +++ b/seisflows/system/cluster.py @@ -22,8 +22,12 @@ class Cluster(Workstation): """ - [system.cluster] generic or common HPC/cluster interfacing commands + Cluster System + ------------------ + Generic or common HPC/cluster interfacing commands + Parameters + ---------- :type title: str :param title: The name used to submit jobs to the system, defaults to the name of the current working directory @@ -44,6 +48,10 @@ class Cluster(Workstation): :param environs: Optional environment variables to be provided in the following format VAR1=var1,VAR2=var2... Will be set using os.environs + + Paths + ----- + *** """ __doc__ = Workstation.__doc__ + __doc__ diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index be1cf4fe..17f3af68 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -19,21 +19,28 @@ import time import subprocess -from seisflows import logger +from seisflows import ROOT_DIR, logger from seisflows.system.cluster import Cluster from seisflows.tools import msg -from seisflows.tools.config import ROOT_DIR class Slurm(Cluster): """ - [system.slurm] Interface for submitting jobs to Simple Linux Utility for + System Slurm + ------------------ + Runs tasks in serial on a local machine.Interface for submitting jobs to Simple Linux Utility for Resource Management (SLURM) system. + Parameters + ---------- :type slurm_args: str :param slurm_args: Any (optional) additional SLURM arguments that will be passed to the SBATCH scripts. Should be in the form: '--key1=value1 --key2=value2" + + Paths + ----- + *** """ __doc__ = Cluster.__doc__ + __doc__ diff --git a/seisflows/system/workstation.py b/seisflows/system/workstation.py index 74fd1441..1486f682 100644 --- a/seisflows/system/workstation.py +++ b/seisflows/system/workstation.py @@ -14,8 +14,12 @@ class Workstation: """ - [system.workstation] runs tasks in serial on a local machine. + Workstation System + ------------------ + Runs tasks in serial on a local machine. + Parameters + ---------- :type ntask: int :param ntask: number of individual tasks/events to run during workflow. Must be <= the number of source files in `path_specfem_data` @@ -29,7 +33,8 @@ class Workstation: name, line number and message type. Useful for debugging but also very verbose. - [path structure] + Paths + ----- :type path_output_log: str :param path_output_log: path to a text file used to store the outputs of the package wide logger, which are also written to stdout @@ -39,6 +44,7 @@ class Workstation: :type path_log_files: str :param path_log_files: path to a directory where individual log files are saved whenever a number of parallel tasks are run on the system. + *** """ def __init__(self, ntask=1, nproc=1, log_level="DEBUG", verbose=False, workdir=os.getcwd(), path_output=None, path_system=None, diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index bee3527e..e249721a 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -16,9 +16,13 @@ class Forward: """ - [workflow.forward] Run forward solver in parallel and (optionally) calculate + Forward Workflow + ---------------- + Run forward solver in parallel and (optionally) calculate data-synthetic misfit and adjoint sources. + Parameters + ---------- :type modules: list of module :param modules: instantiated SeisFlows modules which should have been generated by the function `seisflows.config.import_seisflows` with a @@ -40,8 +44,8 @@ class Forward: are generated by the external solver to `path_output`. If False, residuals stored in scratch may be discarded at any time in the workflow - [path structure] - + Paths + ----- :type workdir: str :param workdir: working directory in which to look for data and store results. Defaults to current working directory @@ -63,6 +67,7 @@ class Forward: :type path_eval_grad: str :param path_eval_grad: scratch path to store files for gradient evaluation, including models, kernels, gradient and residuals. + *** """ def __init__(self, modules=None, data_case="data", export_traces=False, export_residuals=False, workdir=os.getcwd(), path_output=None, @@ -236,7 +241,7 @@ def setup(self): def checkpoint(self): """ - Saves active SeisFlows working state to disk as Pickle files such that + Saves active SeisFlows working state to disk as a text files such that the workflow can be resumed following a crash, pause or termination of workflow. """ diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index 6072847c..dedada05 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -30,9 +30,13 @@ class Inversion(Migration): """ - [workflow.inversion] Peforms iterative nonlinear inversion using the - built-in optimization library. + Inversion Workflow + ------------------ + Peforms iterative nonlinear inversion using the machinery of the Forward + and Migration workflows, as well as a built-in optimization library. + Parameters + ---------- :type start: int :param start: start inversion workflow at this iteration. 1 <= start <= inf :type end: int @@ -50,11 +54,12 @@ class Inversion(Migration): :param export_model: export best-fitting model from the line search to disk. If False, new models can be discarded from scratch at any time. - [path structure] - + Paths + ----- :type path_eval_func: str :param path_eval_func: scratch path to store files for line search objective function evaluations, including models, misfit and residuals + *** """ __doc__ = Migration.__doc__ + __doc__ @@ -228,13 +233,15 @@ def evaluate_initial_misfit(self): logger.info(msg.mnr("THRIFTY INVERSION; SKIP MISFIT EVAL")) else: logger.info(msg.mnr("EVALUATING MISFIT FOR MODEL `m_new`")) - # Previous line search will have saved `m_new` as the initial model, - # export in SPECFEM format to a path discoverable by all solvers + # Previous line search will have saved `m_new` as the initial + # model, export in SPECFEM format to a path discoverable by all + # solvers path_model = os.path.join(self.path.eval_grad, "model") m_new = self.optimize.load_vector("m_new") m_new.write(path=path_model) - # Run forward simulation/misfit quantification with previous model + # Run forward simulation/misfit quantification with previous + # model self.system.run( [self.run_forward_simulations, self.evaluate_objective_function], diff --git a/seisflows/workflow/migration.py b/seisflows/workflow/migration.py index 9508c064..cf065dcc 100644 --- a/seisflows/workflow/migration.py +++ b/seisflows/workflow/migration.py @@ -27,10 +27,14 @@ class Migration(Forward): """ - [workflow.migration] Run forward and adjoint solver to produce - event-dependent misfit kernels. Sum and postprocess kernels to produce - gradient. In seismic exploration this is 'reverse time migration'. - + Migration Workflow + ------------------ + Run forward and adjoint solver to produce event-dependent misfit kernels. + Sum and postprocess kernels to produce gradient. In seismic exploration + this is 'reverse time migration'. + + Parameters + ---------- :type export_gradient: bool :param export_gradient: export the gradient after it has been generated in the scratch directory. If False, gradient can be discarded from @@ -40,12 +44,13 @@ class Migration(Forward): been generated in the scratch directory. If False, gradient can be discarded from scratch at any time in the workflow - [path structure] - + Paths + ----- :type path_mask: str :param path_mask: optional path to a masking function which is used to mask out or scale parts of the gradient. The user-defined mask must match the file format of the input model (e.g., .bin files). + *** """ __doc__ = Forward.__doc__ + __doc__ diff --git a/seisflows/workflow/thrifty_inversion.py b/seisflows/workflow/thrifty_inversion.py deleted file mode 100644 index 7f7183b8..00000000 --- a/seisflows/workflow/thrifty_inversion.py +++ /dev/null @@ -1,97 +0,0 @@ -#!/usr/bin/env python3 -""" -Thrifty: using resources carefully and not wastefully - -A thrifty inversion skips the costly intialization step (i.e., forward -simulations and misfit quantification) if the final forward simulations from -the previous iteration's line search can be used in the current one. Otherwise -it performs the same as the Inversion workflow -""" -from seisflows import logger -from seisflows.workflow.inversion import Inversion -from seisflows.tools import unix, msg - - -class ThriftyInversion(Inversion): - """ - [workflow.thrifty_inversion] an inversion that attempts to save resources - by re-using previous line search results for the current iteration. - - :type line_search_method: str - :param line_search_method: chosen line_search algorithm. Currently available - are 'bracket' and 'backtrack'. See seisflows.plugins.line_search - for all available options - """ - __doc__ = Inversion.__doc__ + __doc__ - - def __init__(self, line_search_method): - """Thrifty does not require input parameters - - """ - super().__init__() - - self._line_search_method = line_search_method - self._thrifty_status = False - - def check(self): - """ - Checks that we have the correct line search - """ - super().check() - - assert(self._line_search_method.title() == "Backtrack"), ( - "Thrifty inversion requires `line_search_method` == 'backtrack'" - ) - - def evaluate_initial_misfit(self): - """ - If line search can be carried over, skip initialization step - Or if manually starting a new run, start with normal inversion init - """ - if not self._thrifty_status or (self.optimize.iteration == self.start): - super().evaluate_initial_misfit() - else: - logger.info(msg.mnr("THRIFTY INVERSION, SKIPPING INITIAL MISFIT " - "EVALUATION")) - - def clean_scratch_directory(self): - """ - Determine if forward simulation from line search can be carried over. - We assume clean() is the final flow() argument so that we can update - the thrifty status here. - """ - self._thrifty_status = self._update_status() - - if self._thrifty_status: - logger.info( - msg.mnr("THRIFTY CLEANING WORKDIR FOR NEXT ITERATION") - ) - unix.rm(self.path.eval_grad) - # Last line search evaluation becomes the new gradient evaluation - unix.mv(self.path.eval_func, self.path.eval_grad) - unix.mkdir(self.path.eval_func) - else: - super().clean_scratch_directory() - - def _update_status(self): - """ - Determine if line search forward simulation can be carried over based - on a variety of criteria relating to location in the inversion. - """ - logger.info("updating thrifty inversion status") - if self.optimize.iter == self.start: - logger.info("1st iteration, defaulting to inversion workflow") - thrifty = False - elif self.optimize.restarted: - logger.info("optimization has been restarted, defaulting to " - "inversion workflow") - thrifty = False - elif self.optimize.iter == self.end: - logger.info("final iteration, defaulting to inversion workflow") - thrifty = False - else: - logger.info("continuing with thrifty inversion workflow") - thrifty = True - - return thrifty - From 991fcbaaf7eb347bac6944790526357f25f8bc3d Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Wed, 3 Aug 2022 21:01:40 -0800 Subject: [PATCH 102/195] changed default preprocess inputs to match pyaflowa call structure both examples 1 and 2 setup and run working properly forward workflow moved model check into initial misfit check rather than setup because inherited setup after iteration 1 doesn't need to check these values anymore bug fix inversion workflow saving residuals but looking for 'residuals.txt' --- ...pecfem2d_workstation_inversion_w_pyatoa.py | 17 ++++--- seisflows/examples/sfexample2d.py | 17 ++++--- seisflows/preprocess/default.py | 46 ++++++++++++++----- seisflows/preprocess/pyaflowa.py | 2 +- seisflows/workflow/forward.py | 20 ++++---- seisflows/workflow/inversion.py | 10 ++-- 6 files changed, 68 insertions(+), 44 deletions(-) diff --git a/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py b/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py index 9d0ab43c..55415500 100644 --- a/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py +++ b/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py @@ -83,15 +83,16 @@ def setup_seisflows_working_directory(self): print("> EX2: Setting SeisFlows parameters for Pyatao preprocessing") self.sf.setup(force=True) # Force will delete existing parameter file - self.sf.par("preprocess", "pyatoa") + self.sf.par("workflow", "inversion") + self.sf.par("preprocess", "pyaflowa") self.sf.configure() self.sf.par("end", 1) # only 1 iteration self.sf.par("ntask", self.ntask) # 3 sources for this example self.sf.par("materials", "elastic") # how velocity model parameterized self.sf.par("density", "constant") # update density or keep constant - self.sf.par("format", "ascii") # how to output synthetic seismograms - self.sf.par("case", "synthetic") # synthetic-synthetic inversion + self.sf.par("data_format", "ascii") # output synthetic seismograms + self.sf.par("data_case", "synthetic") # synthetic-synthetic inversion self.sf.par("attenuation", False) self.sf.par("components", "Y") @@ -99,14 +100,12 @@ def setup_seisflows_working_directory(self): self.sf.par("unit_output", "DISP") self.sf.par("min_period", 10) # filter bounds define window selection self.sf.par("max_period", 200) - self.sf.par("start_pad", 48) # T0 set in Par_file - self.sf.par("end_pad", 5000 * .06) # nt * dt defined by Par_file # self.sf.par("pyflex_preset", "") # To turn off windowing completely - self.sf.par("specfem_bin", self.workdir_paths.bin) - self.sf.par("specfem_data", self.workdir_paths.data) - self.sf.par("model_init", self.workdir_paths.model_init) - self.sf.par("model_true", self.workdir_paths.model_true) + self.sf.par("path_specfem_bin", self.workdir_paths.bin) + self.sf.par("path_specfem_data", self.workdir_paths.data) + self.sf.par("path_model_init", self.workdir_paths.model_init) + self.sf.par("path_model_true", self.workdir_paths.model_true) def finalize_specfem2d_par_file(self): """ diff --git a/seisflows/examples/sfexample2d.py b/seisflows/examples/sfexample2d.py index c4ed3e0e..8dbd9d91 100644 --- a/seisflows/examples/sfexample2d.py +++ b/seisflows/examples/sfexample2d.py @@ -287,21 +287,24 @@ def setup_seisflows_working_directory(self): cd(self.cwd) self.sf.setup(force=True) # Force will delete existing parameter file + self.sf.par("workflow", "inversion") self.sf.configure() self.sf.par("ntask", self.ntask) # default 3 sources for this example self.sf.par("materials", "elastic") # how velocity model parameterized self.sf.par("density", "constant") # update density or keep constant - self.sf.par("format", "ascii") # how to output synthetic seismograms - self.sf.par("begin", 1) # first iteration + self.sf.par("data_format", "ascii") # how to output synthetic seismograms + self.sf.par("start", 1) # first iteration self.sf.par("end", self.niter) # final iteration -- we will run 2 - self.sf.par("case", "synthetic") # synthetic-synthetic inversion + self.sf.par("step_count_max", 5) # will cause iteration 2 to fail + self.sf.par("data_case", "synthetic") # synthetic-synthetic inversion + self.sf.par("components", "Y") # only Y component seismograms avail. self.sf.par("attenuation", False) - self.sf.par("specfem_bin", self.workdir_paths.bin) - self.sf.par("specfem_data", self.workdir_paths.data) - self.sf.par("model_init", self.workdir_paths.model_init) - self.sf.par("model_true", self.workdir_paths.model_true) + self.sf.par("path_specfem_bin", self.workdir_paths.bin) + self.sf.par("path_specfem_data", self.workdir_paths.data) + self.sf.par("path_model_init", self.workdir_paths.model_init) + self.sf.par("path_model_true", self.workdir_paths.model_true) def finalize_specfem2d_par_file(self): """ diff --git a/seisflows/preprocess/default.py b/seisflows/preprocess/default.py index a26fc323..bdb94c9e 100644 --- a/seisflows/preprocess/default.py +++ b/seisflows/preprocess/default.py @@ -7,6 +7,7 @@ """ import os import numpy as np +from glob import glob from obspy import read as obspy_read from obspy import Stream, Trace, UTCDateTime @@ -86,7 +87,8 @@ def __init__(self, data_format="ascii", misfit="waveform", min_period=None, max_period=None, min_freq=None, max_freq=None, mute=None, early_slope=None, early_const=None, late_slope=None, late_const=None, short_dist=None, long_dist=None, - workdir=os.getcwd(), path_preprocess=None, **kwargs): + workdir=os.getcwd(), path_preprocess=None, path_solver=None, + **kwargs): """ Preprocessing module parameters @@ -124,7 +126,8 @@ def __init__(self, data_format="ascii", misfit="waveform", self.path = Dict( scratch=path_preprocess or os.path.join(workdir, "scratch", - "preprocess") + "preprocess"), + solver=path_solver or os.path.join(workdir, "scratch", "solver") ) self._acceptable_data_formats = ["SU", "ASCII"] @@ -338,8 +341,21 @@ def _rename_as_adjoint_source(self, fid): fid = fid.replace(og_extension, ".adj") return fid - def quantify_misfit(self, observed, synthetic, - save_residuals=None, save_adjsrcs=None, **kwargs): + def _setup_quantify_misfit(self, source_name): + """ + Gather waveforms from the Solver scratch directory and + """ + obs_path = os.path.join(self.path.solver, source_name, "traces", "obs") + syn_path = os.path.join(self.path.solver, source_name, "traces", "syn") + + observed = sorted(glob(os.path.join(obs_path, "*"))) + synthetic = sorted(glob(os.path.join(syn_path, "*"))) + + return observed, synthetic + + def quantify_misfit(self, source_name=None, save_residuals=None, + save_adjsrcs=None, iteration=1, step_count=0, + **kwargs): """ Prepares solver for gradient evaluation by writing residuals and adjoint traces. Meant to be called by solver.eval_func(). @@ -350,19 +366,25 @@ def quantify_misfit(self, observed, synthetic, TODO use concurrent futures to parallelize this - .. note:: - Meant to be called by solver.eval_func(), may have unused arguments - to keep functions general across subclasses. - - :type observed: list - :param observed: list of observed waveforms - :type synthetic: list - :param synthetic: list of synthetic waveforms + :type source_name: str + :param source_name: name of the event to quantify misfit for. If not + given, will attempt to gather event id from the given task id which + is assigned by system.run() :type save_residuals: str :param save_residuals: if not None, path to write misfit/residuls to :type save_adjsrcs: str :param save_adjsrcs: if not None, path to write adjoint sources to + :type iteration: int + :param iteration: current iteration of the workflow, information should + be provided by `workflow` module if we are running an inversion. + Defaults to 1 if not given (1st iteration) + :type step_count: int + :param step_count: current step count of the line search. Information + should be provided by the `optimize` module if we are running an + inversion. Defaults to 0 if not given (1st evaluation) """ + observed, synthetic = self._setup_quantify_misfit(source_name) + for obs_fid, syn_fid in zip(observed, synthetic): obs = self.read(fid=obs_fid) syn = self.read(fid=syn_fid) diff --git a/seisflows/preprocess/pyaflowa.py b/seisflows/preprocess/pyaflowa.py index 33b45216..604716f6 100644 --- a/seisflows/preprocess/pyaflowa.py +++ b/seisflows/preprocess/pyaflowa.py @@ -24,7 +24,7 @@ class Pyaflowa: """ - Preprocess Pyaflowa + Pyaflowa Preprocess ------------------- Preprocessing and misfit quantification using Python's Adjoint Tomography Operations Assistance (Pyatoa) diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index e249721a..69c65b5a 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -223,16 +223,6 @@ def setup(self): f.write(f"# Acceptable states: 'completed', 'failed'\n") f.write(f"# =======================================\n") - # Load in the initial model and check its poissons ratio - if self.path.model_init: - logger.info("checking initial model parameters") - _model = Model(os.path.join(self.path.model_init)) - _model.check() - if self.path.model_true: - logger.info("checking true/target model parameters") - _model = Model(os.path.join(self.path.model_true)) - _model.check() - # Distribute modules to the class namespace. We don't do this at init # incase _modules was set as NoneType self.solver = self._modules.solver # NOQA @@ -299,6 +289,16 @@ def evaluate_initial_misfit(self): """ logger.info(msg.mnr("EVALUATING MISFIT FOR INITIAL MODEL")) + # Load in the initial model and check its poissons ratio + if self.path.model_init: + logger.info("checking initial model parameters") + _model = Model(os.path.join(self.path.model_init)) + _model.check() + if self.path.model_true: + logger.info("checking true/target model parameters") + _model = Model(os.path.join(self.path.model_true)) + _model.check() + self.system.run( [self.prepare_data_for_solver, self.run_forward_simulations, diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index dedada05..6cd55ecf 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -182,6 +182,8 @@ def run(self): logger.info(msg.mnr(f"RUNNING ITERATION {self.iteration:0>2}")) super().run() # Runs task list logger.info(msg.mnr(f"COMPLETED ITERATION {self.iteration:0>2}")) + self.iteration += 1 + logger.info(f"setting current iteration to: {self.iteration}") # Clear the state file for new iteration self._states = {} self.checkpoint() @@ -247,7 +249,7 @@ def evaluate_initial_misfit(self): self.evaluate_objective_function], path_model=path_model, save_residuals=os.path.join(self.path.eval_grad, - "residuals") + "residuals.txt") ) # Override function to sum residuals into the optimization library @@ -379,10 +381,10 @@ def _evaluate_line_search_misfit(self): [self.run_forward_simulations, self.evaluate_objective_function], path_model=os.path.join(self.path.eval_func, "model"), - save_residuals=os.path.join(self.path.eval_func, "residuals") + save_residuals=os.path.join(self.path.eval_func, "residuals.txt") ) residuals = np.loadtxt(os.path.join(self.path.eval_func, - "residuals")) + "residuals.txt")) total_misfit = self.preprocess.sum_residuals(residuals) logger.debug(f"misfit for trial model (f_try) == {total_misfit:.2E}") self.optimize.save_vector(name="f_try", m=total_misfit) @@ -403,8 +405,6 @@ def finalize_iteration(self): ) # Update optimization - self.iteration += 1 - logger.info(f"setting current iteration to: {self.iteration}") self.optimize.checkpoint() # Clear out the scratch directory From b7a3d906144814967c1f458d67b57ca24327e03e Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Thu, 4 Aug 2022 10:55:47 -0800 Subject: [PATCH 103/195] unix nproc command was failing on mac, replaced with os.cpu_count() which should work regardless of system --- seisflows/tools/unix.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/seisflows/tools/unix.py b/seisflows/tools/unix.py index 0b07e3af..d8841b8c 100644 --- a/seisflows/tools/unix.py +++ b/seisflows/tools/unix.py @@ -266,16 +266,19 @@ def nproc(): Get the number of processors available. Same as calling 'nproc' from Linux command line. - TODO probably replace this with multiprocessing.cpu_count() + TODO replace all instances of nproc() with os.cpu_count() + :rtype: int :return: number of processors :raises EnvironmentError: if nproc cannot be determined """ + _nproc = os.cpu_count() # Method 1 calls 'nproc'. May fail and return '' if 'nproc' not avail. - _nproc = subprocess.run("nproc", shell=True, text=True, - stdout=subprocess.PIPE).stdout.strip() + if not _nproc: + _nproc = subprocess.run("nproc", shell=True, text=True, + stdout=subprocess.PIPE).stdout.strip() # Method 2 checks /proc/cpuinfo if not _nproc: if os.path.exists("/proc/cpuinfo"): From cf80dcf8c9a30c5e5e9b28481b4279b8bf40d3fd Mon Sep 17 00:00:00 2001 From: bch0w Date: Thu, 11 Aug 2022 11:20:17 -0800 Subject: [PATCH 104/195] hiding specfem2d f0 variable as it should not be set in par file fixing seisflows cli print functions --- seisflows/seisflows.py | 97 +++++++++++++---------------------- seisflows/solver/specfem2d.py | 10 ++-- 2 files changed, 43 insertions(+), 64 deletions(-) diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 7e831444..fee6d7f2 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -26,7 +26,7 @@ from seisflows import logger, ROOT_DIR, NAMES from seisflows.tools import unix, msg from seisflows.tools.config import (Dict, load_yaml, custom_import, - import_seisflows, config_logger) + import_seisflows) from seisflows.tools.specfem import (getpar, setpar, getpar_vel_model, setpar_vel_model) @@ -938,7 +938,7 @@ def print(self, choice=None, **kwargs): :param choice: underlying sub-function to choose """ acceptable_args = {"modules": self._print_modules, - "flow": self._print_flow, + "tasks": self._print_tasks, "inherit": self._print_inheritance} # Ensure that help message is thrown for empty commands @@ -951,6 +951,8 @@ def print(self, choice=None, **kwargs): def reset(self, choice=None, **kwargs): """ Mid-level function to wrap lower level reset functions + + TODO re-write '_reset_line_search' """ acceptable_args = {"line_search": self._reset_line_search,} @@ -959,12 +961,9 @@ def reset(self, choice=None, **kwargs): self._subparser.print_help() sys.exit(0) - self._register_parameters() - self._load_modules() acceptable_args[choice](*self._args.args, **kwargs) - @staticmethod - def _inspect_class_that_defined_method(name, func, **kwargs): + def _inspect_class_that_defined_method(self, name, func, **kwargs): """ Given a function name and generalized module (e.g. solver), inspect which of the subclasses actually defined the function. Makes it easier @@ -979,17 +978,21 @@ def _inspect_class_that_defined_method(name, func, **kwargs): :param func: Corresponding method/function name for the given module """ # Dynamically get the correct module and function based on names - try: - module = sys.modules[f"seisflows_{name}"] - except KeyError: - print(msg.cli(f"SeisFlows has no module: {name}")) - sys.exit(-1) - try: - method = getattr(module, func) - except AttributeError: - print(msg.cli(f"SeisFlows.{name} has no function: {func}")) - sys.exit(-1) + # try: + # module = sys.modules[f"seisflows_{name}"] + # except KeyError: + # print(msg.cli(f"SeisFlows has no module: {name}")) + # sys.exit(-1) + # try: + # method = getattr(module, func) + # except AttributeError: + # print(msg.cli(f"SeisFlows.{name} has no function: {func}")) + # sys.exit(-1) + parameters = load_yaml(os.path.join(self._args.workdir, + self._args.parameter_file)) + module = custom_import(name, parameters[name])() + method = getattr(module, func) method_name = method.__name__ if method.__self__: classes = [method.__self__.__class__] @@ -1006,8 +1009,7 @@ def _inspect_class_that_defined_method(name, func, **kwargs): print(msg.cli(f"Error matching class for SeisFlows.{name}.{func}")) sys.exit(-1) - @staticmethod - def _inspect_module_hierarchy(name=None, **kwargs): + def _inspect_module_hierarchy(self, name=None, **kwargs): """ Determine the order of class hierarchy for a given SeisFlows module. @@ -1021,48 +1023,25 @@ def _inspect_module_hierarchy(name=None, **kwargs): :param name: choice of module, if None, will print hierarchies for all modules. """ + parameters = load_yaml(os.path.join(self._args.workdir, + self._args.parameter_file)) + items = [] for NAME in NAMES: if name and NAME != name: continue - module = sys.modules[f"seisflows_{NAME}"] + module = custom_import(NAME, parameters[NAME])() item_str = f"{NAME.upper():<12}" for i, cls in enumerate(inspect.getmro(type(module))[::-1]): + # The base inheritance is always 'object', skip printing this. + if i == 0: + continue item_str += f"> {cls.__name__:<10}" items.append(item_str) print(msg.cli(items=items, header="seisflows inheritance")) - def _reset_line_search(self, **kwargs): - """ - TODO Delete me - - Reset the machinery of the line search. This is useful for if a line - search fails or stagnates but the User does not want to re-run the - entire iteration. They can reset the line search and resume the workflow - from the line search step - - The following rubric details how you might use this from command line: - - .. rubric:: - $ seisflows reset line_search - $ seisflows par resume_from line_search - $ seisflows resume_from -f - """ - optimize = sys.modules["seisflows_optimize"] - workflow = sys.modules["seisflows_workflow"] - - current_step = optimize.line_search.step_count - optimize.line_search.reset() - - # Manually set step count back to 0, this usually happens in - # optimize.finalize_search() - optimize.line_search.step_count = 0 - - print(msg.cli(f"resetting line search machinery. step count: " - f"{current_step} -> {optimize.line_search.step_count }")) - workflow.checkpoint() - def _print_modules(self, name=None, package=None, **kwargs): + def _print_modules(self, package=None, **kwargs): """ Print out available modules in the SeisFlows name space for all available packages and modules. @@ -1084,10 +1063,10 @@ def _print_modules(self, name=None, package=None, **kwargs): items.append(f"- {module_}".expandtabs(tabsize=4)) for module_ in module_list: items.append(f"\t* {module_}".expandtabs(tabsize=4)) - print(msg.cli("'+': package, '-': module, '*': class", items=items, + print(msg.cli("'-': module, '*': class", items=items, header="seisflows modules")) - def _print_flow(self, **kwargs): + def _print_tasks(self, **kwargs): """ Simply print out the seisflows.workflow.main() flow variable which describes what order workflow functions will be run. Useful for @@ -1096,14 +1075,12 @@ def _print_flow(self, **kwargs): .. rubric:: $ seisflows print flow """ - self._register_parameters() - self._load_modules() - - workflow = custom_import("workflow")() - flow = workflow.main(return_flow=True) - items = [f"{a+1}: {b.__name__}" for a, b in enumerate(flow)] - print(msg.cli(f"Flow arguments for {type(workflow)}", items=items, - header="seisflows workflow main")) + parameters = load_yaml(os.path.join(self._args.workdir, + self._args.parameter_file)) + wf = custom_import("workflow", parameters["workflow"])() + items = [f"{a+1}: {b.__name__}" for a, b in enumerate(wf.task_list)] + print(msg.cli(f"Task list for {type(wf)}", items=items, + header="seisflows workflow task list")) def _print_inheritance(self, name=None, func=None, **kwargs): """ @@ -1129,8 +1106,6 @@ def _print_inheritance(self, name=None, func=None, **kwargs): seisflows inspect solver eval_func """ - self._register_parameters() - self._load_modules() if func is None: self._inspect_module_hierarchy(name, **kwargs) else: diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index b7d98c55..c0f8b761 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -1,7 +1,11 @@ #!/usr/bin/env python3 """ This class provides utilities for the Seisflows solver interactions with -Specfem2D. +Specfem2D. It builds upon the base Specfem class which generalizes all solver +interactions with various versions of Specfem. + +TODO + Internal paramater f0 is not currently used. Can we remove or integrate? """ import os from glob import glob @@ -36,7 +40,7 @@ def __init__(self, source_prefix="SOURCE", multiples=False, **kwargs): super().__init__(source_prefix=source_prefix, **kwargs) self.multiples = multiples - self.f0 = None + self._f0 = None # Define parameters based on material type if self.materials.upper() == "ACOUSTIC": @@ -47,7 +51,7 @@ def __init__(self, source_prefix="SOURCE", multiples=False, **kwargs): def setup(self): """Setup the SPECFEM2D solver interface in a SeisFlows workflow""" source_file = os.path.join(self.path.specfem_data, self.source_prefix) - self.f0 = getpar(key="f0", file=source_file)[1] + self._f0 = getpar(key="f0", file=source_file)[1] par_file = os.path.join(self.path.specfem_data, "Par_file") if self.multiples: From b2ca7a3df568c1b2194fb2008117d044bb92f36f Mon Sep 17 00:00:00 2001 From: bch0w Date: Thu, 11 Aug 2022 11:42:09 -0800 Subject: [PATCH 105/195] fixed failing seisflows cli tests which was due to changed cli architecture --- seisflows/seisflows.py | 182 ++++++++++++---------- seisflows/tests/test_data/parameters.yaml | 1 + seisflows/tests/test_seisflows.py | 105 +++++-------- 3 files changed, 140 insertions(+), 148 deletions(-) create mode 120000 seisflows/tests/test_data/parameters.yaml diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index fee6d7f2..aafc9984 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -320,16 +320,32 @@ class SeisFlows: any checks we must load the entire SeisFlows environment, which is slow but provides the most flexibility when accessing internal information """ - def __init__(self): + def __init__(self, workdir=None, parameter_file=None): """ Parse user-defined arguments and establish internal parameters used to control which functions execute and how. Instance must be called to execute internal functions + + .. note:: + Normally the `workdir` and `parameter_file` paramters should be + set through the argparser (i.e., command line) but we allow + it to be set in a Python environment through __init__ incase there + is no access to the argparser through the command line (e.g., when + testing functionality using pytest) + + :type workdir: str + :param workdir: the working directory to initiate a SeisFlows workflow. + :type parameter_file: str + :param parameter_file: full path to the parameter file """ self._parser, self._subparser = sfparser() self._paths = None self._parameters = None self._args = self._parser.parse_args() + if workdir is not None: + self._args.workdir = workdir + if parameter_file is not None: + self._args.parameter_file = parameter_file def __call__(self, command=None, **kwargs): """ @@ -1111,88 +1127,88 @@ def _print_inheritance(self, name=None, func=None, **kwargs): else: self._inspect_class_that_defined_method(name, func, **kwargs) - def _check_model_parameters(self, src=None, **kwargs): - """ - Print out the min/max values from one or all of the currently available - models. Useful for checking what models are associated with what part of - the workflow, e.g. evaluate function, evaluate gradient. - - :type src: str - :param src: the name of a specific model to check, e.g. 'm_try', - otherwise will check parameters for all models - """ - optimize = sys.modules["seisflows_optimize"] - PATH = sys.modules["seisflows_paths"] - - avail = glob(os.path.join(PATH.OPTIMIZE, "m_*")) - srcs = [os.path.basename(_) for _ in avail] - if src: - if src not in srcs: - print(msg.cli(f"{src} not in available models: {avail}")) - sys.exit(-1) - srcs = [src] - for tag in srcs: - m = optimize.load(tag) - m.check() - - def _check_current_iteration(self, **kwargs): - """ - Display the current point in the workflow in terms of the iteration - and step count number. Args are not used by allow for a more general - check() function. - """ - optimize = sys.modules["seisflows_optimize"] - try: - items = [] - ln = optimize.line_search - items.append(f"Iteration: {optimize.iter}") - items.append(f"Step Count: {ln.step_count} / {ln.step_count_max}") - print(msg.cli(items=items)) - except AttributeError: - print(msg.cli("OPTIMIZATION module has not been initialized yet, " - "cannot retrieve iteration or step count values.")) - sys.exit(-1) - - def _check_source_names(self, source_name=None, **kwargs): - """ - Sources are tagged by name but also by index in the source names which - can be confusing and usually requires doubling checking. This check - just prints out source names next to their respective index, or if a - source name is requested, provides the index for that - - :type source_name: str - :param source_name: name of source to check index, if None will simply - print out all sources - """ - try: - source_names = sys.modules["seisflows_solver"].source_names - except FileNotFoundError as e: - print(msg.cli(str(e))) - sys.exit(-1) - - if source_name: - print(msg.cli(f"{source_names.index(source_name)}: {source_name}")) - else: - items = [] - for i, source_name in enumerate(source_names): - items.append(f"{i:>3}: {source_name}") - print(msg.cli(items=items, header="source names")) - - def _check_source_index(self, idx=None, **kwargs): - """ - Look up source name by index - - :type idx: int - :param idx: index of source to look up - """ - if idx is None: - self._check_source_names(source_name=None) - else: - solver = sys.modules["seisflows_solver"] - try: - print(msg.cli(f"{idx}: {solver.source_names[int(idx)]}")) - except IndexError: - print(msg.cli(f"idx out of range: {len(solver.source_names)}")) + # def _check_model_parameters(self, src=None, **kwargs): + # """ + # Print out the min/max values from one or all of the currently available + # models. Useful for checking what models are associated with what part of + # the workflow, e.g. evaluate function, evaluate gradient. + # + # :type src: str + # :param src: the name of a specific model to check, e.g. 'm_try', + # otherwise will check parameters for all models + # """ + # optimize = sys.modules["seisflows_optimize"] + # PATH = sys.modules["seisflows_paths"] + # + # avail = glob(os.path.join(PATH.OPTIMIZE, "m_*")) + # srcs = [os.path.basename(_) for _ in avail] + # if src: + # if src not in srcs: + # print(msg.cli(f"{src} not in available models: {avail}")) + # sys.exit(-1) + # srcs = [src] + # for tag in srcs: + # m = optimize.load(tag) + # m.check() + # + # def _check_current_iteration(self, **kwargs): + # """ + # Display the current point in the workflow in terms of the iteration + # and step count number. Args are not used by allow for a more general + # check() function. + # """ + # optimize = sys.modules["seisflows_optimize"] + # try: + # items = [] + # ln = optimize.line_search + # items.append(f"Iteration: {optimize.iter}") + # items.append(f"Step Count: {ln.step_count} / {ln.step_count_max}") + # print(msg.cli(items=items)) + # except AttributeError: + # print(msg.cli("OPTIMIZATION module has not been initialized yet, " + # "cannot retrieve iteration or step count values.")) + # sys.exit(-1) + # + # def _check_source_names(self, source_name=None, **kwargs): + # """ + # Sources are tagged by name but also by index in the source names which + # can be confusing and usually requires doubling checking. This check + # just prints out source names next to their respective index, or if a + # source name is requested, provides the index for that + # + # :type source_name: str + # :param source_name: name of source to check index, if None will simply + # print out all sources + # """ + # try: + # source_names = sys.modules["seisflows_solver"].source_names + # except FileNotFoundError as e: + # print(msg.cli(str(e))) + # sys.exit(-1) + # + # if source_name: + # print(msg.cli(f"{source_names.index(source_name)}: {source_name}")) + # else: + # items = [] + # for i, source_name in enumerate(source_names): + # items.append(f"{i:>3}: {source_name}") + # print(msg.cli(items=items, header="source names")) + # + # def _check_source_index(self, idx=None, **kwargs): + # """ + # Look up source name by index + # + # :type idx: int + # :param idx: index of source to look up + # """ + # if idx is None: + # self._check_source_names(source_name=None) + # else: + # solver = sys.modules["seisflows_solver"] + # try: + # print(msg.cli(f"{idx}: {solver.source_names[int(idx)]}")) + # except IndexError: + # print(msg.cli(f"idx out of range: {len(solver.source_names)}")) def return_modules(): diff --git a/seisflows/tests/test_data/parameters.yaml b/seisflows/tests/test_data/parameters.yaml new file mode 120000 index 00000000..bc0c1173 --- /dev/null +++ b/seisflows/tests/test_data/parameters.yaml @@ -0,0 +1 @@ +../../examples/parameters.yaml \ No newline at end of file diff --git a/seisflows/tests/test_seisflows.py b/seisflows/tests/test_seisflows.py index 539d96cb..c6c3068a 100644 --- a/seisflows/tests/test_seisflows.py +++ b/seisflows/tests/test_seisflows.py @@ -14,102 +14,78 @@ from seisflows.tools.config import Dict from seisflows import ROOT_DIR from seisflows.seisflows import SeisFlows -from seisflows.tools.config import NAMES from seisflows.tools.config import load_yaml TEST_DIR = os.path.join(ROOT_DIR, "tests") @pytest.fixture -def filled_par_file(): - """A parameter file that is completely filled out and can be read in""" - return os.path.join(TEST_DIR, "test_data", "test_filled_parameters.yaml") - - -@pytest.fixture -def conf_par_file(): - """A par file that has been configured but requires user-defined values""" - return os.path.join(TEST_DIR, "test_data", "test_conf_parameters.yaml") - - -@pytest.fixture -def setup_par_file(): - """A barebones par file that only contains module names""" - return os.path.join(TEST_DIR, "test_data", "test_setup_parameters.yaml") - - -@pytest.fixture -def copy_par_file(tmpdir, filled_par_file): +def par_file(): """ - Copy the template parameter file into the temporary test directory - :rtype: str - :return: location of the parameter file + Return the test parameter file as a dictionary object + :rtype: seisflows.config.Dict + :return: dictionary of parameters """ - src = filled_par_file - dst = os.path.join(tmpdir, "parameters.yaml") - shutil.copy(src, dst) + return os.path.join(TEST_DIR, "test_data", "parameters.yaml") @pytest.fixture -def par_file_dict(filled_par_file): +def par_file_dict(par_file): """ Return the test parameter file as a dictionary object :rtype: seisflows.config.Dict :return: dictionary of parameters """ - return Dict(load_yaml(filled_par_file)) + return Dict(load_yaml(par_file)) -def test_call_seisflows(tmpdir, par_file_dict, copy_par_file): +def test_call_seisflows(par_file, par_file_dict): """ Test calling the 'par' command from command line and inside a python environemnt. Check that case-insensitivity is also honored Check against the actual value coming from the parameter file Also tests the seisflows 'par' command """ - copy_par_file - os.chdir(tmpdir) - check_val = par_file_dict.LINESEARCH + check_val = par_file_dict.workflow # Mock argv to match the actual scenario in which SeisFlows will be called with patch.object(sys, "argv", ["seisflows"]): - for name in ["linesearch", "LINESEARCH", "LineSearch"]: - # From the command line - cmd_line_arg = ["seisflows", "par", name] + for name in ["Workflow", "WORKFLOW", "WorkFlow"]: + # $ seisflows par workflow -p path/to/parameters.yaml + cmd_line_arg = ["seisflows", "-p", par_file, "par", name] out = subprocess.run(cmd_line_arg, capture_output=True, universal_newlines=True) - assert(out.stdout.strip() == f"{name.upper()}: {check_val}") + assert(out.stdout.strip() == f"{name.lower()}: {check_val}") # Test from inside a Python environment; we need to redirect stdout # to make sure the print statement is working as expected f = io.StringIO() with contextlib.redirect_stdout(f): - sf = SeisFlows() + sf = SeisFlows(parameter_file=par_file) sf(command="par", parameter=name) stdout = f.getvalue() - assert(stdout.strip() == f"{name.upper()}: {check_val}") + assert(stdout.strip() == f"{name.lower()}: {check_val}") -def test_edited_parameter_file_name(tmpdir, par_file_dict, filled_par_file): +def test_edited_parameter_file_name(tmpdir, par_file, par_file_dict): """ Similar test as call_seisflows but just make sure that arbitrary naming of the parameter file still works """ + # Copy given parameter file with a weird name par_fid = "CRAZY_PARAMETER_NAME-0x123kd.yaml" - par_name = "linesearch" - - src = filled_par_file + src = par_file dst = os.path.join(tmpdir, par_fid) shutil.copy(src, dst) + check_key = "workflow" + check_val = par_file_dict[check_key] - os.chdir(tmpdir) - check_val = par_file_dict.LINESEARCH - cmd_line_arg = ["seisflows", "-p", par_fid, "par", par_name] + cmd_line_arg = ["seisflows", "-p", dst, "par", check_key] # Mock argv to match the actual scenario in which SeisFlows will be called with patch.object(sys, "argv", ["seisflows"]): out = subprocess.run(cmd_line_arg, capture_output=True, universal_newlines=True) - assert(out.stdout.strip() == f"{par_name.upper()}: {check_val}") + assert(out.stdout.strip() == f"{check_key.lower()}: {check_val}") def test_cmd_setup(tmpdir): @@ -209,55 +185,54 @@ def test_cmd_setup(tmpdir): # # del sys.modules[PATH] -def test_cmd_par(tmpdir, copy_par_file): +def test_cmd_par(tmpdir, par_file): """ Make sure the 'par' command can print and edit the parameter file :param tmpdir: :return: """ - # Run init first to create a working state - os.chdir(tmpdir) - copy_par_file + # Copy given parameter file with a weird name + src = par_file + dst = os.path.join(tmpdir, "parameters.yaml") + shutil.copy(src, dst) - parameter = "begin" - expected_val = "1" - new_val = "2" + parameter = "workflow" + expected_val = "forward" + new_val = "migration" # testing the get option: seisflows par `parameter` with patch.object(sys, "argv", ["seisflows"]): - sf = SeisFlows() # Run this with subprocess so we can capture the print statement - cmd_line_arg = ["seisflows", "par", parameter] + cmd_line_arg = ["seisflows", "-p", dst, "par", parameter] out = subprocess.run(cmd_line_arg, capture_output=True, universal_newlines=True) # Check that we printed out the correct value par, val = out.stdout.strip().split(":") - assert(par.upper() == parameter.upper()) - assert(int(val) == int(expected_val)) + assert(par.strip() == parameter) + assert(val.strip() == expected_val) # testing the set option: seisflows par `parameter` `value` with patch.object(sys, "argv", ["seisflows"]): - sf = SeisFlows() # Run this with subprocess so we can capture the print statement - cmd_line_arg = ["seisflows", "par", parameter, new_val] + cmd_line_arg = ["seisflows","-p", dst, "par", parameter, new_val] out1 = subprocess.run(cmd_line_arg, capture_output=True, universal_newlines=True) # Run this with subprocess so we can capture the print statement - cmd_line_arg = ["seisflows", "par", parameter] + cmd_line_arg = ["seisflows", "-p", dst, "par", parameter] out2 = subprocess.run(cmd_line_arg, capture_output=True, universal_newlines=True) # Check that the changed print statement works par, vals = out1.stdout.strip().split(":") val_old, val_new = vals.strip().split(" -> ") - assert(par.upper() == parameter.upper()) - assert(int(val_old) == int(expected_val)) - assert(int(val_new) == int(new_val)) + assert(par.strip() == parameter) + assert(val_old.strip() == expected_val) + assert(val_new.strip() == new_val) # Check that we printed out the correctly changed value par, val = out2.stdout.strip().split(":") - assert(par.upper() == parameter.upper()) - assert(int(val) == int(new_val)) + assert(par.strip() == parameter) + assert(val.strip() == new_val) From cbed51f991436bbe33e27b4445a16903afcc0adc Mon Sep 17 00:00:00 2001 From: bch0w Date: Thu, 11 Aug 2022 12:15:09 -0800 Subject: [PATCH 106/195] fixing preprocess tests, all existing tests pass npow --- seisflows/preprocess/default.py | 14 ++++++++++++-- seisflows/preprocess/pyaflowa.py | 2 +- seisflows/tests/test_preprocess.py | 29 ++++++++++++++++------------- 3 files changed, 29 insertions(+), 16 deletions(-) diff --git a/seisflows/preprocess/default.py b/seisflows/preprocess/default.py index bdb94c9e..38147999 100644 --- a/seisflows/preprocess/default.py +++ b/seisflows/preprocess/default.py @@ -13,7 +13,7 @@ from seisflows import logger from seisflows.tools import signal, unix -from seisflows.tools.config import Dict +from seisflows.tools.config import Dict, get_task_id from seisflows.plugins.preprocess import misfit as misfit_functions from seisflows.plugins.preprocess import adjoint as adjoint_sources @@ -345,15 +345,25 @@ def _setup_quantify_misfit(self, source_name): """ Gather waveforms from the Solver scratch directory and """ + source_name = source_name or self._source_names[get_task_id()] + obs_path = os.path.join(self.path.solver, source_name, "traces", "obs") syn_path = os.path.join(self.path.solver, source_name, "traces", "syn") observed = sorted(glob(os.path.join(obs_path, "*"))) synthetic = sorted(glob(os.path.join(syn_path, "*"))) + assert(len(observed) == len(synthetic)), ( + f"number of observed traces does not match length of synthetic for " + f"source: {source_name}" + ) + + assert(len(observed) != 0 and len(synthetic) != 0), \ + f"cannot quantify misfit, missing observed or synthetic traces" + return observed, synthetic - def quantify_misfit(self, source_name=None, save_residuals=None, + def quantify_misfit(self, source_name=None, save_residuals=None, save_adjsrcs=None, iteration=1, step_count=0, **kwargs): """ diff --git a/seisflows/preprocess/pyaflowa.py b/seisflows/preprocess/pyaflowa.py index 604716f6..3dda7d37 100644 --- a/seisflows/preprocess/pyaflowa.py +++ b/seisflows/preprocess/pyaflowa.py @@ -481,7 +481,7 @@ def _quantify_misfit_station(self, config, station_code, else: ds = None mgmt = Manager(config=config, ds=ds) - # If data gather fails, return because theres nothing else we can do + # If data gather fails, return because there's nothing else we can do try: # `gather` function uses Config path structure and Client attribute # to search for data on disk or via webservices (if requested). diff --git a/seisflows/tests/test_preprocess.py b/seisflows/tests/test_preprocess.py index f826c95a..60ecd18e 100644 --- a/seisflows/tests/test_preprocess.py +++ b/seisflows/tests/test_preprocess.py @@ -4,6 +4,7 @@ """ import os import numpy as np +import pytest from glob import glob from seisflows import ROOT_DIR from seisflows.tools import unix @@ -74,28 +75,30 @@ def test_default_quantify_misfit(tmpdir): Quantify misfit with some example data """ preprocess = Default(data_format="ascii", misfit="waveform", - adjoint="waveform", path_preprocess=tmpdir) + adjoint="waveform", path_preprocess=tmpdir, + path_solver=TEST_SOLVER, source_prefix="SOURCE", + ntask=2, + ) preprocess.setup() - data_filenames = glob(os.path.join(TEST_DATA, "*semd")) preprocess.quantify_misfit( - observed=data_filenames, synthetic=data_filenames, + source_name="001", save_residuals=os.path.join(tmpdir, "residuals_ascii"), save_adjsrcs=tmpdir ) - preprocess.data_format = "SU" - data_filenames = glob(os.path.join(TEST_DATA, "*su")) - preprocess.quantify_misfit( - observed=data_filenames, synthetic=data_filenames, - save_residuals=os.path.join(tmpdir, "residuals_su"), - save_adjsrcs=tmpdir - ) + # !!! throws a segy error because data are not in the right format + # preprocess.data_format = "SU" + # preprocess.quantify_misfit( + # source_name="001", + # save_residuals=os.path.join(tmpdir, "residuals_su"), + # save_adjsrcs=tmpdir + # ) - assert(len(glob(os.path.join(tmpdir, "*"))) == 4) + assert(len(glob(os.path.join(tmpdir, "*"))) == 3) residuals = open(os.path.join(tmpdir, "residuals_ascii")).readlines() - assert(len(residuals) == 1) - assert(float(residuals[0]) == 0) + assert(len(residuals) == 2) + assert(float(residuals[0]) == pytest.approx(0.0269, 3)) def test_pyaflowa_setup(tmpdir): From 56a47457cc8fd866975cab7067e6d9916be099bb Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Fri, 12 Aug 2022 15:06:50 -0700 Subject: [PATCH 107/195] fixing example problems and tests, will need to write more tests to cover more of codebase but good base for now --- ...pecfem2d_workstation_inversion_w_pyatoa.py | 3 +- seisflows/examples/sfexample2d.py | 2 +- seisflows/seisflows.py | 2 + seisflows/tests/test_seisflows.py | 83 +++++-------------- 4 files changed, 27 insertions(+), 63 deletions(-) diff --git a/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py b/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py index 55415500..fa38d25c 100644 --- a/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py +++ b/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py @@ -26,7 +26,7 @@ class SFPyatoaEx2D(SFExample2D): advantage of the default SPECFEM2D stuff, onyl changes the generation of MODEL TRUE, the number of stations, and the setup of the parameter file. """ - def __init__(self, ntask=2, niter=1, nsta=5): + def __init__(self, ntask=2, niter=2, nsta=5): """ Overload init and attempt to import Pyatoa before running example, overload the default number of tasks to 2, and add a new init parameter @@ -85,6 +85,7 @@ def setup_seisflows_working_directory(self): self.sf.setup(force=True) # Force will delete existing parameter file self.sf.par("workflow", "inversion") self.sf.par("preprocess", "pyaflowa") + self.sf.par("optimize", "LBFGS") self.sf.configure() self.sf.par("end", 1) # only 1 iteration diff --git a/seisflows/examples/sfexample2d.py b/seisflows/examples/sfexample2d.py index 8dbd9d91..010b9630 100644 --- a/seisflows/examples/sfexample2d.py +++ b/seisflows/examples/sfexample2d.py @@ -320,7 +320,7 @@ def run_sf_example(self): Use subprocess to run the SeisFlows example we just set up """ cd(self.cwd) - subprocess.run("seisflows submit -f", check=False, shell=True) + subprocess.run("seisflows submit", check=False, shell=True) def main(self): """ diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index aafc9984..cf1a61ff 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -526,6 +526,8 @@ def split_module_docstring(mod, idx): continue if val is None: val = "null" + if absolute_paths: + val = os.path.abspath(val) f.write(f"path_{key}: {val}\n") written.append(key) except Exception: diff --git a/seisflows/tests/test_seisflows.py b/seisflows/tests/test_seisflows.py index c6c3068a..ecb1b83a 100644 --- a/seisflows/tests/test_seisflows.py +++ b/seisflows/tests/test_seisflows.py @@ -123,66 +123,27 @@ def test_cmd_setup(tmpdir): assert(test_phrase not in text) -# def test_cmd_submit(tmpdir): -# """ -# Test submit, also test the functionality of resume and restart which -# are essentially wrappers for this call -# :param tmpdir: -# :return: -# """ -# pass -# -# -# def test_cmd_clean(tmpdir): -# """ -# -# :param tmpdir: -# :return: -# """ - pass - - -# def test_cmd_configure(tmpdir, setup_par_file, conf_par_file): -# """ -# Test configuring a parameter file from a template par file -# -# .. note:: -# I don't know exactly why, but this test needs to be run AFTER any other -# test which runs seisflows.init(), otherwise the parameters are not -# instantiated properly (you will hit a KeyError when trying to access -# PAR). I think this is because of how seisflows.configure() registers -# a relatively empty parameter file (only modules are defined), and this -# gets saved into sys modules, affecting subsequent tests which end up -# accessing sys.modules. I tried flushing sys.modules but it didn't work. -# This behavior shouldn't get encountered in a real run because we -# won't need to run init() and configure() in the same python -# runtime environment, but I leave this warning here -# wondering if I'll have to fix it at some point... -B -# """ -# os.chdir(tmpdir) -# -# # Copy in the setup par file so we can configure it -# src = setup_par_file -# dst = os.path.join(tmpdir, "parameters.yaml") -# shutil.copy(src, dst) -# -# # run seisflows init -# with patch.object(sys, "argv", ["seisflows"]): -# sf = SeisFlows() -# sf.configure(relative_paths=False) -# -# # Simple check that the configuration parameter file has the same number -# # of lines as the one that has been created by configure -# lines_conf = open(conf_par_file, "r").readlines() -# lines_fill = open("parameters.yaml", "r").readlines() -# assert (len(lines_conf) == len(lines_fill)) -# -# # My attempt to flush sys.modules which did NOT work -# # from seisflows.tools.config import NAMES, PAR, PATH -# # for name in NAMES: -# # del sys.modules[f"seisflows_{name}"] -# # del sys.modules[PAR] -# # del sys.modules[PATH] +def test_cmd_configure(tmpdir, par_file): + """ + Test configuring a parameter file from a template par file + """ + os.chdir(tmpdir) + + # Copy in the setup par file so we can configure it + src = par_file + dst = os.path.join(tmpdir, "parameters.yaml") + shutil.copy(src, dst) + + # run seisflows configure + with patch.object(sys, "argv", ["seisflows"]): + sf = SeisFlows(workdir=tmpdir, parameter_file="parameters.yaml") + sf.configure() + + # Check some random values that were not in the template file + parameters = load_yaml(dst) + assert("path_model_init" in parameters.keys()) + assert("smooth_h" in parameters.keys()) + assert("ntask" in parameters.keys()) def test_cmd_par(tmpdir, par_file): @@ -215,7 +176,7 @@ def test_cmd_par(tmpdir, par_file): # testing the set option: seisflows par `parameter` `value` with patch.object(sys, "argv", ["seisflows"]): # Run this with subprocess so we can capture the print statement - cmd_line_arg = ["seisflows","-p", dst, "par", parameter, new_val] + cmd_line_arg = ["seisflows", "-p", dst, "par", parameter, new_val] out1 = subprocess.run(cmd_line_arg, capture_output=True, universal_newlines=True) From 5c3e4293c5d03a8e46fa8697cad213e3bf979908 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Fri, 12 Aug 2022 15:29:31 -0700 Subject: [PATCH 108/195] small bugfix optimization wasnt able to access checkpoint for editing --- seisflows/optimize/LBFGS.py | 4 ++-- seisflows/optimize/gradient.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/seisflows/optimize/LBFGS.py b/seisflows/optimize/LBFGS.py index 13219ad2..1d68f8e7 100644 --- a/seisflows/optimize/LBFGS.py +++ b/seisflows/optimize/LBFGS.py @@ -103,11 +103,11 @@ def checkpoint(self): Overwrite default checkpointing to store internal L-BFGS Attributes """ super().checkpoint() - checkpoint_dict = np.load(self.path._checkpoint) + checkpoint_dict = dict(np.load(self.path._checkpoint)) checkpoint_dict["LBFGS_iter"] = self._LBFGS_iter checkpoint_dict["memory_used"] = self._memory_used - np.savez(file=self.path._checkpoint, **dict_out) # NOQA + np.savez(file=self.path._checkpoint, **checkpoint_dict) # NOQA def load_checkpoint(self): """ diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index 917c9d9a..af119bd9 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -108,7 +108,7 @@ def __init__(self, line_search_method="bracket", # Hidden paths to store checkpoint file in scratch directory self.path["_checkpoint"] = os.path.join(self.path.scratch, - "checkpoint") + "checkpoint.npz") self.path["_stats_file"] = os.path.join(self.path.scratch, "output_optim.txt") From 21d5ddb5cb5ec12a518cd8c61c221e57d187a44a Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Mon, 15 Aug 2022 09:35:21 -0700 Subject: [PATCH 109/195] updating docs to match latest version of seisflows, finished command line tool update --- docs/changelog.rst | 52 ++- docs/citeme.rst | 2 +- docs/command_line_tool.rst | 10 +- docs/conf.py | 2 +- docs/design.rst | 10 + docs/index.rst | 13 +- docs/notebooks/command_line_tool.ipynb | 449 +++++++++---------------- docs/overview.rst | 6 +- docs/start_here.rst | 27 +- seisflows/seisflows.py | 24 +- 10 files changed, 240 insertions(+), 355 deletions(-) create mode 100644 docs/design.rst diff --git a/docs/changelog.rst b/docs/changelog.rst index 6e07fee6..cef7e222 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -2,10 +2,23 @@ Change Log =============== The following list documents changes made from ``SeisFlows Legacy`` -codebase (Last updated April 18, 2022). +codebase (Last updated Aug. 12, 2022). Major ------ +* Reworks the entire parameter system to take advantage of Python's native init + and internal attributes, rather than setting global parameters in sys.modules. +* Removes reliance on globally accessible sys.modules, which was used so that all + modules had access to one another, but also prevented standalone imports of modules + and efficient unit testing. This has been replaced with explicit I/O for modules to + talk to each other, and shared parameters that are instantiated by a single parameter + file. +* Sys.modules was also used to checkpoint workflows, as all class instances were saved + as pickle files that could be re-loaded. However this required a specific startup + procedure anytime a workflow had to be instantiated. This has been replaced by an + explicit checkpointing system which takes advantage of simpler text files which save + only relevant checkpoint information. +* Implemented a unit/integration test structure using Pytest, for all modules and utilities. * 'seisflows' command line tool which now acts as the main entry point into package. Replaces the old sf* scripts and adds functionality to inspect the package and an active workflow. @@ -24,6 +37,28 @@ Major Moderate -------- +* All modules now have their own setup() and check() functions which help + establish a workflow before anything needs to be submitted to the system. +* System run functions no longer require global pickling into sys modules, but + instead simply pickle the required functions and their keyword arguments. This + greatly simplifies the run capabilities on external systems, and removes the need + to define specific pickling functions (copyreg) within the package. +* Solver has been separated from all model manipulations. Replaced by a new + standalone Model class which completely characterizes a Specfem model and can + read/write models as numpy vectors or as Specfem-readable binary files. +* Postprocess module has been scrapped and its functionalities absorbed by the + Solver and Workflow modules. The reasoning behind this was that the postprocess + really had no agency of its own, and was just a wrapper for solver functionality. + It may be reintroduced in the future if we find need for very complex postprocessing + steps. +* Optimization test suite now runs Rosenbrock problem (from legacy code) for all + optimization schema and tests line search capabilities. +* Optimization also checkpoints itself and the line search during a workflow, allowing + Users to restart workflows from any point effortlessly and with minimal loss of compute time. + Pyatoa preprocessing renamed to 'Pyaflowa'. All of the old Pyaflowa class functions + (previously contained in the Pyatoa package) are now exposed in the SeisFlows Pyaflowa + preprocessing class. This is so that users do not necessarily have to look at the Pyatoa + source code to understand what's going on in SeisFlows * Renamed and restructured SeisFlows working directory structure. - output.logs -> logs - output.stats -> stats @@ -59,6 +94,21 @@ Moderate Minor ------ +* Removed a lot of agency from individual modules (i.e., writing their own files, + manipulating other modules) and gave it mainly to the Workflow module. This is so + that when users need to look at where changes are disk are happening, they only have + to look at the Workflow module, rather than chasing through various other + modules/sub-modules. +* Rearranged directory structure to be more easily navigable, with less top-directory scripts. +* SeisFlows now relies more heavily on inheritance and super() functions throughout, + whereas previously it was used quite sparingly. +* Workflow module now relies more heavily on inheritance. Forward workflows build out most + of the functionality of the forward solver, and the Migration and Inversion workflows build + on top of this. +* Reworked docstring structure to aid in 'seisflows configure' command which now builds new + parameter files directly from init and class docstrings +* Gutted most of the old internal start up procedures such as loading parameters and modules. + The new start up procedure is now more Pythonic and efficient. * reST format docstrings for all classes and functions. * Comments added throughout codebase to explain naming, logic etc. * Replaced all string formatters with f-strings if possible, .format() otherwise diff --git a/docs/citeme.rst b/docs/citeme.rst index 2f19a8be..9ff3e25e 100644 --- a/docs/citeme.rst +++ b/docs/citeme.rst @@ -10,6 +10,6 @@ If you use SeisFlows for your work, please cite: SeisFlows — Flexible waveform inversion software. Computers & geosciences, 115, 88-95. -Publications which have used (and cited) SeisFlows (and SeisFlows3) can be +Publications which have used (and cited) SeisFlows can be found on `Google Scholar `__. diff --git a/docs/command_line_tool.rst b/docs/command_line_tool.rst index 171b7251..45a4afcd 100644 --- a/docs/command_line_tool.rst +++ b/docs/command_line_tool.rst @@ -1,12 +1,10 @@ Commmand Line Tool ================== -``SeisFlows`` is primarily interacted with via command line calls and a -parameter file. In this page we explain how to use this command line -tool to create a SeisFlows parameters file, edit and configure it, and -establish a SeisFlows working directory. We also provide explanation -for other command line options which act as helper utilities for -improved package control. +``SeisFlows`` is primarily interacted with via a parameter file and command line tool. +In this page we explain how to use this command line tool to create a SeisFlows parameters +file, edit and configure it, and run a workflow. We also provide explanation +for other command line options which act as helper utilities for package control. After installing SeisFlows into a Conda environment, the ``seisflows`` command will be available directly from the command line. To access the diff --git a/docs/conf.py b/docs/conf.py index 623839df..7fd4cb47 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -59,5 +59,5 @@ # AutoAPI variables autoapi_type = "python" -autoapi_dirs = ["../seisflows", "../seisflows-super"] +autoapi_dirs = ["../seisflows"] autoapi_add_toctree_entry = True diff --git a/docs/design.rst b/docs/design.rst new file mode 100644 index 00000000..2978e0bd --- /dev/null +++ b/docs/design.rst @@ -0,0 +1,10 @@ +Design Philosophy +================================= +Although SeisFlows was written with 'Pythonic' style in mind, there are some design +choices within the internal structure of the package that may be unique to SeisFlows. +Throughout this documentation page we point out some of these choices, and provide +detail on why and how they were implemented. This page is intended for developers and +super users who want to explore the source code deeply or edit/extend SeisFlows for other +purposes. + +`Page under construction` diff --git a/docs/index.rst b/docs/index.rst index 4301aba4..1d956e32 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -24,6 +24,7 @@ optimization problems. Major backwards-incompatible changes from the legacy codebase include: - > complete shift to Python3.7 source code, abandoning Python2 support + - > reworked internal architecture to be more 'Pythonic' - > richer source code emphasizing readability and standards - > a new command line tool for improved package control - > redesigned, dynamically-generated parameter file @@ -56,7 +57,7 @@ environment. .. code:: bash - $ conda create -n seisflows python=3.7 + $ conda create -n seisflows python=3.10 $ conda activate seisflows $ git clone --branch devel https://github.com/adjtomo/seisflows.git $ cd seisflows @@ -76,16 +77,9 @@ SeisFlows + Pyatoa -------------------- To include the waveform measurement capabilities of Pyatoa, you must install separately. See the `Pyatoa documentation -`__ for the most up to date +`__ for the most up to date install instructions. -.. code:: bash - - $ cd .. - $ git clone --branch devel https://github.com/bch0w/pyatoa.git - $ cd pyatoa - $ pip install . - .. toctree:: :maxdepth: 1 @@ -125,6 +119,7 @@ install instructions. :caption: Development background + design changelog code_dev_plan diff --git a/docs/notebooks/command_line_tool.ipynb b/docs/notebooks/command_line_tool.ipynb index 939de50c..558ebe56 100644 --- a/docs/notebooks/command_line_tool.ipynb +++ b/docs/notebooks/command_line_tool.ipynb @@ -6,14 +6,14 @@ "source": [ "Commmand Line Tool\n", "==========================\n", - "`SeisFlows3` is primarily interacted with via command line calls and a parameter file. In this page we explain how to use this command line tool to create a SeisFlows3 parameters file, edit and configure it, and establish a SeisFlows3 working directory. We also provide explanation for other command line options which act as helper utilities for improved package control.\n", + "`SeisFlows` is primarily interacted with via command line calls and a parameter file. In this page we explain how to use this command line tool to create a SeisFlows parameters file, edit and configure it, and establish a SeisFlows working directory. We also provide explanation for other command line options which act as helper utilities for improved package control.\n", " \n", - "After installing SeisFlows3 into a Conda environment, the `seisflows` command will be available directly from the command line. To access the help dialogue, you can type `seisflows` or `seisflows -h`" + "After installing SeisFlows into a Conda environment, the `seisflows` command will be available directly from the command line. To access the help dialogue, you can type `seisflows` or `seisflows -h`" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -21,12 +21,12 @@ "output_type": "stream", "text": [ "usage: seisflows [-h] [-w [WORKDIR]] [-p [PARAMETER_FILE]]\r\n", - " {setup,configure,init,submit,resume,restart,clean,par,sempar,check,print,convert,reset,debug,edit,examples}\r\n", + " {setup,configure,swap,init,submit,resume,restart,clean,par,sempar,check,print,reset,debug,examples}\r\n", " ...\r\n", "\r\n", "================================================================================\r\n", "\r\n", - " SeisFlows3: Waveform Inversion Package \r\n", + " SeisFlows: Waveform Inversion Package \r\n", "\r\n", "================================================================================\r\n", "\r\n", @@ -42,19 +42,18 @@ "\r\n", " setup Setup working directory from scratch\r\n", " configure Fill parameter file with defaults\r\n", + " swap Swap module parameters in an existing parameter file\r\n", " init Initiate working environment\r\n", " submit Submit initial workflow to system\r\n", " resume Re-submit previous workflow to system\r\n", " restart Remove current environment and submit new workflow\r\n", " clean Remove files relating to an active working environment\r\n", - " par View and edit SeisFlows3 parameter file\r\n", + " par View and edit SeisFlows parameter file\r\n", " sempar View and edit SPECFEM parameter file\r\n", " check Check state of an active environment\r\n", " print Print information related to an active environment\r\n", - " convert Convert model file format\r\n", " reset Reset modules within an active state\r\n", " debug Start interactive debug environment\r\n", - " edit Open source code file in text editor\r\n", " examples Look at and run pre-configured example problems\r\n", "\r\n", "'seisflows [command] -h' for more detailed descriptions of each command.\r\n" @@ -72,37 +71,38 @@ "### Setting up a parameter file\n", "#### seisflows setup\n", "\n", - "The first step of any SeisFlows3 workflow is to set up a working directory, which begins by establishing a blank parameter file. The `seisflows setup` command copies in a template parameter file. Ideally your working directory will be empty to avoid file conflicts." + "The first step of any SeisFlows workflow is to setting up a parameter file. The `seisflows setup` command copies in a template parameter file." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "/home/bchow/Work/scratch\n" + "/Users/Chow/Scratch\n" ] } ], "source": [ - "%cd ~/Work/scratch\n", + "%cd ~/Scratch\n", + "! rm *\n", "! ls" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "usage: seisflows setup [-h] [-s] [-f]\r\n", + "usage: seisflows setup [-h] [-f]\r\n", "\r\n", "In the specified working directory, copy template parameter file containing\r\n", "only module choices, and symlink source code for both the base and super\r\n", @@ -111,9 +111,8 @@ "they want to overwrite.\r\n", "\r\n", "optional arguments:\r\n", - " -h, --help show this help message and exit\r\n", - " -s, --symlink symlink source code into the working directory\r\n", - " -f, --force automatically overwrites existing parameter file\r\n" + " -h, --help show this help message and exit\r\n", + " -f, --force automatically overwrites existing parameter file\r\n" ] } ], @@ -123,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -135,7 +134,7 @@ } ], "source": [ - "# The '-f' flag (force) will overwrite any existing parameter file\n", + "# The '-f' flag (overwrite) will overwrite any existing parameter file\n", "! seisflows setup -f" ] }, @@ -143,20 +142,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Having a look at the template parameters.yaml file that was just generated, we can see that it contains some pre-defined default values for the core SeisFlows3 modules. Each of these modules defines it's own set of unique parameters which make up a workflow." + "Having a look at the template `parameters.yaml` file that was just generated, we can see that it contains some pre-defined default values for the core SeisFlows modules. Each of these modules defines it's own set of unique parameters which make up a workflow." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "parameters.yaml\n", - "32 parameters.yaml\n" + "parameters.yaml sflog.txt\n", + " 30 parameters.yaml\n" ] } ], @@ -167,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -176,12 +175,12 @@ "text": [ "# //////////////////////////////////////////////////////////////////////////////\r\n", "#\r\n", - "# SeisFlows3 YAML Parameter File\r\n", + "# SeisFlows YAML Parameter File\r\n", "#\r\n", "# //////////////////////////////////////////////////////////////////////////////\r\n", "#\r\n", "# Modules correspond to the structure of the source code, and determine\r\n", - "# SeisFlows3' behavior at runtime. Each module requires its own sub-parameters.\r\n", + "# SeisFlows' behavior at runtime. Each module requires its own sub-parameters.\r\n", "#\r\n", "# .. rubric::\r\n", "# - To determine available options for modules listed below, run:\r\n", @@ -193,19 +192,17 @@ "#\r\n", "# MODULES\r\n", "# ///////\r\n", - "# WORKFLOW (str): The method for running SeisFlows3; equivalent to main()\r\n", - "# SOLVER (str): External numerical solver to use for waveform simulations\r\n", - "# SYSTEM (str): Computer architecture of the system being used\r\n", - "# OPTIMIZE (str): Optimization algorithm for the inverse problem\r\n", - "# PREPROCESS (str): Preprocessing schema for waveform data\r\n", - "# POSTPROCESS (str): Postprocessing schema for kernels and gradients\r\n", + "# workflow (str): The types and order of functions for running SeisFlows\r\n", + "# system (str): Computer architecture of the system being used\r\n", + "# solver (str): External numerical solver to use for waveform simulations\r\n", + "# preprocess (str): Preprocessing schema for waveform data\r\n", + "# optimize (str): Optimization algorithm for the inverse problem\r\n", "# ==============================================================================\r\n", - "WORKFLOW: inversion\r\n", - "SOLVER: specfem2d\r\n", - "SYSTEM: workstation\r\n", - "OPTIMIZE: LBFGS \r\n", - "PREPROCESS: base\r\n", - "POSTPROCESS: base\r\n" + "workflow: forward\r\n", + "system: workstation\r\n", + "solver: specfem2d\r\n", + "preprocess: default\r\n", + "optimize: gradient\r\n" ] } ], @@ -224,14 +221,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "usage: seisflows configure [-h] [-r]\r\n", + "usage: seisflows configure [-h] [-a]\r\n", "\r\n", "SeisFlows parameter files will vary depending on chosen modules and their\r\n", "respective required parameters. This function will dynamically traverse the\r\n", @@ -243,7 +240,7 @@ "\r\n", "optional arguments:\r\n", " -h, --help show this help message and exit\r\n", - " -r, --relative_paths Set default paths relative to cwd\r\n" + " -a, --absolute_paths Set default paths relative to cwd\r\n" ] } ], @@ -253,114 +250,106 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "filling parameters.yaml w/ default values\r\n" - ] - } - ], + "outputs": [], "source": [ "! seisflows configure" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "# :param smooth_v: Gaussian half-width for vertical smoothing in units\n", + "# of meters.\n", + "# :type components: str\n", + "# :param components: components to consider and tag data with. Should be\n", + "# string of letters such as 'RTZ'\n", + "# :type solver_io: str\n", + "# :param solver_io: format of model/kernel/gradient files expected by the\n", + "# numerical solver. Available: ['fortran_binary': default .bin files].\n", + "# TODO: ['adios': ADIOS formatted files]\n", + "# :type source_prefix: str\n", + "# :param source_prefix: prefix of source/event/earthquake files. If None,\n", + "# will attempt to guess based on the specific solver chosen.\n", + "# :type mpiexec: str\n", + "# :param mpiexec: MPI executable used to run parallel processes. Should also\n", + "# be defined for the system module\n", + "#\n", + "# \n", + "# Solver SPECFEM2D\n", + "# ----------------\n", + "# SPECFEM2D-specific alterations to the base SPECFEM module\n", + "#\n", + "# Parameters\n", + "# ----------\n", + "# :type source_prefix: str\n", + "# :param source_prefix: Prefix of source files in path SPECFEM_DATA. Defaults\n", + "# to 'SOURCE'\n", + "# :type multiples: bool\n", + "# :param multiples: set an absorbing top-boundary condition\n", + "#\n", + "# \n", "# =============================================================================\n", - "# SOLVER \n", - "# ////// \n", - "# MATERIALS (str):\n", - "# Material parameters used to define model. Available: ['ELASTIC': Vp, Vs,\n", - "# 'ACOUSTIC': Vp, 'ISOTROPIC', 'ANISOTROPIC']\n", - "# DENSITY (str):\n", - "# How to treat density during inversion. Available: ['CONSTANT': Do not\n", - "# update density, 'VARIABLE': Update density]\n", - "# ATTENUATION (str):\n", - "# If True, turn on attenuation during forward simulations, otherwise set\n", - "# attenuation off. Attenuation is always off for adjoint simulations.\n", - "# COMPONENTS (str):\n", - "# Components used to generate data, formatted as a single string, e.g. ZNE\n", - "# or NZ or E\n", - "# SOLVERIO (int):\n", - "# The format external solver files. Available: ['fortran_binary', 'adios']\n", - "# NT (float):\n", - "# Number of time steps set in the SPECFEM Par_file\n", - "# DT (float):\n", - "# Time step or delta set in the SPECFEM Par_file\n", - "# F0 (float):\n", - "# Dominant source frequency\n", - "# FORMAT (float):\n", - "# Format of synthetic waveforms used during workflow, available options:\n", - "# ['ascii', 'su']\n", - "# SOURCE_PREFIX (str):\n", - "# Prefix of SOURCE files in path SPECFEM_DATA. By default, 'SOURCE' for\n", - "# SPECFEM2D\n", + "data_format: ascii\n", + "materials: acoustic\n", + "density: False\n", + "attenuation: False\n", + "smooth_h: 0.0\n", + "smooth_v: 0.0\n", + "components: ZNE\n", + "source_prefix: SOURCE\n", + "multiples: False\n", "# =============================================================================\n", - "MATERIALS: !!! REQUIRED PARAMETER !!!\n", - "DENSITY: !!! REQUIRED PARAMETER !!!\n", - "ATTENUATION: !!! REQUIRED PARAMETER !!!\n", - "COMPONENTS: ZNE\n", - "SOLVERIO: fortran_binary\n", - "NT: !!! REQUIRED PARAMETER !!!\n", - "DT: !!! REQUIRED PARAMETER !!!\n", - "F0: !!! REQUIRED PARAMETER !!!\n", - "FORMAT: !!! REQUIRED PARAMETER !!!\n", - "SOURCE_PREFIX: SOURCE\n", + "#\n", + "# Default Preprocess\n", + "# ------------------\n", + "# Data processing for seismic traces, with options for data misfit,\n", + "# filtering, normalization and muting.\n", + "#\n", + "# Parameters\n", + "# ----------\n", + "# :type data_format: str\n", + "# :param data_format: data format for reading traces into memory. For\n", + "# available see: seisflows.plugins.preprocess.readers\n", + "# :type misfit: str\n", + "# :param misfit: misfit function for waveform comparisons. For available\n", + "# see seisflows.plugins.preprocess.misfit\n", + "# :type backproject: str\n", + "# :param backproject: backprojection function for migration, or the\n", + "# objective function in FWI. For available see\n", + "# seisflows.plugins.preprocess.adjoint\n", + "# :type normalize: str\n", + "# :param normalize: Data normalization parameters used to normalize the\n", + "# amplitudes of waveforms. Choose from two sets:\n", + "# ENORML1: normalize per event by L1 of traces; OR\n", + "# ENORML2: normalize per event by L2 of traces;\n", + "# &\n", + "# TNORML1: normalize per trace by L1 of itself; OR\n", + "# TNORML2: normalize per trace by L2 of itself\n", + "# :type filter: str\n", + "# :param filter: Data filtering type, available options are:\n", + "# BANDPASS (req. MIN/MAX PERIOD/FREQ);\n", + "# LOWPASS (req. MAX_FREQ or MIN_PERIOD);\n", + "# HIGHPASS (req. MIN_FREQ or MAX_PERIOD)\n", + "# :type min_period: float\n", + "# :param min_period: Minimum filter period applied to time series.\n", + "# See also MIN_FREQ, MAX_FREQ, if User defines FREQ parameters, they\n", + "# will overwrite PERIOD parameters.\n", + "# :type max_period: float\n", + "# :param max_period: Maximum filter period applied to time series. See\n", + "# also MIN_FREQ, MAX_FREQ, if User defines FREQ parameters, they will\n", + "# overwrite PERIOD parameters.\n", + "# :type min_freq: float\n", + "# :param min_freq: Maximum filter frequency applied to time series,\n", "\n", - "# =============================================================================\n", - "# POSTPROCESS \n", - "# /////////// \n", - "# SMOOTH_H (float):\n", - "# Gaussian half-width for horizontal smoothing in units of meters. If 0.,\n", - "# no smoothing applied\n", - "# SMOOTH_V (float):\n", - "# Gaussian half-width for vertical smoothing in units of meters\n", - "# TASKTIME_SMOOTH (int):\n", - "# Large radii smoothing may take longer than normal tasks. Allocate\n", - "# additional smoothing task time as a multiple of TASKTIME\n", - "# =============================================================================\n", - "SMOOTH_H: 0.0\n", - "SMOOTH_V: 0.0\n", - "TASKTIME_SMOOTH: 1\n", - "\n", - "# =============================================================================\n", - "# OPTIMIZE \n", - "# //////// \n", - "# LINESEARCH (str):\n", - "# Algorithm to use for line search, see seisflows.plugins.line_search for\n", - "# available choices\n", - "# PRECOND (str):\n", - "# Algorithm to use for preconditioning gradients, see\n", - "# seisflows.plugins.preconds for available choices\n", - "# STEPCOUNTMAX (int):\n", - "# Max number of trial steps in line search before a change in line search\n", - "# behavior\n", - "# STEPLENINIT (float):\n", - "# Initial line search step length, as a fraction of current model\n", - "# parameters\n", - "# STEPLENMAX (float):\n", - "# Max allowable step length, as a fraction of current model parameters\n", - "# LBFGSMEM (int):\n", - "# Max number of previous gradients to retain in local memory\n", - "# LBFGSMAX (int):\n", - "# LBFGS periodic restart interval, between 1 and 'inf'\n", - "# LBFGSTHRESH (float):\n", - "# LBFGS angle restart threshold\n", - "# =============================================================================\n", - "LINESEARCH: Backtrack\n", - "\n", - "306 parameters.yaml\n" + " 337 parameters.yaml\n" ] } ], @@ -374,9 +363,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can see that our parameter file is over 300 lines now, too cumbersome to print on the page. The length of the file mostly arises from the header, as each parameter gets it's own entry with the parameter's type, docstring, and any available options.\n", + "We can see that our parameter file is over 300 lines, a bit too cumbersome to print on the page. The length of the file mostly arises from the header, as each parameter gets it's own entry with the parameter's type, docstring, and any available options. Each set of parameters are separated by their relevant module, and their respective docstrings should help users understand how and when they are used in a SeisFlows workflow.\n", "\n", - "Parameters that are required by the workflow but do not come with pre-set default values will be labelled with `!!! REQUIRED PARAMETER !!!`. Similarly required path definitions, which come at the end of the file, are labelled with the `!!! REQUIRED PATH !!!` value." + ">__NOTE__: Many parameters have sensible default values chosen, but it is up to the user to decide which parameters are relevant to them, and how they would like them set. Internal check functions throughout the package will raise AssertionErrors for incorrectly or improperly set parameters." ] }, { @@ -386,19 +375,19 @@ "### Filling out the parameter file\n", "#### seisflows par\n", "\n", - "It's easy enough to open your favorite text editor to make adjustments to the parameter file, however the `seisflows par` command makes things easier by allowing you to view and edit values from the command line. This makes it convenient to change parameters, and also allows you to script your workflow setup for improved reproducibility. " + "You can always open your favorite text editor to make changes to the parameter file, however the `seisflows par` command makes things easier by allowing you to view and edit values from the command line. This makes it convenient to change parameters quickly and allows you to script your parameter file setup for improved reproducibility. " ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "usage: seisflows par [-h] [-p] [-r] [parameter] [value]\r\n", + "usage: seisflows par [-h] [-p] [parameter] [value]\r\n", "\r\n", "Directly edit values in the parameter file by providing the parameter and\r\n", "corresponding value. If no value is provided, will simply print out the\r\n", @@ -414,10 +403,7 @@ "optional arguments:\r\n", " -h, --help show this help message and exit\r\n", " -p, --skip_print Skip the print statement which is typically sent to stdout\r\n", - " after changing parameters.\r\n", - " -r, --required Only list parameters which have not been set as a default\r\n", - " value, typically set with some attention catching\r\n", - " argument. 'parameter' and 'value' will be ignored.\r\n" + " after changing parameters.\r\n" ] } ], @@ -429,87 +415,52 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The -r (--required) flag tells us which parameters need to be set by the user" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "!!! REQUIRED PARAMETER !!!\r\n", - "==========================\r\n", - "\tMATERIALS\r\n", - "\tDENSITY\r\n", - "\tATTENUATION\r\n", - "\tNT\r\n", - "\tDT\r\n", - "\tF0\r\n", - "\tFORMAT\r\n", - "\tCASE\r\n", - "\tEND\r\n", - "!!! REQUIRED PATH !!!\r\n", - "=====================\r\n", - "\tSPECFEM_BIN\r\n", - "\tSPECFEM_DATA\r\n", - "\tMODEL_INIT\r\n" - ] - } - ], - "source": [ - "! seisflows par -r" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can view (but not modify) parameters by giving a single argument to the par command" + "The call structure of the `par` command is provided in the help message:\n", + "\n", + "> seisflows par [parameter] [value (optional)]\n", + "\n", + "We can view parameters by providing a single 'parameter' argument to the `par` command" ] }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "END: !!! REQUIRED PARAMETER !!!\r\n" + "ntask: 1\r\n" ] } ], "source": [ - "! seisflows par end" + "! seisflows par ntask # ntask is the number of tasks/events to be run during a workflow" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "and we can edit the given parameter by providing a second argument to the par command" + "We can change a given parameter from it's original value by providing a second 'value' argument" ] }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "END: !!! REQUIRED PARAMETER !!! -> 1\r\n" + "ntask: 1 -> 3\r\n" ] } ], "source": [ - "! seisflows par end 1" + "! seisflows par ntask 3" ] }, { @@ -520,71 +471,14 @@ "\n", "The `seisflows sempar` command behaves the same as the `par` command, except is used to edit a SPECFEM2D/3D/3D_GLOBE Par_file. It has the same call structure as `par`.\n", "\n", - "### Setting up an active working state\n", - "\n", - "An active SeisFlows3 working state is simply a Python environment with the SeisFlows3 library defined based on the given parameter file. In order to establish a working state, we need to set all required paths and parameters. We can look at the parameter file header to determine valid options for each parameter." - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# ////// \r\n", - "# MATERIALS (str):\r\n", - "# Material parameters used to define model. Available: ['ELASTIC': Vp, Vs,\r\n", - "# 'ACOUSTIC': Vp, 'ISOTROPIC', 'ANISOTROPIC']\r\n", - "# DENSITY (str):\r\n", - "# How to treat density during inversion. Available: ['CONSTANT': Do not\r\n", - "# update density, 'VARIABLE': Update density]\r\n", - "# ATTENUATION (str):\r\n", - "# If True, turn on attenuation during forward simulations, otherwise set\r\n", - "# attenuation off. Attenuation is always off for adjoint simulations.\r\n" - ] - } - ], - "source": [ - "! head -130 parameters.yaml | tail -n 10 " - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "# We use the `-p` flag to turn off stdout printing\n", - "! seisflows par materials elastic -p\n", - "! seisflows par density constant -p\n", - "! seisflows par attenuation False -p\n", - "! seisflows par nt 100 -p\n", - "! seisflows par dt .01 -p\n", - "! seisflows par f0 .5 -p\n", - "! seisflows par format ascii -p\n", - "! seisflows par case synthetic -p\n", - "\n", - "# Required paths can similarly be set the `par` command\n", - "! seisflows par specfem_bin ./ -p\n", - "! seisflows par specfem_data ./ -p\n", - "! seisflows par model_init ./ -p" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### seisflows init\n", + "#### seisflows check\n", "\n", - "To initiate a working state, we run `seisflows init`. This registers the parameter file into Python's sys.modules. It runs parameter check functions to ensure that parameters have been set correctly, and then saves the active working state as a set of pickle (.p) files which can be used to resume active workflows." + "Each module contains it's own internal set of parameter checks which make sure that reasonable parameter values and types have been chosen. This is especially important when submitting large jobs on clusters as the `check` function will allow the User to catch errors without having to wait on queue times or waste computational resources." ] }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -593,65 +487,34 @@ "text": [ "\r\n", "================================================================================\r\n", - " MODULE CHECK ERROR \r\n", - " ////////////////// \r\n", - "seisflows.config module check failed with:\r\n", - "\r\n", - "workflow: CASE == SYNTHETIC requires PATH.MODEL_TRUE\r\n", + " PARAMETER ERRROR \r\n", + " //////////////// \r\n", + "`path_specfem_bin` must exist and must point to directory containing SPECFEM\r\n", + "executables\r\n", "================================================================================\r\n" ] } ], "source": [ - "! seisflows init" + "! seisflows check" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Oops, as we can see the parameter check has caught that a given parameter requires a certain path to be set which is currently blank. Let's amend and try again" + "Here we can see that a given path has not been set correctly in the parameter file." ] }, { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "instantiating SeisFlows3 working state in directory: output\r\n" - ] - } - ], - "source": [ - "! seisflows par model_true ./ -p\n", - "! seisflows init" - ] - }, - { - "cell_type": "code", - "execution_count": 65, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "output\tparameters.yaml\n", - "\n", - "seisflows_optimize.p\t seisflows_postprocess.p seisflows_system.p\n", - "seisflows_parameters.json seisflows_preprocess.p seisflows_workflow.p\n", - "seisflows_paths.json\t seisflows_solver.p\n" - ] - } - ], "source": [ - "! ls\n", - "! echo\n", - "! ls output" + "#### seisflows submit\n", + "\n", + "To run SeisFlows, we use the `submit` call. This will submit the `workflow` to the `system` and continue until a User-defined stop criteria is met. \n", + "\n", + "Under the hood, the `submit` function will differ depending on the chosen `system`. For Users running on laptops and workstations, `submit` will simply launch a Python process and step through the tasks in the `workflow` task list. On clusters, `submit` will launch a master job on a compute node, which will itself step through tasks in the task list, ensuring that no processing is run on login nodes. " ] } ], @@ -671,7 +534,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/docs/overview.rst b/docs/overview.rst index 25c2cc87..652679df 100644 --- a/docs/overview.rst +++ b/docs/overview.rst @@ -37,12 +37,10 @@ SeisFlows Modules: internal structure makes it relatively seamless to switch between workstation problems, and HPC jobs (Examples: workstation, Slurm) * **Solver**: External numerical solver used to generate models, synthetics, - and kernels (Examples: SPECFEM2D, SPECFEM3D Cartesian, SPECFEM3D Globe) + and kernels (Examples: SPECFEM2D, SPECFEM3D Cartesian, SPECFEM3D Globe), and + to smooth and manipulate kernels and gradients. * **Preprocessing**: Signal processing operations performed on time series, including downsampling, detrending, filtering, etc. - * **Postprocessing**: Regularization and image processing operations - carried out on the gradient computations. Typical tasks involve - smoothing, preconditioning and masking * **Optimization**: Nonlinear optimization algorithms used to find the minimum of a waveform-based objective function (Examples: L-BFGS, NLCG, steepest descent) diff --git a/docs/start_here.rst b/docs/start_here.rst index 9c3d8ca5..d196ccb7 100644 --- a/docs/start_here.rst +++ b/docs/start_here.rst @@ -1,5 +1,5 @@ Getting started -================================= +================ Assuming installation detailed on the `home page `__ has completed successfully, you can start playing around with SeisFlows. @@ -22,8 +22,10 @@ For more information on the SeisFlows command line tool, see the Running tests ~~~~~~~~~~~~~ -SeisFlows has some unit tests that ensure the capabilities of the command line -tool and package organization are working as intended. To run the tests: +SeisFlows has some unit/integration tests that ensure the capabilities of +the package are working as intended. Tests should be run before and after any +edits to the source code are made. To run the tests, from the top level +`seisflows` directory: .. parsed-literal:: @@ -35,25 +37,6 @@ If developing SeisFlows, please ensure that you run these tests before and after any changes are made to ensure that your changes do not break intended package functionality. -Running the test problem -~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -SeisFlows comes with a small test problem that is intended to not only test -the workflow capabilities of the package, but also for users to rapidly develop -new modules without needing to run large, potentially expensive, workflows. - -In order to set up the test problem: - -.. parsed-literal:: - - cd path/to/working/directory # ideally this directory is empty - seisflows setup # this will create a template parameters.yaml file - seisflows par workflow test - seisflows configure - -At this stage, you will have a full SeisFlows parameter file. Certain system or -module specific - Running an example problem ~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index cf1a61ff..a5707c18 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -193,11 +193,6 @@ def _format_action(self, action): par.add_argument("-p", "--skip_print", action="store_true", default=False, help="Skip the print statement which is typically " "sent to stdout after changing parameters.") - par.add_argument("-r", "--required", action="store_true", default=False, - help="Only list parameters which have not been set as a " - "default value, typically set with some attention " - "catching argument. 'parameter' and 'value' will be " - "ignored.") # ========================================================================= sempar = subparser.add_parser( "sempar", help="View and edit SPECFEM parameter file", @@ -628,7 +623,7 @@ def clean(self, force=False, **kwargs): Clean the SeisFlows working directory except for the parameter file. :type force: bool - :param force: ignore the warning check that precedes the clean() + :param force: ignore the warning check that precedes the clean() function, useful if you don't want any input messages popping up """ # Check if the filepaths exist @@ -712,7 +707,7 @@ def sempar(self, parameter, value=None, skip_print=False, seisflows sempar velocity_model to edit the values of a velocity model (SPECFEM2D) - + seisflows sempar velocity_model \ "1 1 2600.d0 5800.d0 3500.0d0 0 0 10.d0 10.d0 0 0 0 0 0 0" @@ -732,8 +727,8 @@ def sempar(self, parameter, value=None, skip_print=False, :type value: str :param value: value to set for parameter. if none, will simply print out the current parameter value. to set as nonetype, set to 'null' - SPECFEM2D: if set to 'velocity_model' allows the user to set and - edit the velocity model defined in the SPECMFE2D Par_file. Not a + SPECFEM2D: if set to 'velocity_model' allows the user to set and + edit the velocity model defined in the SPECMFE2D Par_file. Not a very smart capability, likely easier to do this manually. :type par_file: str :param par_file: name of the SPECFEM parameter file, defaults: Par_file @@ -784,8 +779,7 @@ def sempar(self, parameter, value=None, skip_print=False, if not skip_print: print(msg.cli(f"{key}: {cur_val} -> {value}")) - def par(self, parameter, value=None, skip_print=False, required=False, - **kwargs): + def par(self, parameter, value=None, skip_print=False, **kwargs): """ Check or set parameters in the seisflows parameter file. @@ -814,20 +808,14 @@ def par(self, parameter, value=None, skip_print=False, required=False, :type skip_print: bool :param skip_print: skip the print statement which is typically sent to stdout after changing parameters. - :type required: bool - :param required: Only list parameters which have not been set as a - default value, 'parameter' and 'value' will be ignored. """ if not os.path.exists(self._args.parameter_file): sys.exit(f"\n\tparameter file '{self._args.parameter_file}' " f"does not exist\n") - if parameter is None and not required: + if parameter is None: self._subparser.print_help() sys.exit(0) - elif required: - self._par_required() - sys.exit(0) # SeisFlows parameter file dictates upper-case parameters parameter = parameter.upper() From ced2be3a0df6f80923c9664c433c3aee71a1afa8 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Mon, 15 Aug 2022 15:24:14 -0700 Subject: [PATCH 110/195] bugfix inversion was updating all model parameter rather than those chosen by the user. needed to fix parameters when writing model vectors --- seisflows/optimize/gradient.py | 4 ---- seisflows/workflow/forward.py | 22 +++++++++++++++++++--- seisflows/workflow/inversion.py | 24 +++++++++++++++--------- 3 files changed, 34 insertions(+), 16 deletions(-) diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index af119bd9..64497521 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -428,10 +428,6 @@ def finalize_search(self): Removes old model/search parameters, moves current parameters to old, sets up new current parameters and writes statistic outputs - - TODO does moving 'm_try' to 'm_new' actually make sense with bracketing - line search where our last trial model will potentially have a - higher misfit due to the 'bracket'ing nature? """ unix.cd(self.path.scratch) diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index 69c65b5a..bab0a073 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -35,6 +35,11 @@ class Forward: synthetic': 'data' will be generated as synthetic seismograms using a target model provided in `path_model_true`. If None, workflow will not attempt to generate data. + :type stop_after: str + :param stop_after: optional name of task in task list (use + `seisflows print tasks` to get task list for given workflow) to stop + workflow after, allowing user to prematurely stop a workflow to explore + intermediate results or debug. :type export_traces: bool :param export_traces: export all waveforms that are generated by the external solver to `path_output`. If False, solver traces stored in @@ -69,9 +74,10 @@ class Forward: including models, kernels, gradient and residuals. *** """ - def __init__(self, modules=None, data_case="data", export_traces=False, - export_residuals=False, workdir=os.getcwd(), path_output=None, - path_data=None, path_state_file=None, path_model_init=None, + def __init__(self, modules=None, data_case="data", stop_after=None, + export_traces=False, export_residuals=False, + workdir=os.getcwd(), path_output=None, path_data=None, + path_state_file=None, path_model_init=None, path_model_true=None, path_eval_grad=None, **kwargs): """ Set default forward workflow parameters @@ -86,6 +92,7 @@ def __init__(self, modules=None, data_case="data", export_traces=False, self._modules = modules self.data_case = data_case + self.stop_after = stop_after self.export_traces = export_traces self.export_residuals = export_residuals @@ -182,6 +189,11 @@ def check(self): f"be able to find data for data-synthetic comparison" ) + if self.stop_after is not None: + _task_names = [task.__name__ for task in self.task_list] + assert(self.stop_after in _task_names), \ + f"workflow parameter `stop_after` must match {_task_names}" + def setup(self): """ Assigns modules as attributes of the workflow. I.e., `self.solver` to @@ -273,6 +285,10 @@ def run(self): self._states[func.__name__] = "failed" self.checkpoint() raise + # Allow user to prematurely stop a workflow after a given task + if self.stop_after and func.__name__ == self.stop_after: + logger.info(f"stop workflow at `stop_after`: {self.stop_after}") + break self.checkpoint() diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index 6cd55ecf..219eba2d 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -181,12 +181,16 @@ def run(self): while self.iteration < self.end + 1: logger.info(msg.mnr(f"RUNNING ITERATION {self.iteration:0>2}")) super().run() # Runs task list - logger.info(msg.mnr(f"COMPLETED ITERATION {self.iteration:0>2}")) - self.iteration += 1 - logger.info(f"setting current iteration to: {self.iteration}") - # Clear the state file for new iteration - self._states = {} - self.checkpoint() + # Assuming that if `stop_after` is used, that we are NOT iterating + if self.stop_after is None: + logger.info(msg.mnr(f"COMPLETE ITERATION {self.iteration:0>2}")) + self.iteration += 1 + logger.info(f"setting current iteration to: {self.iteration}") + # Clear the state file for new iteration + self._states = {} + self.checkpoint() + else: + break def checkpoint(self): """ @@ -240,7 +244,7 @@ def evaluate_initial_misfit(self): # solvers path_model = os.path.join(self.path.eval_grad, "model") m_new = self.optimize.load_vector("m_new") - m_new.write(path=path_model) + m_new.write(path=path_model, parameters=self.solver._parameters) # Run forward simulation/misfit quantification with previous # model @@ -305,7 +309,8 @@ def initialize_line_search(self): self.optimize.checkpoint() # Expose model `m_try` to the solver by placing it in eval_func dir. - m_try.write(path=os.path.join(self.path.eval_func, "model")) + m_try.write(path=os.path.join(self.path.eval_func, "model"), + parameters=self.solver._parameters) def perform_line_search(self): """ @@ -401,7 +406,8 @@ def finalize_iteration(self): if self.export_model: model = self.optimize.load_vector("m_new") model.write(path=os.path.join(self.path.output, - f"M{self.iteration:0>2}") + f"M{self.iteration:0>2}"), + parameters=self.solver._parameters ) # Update optimization From e8ea3f70ebf9b1e2a7d1fb3b0fde44ef745cb799 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Mon, 15 Aug 2022 17:17:01 -0700 Subject: [PATCH 111/195] updating doc notebooks to reflect change to seisflows (from seisflows3) and new directory structure, weird ls coloring happening, will need to fix or run doc on another machine --- docs/command_line_tool.rst | 623 +++-- docs/notebooks/convert.py | 7 +- docs/notebooks/inheritance.ipynb | 2 +- docs/notebooks/parameter_file.ipynb | 634 ++--- .../notebooks/scratch/specfem2d_example.ipynb | 2047 ++++++++++++++++ docs/notebooks/sflog.txt | 0 docs/notebooks/specfem2d_example.ipynb | 1957 +++++++-------- docs/notebooks/working_directory.ipynb | 77 +- docs/parameter_file.rst | 805 +++---- docs/specfem2d_example.rst | 2094 ++++++++--------- seisflows/solver/specfem.py | 4 + seisflows/tools/specfem.py | 2 +- seisflows/workflow/inversion.py | 23 +- 13 files changed, 4773 insertions(+), 3502 deletions(-) create mode 100644 docs/notebooks/scratch/specfem2d_example.ipynb create mode 100644 docs/notebooks/sflog.txt diff --git a/docs/command_line_tool.rst b/docs/command_line_tool.rst index 45a4afcd..2ec34f47 100644 --- a/docs/command_line_tool.rst +++ b/docs/command_line_tool.rst @@ -1,10 +1,12 @@ Commmand Line Tool ================== -``SeisFlows`` is primarily interacted with via a parameter file and command line tool. -In this page we explain how to use this command line tool to create a SeisFlows parameters -file, edit and configure it, and run a workflow. We also provide explanation -for other command line options which act as helper utilities for package control. +``SeisFlows`` is primarily interacted with via command line calls and a +parameter file. In this page we explain how to use this command line +tool to create a SeisFlows parameters file, edit and configure it, and +establish a SeisFlows working directory. We also provide explanation for +other command line options which act as helper utilities for improved +package control. After installing SeisFlows into a Conda environment, the ``seisflows`` command will be available directly from the command line. To access the @@ -17,44 +19,43 @@ help dialogue, you can type ``seisflows`` or ``seisflows -h`` .. parsed-literal:: - usage: seisflows [-h] [-w [WORKDIR]] [-p [PARAMETER_FILE]] - {setup,configure,init,submit,resume,restart,clean,par,sempar,check,print,convert,reset,debug,edit,examples} - ... - - ================================================================================ - - SeisFlows: Waveform Inversion Package - - ================================================================================ - - optional arguments: - -h, --help show this help message and exit - -w [WORKDIR], --workdir [WORKDIR] - The SeisFlows working directory, default: cwd - -p [PARAMETER_FILE], --parameter_file [PARAMETER_FILE] - Parameters file, default: 'parameters.yaml' - - command: - Available SeisFlows arguments and their intended usages - - setup Setup working directory from scratch - configure Fill parameter file with defaults - init Initiate working environment - submit Submit initial workflow to system - resume Re-submit previous workflow to system - restart Remove current environment and submit new workflow - clean Remove files relating to an active working environment - par View and edit SeisFlows parameter file - sempar View and edit SPECFEM parameter file - check Check state of an active environment - print Print information related to an active environment - convert Convert model file format - reset Reset modules within an active state - debug Start interactive debug environment - edit Open source code file in text editor - examples Look at and run pre-configured example problems - - 'seisflows [command] -h' for more detailed descriptions of each command. + usage: seisflows [-h] [-w [WORKDIR]] [-p [PARAMETER_FILE]] + {setup,configure,swap,init,submit,resume,restart,clean,par,sempar,check,print,reset,debug,examples} + ... + + ================================================================================ + + SeisFlows: Waveform Inversion Package + + ================================================================================ + + optional arguments: + -h, --help show this help message and exit + -w [WORKDIR], --workdir [WORKDIR] + The SeisFlows working directory, default: cwd + -p [PARAMETER_FILE], --parameter_file [PARAMETER_FILE] + Parameters file, default: 'parameters.yaml' + + command: + Available SeisFlows arguments and their intended usages + + setup Setup working directory from scratch + configure Fill parameter file with defaults + swap Swap module parameters in an existing parameter file + init Initiate working environment + submit Submit initial workflow to system + resume Re-submit previous workflow to system + restart Remove current environment and submit new workflow + clean Remove files relating to an active working environment + par View and edit SeisFlows parameter file + sempar View and edit SPECFEM parameter file + check Check state of an active environment + print Print information related to an active environment + reset Reset modules within an active state + debug Start interactive debug environment + examples Look at and run pre-configured example problems + + 'seisflows [command] -h' for more detailed descriptions of each command. Setting up a parameter file @@ -63,20 +64,20 @@ Setting up a parameter file seisflows setup ^^^^^^^^^^^^^^^ -The first step of any SeisFlows workflow is to set up a working -directory, which begins by establishing a blank parameter file. The -``seisflows setup`` command copies in a template parameter file. Ideally -your working directory will be empty to avoid file conflicts. +The first step of any SeisFlows workflow is to setting up a parameter +file. The ``seisflows setup`` command copies in a template parameter +file. .. code:: ipython3 - %cd ~/Work/scratch + %cd ~/Scratch + ! rm * ! ls .. parsed-literal:: - /home/bchow/Work/scratch + /Users/Chow/Scratch .. code:: ipython3 @@ -86,32 +87,31 @@ your working directory will be empty to avoid file conflicts. .. parsed-literal:: - usage: seisflows setup [-h] [-s] [-f] - - In the specified working directory, copy template parameter file containing - only module choices, and symlink source code for both the base and super - repositories for easy edit access. If a parameter file matching the provided - name exists in the working directory, a prompt will appear asking the user if - they want to overwrite. - - optional arguments: - -h, --help show this help message and exit - -s, --symlink symlink source code into the working directory - -f, --force automatically overwrites existing parameter file + usage: seisflows setup [-h] [-f] + + In the specified working directory, copy template parameter file containing + only module choices, and symlink source code for both the base and super + repositories for easy edit access. If a parameter file matching the provided + name exists in the working directory, a prompt will appear asking the user if + they want to overwrite. + + optional arguments: + -h, --help show this help message and exit + -f, --force automatically overwrites existing parameter file .. code:: ipython3 - # The '-f' flag (force) will overwrite any existing parameter file + # The '-f' flag (overwrite) will overwrite any existing parameter file ! seisflows setup -f .. parsed-literal:: - creating parameter file: parameters.yaml + creating parameter file: parameters.yaml -Having a look at the template parameters.yaml file that was just +Having a look at the template ``parameters.yaml`` file that was just generated, we can see that it contains some pre-defined default values for the core SeisFlows modules. Each of these modules defines it’s own set of unique parameters which make up a workflow. @@ -124,8 +124,8 @@ set of unique parameters which make up a workflow. .. parsed-literal:: - parameters.yaml - 32 parameters.yaml + parameters.yaml sflog.txt + 30 parameters.yaml .. code:: ipython3 @@ -135,38 +135,36 @@ set of unique parameters which make up a workflow. .. parsed-literal:: - # ////////////////////////////////////////////////////////////////////////////// - # - # SeisFlows YAML Parameter File - # - # ////////////////////////////////////////////////////////////////////////////// - # - # Modules correspond to the structure of the source code, and determine - # SeisFlows' behavior at runtime. Each module requires its own sub-parameters. - # - # .. rubric:: - # - To determine available options for modules listed below, run: - # > seisflows print modules - # - To auto-fill with docstrings and default values (recommended), run: - # > seisflows configure - # - To set values as NoneType, use: null - # - To set values as infinity, use: inf - # - # MODULES - # /////// - # WORKFLOW (str): The method for running SeisFlows; equivalent to main() - # SOLVER (str): External numerical solver to use for waveform simulations - # SYSTEM (str): Computer architecture of the system being used - # OPTIMIZE (str): Optimization algorithm for the inverse problem - # PREPROCESS (str): Preprocessing schema for waveform data - # POSTPROCESS (str): Postprocessing schema for kernels and gradients - # ============================================================================== - WORKFLOW: inversion - SOLVER: specfem2d - SYSTEM: workstation - OPTIMIZE: LBFGS - PREPROCESS: base - POSTPROCESS: base + # ////////////////////////////////////////////////////////////////////////////// + # + # SeisFlows YAML Parameter File + # + # ////////////////////////////////////////////////////////////////////////////// + # + # Modules correspond to the structure of the source code, and determine + # SeisFlows' behavior at runtime. Each module requires its own sub-parameters. + # + # .. rubric:: + # - To determine available options for modules listed below, run: + # > seisflows print modules + # - To auto-fill with docstrings and default values (recommended), run: + # > seisflows configure + # - To set values as NoneType, use: null + # - To set values as infinity, use: inf + # + # MODULES + # /////// + # workflow (str): The types and order of functions for running SeisFlows + # system (str): Computer architecture of the system being used + # solver (str): External numerical solver to use for waveform simulations + # preprocess (str): Preprocessing schema for waveform data + # optimize (str): Optimization algorithm for the inverse problem + # ============================================================================== + workflow: forward + system: workstation + solver: specfem2d + preprocess: default + optimize: gradient seisflows configure @@ -183,31 +181,25 @@ file. .. parsed-literal:: - usage: seisflows configure [-h] [-r] - - SeisFlows parameter files will vary depending on chosen modules and their - respective required parameters. This function will dynamically traverse the - source code and generate a template parameter file based on module choices. - The resulting file incldues docstrings and type hints for each parameter. - Optional parameters will be set with default values and required parameters - and paths will be marked appropriately. Required parameters must be set before - a workflow can be submitted. - - optional arguments: - -h, --help show this help message and exit - -r, --relative_paths Set default paths relative to cwd + usage: seisflows configure [-h] [-a] + + SeisFlows parameter files will vary depending on chosen modules and their + respective required parameters. This function will dynamically traverse the + source code and generate a template parameter file based on module choices. + The resulting file incldues docstrings and type hints for each parameter. + Optional parameters will be set with default values and required parameters + and paths will be marked appropriately. Required parameters must be set before + a workflow can be submitted. + + optional arguments: + -h, --help show this help message and exit + -a, --absolute_paths Set default paths relative to cwd .. code:: ipython3 ! seisflows configure - -.. parsed-literal:: - - filling parameters.yaml w/ default values - - .. code:: ipython3 ! head -200 parameters.yaml | tail -n 82 # have a look at the middle of the file @@ -217,102 +209,105 @@ file. .. parsed-literal:: + # :param smooth_v: Gaussian half-width for vertical smoothing in units + # of meters. + # :type components: str + # :param components: components to consider and tag data with. Should be + # string of letters such as 'RTZ' + # :type solver_io: str + # :param solver_io: format of model/kernel/gradient files expected by the + # numerical solver. Available: ['fortran_binary': default .bin files]. + # TODO: ['adios': ADIOS formatted files] + # :type source_prefix: str + # :param source_prefix: prefix of source/event/earthquake files. If None, + # will attempt to guess based on the specific solver chosen. + # :type mpiexec: str + # :param mpiexec: MPI executable used to run parallel processes. Should also + # be defined for the system module + # + # + # Solver SPECFEM2D + # ---------------- + # SPECFEM2D-specific alterations to the base SPECFEM module + # + # Parameters + # ---------- + # :type source_prefix: str + # :param source_prefix: Prefix of source files in path SPECFEM_DATA. Defaults + # to 'SOURCE' + # :type multiples: bool + # :param multiples: set an absorbing top-boundary condition + # + # # ============================================================================= - # SOLVER - # ////// - # MATERIALS (str): - # Material parameters used to define model. Available: ['ELASTIC': Vp, Vs, - # 'ACOUSTIC': Vp, 'ISOTROPIC', 'ANISOTROPIC'] - # DENSITY (str): - # How to treat density during inversion. Available: ['CONSTANT': Do not - # update density, 'VARIABLE': Update density] - # ATTENUATION (str): - # If True, turn on attenuation during forward simulations, otherwise set - # attenuation off. Attenuation is always off for adjoint simulations. - # COMPONENTS (str): - # Components used to generate data, formatted as a single string, e.g. ZNE - # or NZ or E - # SOLVERIO (int): - # The format external solver files. Available: ['fortran_binary', 'adios'] - # NT (float): - # Number of time steps set in the SPECFEM Par_file - # DT (float): - # Time step or delta set in the SPECFEM Par_file - # F0 (float): - # Dominant source frequency - # FORMAT (float): - # Format of synthetic waveforms used during workflow, available options: - # ['ascii', 'su'] - # SOURCE_PREFIX (str): - # Prefix of SOURCE files in path SPECFEM_DATA. By default, 'SOURCE' for - # SPECFEM2D - # ============================================================================= - MATERIALS: !!! REQUIRED PARAMETER !!! - DENSITY: !!! REQUIRED PARAMETER !!! - ATTENUATION: !!! REQUIRED PARAMETER !!! - COMPONENTS: ZNE - SOLVERIO: fortran_binary - NT: !!! REQUIRED PARAMETER !!! - DT: !!! REQUIRED PARAMETER !!! - F0: !!! REQUIRED PARAMETER !!! - FORMAT: !!! REQUIRED PARAMETER !!! - SOURCE_PREFIX: SOURCE - - # ============================================================================= - # POSTPROCESS - # /////////// - # SMOOTH_H (float): - # Gaussian half-width for horizontal smoothing in units of meters. If 0., - # no smoothing applied - # SMOOTH_V (float): - # Gaussian half-width for vertical smoothing in units of meters - # TASKTIME_SMOOTH (int): - # Large radii smoothing may take longer than normal tasks. Allocate - # additional smoothing task time as a multiple of TASKTIME - # ============================================================================= - SMOOTH_H: 0.0 - SMOOTH_V: 0.0 - TASKTIME_SMOOTH: 1 - - # ============================================================================= - # OPTIMIZE - # //////// - # LINESEARCH (str): - # Algorithm to use for line search, see seisflows.plugins.line_search for - # available choices - # PRECOND (str): - # Algorithm to use for preconditioning gradients, see - # seisflows.plugins.preconds for available choices - # STEPCOUNTMAX (int): - # Max number of trial steps in line search before a change in line search - # behavior - # STEPLENINIT (float): - # Initial line search step length, as a fraction of current model - # parameters - # STEPLENMAX (float): - # Max allowable step length, as a fraction of current model parameters - # LBFGSMEM (int): - # Max number of previous gradients to retain in local memory - # LBFGSMAX (int): - # LBFGS periodic restart interval, between 1 and 'inf' - # LBFGSTHRESH (float): - # LBFGS angle restart threshold + data_format: ascii + materials: acoustic + density: False + attenuation: False + smooth_h: 0.0 + smooth_v: 0.0 + components: ZNE + source_prefix: SOURCE + multiples: False # ============================================================================= - LINESEARCH: Backtrack + # + # Default Preprocess + # ------------------ + # Data processing for seismic traces, with options for data misfit, + # filtering, normalization and muting. + # + # Parameters + # ---------- + # :type data_format: str + # :param data_format: data format for reading traces into memory. For + # available see: seisflows.plugins.preprocess.readers + # :type misfit: str + # :param misfit: misfit function for waveform comparisons. For available + # see seisflows.plugins.preprocess.misfit + # :type backproject: str + # :param backproject: backprojection function for migration, or the + # objective function in FWI. For available see + # seisflows.plugins.preprocess.adjoint + # :type normalize: str + # :param normalize: Data normalization parameters used to normalize the + # amplitudes of waveforms. Choose from two sets: + # ENORML1: normalize per event by L1 of traces; OR + # ENORML2: normalize per event by L2 of traces; + # & + # TNORML1: normalize per trace by L1 of itself; OR + # TNORML2: normalize per trace by L2 of itself + # :type filter: str + # :param filter: Data filtering type, available options are: + # BANDPASS (req. MIN/MAX PERIOD/FREQ); + # LOWPASS (req. MAX_FREQ or MIN_PERIOD); + # HIGHPASS (req. MIN_FREQ or MAX_PERIOD) + # :type min_period: float + # :param min_period: Minimum filter period applied to time series. + # See also MIN_FREQ, MAX_FREQ, if User defines FREQ parameters, they + # will overwrite PERIOD parameters. + # :type max_period: float + # :param max_period: Maximum filter period applied to time series. See + # also MIN_FREQ, MAX_FREQ, if User defines FREQ parameters, they will + # overwrite PERIOD parameters. + # :type min_freq: float + # :param min_freq: Maximum filter frequency applied to time series, - 306 parameters.yaml + 337 parameters.yaml -We can see that our parameter file is over 300 lines now, too cumbersome -to print on the page. The length of the file mostly arises from the -header, as each parameter gets it’s own entry with the parameter’s type, -docstring, and any available options. +We can see that our parameter file is over 300 lines, a bit too +cumbersome to print on the page. The length of the file mostly arises +from the header, as each parameter gets it’s own entry with the +parameter’s type, docstring, and any available options. Each set of +parameters are separated by their relevant module, and their respective +docstrings should help users understand how and when they are used in a +SeisFlows workflow. -Parameters that are required by the workflow but do not come with -pre-set default values will be labelled with -``!!! REQUIRED PARAMETER !!!``. Similarly required path definitions, -which come at the end of the file, are labelled with the -``!!! REQUIRED PATH !!!`` value. + **NOTE**: Many parameters have sensible default values chosen, but it + is up to the user to decide which parameters are relevant to them, + and how they would like them set. Internal check functions throughout + the package will raise AssertionErrors for incorrectly or improperly + set parameters. Filling out the parameter file ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -320,11 +315,11 @@ Filling out the parameter file seisflows par ^^^^^^^^^^^^^ -It’s easy enough to open your favorite text editor to make adjustments -to the parameter file, however the ``seisflows par`` command makes -things easier by allowing you to view and edit values from the command -line. This makes it convenient to change parameters, and also allows you -to script your workflow setup for improved reproducibility. +You can always open your favorite text editor to make changes to the +parameter file, however the ``seisflows par`` command makes things +easier by allowing you to view and edit values from the command line. +This makes it convenient to change parameters quickly and allows you to +script your parameter file setup for improved reproducibility. .. code:: ipython3 @@ -333,80 +328,54 @@ to script your workflow setup for improved reproducibility. .. parsed-literal:: - usage: seisflows par [-h] [-p] [-r] [parameter] [value] - - Directly edit values in the parameter file by providing the parameter and - corresponding value. If no value is provided, will simply print out the - current value of the given parameter. Works also with path names. - - positional arguments: - parameter Parameter to edit or view, (case independent). - value Optional value to set parameter to. If not given, will - print out current parameter. If given, will replace - current parameter with new value. Set as 'null' for - NoneType and set '' for empty string - - optional arguments: - -h, --help show this help message and exit - -p, --skip_print Skip the print statement which is typically sent to stdout - after changing parameters. - -r, --required Only list parameters which have not been set as a default - value, typically set with some attention catching - argument. 'parameter' and 'value' will be ignored. - - -The -r (–required) flag tells us which parameters need to be set by the -user + usage: seisflows par [-h] [-p] [parameter] [value] + + Directly edit values in the parameter file by providing the parameter and + corresponding value. If no value is provided, will simply print out the + current value of the given parameter. Works also with path names. + + positional arguments: + parameter Parameter to edit or view, (case independent). + value Optional value to set parameter to. If not given, will + print out current parameter. If given, will replace + current parameter with new value. Set as 'null' for + NoneType and set '' for empty string + + optional arguments: + -h, --help show this help message and exit + -p, --skip_print Skip the print statement which is typically sent to stdout + after changing parameters. + + +The call structure of the ``par`` command is provided in the help +message: + + seisflows par [parameter] [value (optional)] + +We can view parameters by providing a single ‘parameter’ argument to the +``par`` command .. code:: ipython3 - ! seisflows par -r + ! seisflows par ntask # ntask is the number of tasks/events to be run during a workflow .. parsed-literal:: - !!! REQUIRED PARAMETER !!! - ========================== - MATERIALS - DENSITY - ATTENUATION - NT - DT - F0 - FORMAT - CASE - END - !!! REQUIRED PATH !!! - ===================== - SPECFEM_BIN - SPECFEM_DATA - MODEL_INIT - - -We can view (but not modify) parameters by giving a single argument to -the par command - -.. code:: ipython3 + ntask: 1 - ! seisflows par end - -.. parsed-literal:: - - END: !!! REQUIRED PARAMETER !!! - - -and we can edit the given parameter by providing a second argument to -the par command +We can change a given parameter from it’s original value by providing a +second ‘value’ argument .. code:: ipython3 - ! seisflows par end 1 + ! seisflows par ntask 3 .. parsed-literal:: - END: !!! REQUIRED PARAMETER !!! -> 1 + ntask: 1 -> 3 seisflows sempar @@ -416,104 +385,44 @@ The ``seisflows sempar`` command behaves the same as the ``par`` command, except is used to edit a SPECFEM2D/3D/3D_GLOBE Par_file. It has the same call structure as ``par``. -Setting up an active working state -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -An active SeisFlows working state is simply a Python environment with -the SeisFlows library defined based on the given parameter file. In -order to establish a working state, we need to set all required paths -and parameters. We can look at the parameter file header to determine -valid options for each parameter. - -.. code:: ipython3 - - ! head -130 parameters.yaml | tail -n 10 - - -.. parsed-literal:: - - # ////// - # MATERIALS (str): - # Material parameters used to define model. Available: ['ELASTIC': Vp, Vs, - # 'ACOUSTIC': Vp, 'ISOTROPIC', 'ANISOTROPIC'] - # DENSITY (str): - # How to treat density during inversion. Available: ['CONSTANT': Do not - # update density, 'VARIABLE': Update density] - # ATTENUATION (str): - # If True, turn on attenuation during forward simulations, otherwise set - # attenuation off. Attenuation is always off for adjoint simulations. - - -.. code:: ipython3 - - # We use the `-p` flag to turn off stdout printing - ! seisflows par materials elastic -p - ! seisflows par density constant -p - ! seisflows par attenuation False -p - ! seisflows par nt 100 -p - ! seisflows par dt .01 -p - ! seisflows par f0 .5 -p - ! seisflows par format ascii -p - ! seisflows par case synthetic -p - - # Required paths can similarly be set the `par` command - ! seisflows par specfem_bin ./ -p - ! seisflows par specfem_data ./ -p - ! seisflows par model_init ./ -p - -seisflows init -^^^^^^^^^^^^^^ - -To initiate a working state, we run ``seisflows init``. This registers -the parameter file into Python’s sys.modules. It runs parameter check -functions to ensure that parameters have been set correctly, and then -saves the active working state as a set of pickle (.p) files which can -be used to resume active workflows. - -.. code:: ipython3 - - ! seisflows init - - -.. parsed-literal:: - - - ================================================================================ - MODULE CHECK ERROR - ////////////////// - seisflows.config module check failed with: - - workflow: CASE == SYNTHETIC requires PATH.MODEL_TRUE - ================================================================================ - +seisflows check +^^^^^^^^^^^^^^^ -Oops, as we can see the parameter check has caught that a given -parameter requires a certain path to be set which is currently blank. -Let’s amend and try again +Each module contains it’s own internal set of parameter checks which +make sure that reasonable parameter values and types have been chosen. +This is especially important when submitting large jobs on clusters as +the ``check`` function will allow the User to catch errors without +having to wait on queue times or waste computational resources. .. code:: ipython3 - ! seisflows par model_true ./ -p - ! seisflows init + ! seisflows check .. parsed-literal:: - instantiating SeisFlows working state in directory: output - + + ================================================================================ + PARAMETER ERRROR + //////////////// + `path_specfem_bin` must exist and must point to directory containing SPECFEM + executables + ================================================================================ -.. code:: ipython3 - ! ls - ! echo - ! ls output +Here we can see that a given path has not been set correctly in the +parameter file. +seisflows submit +^^^^^^^^^^^^^^^^ -.. parsed-literal:: - - output parameters.yaml - - seisflows_optimize.p seisflows_postprocess.p seisflows_system.p - seisflows_parameters.json seisflows_preprocess.p seisflows_workflow.p - seisflows_paths.json seisflows_solver.p +To run SeisFlows, we use the ``submit`` call. This will submit the +``workflow`` to the ``system`` and continue until a User-defined stop +criteria is met. +Under the hood, the ``submit`` function will differ depending on the +chosen ``system``. For Users running on laptops and workstations, +``submit`` will simply launch a Python process and step through the +tasks in the ``workflow`` task list. On clusters, ``submit`` will launch +a master job on a compute node, which will itself step through tasks in +the task list, ensuring that no processing is run on login nodes. diff --git a/docs/notebooks/convert.py b/docs/notebooks/convert.py index 7b279e49..9a91ec81 100644 --- a/docs/notebooks/convert.py +++ b/docs/notebooks/convert.py @@ -1,8 +1,13 @@ #! /usr/bin/env python """ -Run an IPython notebook (execute all cells) and then convert the notebook to a \ +Run an IPython notebook (execute all cells) and then convert the notebook to a Sphinx .rst doc page in HTML +.. rubric:: + python convert.py # to convert all .ipynb files + OR + python convert.py {notebook}.ipynb # to convert a given notebook + .. note:: You will not need this if using the nbsphinx extension. diff --git a/docs/notebooks/inheritance.ipynb b/docs/notebooks/inheritance.ipynb index 6827b765..a68e64ff 100644 --- a/docs/notebooks/inheritance.ipynb +++ b/docs/notebooks/inheritance.ipynb @@ -362,7 +362,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/docs/notebooks/parameter_file.ipynb b/docs/notebooks/parameter_file.ipynb index bad3a0bb..5e249fd6 100644 --- a/docs/notebooks/parameter_file.ipynb +++ b/docs/notebooks/parameter_file.ipynb @@ -6,16 +6,43 @@ "source": [ "# Parameter File\n", "\n", - "The parameter file is the central control object for a SeisFlows3 workflow. Here we take a look at the anatomy of a parameter file. Parameter files in SeisFlows3 are formatted in the [YAML format (YAML Ain't Markup Language)](https://pyyaml.org/wiki/PyYAMLDocumentation).\n", + "The parameter file is the central control object for a SeisFlows workflow. Here we take a look at the anatomy of a parameter file. Parameter files in SeisFlows are formatted in the [YAML format (YAML Ain't Markup Language)](https://pyyaml.org/wiki/PyYAMLDocumentation).\n", "\n", "## Template\n", "\n", - "Each workflow starts with the module-only template parameter file which defines the core modules which make up the package. Running `seisflows setup` from the command line will create this file. " + "Each workflow starts with the module-only template parameter file which defines the core modules of the package. Your choices for each of these modules will determine which paths and parameters are included in the full parameter file. Running `seisflows setup` from the command line will create the template file. " ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "usage: seisflows setup [-h] [-f]\r\n", + "\r\n", + "In the specified working directory, copy template parameter file containing\r\n", + "only module choices, and symlink source code for both the base and super\r\n", + "repositories for easy edit access. If a parameter file matching the provided\r\n", + "name exists in the working directory, a prompt will appear asking the user if\r\n", + "they want to overwrite.\r\n", + "\r\n", + "optional arguments:\r\n", + " -h, --help show this help message and exit\r\n", + " -f, --force automatically overwrites existing parameter file\r\n" + ] + } + ], + "source": [ + "! seisflows setup -h" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -32,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -41,12 +68,12 @@ "text": [ "# //////////////////////////////////////////////////////////////////////////////\r\n", "#\r\n", - "# SeisFlows3 YAML Parameter File\r\n", + "# SeisFlows YAML Parameter File\r\n", "#\r\n", "# //////////////////////////////////////////////////////////////////////////////\r\n", "#\r\n", "# Modules correspond to the structure of the source code, and determine\r\n", - "# SeisFlows3' behavior at runtime. Each module requires its own sub-parameters.\r\n", + "# SeisFlows' behavior at runtime. Each module requires its own sub-parameters.\r\n", "#\r\n", "# .. rubric::\r\n", "# - To determine available options for modules listed below, run:\r\n", @@ -58,19 +85,17 @@ "#\r\n", "# MODULES\r\n", "# ///////\r\n", - "# WORKFLOW (str): The method for running SeisFlows3; equivalent to main()\r\n", - "# SOLVER (str): External numerical solver to use for waveform simulations\r\n", - "# SYSTEM (str): Computer architecture of the system being used\r\n", - "# OPTIMIZE (str): Optimization algorithm for the inverse problem\r\n", - "# PREPROCESS (str): Preprocessing schema for waveform data\r\n", - "# POSTPROCESS (str): Postprocessing schema for kernels and gradients\r\n", + "# workflow (str): The types and order of functions for running SeisFlows\r\n", + "# system (str): Computer architecture of the system being used\r\n", + "# solver (str): External numerical solver to use for waveform simulations\r\n", + "# preprocess (str): Preprocessing schema for waveform data\r\n", + "# optimize (str): Optimization algorithm for the inverse problem\r\n", "# ==============================================================================\r\n", - "WORKFLOW: inversion\r\n", - "SOLVER: specfem2d\r\n", - "SYSTEM: workstation\r\n", - "OPTIMIZE: LBFGS \r\n", - "PREPROCESS: base\r\n", - "POSTPROCESS: base\r\n" + "workflow: forward\r\n", + "system: workstation\r\n", + "solver: specfem2d\r\n", + "preprocess: default\r\n", + "optimize: gradient\r\n" ] } ], @@ -82,67 +107,62 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### How do I choose my modules?\n", + "### How do I choose modules?\n", "\n", - "As seen above, each of the modules comes with a default value. But you may want to run a migration, not an inversion. Or run with SPECFEM3D not 2D. As stated in the comments at the top of the file, the `seisflows print modules` command lists out all available options. Don't see an option that works for you? Learn to extend the SeisFlows3 package here: **!!! docs page link here !!!**" + "As seen above, each of the modules comes with a default value which represents the base class* for this module. " + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "* For an explanation of base classes and Python inheritance, see the `inheritance page.`__ " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These default values are likely not suitable for all, e.g., if you want to run an inversion and not a forward workflow, or use SPECFEM3D not SPECFEM2D. To see all available module options, use the `seisflows print modules` command." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " SEISFLOWS3 MODULES \r\n", - " ////////////////// \r\n", - "'+': package, '-': module, '*': class\r\n", + " SEISFLOWS MODULES \r\n", + " ///////////////// \r\n", + "'-': module, '*': class\r\n", "\r\n", - "+ SYSTEM\r\n", - " - seisflows\r\n", - " * base\r\n", - " * cluster\r\n", - " * lsf\r\n", - " * slurm\r\n", - " * workstation\r\n", - " - seisflows-super\r\n", - " * chinook\r\n", - " * maui\r\n", - "+ PREPROCESS\r\n", - " - seisflows\r\n", - " * base\r\n", - " * pyatoa\r\n", - " - seisflows-super\r\n", - " * pyatoa_nz\r\n", - "+ SOLVER\r\n", - " - seisflows\r\n", - " * base\r\n", - " * specfem2d\r\n", - " * specfem3d\r\n", - " * specfem3d_globe\r\n", - " - seisflows-super\r\n", - " * specfem3d_maui\r\n", - "+ POSTPROCESS\r\n", - " - seisflows\r\n", - " * base\r\n", - " - seisflows-super\r\n", - "+ OPTIMIZE\r\n", - " - seisflows\r\n", - " * LBFGS\r\n", - " * NLCG\r\n", - " * base\r\n", - " - seisflows-super\r\n", - "+ WORKFLOW\r\n", - " - seisflows\r\n", - " * base\r\n", - " * inversion\r\n", - " * migration\r\n", - " * test\r\n", - " - seisflows-super\r\n", - " * thrifty_inversion\r\n", - " * thrifty_maui\r\n" + "- workflow\r\n", + " * forward\r\n", + " * inversion\r\n", + " * migration\r\n", + "- system\r\n", + " * chinook\r\n", + " * cluster\r\n", + " * frontera\r\n", + " * lsf\r\n", + " * maui\r\n", + " * slurm\r\n", + " * workstation\r\n", + "- solver\r\n", + " * specfem\r\n", + " * specfem2d\r\n", + " * specfem3d\r\n", + " * specfem3d_globe\r\n", + "- preprocess\r\n", + " * default\r\n", + " * pyaflowa\r\n", + "- optimize\r\n", + " * LBFGS\r\n", + " * NLCG\r\n", + " * gradient\r\n" ] } ], @@ -156,7 +176,14 @@ "source": [ "### How do I change modules?\n", "\n", - "Feel free to use any old text editor to edit the YAML file, or you can use the `seisflows par` command to make changes directly from the command line. For example, say we want to use SPECFEM3D" + "Feel free to use any text editor, or use the `seisflows par` command to make changes directly from the command line. For example, say we want to use SPECFEM3D as our solver module. " + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "This is also covered in the `command line tool page.`__" ] }, { @@ -168,7 +195,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "SOLVER: specfem2d -> specfem3d\r\n" + "solver: specfem2d -> specfem3d\r\n" ] } ], @@ -186,7 +213,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "SOLVER: specfem3d\r\n" + "solver: specfem3d\r\n" ] } ], @@ -199,9 +226,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### How do I get to a full parameter file?\n", + "### How do I create a full parameter file?\n", "\n", - "The module-only parameter file serves as as a template for dynamically generating a full parameter file. Since each module requires it's own unique set of parameters and paths, each parameter file will look different. We can use the `seisflows configure` command to complete our parmater file, based on the chosen modules." + "The module-only parameter file serves as as a template for dynamically generating the full parameter file. Since each module requires it's own unique set of parameters and paths, each parameter file will look different. We use the `seisflows configure` command to complete the file." ] }, { @@ -213,10 +240,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "filling parameters.yaml w/ default values\r\n" + "usage: seisflows configure [-h] [-a]\r\n", + "\r\n", + "SeisFlows parameter files will vary depending on chosen modules and their\r\n", + "respective required parameters. This function will dynamically traverse the\r\n", + "source code and generate a template parameter file based on module choices.\r\n", + "The resulting file incldues docstrings and type hints for each parameter.\r\n", + "Optional parameters will be set with default values and required parameters\r\n", + "and paths will be marked appropriately. Required parameters must be set before\r\n", + "a workflow can be submitted.\r\n", + "\r\n", + "optional arguments:\r\n", + " -h, --help show this help message and exit\r\n", + " -a, --absolute_paths Set default paths relative to cwd\r\n" ] } ], + "source": [ + "! seisflows configure -h" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], "source": [ "! seisflows configure" ] @@ -225,9 +273,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Anatomy of the parameter file\n", + "Below we will take a look at the parameter file we just created" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Anatomy of a parameter file\n", "\n", - "As we will see below, the parameter file has now been generated. Each module will define its own section, separated by a header of comments. Within each header, parameter names, types and descriptions are listed. At the bottom of the parameter file, there is a section defining paths required by the workflow. Section headers will look something: " + "Each of SeisFlows' modules will define its own section in the parameter file, separated by a header of comments representing the docstring. Within each header, parameter names, types and descriptions are listed. At the bottom of the parameter file, there is a section defining paths required by SeisFlows. Section headers will look something: " ] }, { @@ -237,18 +292,22 @@ "outputs": [], "source": [ "# =============================================================================\n", - "# MODULE\n", - "# ////// \n", - "# PARAMETER_NAME (type):\n", - "# Description\n", + "# MODULE\n", + "# ------\n", + "# Module description \n", + "#\n", + "# Parameters\n", + "# ----------\n", + "# :type parameter: type\n", + "# :param paramter: description\n", "# ...\n", "# =============================================================================\n", - "PARAMETER_NAME: parameter_value" + "parameter: value" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -257,12 +316,12 @@ "text": [ "# //////////////////////////////////////////////////////////////////////////////\r\n", "#\r\n", - "# SeisFlows3 YAML Parameter File\r\n", + "# SeisFlows YAML Parameter File\r\n", "#\r\n", "# //////////////////////////////////////////////////////////////////////////////\r\n", "#\r\n", "# Modules correspond to the structure of the source code, and determine\r\n", - "# SeisFlows3' behavior at runtime. Each module requires its own sub-parameters.\r\n", + "# SeisFlows' behavior at runtime. Each module requires its own sub-parameters.\r\n", "#\r\n", "# .. rubric::\r\n", "# - To determine available options for modules listed below, run:\r\n", @@ -274,67 +333,67 @@ "#\r\n", "# MODULES\r\n", "# ///////\r\n", - "# WORKFLOW (str): The method for running SeisFlows3; equivalent to main()\r\n", - "# SOLVER (str): External numerical solver to use for waveform simulations\r\n", - "# SYSTEM (str): Computer architecture of the system being used\r\n", - "# OPTIMIZE (str): Optimization algorithm for the inverse problem\r\n", - "# PREPROCESS (str): Preprocessing schema for waveform data\r\n", - "# POSTPROCESS (str): Postprocessing schema for kernels and gradients\r\n", + "# workflow (str): The types and order of functions for running SeisFlows\r\n", + "# system (str): Computer architecture of the system being used\r\n", + "# solver (str): External numerical solver to use for waveform simulations\r\n", + "# preprocess (str): Preprocessing schema for waveform data\r\n", + "# optimize (str): Optimization algorithm for the inverse problem\r\n", "# ==============================================================================\r\n", - "WORKFLOW: inversion\r\n", - "SOLVER: specfem3d\r\n", - "SYSTEM: workstation\r\n", - "OPTIMIZE: LBFGS \r\n", - "PREPROCESS: base\r\n", - "POSTPROCESS: base\r\n", - "\r\n", + "workflow: forward\r\n", + "system: workstation\r\n", + "solver: specfem3d\r\n", + "preprocess: default\r\n", + "optimize: gradient\r\n", "# =============================================================================\r\n", - "# SYSTEM \r\n", - "# ////// \r\n", - "# TITLE (str):\r\n", - "# The name used to submit jobs to the system, defaults to the name of the\r\n", - "# working directory\r\n", - "# PRECHECK (list):\r\n", - "# A list of parameters that will be displayed to stdout before 'submit' or\r\n", - "# 'resume' is run. Useful for manually reviewing important parameters prior\r\n", - "# to system submission\r\n", - "# LOG_LEVEL (str):\r\n", - "# Verbosity output of SF3 logger. Available from least to most verbosity:\r\n", - "# 'CRITICAL', 'WARNING', 'INFO', 'DEBUG'; defaults to 'DEBUG'\r\n", - "# VERBOSE (bool):\r\n", - "# Level of verbosity provided to the output log. If True, log statements\r\n", - "# will declare what module/class/function they are being called from.\r\n", - "# Useful for debugging but also very noisy.\r\n", - "# MPIEXEC (str):\r\n", - "# Function used to invoke executables on the system. For example 'srun' on\r\n", - "# SLURM systems, or './' on a workstation. If left blank, will guess based\r\n", - "# on the system.\r\n", - "# NTASK (int):\r\n", - "# Number of separate, individual tasks. Also equal to the number of desired\r\n", - "# sources in workflow\r\n", - "# NPROC (int):\r\n", - "# Number of processor to use for each simulation\r\n", + "#\r\n", + "# Forward Workflow\r\n", + "# ----------------\r\n", + "# Run forward solver in parallel and (optionally) calculate\r\n", + "# data-synthetic misfit and adjoint sources.\r\n", + "#\r\n", + "# Parameters\r\n", + "# ----------\r\n", + "# :type modules: list of module\r\n", + "# :param modules: instantiated SeisFlows modules which should have been\r\n", + "# generated by the function `seisflows.config.import_seisflows` with a\r\n", + "# parameter file generated by seisflows.configure\r\n", + "# :type data_case: str\r\n", + "# :param data_case: How to address 'data' in the workflow, available options:\r\n", + "# 'data': real data will be provided by the user in\r\n", + "# `path_data/{source_name}` in the same format that the solver will\r\n", + "# produce synthetics (controlled by `solver.format`) OR\r\n", + "# synthetic': 'data' will be generated as synthetic seismograms using\r\n", + "# a target model provided in `path_model_true`. If None, workflow will\r\n", + "# not attempt to generate data.\r\n", + "# :type export_traces: bool\r\n", + "# :param export_traces: export all waveforms that are generated by the\r\n", + "# external solver to `path_output`. If False, solver traces stored in\r\n", + "# scratch may be discarded at any time in the workflow\r\n", + "# :type export_residuals: bool\r\n", + "# :param export_residuals: export all residuals (data-synthetic misfit) that\r\n", + "# are generated by the external solver to `path_output`. If False,\r\n", + "# residuals stored in scratch may be discarded at any time in the workflow\r\n", + "#\r\n", + "# \r\n", "# =============================================================================\r\n", - "TITLE: docs\r\n", - "PRECHECK:\r\n", - " - TITLE\r\n", - "LOG_LEVEL: DEBUG\r\n", - "VERBOSE: False\r\n", - "MPIEXEC:\r\n", - "NTASK: 1\r\n", - "NPROC: 1\r\n", - "\r\n", + "data_case: data\r\n", + "export_traces: False\r\n", + "export_residuals: False\r\n", "# =============================================================================\r\n", - "# PREPROCESS \r\n", - "# ////////// \r\n", - "# MISFIT (str):\r\n", - "# Misfit function for waveform comparisons, for available see\r\n", - "# seisflows.plugins.misfit\r\n", - "# BACKPROJECT (str):\r\n", - "# Backprojection function for migration, for available see\r\n", - "# seisflows.plugins.adjoint\r\n", - "# NORMALIZE (list):\r\n", - "# Data normalization parameters used to normalize the amplitudes of\r\n" + "#\r\n", + "# Workstation System\r\n", + "# ------------------\r\n", + "# Runs tasks in serial on a local machine.\r\n", + "#\r\n", + "# Parameters\r\n", + "# ----------\r\n", + "# :type ntask: int\r\n", + "# :param ntask: number of individual tasks/events to run during workflow.\r\n", + "# Must be <= the number of source files in `path_specfem_data`\r\n", + "# :type nproc: int\r\n", + "# :param nproc: number of processors to use for each simulation\r\n", + "# :type log_level: str\r\n", + "# :param log_level: logger level to pass to logging module.\r\n" ] } ], @@ -344,310 +403,65 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "# =============================================================================\r\n", - "# PATHS \r\n", - "# ///// \r\n", - "# SCRATCH:\r\n", - "# scratch path to hold temporary data during workflow\r\n", - "# OUTPUT:\r\n", - "# directory to save workflow outputs to disk\r\n", - "# SYSTEM:\r\n", - "# scratch path to hold any system related data\r\n", - "# LOCAL:\r\n", - "# path to local data to be used during workflow\r\n", - "# LOGFILE:\r\n", - "# the main output log file where all processes will track their status\r\n", - "# SOLVER:\r\n", - "# scratch path to hold solver working directories\r\n", - "# SPECFEM_BIN:\r\n", - "# path to the SPECFEM binary executables\r\n", - "# SPECFEM_DATA:\r\n", - "# path to the SPECFEM DATA/ directory containing the 'Par_file', 'STATIONS'\r\n", - "# file and 'CMTSOLUTION' files\r\n", - "# DATA:\r\n", - "# path to data available to workflow\r\n", - "# MASK:\r\n", - "# Directory to mask files for gradient masking\r\n", - "# OPTIMIZE:\r\n", - "# scratch path to store data related to nonlinear optimization\r\n", - "# MODEL_INIT:\r\n", - "# location of the initial model to be used for workflow\r\n", - "# MODEL_TRUE:\r\n", - "# Target model to be used for PAR.CASE == 'synthetic'\r\n", - "# FUNC:\r\n", - "# scratch path to store data related to function evaluations\r\n", - "# GRAD:\r\n", - "# scratch path to store data related to gradient evaluations\r\n", - "# HESS:\r\n", - "# scratch path to store data related to Hessian evaluations\r\n", - "# =============================================================================\r\n", - "PATHS:\r\n", - " SCRATCH: /home/bchow/REPOSITORIES/seisflows/seisflows/docs/scratch\r\n", - " OUTPUT: /home/bchow/REPOSITORIES/seisflows/seisflows/docs/output\r\n", - " SYSTEM: /home/bchow/REPOSITORIES/seisflows/seisflows/docs/scratch/system\r\n", - " LOCAL:\r\n", - " LOGFILE: /home/bchow/REPOSITORIES/seisflows/seisflows/docs/output_sf3.txt\r\n", - " SOLVER: /home/bchow/REPOSITORIES/seisflows/seisflows/docs/scratch/solver\r\n", - " SPECFEM_BIN: !!! REQUIRED PATH !!!\r\n", - " SPECFEM_DATA: !!! REQUIRED PATH !!!\r\n", - " DATA:\r\n", - " MASK:\r\n", - " OPTIMIZE: /home/bchow/REPOSITORIES/seisflows/seisflows/docs/scratch/optimize\r\n", - " MODEL_INIT: !!! REQUIRED PATH !!!\r\n", - " MODEL_TRUE:\r\n", - " FUNC: /home/bchow/REPOSITORIES/seisflows/seisflows/docs/scratch/scratch\r\n", - " GRAD: /home/bchow/REPOSITORIES/seisflows/seisflows/docs/scratch/evalgrad\r\n", - " HESS: /home/bchow/REPOSITORIES/seisflows/seisflows/docs/scratch/evalhess\r\n" + "path_model_true: null\r\n", + "path_state_file: /Users/Chow/Repositories/seisflows/docs/notebooks/sfstate.txt\r\n", + "path_data: null\r\n", + "path_par_file: /Users/Chow/Repositories/seisflows/docs/notebooks/parameters.yaml\r\n", + "path_log_files: /Users/Chow/Repositories/seisflows/docs/notebooks/logs\r\n", + "path_output_log: /Users/Chow/Repositories/seisflows/docs/notebooks/sflog.txt\r\n", + "path_specfem_bin: null\r\n", + "path_specfem_data: null\r\n", + "path_solver: /Users/Chow/Repositories/seisflows/docs/notebooks/scratch/solver\r\n", + "path_preconditioner: null\r\n" ] } ], "source": [ - "! tail --lines=54 parameters.yaml" + "! tail parameters.yaml" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### How do I know what parameters need to be set?\n", - "\n", - "> **NOTE**: Required parameters that can not be set to default values will be listed as `!!! REQUIRED PARAMETER !!!`\n", + "### How do I know how parameters need to be set?\n", "\n", - "We can check the required paths and parameters manually by scrolling through the parameter file, or we can use the `seisflows par --required` command to list them out all at once." + "Most SeisFlows parameters come with reasonable default values. The docstrings headers will also list the expected type and available options (if any). You may also run the `seisflows check` command which verifies that parameters are set correctly." ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "!!! REQUIRED PARAMETER !!!\r\n", - "==========================\r\n", - "\tMATERIALS\r\n", - "\tDENSITY\r\n", - "\tATTENUATION\r\n", - "\tNT\r\n", - "\tDT\r\n", - "\tFORMAT\r\n", - "\tCASE\r\n", - "\tEND\r\n", - "!!! REQUIRED PATH !!!\r\n", - "=====================\r\n", - "\tSPECFEM_BIN\r\n", - "\tSPECFEM_DATA\r\n", - "\tMODEL_INIT\r\n" - ] - } - ], - "source": [ - "! seisflows par --required" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Checking parameter validity\n", - "\n", - "You might be asking, how do I know if my parameters are set correctly? SeisFlows3 modules feature check() functions which dictate correct parameter values. You can run `seisflows init` to run these check() functions. Because we have required parameters still left unset in our parameter file, we expect the `seisflows init` function to throw an error." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================================================================\r\n", - " PARAMETER FILE READ ERROR \r\n", - " ///////////////////////// \r\n", - "Please check that your parameter file is properly formatted in the YAML format.\r\n", - "If you have just run 'seisflows configure', you may have some required\r\n", - "parameters that will need to be filled out before you can proceed. The error\r\n", - "message is:\r\n", "\r\n", - "could not determine a constructor for the tag 'tag:yaml.org,2002:!'\r\n", - " in \"parameters.yaml\", line 147, column 12\r\n", + "================================================================================\r\n", + " PARAMETER ERRROR \r\n", + " //////////////// \r\n", + "`path_specfem_bin` must exist and must point to directory containing SPECFEM\r\n", + "executables\r\n", "================================================================================\r\n" ] } ], "source": [ - "! seisflows init" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's set some random variables for the required parameters with the `seisflows par` command and try again." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MATERIALS: !!! REQUIRED PARAMETER !!! -> elastic\n", - "DENSITY: !!! REQUIRED PARAMETER !!! -> constant\n", - "ATTENUATION: !!! REQUIRED PARAMETER !!! -> False\n", - "NT: !!! REQUIRED PARAMETER !!! -> 100\n", - "DT: !!! REQUIRED PARAMETER !!! -> .05\n", - "FORMAT: !!! REQUIRED PARAMETER !!! -> ascii\n", - "CASE: !!! REQUIRED PARAMETER !!! -> data\n", - "END: !!! REQUIRED PARAMETER !!! -> 1\n", - "SPECFEM_BIN: !!! REQUIRED PATH !!! -> ./\n", - "SPECFEM_DATA: !!! REQUIRED PATH !!! -> ./\n", - "MODEL_INIT: !!! REQUIRED PATH !!! -> ./\n" - ] - } - ], - "source": [ - "! seisflows par materials elastic\n", - "! seisflows par density constant\n", - "! seisflows par attenuation False\n", - "! seisflows par nt 100\n", - "! seisflows par dt .05\n", - "! seisflows par format ascii\n", - "! seisflows par case data\n", - "! seisflows par end 1\n", - "! seisflows par specfem_bin ./\n", - "! seisflows par specfem_data ./\n", - "! seisflows par model_init ./" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "instantiating SeisFlows3 working state in directory: output\r\n" - ] - } - ], - "source": [ - "! seisflows init" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Of course we knew that the above parameters were acceptable. But what if we input an unacceptable parameter into the parameter file and try again?" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MATERIALS: elastic -> visibily_incorrect_value\n", - "================================================================================\n", - " MODULE CHECK ERROR \n", - " ////////////////// \n", - "seisflows.config module check failed with:\n", - "\n", - "solver: MATERIALS must be in ['ELASTIC', 'ACOUSTIC', 'ISOTROPIC', 'ANISOTROPIC']\n", - "================================================================================\n" - ] - } - ], - "source": [ - "! rm -r output/\n", - "! seisflows par materials visibily_incorrect_value\n", - "! seisflows init" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And voila, the module check has thrown an error, and told us (the User) how to properly set the value of the materials parameter. Hopefully a combination of thorough explanations in the parameter file section headers, and error catching with `seisflows init` makes crafting your own parameter file a smooth process." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r\n", - "# =============================================================================\r\n", - "# SOLVER \r\n", - "# ////// \r\n", - "# MATERIALS (str):\r\n", - "# Material parameters used to define model. Available: ['ELASTIC': Vp, Vs,\r\n", - "# 'ACOUSTIC': Vp, 'ISOTROPIC', 'ANISOTROPIC']\r\n", - "# DENSITY (str):\r\n", - "# How to treat density during inversion. Available: ['CONSTANT': Do not\r\n", - "# update density, 'VARIABLE': Update density]\r\n", - "# ATTENUATION (str):\r\n", - "# If True, turn on attenuation during forward simulations, otherwise set\r\n", - "# attenuation off. Attenuation is always off for adjoint simulations.\r\n", - "# COMPONENTS (str):\r\n", - "# Components used to generate data, formatted as a single string, e.g. ZNE\r\n", - "# or NZ or E\r\n", - "# SOLVERIO (int):\r\n", - "# The format external solver files. Available: ['fortran_binary', 'adios']\r\n", - "# NT (float):\r\n", - "# Number of time steps set in the SPECFEM Par_file\r\n", - "# DT (float):\r\n", - "# Time step or delta set in the SPECFEM Par_file\r\n", - "# FORMAT (float):\r\n", - "# Format of synthetic waveforms used during workflow, available options:\r\n", - "# ['ascii', 'su']\r\n", - "# SOURCE_PREFIX (str):\r\n", - "# Prefix of SOURCE files in path SPECFEM_DATA. Available ['CMTSOLUTION',\r\n", - "# FORCESOLUTION']\r\n", - "# =============================================================================\r\n", - "MATERIALS: visibily_incorrect_value\r\n", - "DENSITY: constant\r\n", - "ATTENUATION: False\r\n", - "COMPONENTS: ZNE\r\n", - "SOLVERIO: fortran_binary\r\n", - "NT: 100\r\n", - "DT: .05\r\n", - "FORMAT: ascii\r\n", - "SOURCE_PREFIX: CMTSOLUTION\r\n" - ] - } - ], - "source": [ - "! head -155 parameters.yaml | tail --lines=38" + "! seisflows check" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -657,9 +471,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python (docs)", + "display_name": "Python 3", "language": "python", - "name": "docs" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -671,7 +485,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.12" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/docs/notebooks/scratch/specfem2d_example.ipynb b/docs/notebooks/scratch/specfem2d_example.ipynb new file mode 100644 index 00000000..2778a6a8 --- /dev/null +++ b/docs/notebooks/scratch/specfem2d_example.ipynb @@ -0,0 +1,2047 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Specfem2D workstation example\n", + "\n", + "To demonstrate the inversion capabilities of SeisFlows, we will run a __Specfem2D synthetic-synthetic example__ on a __local machine__ (tested on a Linux workstation running CentOS 7, and an Apple Laptop running macOS 10.14.6). Many of the setup steps here may be unique to our OS and workstation, but hopefully they may serve as templates for new Users wanting to explore SeisFlows. \n", + "\n", + "The numerical solver we will use is: [SPECFEM2D](https://geodynamics.org/cig/software/specfem2d/). We'll also be working in our `seisflows` [Conda](https://docs.conda.io/en/latest/) environment, see the installation documentation page for instructions on how to install and activate the required Conda environment.\n", + "\n", + "-----------------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Option 1: Automated run\n", + "\n", + "We have set up this example to run using a single command line argument. The following command will run an example script which will (1) download and compile SPECFEM2D, (2) setup a SPECFEM2D working directory to generate initial and target models, and (3) Run a SeisFlows inversion. " + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + ".. warning:: \n", + " This example attempts to automatically download and compile SPECFEM2D. This step may fail if you are software required by SPECFEM2D, there are issues with the SPECFEM2D repository itself, or the configuration and compiling steps fail. If you run any issues, it is recommended that you manually install and compile SPECFEM2D, and directly provide its path to this example problem when prompted." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# ! seisflows examples run 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--------------\n", + "## Option 2: Manual run\n", + "\n", + "The notebook below details a walkthrough of the automated run shown above. This is meant for those who want to understand what is going on under the hood. You are welcome to follow along on your workstation. The following Table of Contents outlines the steps we will take in this tutorial:\n", + "\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + ".. warning:: \n", + " Navigation links will not work outside of Jupyter. Please use the navigation bar to the left." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. __[Setup SPECFEM2D](#1.-Setup-SPECFEM2D)__ \n", + " a. [Download and compile codebase](#1a.-Download-and-compile-codebase*) \n", + " b. [Create a separate SPECFEM2D working directory](#1b.-Create-a-separate-SPECFEM2D-working-directory) \n", + " c. [Generate initial and target models](#1c.-Generate-initial-and-target-models) \n", + "\n", + "2. __[Initialize SeisFlows (SF)](#2.-Initialize-SeisFlows-(SF))__ \n", + " a. [SeisFlows working directory and parameter file](#2a.-SF-working-directory-and-parameter-file) \n", + "\n", + "3. __[Run SeisFlows](#2.-Run-SeisFlows)__ \n", + " a. [Forward simulations](#3a.-Forward-simulations) \n", + " b. [Exploring the SeisFlows directory structure](#3b.-Exploring-the-SF-directory-structure) \n", + " c. [Adjoint simulations](#3c.-Adjoint-simulations) \n", + " d. [Line search and model update](#3d.-Line-search-and-model-update) \n", + "\n", + "4. __[Conclusions](#4.-Conclusions)__ " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Setup SPECFEM2D \n", + "#### 1a. Download and compile codebase (optional)\n", + "\n", + "> **NOTE**: If you have already downloaded and compiled SPECFEM2D, you can skip most of this subsection (1a). However you will need to edit the first two paths in the following cell (WORKDIR and SPECFEM2D_ORIGINAL), and execute the path structure defined in the cell.\n", + "\n", + "First we'll download and compile SPECFEM2D to generate the binaries necessary to run our simulations. We will then populate a new SPECFEM2D working directory that will be used by SeisFlows. We'll use to Python OS module to do our filesystem processes just to keep everything in Python, but this can easily be accomplished in bash." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import glob\n", + "import shutil\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# vvv USER MUST EDIT THE FOLLOWING PATHS vvv\n", + "# MAC PATHS\n", + "WORKDIR = \"/Users/Chow/Work/work/sf_specfem2d_example\" \n", + "SPECFEM2D = \"/Users/Chow/Repositories/specfem2d\"\n", + "# LINUX PATHS\n", + "# WORKDIR = \"/home/bchow/Work/work/sf_specfem2d_example\" \n", + "# SPECFEM2D = \"/home/bchow/REPOSITORIES/specfem2d\"\n", + "# where WORKDIR: points to your own working directory\n", + "# and SPECFEM2D: points to an existing specfem2D repository if available (if not set as '')\n", + "# ^^^ USER MUST EDIT THE FOLLOWING PATHS ^^^\n", + "# ======================================================================================================\n", + "\n", + "# Distribute the necessary file structure of the SPECFEM2D repository that we will downloaded/reference\n", + "SPECFEM2D_ORIGINAL = os.path.join(WORKDIR, \"specfem2d\")\n", + "SPECFEM2D_BIN_ORIGINAL = os.path.join(SPECFEM2D_ORIGINAL, \"bin\")\n", + "SPECFEM2D_DATA_ORIGINAL = os.path.join(SPECFEM2D_ORIGINAL, \"DATA\")\n", + "TAPE_2007_EXAMPLE = os.path.join(SPECFEM2D_ORIGINAL, \"EXAMPLES\", \"Tape2007\")\n", + "\n", + "# The SPECFEM2D working directory that we will create separate from the downloaded repo\n", + "SPECFEM2D_WORKDIR = os.path.join(WORKDIR, \"specfem2d_workdir\")\n", + "SPECFEM2D_BIN = os.path.join(SPECFEM2D_WORKDIR, \"bin\")\n", + "SPECFEM2D_DATA = os.path.join(SPECFEM2D_WORKDIR, \"DATA\")\n", + "SPECFEM2D_OUTPUT = os.path.join(SPECFEM2D_WORKDIR, \"OUTPUT_FILES\")\n", + "\n", + "# Pre-defined locations of velocity models we will generate using the solver\n", + "SPECFEM2D_MODEL_INIT = os.path.join(SPECFEM2D_WORKDIR, \"OUTPUT_FILES_INIT\")\n", + "SPECFEM2D_MODEL_TRUE = os.path.join(SPECFEM2D_WORKDIR, \"OUTPUT_FILES_TRUE\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SPECFEM2D repository already found, you may skip this subsection\n" + ] + } + ], + "source": [ + "# Download SPECFEM2D from GitHub, devel branch for latest codebase OR symlink from existing repo\n", + "if not os.path.exists(WORKDIR):\n", + " os.makedirs(WORKDIR)\n", + "os.chdir(WORKDIR)\n", + "\n", + "if os.path.exists(\"specfem2d\"):\n", + " print(\"SPECFEM2D repository already found, you may skip this subsection\")\n", + " pass\n", + "elif os.path.exists(SPECFEM2D):\n", + " print(\"Existing SPECMFE2D respository found, symlinking to working directory\")\n", + " os.symlink(SPECFEM2D, \"./specfem2d\")\n", + "else:\n", + " print(\"Cloning respository from GitHub\")\n", + " ! git clone --recursive --branch devel https://github.com/geodynamics/specfem2d.git" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Compile SPECFEM2D to generate the Makefile\n", + "os.chdir(SPECFEM2D_ORIGINAL)\n", + "if not os.path.exists(\"./config.log\"):\n", + " os.system(\"./configure\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Run make to generate SPECFEM2D binaries\n", + "if not os.path.exists(\"bin\"):\n", + " os.system(\"make all\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/Chow/Repositories/specfem2d\n", + "\u001b[1m\u001b[34mxadj_seismogram\u001b[m\u001b[m \u001b[1m\u001b[34mxmeshfem2D\u001b[m\u001b[m\n", + "\u001b[1m\u001b[32mxadj_seismogram.dSYM\u001b[m\u001b[m \u001b[1m\u001b[32mxmeshfem2D.dSYM\u001b[m\u001b[m\n", + "\u001b[1m\u001b[34mxcheck_quality_external_mesh\u001b[m\u001b[m \u001b[1m\u001b[34mxsmooth_sem\u001b[m\u001b[m\n", + "\u001b[1m\u001b[32mxcheck_quality_external_mesh.dSYM\u001b[m\u001b[m \u001b[1m\u001b[32mxsmooth_sem.dSYM\u001b[m\u001b[m\n", + "\u001b[1m\u001b[34mxcombine_sem\u001b[m\u001b[m \u001b[1m\u001b[34mxspecfem2D\u001b[m\u001b[m\n", + "\u001b[1m\u001b[32mxcombine_sem.dSYM\u001b[m\u001b[m \u001b[1m\u001b[32mxspecfem2D.dSYM\u001b[m\u001b[m\n", + "\u001b[1m\u001b[34mxconvolve_source_timefunction\u001b[m\u001b[m \u001b[1m\u001b[34mxsum_kernels\u001b[m\u001b[m\n", + "\u001b[1m\u001b[32mxconvolve_source_timefunction.dSYM\u001b[m\u001b[m \u001b[1m\u001b[32mxsum_kernels.dSYM\u001b[m\u001b[m\n" + ] + } + ], + "source": [ + "# Check out the binary files that have been created\n", + "os.chdir(SPECFEM2D_ORIGINAL)\n", + "! pwd\n", + "! ls bin/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1b. Create a separate SPECFEM2D working directory\n", + "\n", + "Next we'll create a new SPECFEM2D working directory, separate from the original repository. The intent here is to isolate the original SPECFEM2D repository from our working state, to protect it from things like accidental file deletions or manipulations. This is not a mandatory step for using SeisFlows, but it helps keep file structure clean in the long run, and is the SeisFlows3 dev team's preferred method of using SPECFEM. " + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + ".. note::\n", + " All SPECFEM2D/3D/3D_GLOBE need to run successfully are the bin/, DATA/, and OUTPUT_FILES/ directories. Everything else in the repository is not mandatory for running binaries." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial we will be using the [Tape2007 example problem](https://github.com/geodynamics/specfem2d/tree/devel/EXAMPLES/Tape2007) to define our __DATA/__ directory (last tested 8/15/22, bdba4389)." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/Chow/Work/work/sf_specfem2d_example/specfem2d_workdir\n", + "\u001b[1m\u001b[32mDATA\u001b[m\u001b[m \u001b[1m\u001b[32mbin\u001b[m\u001b[m\n" + ] + } + ], + "source": [ + "# Incase we've run this docs page before, delete the working directory before remaking\n", + "if os.path.exists(SPECFEM2D_WORKDIR):\n", + " shutil.rmtree(SPECFEM2D_WORKDIR)\n", + "\n", + "os.mkdir(SPECFEM2D_WORKDIR)\n", + "os.chdir(SPECFEM2D_WORKDIR)\n", + "\n", + "# Copy the binary files incase we update the source code. These can also be symlinked.\n", + "shutil.copytree(SPECFEM2D_BIN_ORIGINAL, \"bin\")\n", + "\n", + "# Copy the DATA/ directory because we will be making edits here frequently and it's useful to\n", + "# retain the original files for reference. We will be running one of the example problems: Tape2007\n", + "shutil.copytree(os.path.join(TAPE_2007_EXAMPLE, \"DATA\"), \"DATA\")\n", + "\n", + "! pwd\n", + "! ls" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -------------------------------------------------------------------------------\r\n", + " -------------------------------------------------------------------------------\r\n", + " D a t e : 15 - 08 - 2022 T i m e : 10:13:31\r\n", + " -------------------------------------------------------------------------------\r\n", + " -------------------------------------------------------------------------------\r\n", + "\r\n", + "see results in directory: OUTPUT_FILES/\r\n", + "\r\n", + "done\r\n", + "Mon Aug 15 10:13:31 PDT 2022\r\n" + ] + } + ], + "source": [ + "# Run the Tape2007 example to make sure SPECFEM2D is working as expected\n", + "os.chdir(TAPE_2007_EXAMPLE)\n", + "! ./run_this_example.sh > output_log.txt\n", + "\n", + "assert(os.path.exists(\"OUTPUT_FILES/forward_image000004800.jpg\")), \\\n", + " (f\"Example did not run, the remainder of this docs page will likely not work.\"\n", + " f\"Please check the following directory: {TAPE_2007_EXAMPLE}\")\n", + "\n", + "! tail output_log.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "------------------------------------\n", + "Now we need to manually set up our SPECFEM2D working directory. As mentioned in the previous cell, the only required elements of this working directory are the following (these files will form the basis for how SeisFlows3 operates within the SPECFEM2D framework):\n", + "\n", + "1. __bin/__ directory containing SPECFEM2D binaries\n", + "2. __DATA/__ directory containing SOURCE and STATION files, as well as a SPECFEM2D Par_file\n", + "3. __OUTPUT_FILES/proc??????_*.bin__ files which define the starting (and target) models" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + ".. note:: \n", + " This file structure is the same for all versions of SPECFEM (2D/3D/3D_GLOBE)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Par_file SOURCE_013\r\n", + "Par_file_Tape2007_132rec_checker SOURCE_014\r\n", + "Par_file_Tape2007_onerec SOURCE_015\r\n", + "\u001b[1m\u001b[35mSOURCE\u001b[m\u001b[m SOURCE_016\r\n", + "SOURCE_001 SOURCE_017\r\n", + "SOURCE_002 SOURCE_018\r\n", + "SOURCE_003 SOURCE_019\r\n", + "SOURCE_004 SOURCE_020\r\n", + "SOURCE_005 SOURCE_021\r\n", + "SOURCE_006 SOURCE_022\r\n", + "SOURCE_007 SOURCE_023\r\n", + "SOURCE_008 SOURCE_024\r\n", + "SOURCE_009 SOURCE_025\r\n", + "SOURCE_010 STATIONS_checker\r\n", + "SOURCE_011 interfaces_Tape2007.dat\r\n", + "SOURCE_012 model_velocity.dat_checker\r\n" + ] + } + ], + "source": [ + "# First we will set the correct SOURCE and STATION files.\n", + "# This is the same task as shown in ./run_this_example.sh\n", + "os.chdir(SPECFEM2D_DATA)\n", + "\n", + "# Symlink source 001 as our main source\n", + "if os.path.exists(\"SOURCE\"):\n", + " os.remove(\"SOURCE\")\n", + "os.symlink(\"SOURCE_001\", \"SOURCE\")\n", + "\n", + "# Copy the correct Par_file so that edits do not affect the original file\n", + "if os.path.exists(\"Par_file\"):\n", + " os.remove(\"Par_file\")\n", + "shutil.copy(\"Par_file_Tape2007_onerec\", \"Par_file\")\n", + "\n", + "! ls" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1c. Generate initial and target models\n", + "\n", + "Since we're doing a synthetic-synthetic inversion, we need to manually set up the velocity models with which we generate our synthetic waveforms. The naming conventions for these models are:\n", + "\n", + "1. __MODEL_INIT:__ The initial or starting model. Used to generate the actual synthetic seismograms. This is considered M00.\n", + "2. __MODEL_TRUE:__ The target or true model. Used to generate 'data' (also synthetic). This is the reference model that our inversion is trying to resolve.\n", + "\n", + "The starting model is defined as a homogeneous halfspace uin the Tape2007 example problem. We will need to run both `xmeshfem2D` and `xspecfem2D` to generate the required velocity model database files. We will generate our target model by slightly perturbing the parameters of the initial model." + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + ".. note::\n", + " We can use the SeisFlows3 command line option `seisflows sempar` to directly edit the SPECFEM2D Par_file in the command line. This will work for the SPECFEM3D Par_file as well." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "setup_with_binary_database: 0 -> 1\n", + "SAVE_MODEL: default -> binary\n", + "save_ASCII_kernels: .true. -> .false.\n" + ] + } + ], + "source": [ + "os.chdir(SPECFEM2D_DATA)\n", + "\n", + "# Ensure that SPECFEM2D outputs the velocity model in the expected binary format\n", + "! seisflows sempar setup_with_binary_database 1 # allow creation of .bin files\n", + "! seisflows sempar save_model binary # output model in .bin database format\n", + "! seisflows sempar save_ascii_kernels .false. # output kernels in .bin format, not ASCII" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m\u001b[32mDATA\u001b[m\u001b[m \u001b[1m\u001b[32mOUTPUT_FILES\u001b[m\u001b[m \u001b[1m\u001b[32mbin\u001b[m\u001b[m\r\n" + ] + } + ], + "source": [ + "# SPECFEM requires that we create the OUTPUT_FILES directory before running\n", + "os.chdir(SPECFEM2D_WORKDIR)\n", + "\n", + "if os.path.exists(SPECFEM2D_OUTPUT):\n", + " shutil.rmtree(SPECFEM2D_OUTPUT)\n", + " \n", + "os.mkdir(SPECFEM2D_OUTPUT)\n", + "\n", + "! ls" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " **********************************************\n", + " **** Specfem 2-D Solver - serial version ****\n", + " **********************************************\n", + "\n", + " Running Git version of the code corresponding to \n", + " dating From \n", + "\n", + "\n", + " NDIM = 2\n", + " -------------------------------------------------------------------------------\n", + " Program SPECFEM2D: \n", + " -------------------------------------------------------------------------------\n", + " -------------------------------------------------------------------------------\n", + " Tape-Liu-Tromp (GJI 2007)\n", + " -------------------------------------------------------------------------------\n", + " -------------------------------------------------------------------------------\n", + " D a t e : 15 - 08 - 2022 T i m e : 10:14:13\n", + " -------------------------------------------------------------------------------\n", + " -------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# GENERATE MODEL_INIT\n", + "os.chdir(SPECFEM2D_WORKDIR)\n", + "\n", + "# Run the mesher and solver to generate our initial model\n", + "! ./bin/xmeshfem2D > OUTPUT_FILES/mesher_log.txt\n", + "! ./bin/xspecfem2D > OUTPUT_FILES/solver_log.txt\n", + "\n", + "# Move the model files (*.bin) into the OUTPUT_FILES directory, where SeisFlows3 expects them\n", + "! mv DATA/*bin OUTPUT_FILES\n", + "\n", + "# Make sure we don't overwrite this initial model when creating our target model in the next step\n", + "! mv OUTPUT_FILES OUTPUT_FILES_INIT\n", + "\n", + "! head OUTPUT_FILES_INIT/solver_log.txt\n", + "! tail OUTPUT_FILES_INIT/solver_log.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-----------------------------\n", + "\n", + "Now we want to perturb the initial model to create our target model (__MODEL_TRUE__). The seisflows command line subargument `seisflows sempar velocity_model` will let us view and edit the velocity model. You can also do this manually by editing the Par_file directly. " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VELOCITY_MODEL:\r\n", + "\r\n", + "1 1 2600.d0 5800.d0 3500.0d0 0 0 10.d0 10.d0 0 0 0 0 0 0\r\n", + "->\r\n", + "1 1 2600.d0 5900.d0 3550.0d0 0 0 10.d0 10.d0 0 0 0 0 0 0\r\n" + ] + } + ], + "source": [ + "# GENERATE MODEL_TRUE\n", + "os.chdir(SPECFEM2D_DATA)\n", + "\n", + "# Edit the Par_file by increasing velocities by ~10% \n", + "! seisflows sempar velocity_model '1 1 2600.d0 5900.d0 3550.0d0 0 0 10.d0 10.d0 0 0 0 0 0 0'" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " **********************************************\n", + " **** Specfem 2-D Solver - serial version ****\n", + " **********************************************\n", + "\n", + " Running Git version of the code corresponding to \n", + " dating From \n", + "\n", + "\n", + " NDIM = 2\n", + " -------------------------------------------------------------------------------\n", + " Program SPECFEM2D: \n", + " -------------------------------------------------------------------------------\n", + " -------------------------------------------------------------------------------\n", + " Tape-Liu-Tromp (GJI 2007)\n", + " -------------------------------------------------------------------------------\n", + " -------------------------------------------------------------------------------\n", + " D a t e : 15 - 08 - 2022 T i m e : 10:14:13\n", + " -------------------------------------------------------------------------------\n", + " -------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# Re-run the mesher and solver to generate our target velocity model\n", + "os.chdir(SPECFEM2D_WORKDIR)\n", + "\n", + "# Make sure the ./OUTPUT_FILES directory exists since we moved the old one\n", + "if os.path.exists(SPECFEM2D_OUTPUT):\n", + " shutil.rmtree(SPECFEM2D_OUTPUT)\n", + "os.mkdir(SPECFEM2D_OUTPUT)\n", + "\n", + "# Run the binaries to generate MODEL_TRUE\n", + "! ./bin/xmeshfem2D > OUTPUT_FILES/mesher_log.txt\n", + "! ./bin/xspecfem2D > OUTPUT_FILES/solver_log.txt\n", + "\n", + "# Move all the relevant files into OUTPUT_FILES \n", + "! mv ./DATA/*bin OUTPUT_FILES\n", + "! mv OUTPUT_FILES OUTPUT_FILES_TRUE\n", + "\n", + "! head OUTPUT_FILES_INIT/solver_log.txt\n", + "! tail OUTPUT_FILES_INIT/solver_log.txt" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m\u001b[32mDATA\u001b[m\u001b[m \u001b[1m\u001b[32mOUTPUT_FILES_INIT\u001b[m\u001b[m \u001b[1m\u001b[32mOUTPUT_FILES_TRUE\u001b[m\u001b[m \u001b[1m\u001b[32mbin\u001b[m\u001b[m\r\n" + ] + } + ], + "source": [ + "# Great, we have all the necessary SPECFEM files to run our SeisFlows inversion!\n", + "! ls" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Initialize SeisFlows (SF)\n", + "In this Section we will look at a SeisFlows working directory, parameter file, and working state.\n", + "\n", + "#### 2a. SeisFlows working directory and parameter file\n", + "\n", + "As with SPECFEM, SeisFlows requires a parameter file (__parameters.yaml__) that controls how an automated workflow will proceed. Because SeisFlows is modular, there are a large number of potential parameters which may be present in a SeisFlows parameter file, as each sub-module may have its own set of unique parameters.\n", + "\n", + "In contrast to SPECFEM's method of listing all available parameters and leaving it up the User to determine which ones are relevant to them, SeisFlows dynamically builds its parameter file based on User inputs. In this subsection we will use the built-in SeisFlows command line tools to generate and populate the parameter file. " + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + ".. note::\n", + " See the `parameter file documentation page `__ for a more in depth exploration of this central SeisFlows file." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the previous section we saw the `sempar` command in action. We can use the `-h` or help flag to list all available SiesFlows3 command line commands." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "usage: seisflows [-h] [-w [WORKDIR]] [-p [PARAMETER_FILE]]\r\n", + " {setup,configure,swap,init,submit,resume,restart,clean,par,sempar,check,print,reset,debug,examples}\r\n", + " ...\r\n", + "\r\n", + "================================================================================\r\n", + "\r\n", + " SeisFlows: Waveform Inversion Package \r\n", + "\r\n", + "================================================================================\r\n", + "\r\n", + "optional arguments:\r\n", + " -h, --help show this help message and exit\r\n", + " -w [WORKDIR], --workdir [WORKDIR]\r\n", + " The SeisFlows working directory, default: cwd\r\n", + " -p [PARAMETER_FILE], --parameter_file [PARAMETER_FILE]\r\n", + " Parameters file, default: 'parameters.yaml'\r\n", + "\r\n", + "command:\r\n", + " Available SeisFlows arguments and their intended usages\r\n", + "\r\n", + " setup Setup working directory from scratch\r\n", + " configure Fill parameter file with defaults\r\n", + " swap Swap module parameters in an existing parameter file\r\n", + " init Initiate working environment\r\n", + " submit Submit initial workflow to system\r\n", + " resume Re-submit previous workflow to system\r\n", + " restart Remove current environment and submit new workflow\r\n", + " clean Remove files relating to an active working environment\r\n", + " par View and edit SeisFlows parameter file\r\n", + " sempar View and edit SPECFEM parameter file\r\n", + " check Check state of an active environment\r\n", + " print Print information related to an active environment\r\n", + " reset Reset modules within an active state\r\n", + " debug Start interactive debug environment\r\n", + " examples Look at and run pre-configured example problems\r\n", + "\r\n", + "'seisflows [command] -h' for more detailed descriptions of each command.\r\n" + ] + } + ], + "source": [ + "! seisflows -h" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'os' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# The command 'setup' creates the 'parameters.yaml' file that controls all of SeisFlows\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m# the '-f' flag removes any exist 'parameters.yaml' file that might be in the directory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mWORKDIR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' seisflows setup -f'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' ls'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'os' is not defined" + ] + } + ], + "source": [ + "# The command 'setup' creates the 'parameters.yaml' file that controls all of SeisFlows\n", + "# the '-f' flag removes any exist 'parameters.yaml' file that might be in the directory\n", + "os.chdir(WORKDIR)\n", + "! seisflows setup -f\n", + "! ls" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# //////////////////////////////////////////////////////////////////////////////\r\n", + "#\r\n", + "# SeisFlows YAML Parameter File\r\n", + "#\r\n", + "# //////////////////////////////////////////////////////////////////////////////\r\n", + "#\r\n", + "# Modules correspond to the structure of the source code, and determine\r\n", + "# SeisFlows' behavior at runtime. Each module requires its own sub-parameters.\r\n", + "#\r\n", + "# .. rubric::\r\n", + "# - To determine available options for modules listed below, run:\r\n", + "# > seisflows print modules\r\n", + "# - To auto-fill with docstrings and default values (recommended), run:\r\n", + "# > seisflows configure\r\n", + "# - To set values as NoneType, use: null\r\n", + "# - To set values as infinity, use: inf\r\n", + "#\r\n", + "# MODULES\r\n", + "# ///////\r\n", + "# workflow (str): The types and order of functions for running SeisFlows\r\n", + "# system (str): Computer architecture of the system being used\r\n", + "# solver (str): External numerical solver to use for waveform simulations\r\n", + "# preprocess (str): Preprocessing schema for waveform data\r\n", + "# optimize (str): Optimization algorithm for the inverse problem\r\n", + "# ==============================================================================\r\n", + "workflow: forward\r\n", + "system: workstation\r\n", + "solver: specfem2d\r\n", + "preprocess: default\r\n", + "optimize: gradient\r\n" + ] + } + ], + "source": [ + "# Let's have a look at this file, which has not yet been populated\n", + "! cat parameters.yaml" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " SEISFLOWS MODULES \r\n", + " ///////////////// \r\n", + "'-': module, '*': class\r\n", + "\r\n", + "- workflow\r\n", + " * forward\r\n", + " * inversion\r\n", + " * migration\r\n", + "- system\r\n", + " * chinook\r\n", + " * cluster\r\n", + " * frontera\r\n", + " * lsf\r\n", + " * maui\r\n", + " * slurm\r\n", + " * workstation\r\n", + "- solver\r\n", + " * specfem\r\n", + " * specfem2d\r\n", + " * specfem3d\r\n", + " * specfem3d_globe\r\n", + "- preprocess\r\n", + " * default\r\n", + " * pyaflowa\r\n", + "- optimize\r\n", + " * LBFGS\r\n", + " * NLCG\r\n", + " * gradient\r\n" + ] + } + ], + "source": [ + "# We can use the `seisflows print modules` command to list out the available options \n", + "! seisflows print modules" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "workflow: forward -> inversion\n", + "# //////////////////////////////////////////////////////////////////////////////\n", + "#\n", + "# SeisFlows YAML Parameter File\n", + "#\n", + "# //////////////////////////////////////////////////////////////////////////////\n", + "#\n", + "# Modules correspond to the structure of the source code, and determine\n", + "# SeisFlows' behavior at runtime. Each module requires its own sub-parameters.\n", + "#\n", + "# .. rubric::\n", + "# - To determine available options for modules listed below, run:\n", + "# > seisflows print modules\n", + "# - To auto-fill with docstrings and default values (recommended), run:\n", + "# > seisflows configure\n", + "# - To set values as NoneType, use: null\n", + "# - To set values as infinity, use: inf\n", + "#\n", + "# MODULES\n", + "# ///////\n", + "# workflow (str): The types and order of functions for running SeisFlows\n", + "# system (str): Computer architecture of the system being used\n", + "# solver (str): External numerical solver to use for waveform simulations\n", + "# preprocess (str): Preprocessing schema for waveform data\n", + "# optimize (str): Optimization algorithm for the inverse problem\n", + "# ==============================================================================\n", + "workflow: inversion\n", + "system: workstation\n", + "solver: specfem2d\n", + "preprocess: default\n", + "optimize: gradient\n" + ] + } + ], + "source": [ + "# For this example, we can use most of the default modules, however we need to \n", + "# change the SOLVER module to let SeisFlows know we're using SPECFEM2D (as opposed to 3D)\n", + "! seisflows par workflow inversion\n", + "! cat parameters.yaml" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-------------------------\n", + "The `seisflows configure` command populates the parameter file based on the chosen modules. SeisFlows will attempt to fill in all parameters with reasonable default values. Docstrings above each module show descriptions and available options for each of these parameters. \n", + "\n", + "In the follownig cell we will use the `seisflows par` command to edit the parameters.yaml file directly, replacing some default parameters with our own values. Comments next to each evaluation describe the choice for each." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# //////////////////////////////////////////////////////////////////////////////\r\n", + "#\r\n", + "# SeisFlows YAML Parameter File\r\n", + "#\r\n", + "# //////////////////////////////////////////////////////////////////////////////\r\n", + "#\r\n", + "# Modules correspond to the structure of the source code, and determine\r\n", + "# SeisFlows' behavior at runtime. Each module requires its own sub-parameters.\r\n", + "#\r\n", + "# .. rubric::\r\n", + "# - To determine available options for modules listed below, run:\r\n", + "# > seisflows print modules\r\n", + "# - To auto-fill with docstrings and default values (recommended), run:\r\n", + "# > seisflows configure\r\n", + "# - To set values as NoneType, use: null\r\n", + "# - To set values as infinity, use: inf\r\n", + "#\r\n", + "# MODULES\r\n", + "# ///////\r\n", + "# workflow (str): The types and order of functions for running SeisFlows\r\n", + "# system (str): Computer architecture of the system being used\r\n", + "# solver (str): External numerical solver to use for waveform simulations\r\n", + "# preprocess (str): Preprocessing schema for waveform data\r\n", + "# optimize (str): Optimization algorithm for the inverse problem\r\n", + "# ==============================================================================\r\n", + "workflow: inversion\r\n", + "system: workstation\r\n", + "solver: specfem2d\r\n", + "preprocess: default\r\n", + "optimize: gradient\r\n", + "# =============================================================================\r\n", + "#\r\n", + "# Forward Workflow\r\n", + "# ----------------\r\n", + "# Run forward solver in parallel and (optionally) calculate\r\n", + "# data-synthetic misfit and adjoint sources.\r\n", + "#\r\n", + "# Parameters\r\n", + "# ----------\r\n", + "# :type modules: list of module\r\n", + "# :param modules: instantiated SeisFlows modules which should have been\r\n", + "# generated by the function `seisflows.config.import_seisflows` with a\r\n", + "# parameter file generated by seisflows.configure\r\n", + "# :type data_case: str\r\n", + "# :param data_case: How to address 'data' in the workflow, available options:\r\n", + "# 'data': real data will be provided by the user in\r\n", + "# `path_data/{source_name}` in the same format that the solver will\r\n", + "# produce synthetics (controlled by `solver.format`) OR\r\n", + "# synthetic': 'data' will be generated as synthetic seismograms using\r\n", + "# a target model provided in `path_model_true`. If None, workflow will\r\n", + "# not attempt to generate data.\r\n", + "# :type stop_after: str\r\n", + "# :param stop_after: optional name of task in task list (use\r\n", + "# `seisflows print tasks` to get task list for given workflow) to stop\r\n", + "# workflow after, allowing user to prematurely stop a workflow to explore\r\n", + "# intermediate results or debug.\r\n", + "# :type export_traces: bool\r\n", + "# :param export_traces: export all waveforms that are generated by the\r\n", + "# external solver to `path_output`. If False, solver traces stored in\r\n", + "# scratch may be discarded at any time in the workflow\r\n", + "# :type export_residuals: bool\r\n", + "# :param export_residuals: export all residuals (data-synthetic misfit) that\r\n", + "# are generated by the external solver to `path_output`. If False,\r\n", + "# residuals stored in scratch may be discarded at any time in the workflow\r\n", + "#\r\n", + "# \r\n", + "# Migration Workflow\r\n", + "# ------------------\r\n", + "# Run forward and adjoint solver to produce event-dependent misfit kernels.\r\n", + "# Sum and postprocess kernels to produce gradient. In seismic exploration\r\n", + "# this is 'reverse time migration'.\r\n", + "#\r\n", + "# Parameters\r\n", + "# ----------\r\n", + "# :type export_gradient: bool\r\n", + "# :param export_gradient: export the gradient after it has been generated\r\n", + "# in the scratch directory. If False, gradient can be discarded from\r\n", + "# scratch at any time in the workflow\r\n", + "# :type export_kernels: bool\r\n", + "# :param export_kernels: export each sources event kernels after they have\r\n", + "# been generated in the scratch directory. If False, gradient can be\r\n", + "# discarded from scratch at any time in the workflow\r\n", + "#\r\n", + "# \r\n", + "# Inversion Workflow\r\n", + "# ------------------\r\n", + "# Peforms iterative nonlinear inversion using the machinery of the Forward\r\n", + "# and Migration workflows, as well as a built-in optimization library.\r\n", + "#\r\n", + "# Parameters\r\n", + "# ----------\r\n", + "# :type start: int\r\n", + "# :param start: start inversion workflow at this iteration. 1 <= start <= inf\r\n", + "# :type end: int\r\n", + "# :param end: end inversion workflow at this iteration. start <= end <= inf\r\n", + "# :type iteration: int\r\n", + "# :param iteration: The current iteration of the workflow. If NoneType, takes\r\n", + "# the value of `start` (i.e., first iteration of the workflow). User can\r\n", + "# also set between `start` and `end` to resume a failed workflow.\r\n", + "# :type thrifty: bool\r\n", + "# :param thrifty: a thrifty inversion skips the costly intialization step\r\n", + "# (i.e., forward simulations and misfit quantification) if the final\r\n", + "# forward simulations from the previous iterations line search can be\r\n", + "# used in the current one. Requires L-BFGS optimization.\r\n", + "# :type export_model: bool\r\n", + "# :param export_model: export best-fitting model from the line search to disk.\r\n", + "# If False, new models can be discarded from scratch at any time.\r\n", + "#\r\n", + "# \r\n", + "# =============================================================================\r\n", + "data_case: data\r\n", + "stop_after: null\r\n", + "export_traces: False\r\n", + "export_residuals: False\r\n", + "export_gradient: False\r\n", + "export_kernels: False\r\n", + "start: 1\r\n", + "end: 1\r\n", + "export_model: True\r\n", + "thrifty: False\r\n", + "iteration: 1\r\n", + "# =============================================================================\r\n", + "#\r\n", + "# Workstation System\r\n", + "# ------------------\r\n", + "# Runs tasks in serial on a local machine.\r\n", + "#\r\n", + "# Parameters\r\n", + "# ----------\r\n", + "# :type ntask: int\r\n", + "# :param ntask: number of individual tasks/events to run during workflow.\r\n", + "# Must be <= the number of source files in `path_specfem_data`\r\n", + "# :type nproc: int\r\n", + "# :param nproc: number of processors to use for each simulation\r\n", + "# :type log_level: str\r\n", + "# :param log_level: logger level to pass to logging module.\r\n", + "# Available: 'debug', 'info', 'warning', 'critical'\r\n", + "# :type verbose: bool\r\n", + "# :param verbose: if True, formats the log messages to include the file\r\n", + "# name, line number and message type. Useful for debugging but\r\n", + "# also very verbose.\r\n", + "#\r\n", + "# \r\n", + "# =============================================================================\r\n", + "ntask: 1\r\n", + "nproc: 1\r\n", + "log_level: DEBUG\r\n", + "verbose: False\r\n", + "# =============================================================================\r\n", + "#\r\n", + "# Solver SPECFEM\r\n", + "# --------------\r\n", + "# Generalized SPECFEM interface to manipulate SPECFEM2D/3D/3D_GLOBE w/ Python\r\n", + "#\r\n", + "# Parameters\r\n", + "# ----------\r\n", + "# :type data_format: str\r\n", + "# :param data_format: data format for reading traces into memory.\r\n", + "# Available: ['SU': seismic unix format, 'ASCII': human-readable ascii]\r\n", + "# :type materials: str\r\n", + "# :param materials: Material parameters used to define model. Available:\r\n", + "# ['ELASTIC': Vp, Vs, 'ACOUSTIC': Vp, 'ISOTROPIC', 'ANISOTROPIC']\r\n", + "# :type density: bool\r\n", + "# :param density: How to treat density during inversion. If True, updates\r\n", + "# density during inversion. If False, keeps it constant.\r\n", + "# TODO allow density scaling during an inversion\r\n", + "# :type attenuation: bool\r\n", + "# :param attenuation: How to treat attenuation during inversion.\r\n", + "# if True, turns on attenuation during forward simulations only. If\r\n", + "# False, attenuation is always set to False. Requires underlying\r\n", + "# attenution (Q_mu, Q_kappa) model\r\n", + "# :type smooth_h: float\r\n", + "# :param smooth_h: Gaussian half-width for horizontal smoothing in units\r\n", + "# of meters. If 0., no smoothing applied\r\n", + "# :type smooth_h: float\r\n", + "# :param smooth_v: Gaussian half-width for vertical smoothing in units\r\n", + "# of meters.\r\n", + "# :type components: str\r\n", + "# :param components: components to consider and tag data with. Should be\r\n", + "# string of letters such as 'RTZ'\r\n", + "# :type solver_io: str\r\n", + "# :param solver_io: format of model/kernel/gradient files expected by the\r\n", + "# numerical solver. Available: ['fortran_binary': default .bin files].\r\n", + "# TODO: ['adios': ADIOS formatted files]\r\n", + "# :type source_prefix: str\r\n", + "# :param source_prefix: prefix of source/event/earthquake files. If None,\r\n", + "# will attempt to guess based on the specific solver chosen.\r\n", + "# :type mpiexec: str\r\n", + "# :param mpiexec: MPI executable used to run parallel processes. Should also\r\n", + "# be defined for the system module\r\n", + "#\r\n", + "# \r\n", + "# Solver SPECFEM2D\r\n", + "# ----------------\r\n", + "# SPECFEM2D-specific alterations to the base SPECFEM module\r\n", + "#\r\n", + "# Parameters\r\n", + "# ----------\r\n", + "# :type source_prefix: str\r\n", + "# :param source_prefix: Prefix of source files in path SPECFEM_DATA. Defaults\r\n", + "# to 'SOURCE'\r\n", + "# :type multiples: bool\r\n", + "# :param multiples: set an absorbing top-boundary condition\r\n", + "#\r\n", + "# \r\n", + "# =============================================================================\r\n", + "data_format: ascii\r\n", + "materials: acoustic\r\n", + "density: False\r\n", + "attenuation: False\r\n", + "smooth_h: 0.0\r\n", + "smooth_v: 0.0\r\n", + "components: ZNE\r\n", + "source_prefix: SOURCE\r\n", + "multiples: False\r\n", + "# =============================================================================\r\n", + "#\r\n", + "# Default Preprocess\r\n", + "# ------------------\r\n", + "# Data processing for seismic traces, with options for data misfit,\r\n", + "# filtering, normalization and muting.\r\n", + "#\r\n", + "# Parameters\r\n", + "# ----------\r\n", + "# :type data_format: str\r\n", + "# :param data_format: data format for reading traces into memory. For\r\n", + "# available see: seisflows.plugins.preprocess.readers\r\n", + "# :type misfit: str\r\n", + "# :param misfit: misfit function for waveform comparisons. For available\r\n", + "# see seisflows.plugins.preprocess.misfit\r\n", + "# :type backproject: str\r\n", + "# :param backproject: backprojection function for migration, or the\r\n", + "# objective function in FWI. For available see\r\n", + "# seisflows.plugins.preprocess.adjoint\r\n", + "# :type normalize: str\r\n", + "# :param normalize: Data normalization parameters used to normalize the\r\n", + "# amplitudes of waveforms. Choose from two sets:\r\n", + "# ENORML1: normalize per event by L1 of traces; OR\r\n", + "# ENORML2: normalize per event by L2 of traces;\r\n", + "# &\r\n", + "# TNORML1: normalize per trace by L1 of itself; OR\r\n", + "# TNORML2: normalize per trace by L2 of itself\r\n", + "# :type filter: str\r\n", + "# :param filter: Data filtering type, available options are:\r\n", + "# BANDPASS (req. MIN/MAX PERIOD/FREQ);\r\n", + "# LOWPASS (req. MAX_FREQ or MIN_PERIOD);\r\n", + "# HIGHPASS (req. MIN_FREQ or MAX_PERIOD)\r\n", + "# :type min_period: float\r\n", + "# :param min_period: Minimum filter period applied to time series.\r\n", + "# See also MIN_FREQ, MAX_FREQ, if User defines FREQ parameters, they\r\n", + "# will overwrite PERIOD parameters.\r\n", + "# :type max_period: float\r\n", + "# :param max_period: Maximum filter period applied to time series. See\r\n", + "# also MIN_FREQ, MAX_FREQ, if User defines FREQ parameters, they will\r\n", + "# overwrite PERIOD parameters.\r\n", + "# :type min_freq: float\r\n", + "# :param min_freq: Maximum filter frequency applied to time series,\r\n", + "# See also MIN_PERIOD, MAX_PERIOD, if User defines FREQ parameters,\r\n", + "# they will overwrite PERIOD parameters.\r\n", + "# :type max_freq: float\r\n", + "# :param max_freq: Maximum filter frequency applied to time series,\r\n", + "# See also MIN_PERIOD, MAX_PERIOD, if User defines FREQ parameters,\r\n", + "# they will overwrite PERIOD parameters.\r\n", + "# :type mute: list\r\n", + "# :param mute: Data mute parameters used to zero out early / late\r\n", + "# arrivals or offsets. Choose any number of:\r\n", + "# EARLY: mute early arrivals;\r\n", + "# LATE: mute late arrivals;\r\n", + "# SHORT: mute short source-receiver distances;\r\n", + "# LONG: mute long source-receiver distances\r\n", + "#\r\n", + "# \r\n", + "# =============================================================================\r\n", + "misfit: waveform\r\n", + "adjoint: waveform\r\n", + "normalize: []\r\n", + "filter: null\r\n", + "min_period: null\r\n", + "max_period: null\r\n", + "min_freq: null\r\n", + "max_freq: null\r\n", + "mute: []\r\n", + "early_slope: null\r\n", + "early_const: null\r\n", + "late_slope: null\r\n", + "late_const: null\r\n", + "short_dist: null\r\n", + "long_dist: null\r", + "\r\n", + "# =============================================================================\r\n", + "#\r\n", + "# Gradient Optimization\r\n", + "# ---------------------\r\n", + "# Gradient/steepest descent optimization algorithm.\r\n", + "#\r\n", + "# Parameters\r\n", + "# ----------\r\n", + "# :type line_search_method: str\r\n", + "# :param line_search_method: chosen line_search algorithm. Currently available\r\n", + "# are 'bracket' and 'backtrack'. See seisflows.plugins.line_search\r\n", + "# for all available options\r\n", + "# :type preconditioner: str\r\n", + "# :param preconditioner: algorithm for preconditioning gradients. Currently\r\n", + "# available: 'diagonal'. Requires `path_preconditioner` to point to a\r\n", + "# set of files that define the preconditioner, formatted the same as the\r\n", + "# input model\r\n", + "# :type step_count_max: int\r\n", + "# :param step_count_max: maximum number of trial steps to perform during\r\n", + "# the line search before a change in line search behavior is\r\n", + "# considered, or a line search is considered to have failed.\r\n", + "# :type step_len_init: float\r\n", + "# :param step_len_init: initial line search step length guess, provided\r\n", + "# as a fraction of current model parameters.\r\n", + "# :type step_len_max: float\r\n", + "# :param step_len_max: maximum allowable step length during the line\r\n", + "# search. Set as a fraction of the current model parameters\r\n", + "#\r\n", + "# \r\n", + "# =============================================================================\r\n", + "preconditioner: null\r\n", + "step_count_max: 10\r\n", + "step_len_init: 0.05\r\n", + "step_len_max: 0.5\r\n", + "line_search_method: bracket\r\n", + "# =============================================================================\r\n", + "#\r\n", + "#\t Paths\r\n", + "#\t -----\r\n", + "# :type workdir: str\r\n", + "# :param workdir: working directory in which to look for data and store\r\n", + "# results. Defaults to current working directory\r\n", + "# :type path_output: str\r\n", + "# :param path_output: path to directory used for permanent storage on disk.\r\n", + "# Results and exported scratch files are saved here.\r\n", + "# :type path_data: str\r\n", + "# :param path_data: path to any externally stored data required by the solver\r\n", + "# :type path_state_file: str\r\n", + "# :param path_state_file: path to a text file used to track the current\r\n", + "# status of a workflow (i.e., what functions have already been completed),\r\n", + "# used for checkpointing and resuming workflows\r\n", + "# :type path_model_init: str\r\n", + "# :param path_model_init: path to the starting model used to calculate the\r\n", + "# initial misfit. Must match the expected `solver_io` format.\r\n", + "# :type path_model_true: str\r\n", + "# :param path_model_true: path to a target model if `case`=='synthetic' and\r\n", + "# a set of synthetic 'observations' are required for workflow.\r\n", + "# :type path_eval_grad: str\r\n", + "# :param path_eval_grad: scratch path to store files for gradient evaluation,\r\n", + "# including models, kernels, gradient and residuals.\r\n", + "# :type path_mask: str\r\n", + "# :param path_mask: optional path to a masking function which is used to\r\n", + "# mask out or scale parts of the gradient. The user-defined mask must\r\n", + "# match the file format of the input model (e.g., .bin files).\r\n", + "# :type path_eval_func: str\r\n", + "# :param path_eval_func: scratch path to store files for line search objective\r\n", + "# function evaluations, including models, misfit and residuals\r\n", + "# \r\n", + "# :type path_output_log: str\r\n", + "# :param path_output_log: path to a text file used to store the outputs of\r\n", + "# the package wide logger, which are also written to stdout\r\n", + "# :type path_par_file: str\r\n", + "# :param path_par_file: path to parameter file which is used to instantiate\r\n", + "# the package\r\n", + "# :type path_log_files: str\r\n", + "# :param path_log_files: path to a directory where individual log files are\r\n", + "# saved whenever a number of parallel tasks are run on the system.\r\n", + "# \r\n", + "# :type path_data: str\r\n", + "# :param path_data: path to any externally stored data required by the solver\r\n", + "# :type path_specfem_bin: str\r\n", + "# :param path_specfem_bin: path to SPECFEM bin/ directory which\r\n", + "# contains binary executables for running SPECFEM\r\n", + "# :type path_specfem_data: str\r\n", + "# :param path_specfem_data: path to SPECFEM DATA/ directory which must\r\n", + "# contain the CMTSOLUTION, STATIONS and Par_file files used for\r\n", + "# running SPECFEM\r\n", + "# \r\n", + "# :type path_preprocess: str\r\n", + "# :param path_preprocess: scratch path for all preprocessing processes,\r\n", + "# including saving files\r\n", + "# \r\n", + "# :type path_preconditioner: str\r\n", + "# :param path_preconditioner: optional path to a set of preconditioner files\r\n", + "# formatted the same as the input model (or output model of solver).\r\n", + "# Required to exist and contain files if `preconditioner`==True\r\n", + "# \r\n", + "# =============================================================================\r\n", + "path_workdir: /Users/Chow/Work/work/sf_specfem2d_example\r\n", + "path_scratch: /Users/Chow/Work/work/sf_specfem2d_example/scratch\r\n", + "path_eval_grad: /Users/Chow/Work/work/sf_specfem2d_example/scratch/eval_grad\r\n", + "path_output: /Users/Chow/Work/work/sf_specfem2d_example/output\r\n", + "path_model_init: null\r\n", + "path_model_true: null\r\n", + "path_state_file: /Users/Chow/Work/work/sf_specfem2d_example/sfstate.txt\r\n", + "path_data: null\r\n", + "path_mask: null\r\n", + "path_eval_func: /Users/Chow/Work/work/sf_specfem2d_example/scratch/eval_func\r\n", + "path_par_file: /Users/Chow/Work/work/sf_specfem2d_example/parameters.yaml\r\n", + "path_log_files: /Users/Chow/Work/work/sf_specfem2d_example/logs\r\n", + "path_output_log: /Users/Chow/Work/work/sf_specfem2d_example/sflog.txt\r\n", + "path_specfem_bin: null\r\n", + "path_specfem_data: null\r\n", + "path_solver: /Users/Chow/Work/work/sf_specfem2d_example/scratch/solver\r\n", + "path_preconditioner: null\r\n" + ] + } + ], + "source": [ + "! seisflows configure\n", + "! cat parameters.yaml" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ntask: 1 -> 3\n", + "materials: acoustic -> elastic\n", + "density: False -> False\n", + "attenuation: False -> False\n", + "start: 1 -> 1\n", + "end: 1 -> 2\n", + "data_case: data -> synthetic\n", + "components: ZNE -> Y\n", + "step_count_max: 10 -> 5\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# EDIT THE SEISFLOWS PARAMETER FILE\n", + "! seisflows par ntask 3 # set the number of sources/events to use\n", + "! seisflows par materials elastic # how the velocity model is parameterized\n", + "! seisflows par density False # update density or keep constant\n", + "! seisflows par attenuation False\n", + "! seisflows par start 1 # first iteration\n", + "! seisflows par end 2 # final iteration -- we will only run 1\n", + "! seisflows par data_case synthetic # synthetic-synthetic means we need both INIT and TRUE models\n", + "! seisflows par components Y # this default example creates Y-component seismograms\n", + "! seisflows par step_count_max 5 # limit the number of steps in the line search\n", + "\n", + "# Use Python syntax here to access path constants\n", + "os.system(f\"seisflows par path_specfem_bin {SPECFEM2D_BIN}\") # set path to SPECFEM2D binaries\n", + "os.system(f\"seisflows par path_specfem_data {SPECFEM2D_DATA}\") # set path to SEPCFEM2D DATA/\n", + "os.system(f\"seisflows par path_model_init {SPECFEM2D_MODEL_INIT}\") # set path to INIT model\n", + "os.system(f\"seisflows par path_model_true {SPECFEM2D_MODEL_TRUE}\") # set path to TRUE model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "------------------------------\n", + "One last thing, we will need to edit the SPECFEM2D Par_file parameter `MODEL` such that `xmeshfem2d` reads our pre-built velocity models (\\*.bin files) rather than the meshing parameters defined in the Par_file." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MODEL: default -> gll\r\n" + ] + } + ], + "source": [ + "os.chdir(SPECFEM2D_DATA)\n", + "! seisflows sempar model gll" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Run SeisFlows\n", + "\n", + "In this Section we will run SeisFlows to generate synthetic seismograms, kernels, a gradient, and an updated velocity model.\n", + "\n", + "#### 3a. Forward simulations\n", + "\n", + "SeisFlows is an automated workflow tool, such that once we run `seisflows submit` we should not need to intervene in the workflow. However the package does allow the User flexibility in how they want the workflow to behave.\n", + "\n", + "For example, we can run our workflow in stages by taking advantage of the `stop_after` parameter. As its name suggests, `stop_after` allows us to stop a workflow prematurely so that we may stop and look at results, or debug a failing workflow.\n", + "\n", + "The `seisflows print flow` command tells us what functions we can use for the `stop_after` parameter. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " SEISFLOWS WORKFLOW TASK LIST \r\n", + " //////////////////////////// \r\n", + "Task list for \r\n", + "\r\n", + "1: evaluate_initial_misfit\r\n", + "2: run_adjoint_simulations\r\n", + "3: postprocess_event_kernels\r\n", + "4: evaluate_gradient_from_kernels\r\n", + "5: initialize_line_search\r\n", + "6: perform_line_search\r\n", + "7: finalize_iteration\r\n" + ] + } + ], + "source": [ + "os.chdir(WORKDIR)\n", + "! seisflows print tasks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-----------------------------\n", + "In the Inversion workflow, the tasks listed are described as follows:\n", + "\n", + "1. __evaluate_initial_misfit:__ \n", + " a. Prepare data for inversion by either copying data from disk or generating 'synthetic data' with MODEL_TRUE \n", + " b. Call numerical solver to run forward simulations using MODEL_INIT, generating synthetics \n", + " c. Evaluate the objective function by performing waveform comparisons \n", + " d. Prepare `run_adjoint_simulations` step by generating adjoint sources and auxiliary files\n", + "2. __run_adjoint_simulations:__ Call numerical solver to run adjoint simulation, generating kernels\n", + "3. __postprocess_event_kernels:__ Combine all event kernels into a misfit kernel. \n", + "4. __evaluate_gradient_from_kernels:__ Smooth and mask the misfit kernel to create the gradient\n", + "4. __initialize_line_search:__ Call on the optimization library to scale the gradient by a step length to compute the search direction. Prepare file structure for line search.\n", + "5. __perform_line_search:__ Perform a line search by algorithmically scaling the gradient and evaluating the misfit function (forward simulations and misfit quantification) until misfit is acceptably reduced.\n", + "6. __finalize_iteration:__ Run any finalization steps such as saving traces, kernels, gradients and models to disk, setting up SeisFlows3 for any subsequent iterations. Clean the scratch/ directory in preparation for subsequent iterations\n", + "\n", + "Let's set the `stop_after` argument to __evaluate_initial_misfit__, this will halt the workflow after the intialization step. We'll also set the `verbose` parameter to 'False', to keep the logging format relatively simple. We will explore the `verbose`==True option in a later cell." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "stop_after: null -> evaluate_initial_misfit\n", + "verbose: False -> False\n" + ] + } + ], + "source": [ + "! seisflows par stop_after evaluate_initial_misfit\n", + "! seisflows par verbose False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-----------------------\n", + "Now let's run SeisFlows. There are two ways to do this: `submit` and `restart`\n", + "\n", + "1. `seisflows submit` is used to run new workflows and resume stopped or failed workflows.\n", + "2. The `restart` command is simply a convenience function that runs `clean` (to remove an active working state) and `submit` (to submit a fresh workflow). \n", + "\n", + "Since this is our first run, we'll use `seisflows submit`." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-08-15 16:11:40 (I) | \n", + "================================================================================\n", + " SETTING UP INVERSION WORKFLOW \n", + "================================================================================\n", + "2022-08-15 16:11:47 (D) | running setup for module 'system.Workstation'\n", + "2022-08-15 16:11:50 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/sflog_001.txt\n", + "2022-08-15 16:11:50 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/parameters_001.yaml\n", + "2022-08-15 16:11:50 (D) | running setup for module 'solver.Specfem2D'\n", + "2022-08-15 16:11:50 (I) | initializing 3 solver directories\n", + "2022-08-15 16:11:50 (D) | initializing solver directory source: 001\n", + "2022-08-15 16:11:58 (D) | linking source '001' as 'mainsolver'\n", + "2022-08-15 16:11:58 (D) | initializing solver directory source: 002\n", + "2022-08-15 16:12:04 (D) | initializing solver directory source: 003\n", + "2022-08-15 16:12:13 (D) | running setup for module 'preprocess.Default'\n", + "2022-08-15 16:12:14 (D) | running setup for module 'optimize.Gradient'\n", + "2022-08-15 16:12:15 (I) | no optimization checkpoint found, assuming first run\n", + "2022-08-15 16:12:16 (I) | re-loading optimization module from checkpoint\n", + "2022-08-15 16:12:16 (I) | \n", + "////////////////////////////////////////////////////////////////////////////////\n", + " RUNNING ITERATION 01 \n", + "////////////////////////////////////////////////////////////////////////////////\n", + "2022-08-15 16:12:16 (I) | \n", + "================================================================================\n", + " RUNNING INVERSION WORKFLOW \n", + "================================================================================\n", + "2022-08-15 16:12:16 (I) | \n", + "////////////////////////////////////////////////////////////////////////////////\n", + " EVALUATING MISFIT FOR INITIAL MODEL \n", + "////////////////////////////////////////////////////////////////////////////////\n", + "2022-08-15 16:12:16 (I) | checking initial model parameters\n", + "2022-08-15 16:12:16 (I) | 2600.00 <= rho <= 2600.00\n", + "2022-08-15 16:12:16 (I) | 3500.00 <= vs <= 3500.00\n", + "2022-08-15 16:12:16 (I) | 5800.00 <= vp <= 5800.00\n", + "2022-08-15 16:12:16 (I) | checking true/target model parameters\n", + "2022-08-15 16:12:16 (I) | 2600.00 <= rho <= 2600.00\n", + "2022-08-15 16:12:16 (I) | 3550.00 <= vs <= 3550.00\n", + "2022-08-15 16:12:16 (I) | 5900.00 <= vp <= 5900.00\n", + "2022-08-15 16:12:16 (I) | preparing observation data for source 001\n", + "2022-08-15 16:12:16 (I) | running forward simulation w/ target model for 001\n", + "2022-08-15 16:12:33 (I) | evaluating objective function for source 001\n", + "2022-08-15 16:12:33 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:12:53 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:12:53 (I) | preparing observation data for source 002\n", + "2022-08-15 16:12:53 (I) | running forward simulation w/ target model for 002\n", + "2022-08-15 16:13:09 (I) | evaluating objective function for source 002\n", + "2022-08-15 16:13:09 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:13:31 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:13:31 (I) | preparing observation data for source 003\n", + "2022-08-15 16:13:31 (I) | running forward simulation w/ target model for 003\n", + "2022-08-15 16:14:16 (I) | evaluating objective function for source 003\n", + "2022-08-15 16:14:16 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:14:33 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:14:33 (I) | stop workflow at `stop_after`: evaluate_initial_misfit\n" + ] + } + ], + "source": [ + "! seisflows submit " + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + ".. note::\n", + " For a detailed exploration of a SeisFlows working directory, see the `working directory `__ documentation page where we explain each of the files and directories that have been generated during this workflow. Below we just look at two files which are required for our adjoint simulation, the adjoint sources (.adj) and STATIONS_ADJOINT file" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -48.0000000 0.0000000\r\n", + " -47.9400000 0.0000000\r\n", + " -47.8800000 0.0000000\r\n", + " -47.8200000 0.0000000\r\n", + " -47.7600000 0.0000000\r\n", + " -47.7000000 0.0000000\r\n", + " -47.6400000 0.0000000\r\n", + " -47.5800000 0.0000000\r\n", + " -47.5200000 0.0000000\r\n", + " -47.4600000 0.0000000\r\n" + ] + } + ], + "source": [ + "# The adjoint source is created in the same format as the synthetics (two-column ASCII) \n", + "! head scratch/solver/001/traces/adj/AA.S0001.BXY.adj" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3b. Adjoint simulations\n", + "\n", + "Now that we have all the required files for running an adjoint simulation (\\*.adj waveforms and STATIONS_ADJOINT file), we can continue with the SeisFlows3 Inversion workflow. No need to edit the Par_file or anything like that, SeisFlows3 will take care of that under the hood. We simply need to tell the workflow (via the parameters.yaml file) to `resume_from` the correct function. We can have a look at these functions again:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " SEISFLOWS WORKFLOW TASK LIST \r\n", + " //////////////////////////// \r\n", + "Task list for \r\n", + "\r\n", + "1: evaluate_initial_misfit\r\n", + "2: run_adjoint_simulations\r\n", + "3: postprocess_event_kernels\r\n", + "4: evaluate_gradient_from_kernels\r\n", + "5: initialize_line_search\r\n", + "6: perform_line_search\r\n", + "7: finalize_iteration\r\n" + ] + } + ], + "source": [ + "! seisflows print tasks" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "stop_after: evaluate_initial_misfit -> evaluate_gradient_from_kernels\r\n" + ] + } + ], + "source": [ + "# We'll stop just before the line search so that we can take a look at the files \n", + "# generated during the middle tasks\n", + "! seisflows par stop_after evaluate_gradient_from_kernels" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-08-15 16:15:06 (D) | setting iteration==1 from state file\n", + "2022-08-15 16:15:06 (I) | \n", + "================================================================================\n", + " SETTING UP INVERSION WORKFLOW \n", + "================================================================================\n", + "2022-08-15 16:15:16 (D) | running setup for module 'system.Workstation'\n", + "2022-08-15 16:15:20 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/sflog_002.txt\n", + "2022-08-15 16:15:20 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/parameters_002.yaml\n", + "2022-08-15 16:15:20 (D) | running setup for module 'solver.Specfem2D'\n", + "2022-08-15 16:15:20 (I) | initializing 3 solver directories\n", + "2022-08-15 16:15:22 (D) | running setup for module 'preprocess.Default'\n", + "2022-08-15 16:15:23 (D) | running setup for module 'optimize.Gradient'\n", + "2022-08-15 16:15:25 (I) | re-loading optimization module from checkpoint\n", + "2022-08-15 16:15:27 (I) | re-loading optimization module from checkpoint\n", + "2022-08-15 16:15:27 (I) | \n", + "////////////////////////////////////////////////////////////////////////////////\n", + " RUNNING ITERATION 01 \n", + "////////////////////////////////////////////////////////////////////////////////\n", + "2022-08-15 16:15:27 (I) | \n", + "================================================================================\n", + " RUNNING INVERSION WORKFLOW \n", + "================================================================================\n", + "2022-08-15 16:15:27 (I) | 'evaluate_initial_misfit' has already been run, skipping\n", + "2022-08-15 16:15:27 (I) | \n", + "////////////////////////////////////////////////////////////////////////////////\n", + " EVALUATING EVENT KERNELS W/ ADJOINT SIMULATIONS \n", + "////////////////////////////////////////////////////////////////////////////////\n", + "2022-08-15 16:15:27 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log'\n", + "2022-08-15 16:16:11 (D) | renaming output event kernels: 'alpha' -> 'vp'\n", + "2022-08-15 16:16:11 (D) | renaming output event kernels: 'beta' -> 'vs'\n", + "2022-08-15 16:16:12 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log'\n", + "2022-08-15 16:16:59 (D) | renaming output event kernels: 'alpha' -> 'vp'\n", + "2022-08-15 16:16:59 (D) | renaming output event kernels: 'beta' -> 'vs'\n", + "2022-08-15 16:16:59 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log'\n", + "2022-08-15 16:17:45 (D) | renaming output event kernels: 'alpha' -> 'vp'\n", + "2022-08-15 16:17:45 (D) | renaming output event kernels: 'beta' -> 'vs'\n", + "2022-08-15 16:17:45 (I) | \n", + "////////////////////////////////////////////////////////////////////////////////\n", + " GENERATING/PROCESSING MISFIT KERNEL \n", + "////////////////////////////////////////////////////////////////////////////////\n", + "2022-08-15 16:17:45 (I) | combining event kernels into single misfit kernel\n", + "2022-08-15 16:17:47 (I) | scaling gradient to absolute model perturbations\n", + "2022-08-15 16:17:49 (I) | stop workflow at `stop_after`: evaluate_gradient_from_kernels\n" + ] + } + ], + "source": [ + "# We can use the `seisflows submit` command to continue an active workflow\n", + "# The state file created during the first run will tell the workflow to resume from the stopped point in the workflow\n", + "! seisflows submit " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----------------\n", + "The function __run_adjoint_simulations()__ has run adjoint simulations to generate event kernels. The functions __postprocess_event_kernels__ and __evaluate_gradient_from_kernels__ will have summed and (optionally) smoothed the kernels to recover the gradient, which will be used to update our starting model.\n", + "\n", + "> **NOTE**: Since we did not specify any smoothing lenghts (PAR.SMOOTH_H and PAR.SMOOTH_V), no smoothing of the gradient has occurred. \n", + "\n", + "Using the gradient-descent optimization algorithm, SeisFlows will now compute a search direction that will be used in the line search to search for a best fitting model which optimally reduces the objective function. We can take a look at where SeisFlows has stored the information relating to kernel generation and the optimization computation." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m\u001b[32mgradient\u001b[m\u001b[m \u001b[1m\u001b[32mkernels\u001b[m\u001b[m \u001b[1m\u001b[32mmisfit_kernel\u001b[m\u001b[m \u001b[1m\u001b[32mmodel\u001b[m\u001b[m residuals.txt\r\n" + ] + } + ], + "source": [ + "# Gradient evaluation files are stored here, the kernels are stored separately from the gradient incase\n", + "# the user wants to manually manipulate them\n", + "! ls scratch/eval_grad" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "proc000000_vp_kernel.bin proc000000_vs_kernel.bin\r\n" + ] + } + ], + "source": [ + "# SeisFlows3 stores all kernels and gradient information as SPECFEM binary (.bin) files\n", + "! ls scratch/eval_grad/gradient" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m\u001b[32m001\u001b[m\u001b[m \u001b[1m\u001b[32m002\u001b[m\u001b[m \u001b[1m\u001b[32m003\u001b[m\u001b[m\r\n" + ] + } + ], + "source": [ + "# Kernels are stored on a per-event basis, and summed together (sum/). If smoothing was performed, \n", + "# we would see both smoothed and unsmoothed versions of the misfit kernel\n", + "! ls scratch/eval_grad/kernels" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "checkpoint.npz f_new.txt g_new.npz m_new.npz\r\n" + ] + } + ], + "source": [ + "# We can see that some new values have been stored in prepartion for the line search,\n", + "# including g_new (current gradient) and p_new (current search direction). These are also\n", + "# stored as vector NumPy arrays (.npy files)\n", + "! ls scratch/optimize" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-1.18126331e-12 2.40273470e-12 3.97045036e-11 ... 9.62017688e-11\n", + " 4.21140102e-11 3.96825021e-12]]\n" + ] + } + ], + "source": [ + "g_new = np.load(\"scratch/optimize/g_new.npz\")\n", + "print(g_new[\"vs_kernel\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--------------------\n", + "#### 3c. Line search and model update\n", + "\n", + "Let's finish off the inversion by running through the line search, which will generate new models using the\n", + "gradient, evaluate the objective function by running forward simulations, and comparing the evaluated objective function with the value obtained in __evalaute_initial_misfit__. \n", + "\n", + "Satisfactory reduction in the objective function will result in a termination of the line search. We are using a bracketing line search here [(Modrak et al. 2018)](https://academic.oup.com/gji/article/206/3/1864/2583505), which requires finding models which both increase and decrease the misfit with respect to the initial evaluation. Therefore it takes atleast two trial steps to complete the line search." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "stop_after: evaluate_gradient_from_kernels -> finalize_iteration\r\n" + ] + } + ], + "source": [ + "! seisflows par stop_after perform_line_search # We don't want to run the finalize_iteration argument so that we can explore the dir" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-08-15 16:21:55 (D) | setting iteration==1 from state file\n", + "2022-08-15 16:21:55 (I) | \n", + "================================================================================\n", + " SETTING UP INVERSION WORKFLOW \n", + "================================================================================\n", + "2022-08-15 16:22:03 (D) | running setup for module 'system.Workstation'\n", + "2022-08-15 16:22:05 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/sflog_003.txt\n", + "2022-08-15 16:22:05 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/parameters_003.yaml\n", + "2022-08-15 16:22:05 (D) | running setup for module 'solver.Specfem2D'\n", + "2022-08-15 16:22:05 (I) | initializing 3 solver directories\n", + "2022-08-15 16:22:07 (D) | running setup for module 'preprocess.Default'\n", + "2022-08-15 16:22:08 (D) | running setup for module 'optimize.Gradient'\n", + "2022-08-15 16:22:09 (I) | re-loading optimization module from checkpoint\n", + "2022-08-15 16:22:11 (I) | re-loading optimization module from checkpoint\n", + "2022-08-15 16:22:11 (I) | \n", + "////////////////////////////////////////////////////////////////////////////////\n", + " RUNNING ITERATION 01 \n", + "////////////////////////////////////////////////////////////////////////////////\n", + "2022-08-15 16:22:11 (I) | \n", + "================================================================================\n", + " RUNNING INVERSION WORKFLOW \n", + "================================================================================\n", + "2022-08-15 16:22:11 (I) | 'evaluate_initial_misfit' has already been run, skipping\n", + "2022-08-15 16:22:11 (I) | 'run_adjoint_simulations' has already been run, skipping\n", + "2022-08-15 16:22:11 (I) | 'postprocess_event_kernels' has already been run, skipping\n", + "2022-08-15 16:22:11 (I) | 'evaluate_gradient_from_kernels' has already been run, skipping\n", + "2022-08-15 16:22:11 (I) | initializing 'bracket'ing line search\n", + "2022-08-15 16:22:11 (I) | enforcing max step length safeguard\n", + "2022-08-15 16:22:11 (D) | step length(s) = 0.00E+00\n", + "2022-08-15 16:22:11 (D) | misfit val(s) = 1.28E-03\n", + "2022-08-15 16:22:11 (I) | try: first evaluation, attempt guess step length, alpha=9.08E+11\n", + "2022-08-15 16:22:11 (I) | try: applying initial step length safegaurd as alpha has exceeded maximum step length, alpha_new=1.44E+10\n", + "2022-08-15 16:22:11 (D) | overwriting initial step length, alpha_new=2.32E+09\n", + "2022-08-15 16:22:11 (I) | trial model 'm_try' parameters: \n", + "2022-08-15 16:22:11 (I) | 5800.00 <= vp <= 5800.00\n", + "2022-08-15 16:22:11 (I) | 3244.51 <= vs <= 3790.00\n", + "2022-08-15 16:22:12 (I) | \n", + "LINE SEARCH STEP COUNT 01\n", + "--------------------------------------------------------------------------------\n", + "2022-08-15 16:22:12 (I) | evaluating objective function for source 001\n", + "2022-08-15 16:22:12 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:22:23 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:22:23 (I) | evaluating objective function for source 002\n", + "2022-08-15 16:22:23 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:22:35 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:22:35 (I) | evaluating objective function for source 003\n", + "2022-08-15 16:22:35 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:22:48 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:22:48 (D) | misfit for trial model (f_try) == 8.65E-04\n", + "2022-08-15 16:22:48 (D) | step length(s) = 0.00E+00, 2.32E+09\n", + "2022-08-15 16:22:48 (D) | misfit val(s) = 1.28E-03, 8.65E-04\n", + "2022-08-15 16:22:48 (I) | try: misfit not bracketed, increasing step length using golden ratio, alpha=3.76E+09\n", + "2022-08-15 16:22:49 (I) | line search model 'm_try' parameters: \n", + "2022-08-15 16:22:49 (I) | 5800.00 <= vp <= 5800.00\n", + "2022-08-15 16:22:49 (I) | 3086.61 <= vs <= 3969.23\n", + "2022-08-15 16:22:49 (I) | trial step unsuccessful. re-attempting line search\n", + "2022-08-15 16:22:49 (I) | \n", + "LINE SEARCH STEP COUNT 02\n", + "--------------------------------------------------------------------------------\n", + "2022-08-15 16:22:49 (I) | evaluating objective function for source 001\n", + "2022-08-15 16:22:49 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:23:01 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:23:01 (I) | evaluating objective function for source 002\n", + "2022-08-15 16:23:01 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:23:13 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:23:13 (I) | evaluating objective function for source 003\n", + "2022-08-15 16:23:13 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:23:25 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:23:25 (D) | misfit for trial model (f_try) == 1.73E-03\n", + "2022-08-15 16:23:25 (D) | step length(s) = 0.00E+00, 2.32E+09, 3.76E+09\n", + "2022-08-15 16:23:25 (D) | misfit val(s) = 1.28E-03, 8.65E-04, 1.73E-03\n", + "2022-08-15 16:23:25 (I) | try: bracket acceptable but step length unreasonable attempting to re-adjust step length alpha=1.59E+09\n", + "2022-08-15 16:23:25 (I) | line search model 'm_try' parameters: \n", + "2022-08-15 16:23:25 (I) | 5800.00 <= vp <= 5800.00\n", + "2022-08-15 16:23:25 (I) | 3325.01 <= vs <= 3698.63\n", + "2022-08-15 16:23:25 (I) | trial step unsuccessful. re-attempting line search\n", + "2022-08-15 16:23:25 (I) | \n", + "LINE SEARCH STEP COUNT 03\n", + "--------------------------------------------------------------------------------\n", + "2022-08-15 16:23:25 (I) | evaluating objective function for source 001\n", + "2022-08-15 16:23:25 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:23:37 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:23:37 (I) | evaluating objective function for source 002\n", + "2022-08-15 16:23:37 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:23:51 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:23:51 (I) | evaluating objective function for source 003\n", + "2022-08-15 16:23:51 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:24:03 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:24:04 (D) | misfit for trial model (f_try) == 2.59E-03\n", + "2022-08-15 16:24:04 (D) | step length(s) = 0.00E+00, 1.59E+09, 2.32E+09, 3.76E+09\n", + "2022-08-15 16:24:04 (D) | misfit val(s) = 1.28E-03, 2.59E-03, 8.65E-04, 1.73E-03\n", + "2022-08-15 16:24:04 (I) | try: bracket acceptable but step length unreasonable attempting to re-adjust step length alpha=2.82E+09\n", + "2022-08-15 16:24:04 (I) | line search model 'm_try' parameters: \n", + "2022-08-15 16:24:04 (I) | 5800.00 <= vp <= 5800.00\n", + "2022-08-15 16:24:04 (I) | 3189.77 <= vs <= 3852.13\n", + "2022-08-15 16:24:04 (I) | trial step unsuccessful. re-attempting line search\n", + "2022-08-15 16:24:04 (I) | \n", + "LINE SEARCH STEP COUNT 04\n", + "--------------------------------------------------------------------------------\n", + "2022-08-15 16:24:04 (I) | evaluating objective function for source 001\n", + "2022-08-15 16:24:04 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:24:15 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:24:15 (I) | evaluating objective function for source 002\n", + "2022-08-15 16:24:15 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:24:27 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:24:27 (I) | evaluating objective function for source 003\n", + "2022-08-15 16:24:27 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:24:39 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:24:39 (D) | misfit for trial model (f_try) == 3.46E-03\n", + "2022-08-15 16:24:39 (D) | step length(s) = 0.00E+00, 1.59E+09, 2.32E+09, 2.82E+09, 3.76E+09\n", + "2022-08-15 16:24:39 (D) | misfit val(s) = 1.28E-03, 2.59E-03, 8.65E-04, 3.46E-03, 1.73E-03\n", + "2022-08-15 16:24:39 (I) | pass: bracket acceptable and step length reasonable. returning minimum line search misfit.\n", + "2022-08-15 16:24:39 (I) | line search model 'm_try' parameters: \n", + "2022-08-15 16:24:39 (I) | 5800.00 <= vp <= 5800.00\n", + "2022-08-15 16:24:39 (I) | 3244.51 <= vs <= 3790.00\n", + "2022-08-15 16:24:39 (I) | trial step successful. finalizing line search\n", + "2022-08-15 16:24:39 (I) | \n", + "FINALIZING LINE SEARCH\n", + "--------------------------------------------------------------------------------\n", + "2022-08-15 16:24:39 (I) | writing optimization stats\n", + "2022-08-15 16:24:39 (I) | renaming current (new) optimization vectors as previous model (old)\n", + "2022-08-15 16:24:39 (I) | setting accepted trial model (try) as current model (new)\n", + "2022-08-15 16:24:39 (I) | misfit of accepted trial model is f=8.645E-04\n", + "2022-08-15 16:24:39 (I) | resetting line search step count to 0\n", + "2022-08-15 16:24:39 (I) | \n", + "////////////////////////////////////////////////////////////////////////////////\n", + " CLEANING WORKDIR FOR NEXT ITERATION \n", + "////////////////////////////////////////////////////////////////////////////////\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-08-15 16:24:41 (I) | thrifty inversion encountering first iteration, defaulting to standard inversion workflow\n", + "2022-08-15 16:24:42 (I) | stop workflow at `stop_after`: finalize_iteration\n" + ] + } + ], + "source": [ + "! seisflows submit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the log statements above, we can see that the SeisFlows line search required 4 trial steps, where it modified values of Vs (shear-wave velocity) until satisfactory reduction in the objective function was met. This was the final step in the iteration, and so the finalization of the line search made preparations for a subsequent iteration. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "alpha.txt f_old.txt m_new.npz p_old.npz\r\n", + "checkpoint.npz f_try.txt m_old.npz\r\n", + "f_new.txt g_old.npz output_optim.txt\r\n" + ] + } + ], + "source": [ + "# We can see that we have 'new' and 'old' values for each of the optimization values,\n", + "# representing the previous model (M00) and the current model (M01).\n", + "! ls scratch/optimize" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step_count,step_length,gradient_norm_L1,gradient_norm_L2,misfit,if_restarted,slope,theta\r\n", + "04,2.323E+09,9.243E-05,1.049E-06,1.279E-03,0,8.263E-13,0.000E+00\r\n" + ] + } + ], + "source": [ + "# The stats/ directory contains text files describing the optimization/line search\n", + "! cat scratch/optimize/output_optim.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Conclusions\n", + "\n", + "We've now seen how SeisFlows runs an __Inversion__ workflow using the __Specfem2D__ solver on a __Workstation__ system. More or less, this is all you need to run SeisFlows with any combination of modules. The specificities of a system or numerical solver are already handled internally by SeisFlows, so if you want to use Specmfe3D_Cartesian as your solver, you would only need to run `seisflows par solver specfem3d` at the beginning of your workflow (you will also need to set up your Specfem3D models, similar to what we did for Specfem2D here). To run on a slurm system like Chinook (University of Alaska Fairbanks), you can run `seisflows par system chinook`. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/notebooks/sflog.txt b/docs/notebooks/sflog.txt new file mode 100644 index 00000000..e69de29b diff --git a/docs/notebooks/specfem2d_example.ipynb b/docs/notebooks/specfem2d_example.ipynb index 9c7482af..6dac591a 100644 --- a/docs/notebooks/specfem2d_example.ipynb +++ b/docs/notebooks/specfem2d_example.ipynb @@ -6,7 +6,7 @@ "source": [ "# Specfem2D workstation example\n", "\n", - "To demonstrate the inversion capabilities of SeisFlows3, we will run a __Specfem2D synthetic-synthetic example__ on a __local machine__ (Linux workstation running CentOS 7). Many of the setup steps here will likely be unique to our OS and workstation, but hopefully they may serve as templates for new Users wanting to explore SeisFlows3. \n", + "To demonstrate the inversion capabilities of SeisFlows, we will run a __Specfem2D synthetic-synthetic example__ on a __local machine__ (tested on a Linux workstation running CentOS 7, and an Apple Laptop running macOS 10.14.6). Many of the setup steps here may be unique to our OS and workstation, but hopefully they may serve as templates for new Users wanting to explore SeisFlows. \n", "\n", "The numerical solver we will use is: [SPECFEM2D](https://geodynamics.org/cig/software/specfem2d/). We'll also be working in our `seisflows` [Conda](https://docs.conda.io/en/latest/) environment, see the installation documentation page for instructions on how to install and activate the required Conda environment.\n", "\n", @@ -19,7 +19,7 @@ "source": [ "## Option 1: Automated run\n", "\n", - "We have set up this example to run using a single command line argument. The following command will run an example script which will (1) download and compile SPECFEM2D, (2) setup a SPECFEM2D working directory to generate initial and target models, and (3) Run a SeisFlows3 inversion. " + "We have set up this example to run using a single command line argument. The following command will run an example script which will (1) download and compile SPECFEM2D, (2) setup a SPECFEM2D working directory to generate initial and target models, and (3) Run a SeisFlows inversion. " ] }, { @@ -34,54 +34,9 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Run example: ex1_specfem2d_workstation_inversion\n", - "\n", - " @@@@@@@@@@ \n", - " .@@@@. .%&( %@. \n", - " @@@@ @@@@ &@@@@@@ ,%@ \n", - " @@@@ @@@, /@@ @ \n", - " @@@ @@@@ @@@ @ \n", - " @@@@ @@@@ @@@ @ @ \n", - " @@@ @@@@ ,@@@ @ @ \n", - " @@@@ @@@@ @@@@ @@ @ @\n", - " @@@@ @@@@@ @@@@@ @@@ @@ @\n", - " @@@@ @@@@@ @@@@@@@@@@@@@@ @@ @\n", - " @@@@ @@@@@@ @@@& @@@ @ \n", - " @@@@@ @@@@@@@@ %@@@@# @@ \n", - " @@@@# @@@@@@@@@@@@@@@@@ @@ \n", - " &@@@@@ @@@@( @@& \n", - " @@@@@@@ /@@@@ \n", - " @@@@@@@@@@@@@@@@@\n", - " @@@@@@@@@@ \n", - "\n", - "================================================================================\n", - " SEISFLOWS3 EXAMPLE 1 \n", - " //////////////////// \n", - "This is a [SPECFEM2D] [WORKSTATION] example, which will run 2 iterations of an\n", - "inversion to assess misfit between two homogeneous halfspace models with\n", - "slightly different velocities, 3 sources and 1 receiver. The tasks involved\n", - "include:\n", - "\n", - "1. (optional) Download, configure, compile SPECFEM2D\n", - "2. Set up a SPECFEM2D working directory\n", - "3. Generate starting model from Tape2007 example\n", - "4. Generate target model w/ perturbed starting model\n", - "5. Set up a SeisFlows3 working directory\n", - "6. Run 2 iterations of an inversion workflow\n", - "================================================================================\n", - "If you have already downloaded SPECMFE2D, please input its path here. If blank,\n", - "this example will pull the latest version from GitHub and attempt to configure\n", - "and make the binaries: >" - ] - } - ], + "outputs": [], "source": [ - "seisflows examples run 1" + "! seisflows examples run 1" ] }, { @@ -112,13 +67,12 @@ " b. [Create a separate SPECFEM2D working directory](#1b.-Create-a-separate-SPECFEM2D-working-directory) \n", " c. [Generate initial and target models](#1c.-Generate-initial-and-target-models) \n", "\n", - "2. __[Initialize SeisFlows3 (SF3)](#2.-Initialize-SeisFlows3-(SF3))__ \n", - " a. [SF3 working directory and parameter file](#2a.-SF3-working-directory-and-parameter-file) \n", - " b. [Initialize SF3 working state](#2b.-Initialize-SF3-working-state) \n", + "2. __[Initialize SeisFlows (SF)](#2.-Initialize-SeisFlows-(SF))__ \n", + " a. [SeisFlows working directory and parameter file](#2a.-SF-working-directory-and-parameter-file) \n", "\n", - "3. __[Run SeisFlows3](#2.-Run-SeisFlows3)__ \n", + "3. __[Run SeisFlows](#2.-Run-SeisFlows)__ \n", " a. [Forward simulations](#3a.-Forward-simulations) \n", - " b. [Exploring the SF3 directory structure](#3b.-Exploring-the-SF3-directory-structure) \n", + " b. [Exploring the SeisFlows directory structure](#3b.-Exploring-the-SF-directory-structure) \n", " c. [Adjoint simulations](#3c.-Adjoint-simulations) \n", " d. [Line search and model update](#3d.-Line-search-and-model-update) \n", "\n", @@ -134,12 +88,12 @@ "\n", "> **NOTE**: If you have already downloaded and compiled SPECFEM2D, you can skip most of this subsection (1a). However you will need to edit the first two paths in the following cell (WORKDIR and SPECFEM2D_ORIGINAL), and execute the path structure defined in the cell.\n", "\n", - "First we'll download and compile SPECFEM2D to generate the binaries necessary to run our simulations. We will then populate a new SPECFEM2D working directory that will be used by SeisFlows3. We'll use to Python OS module to do our filesystem processes just to keep everything in Python, but this can easily be accomplished in bash." + "First we'll download and compile SPECFEM2D to generate the binaries necessary to run our simulations. We will then populate a new SPECFEM2D working directory that will be used by SeisFlows. We'll use to Python OS module to do our filesystem processes just to keep everything in Python, but this can easily be accomplished in bash." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -151,13 +105,17 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# vvv USER MUST EDIT THE FOLLOWING PATHS vvv\n", - "WORKDIR = \"/home/bchow/Work/work/sf3_specfem2d_example\" \n", - "SPECFEM2D = \"/home/bchow/REPOSITORIES/specfem2d\"\n", + "# MAC PATHS\n", + "WORKDIR = \"/Users/Chow/Work/work/sf_specfem2d_example\" \n", + "SPECFEM2D = \"/Users/Chow/Repositories/specfem2d\"\n", + "# LINUX PATHS\n", + "# WORKDIR = \"/home/bchow/Work/work/sf_specfem2d_example\" \n", + "# SPECFEM2D = \"/home/bchow/REPOSITORIES/specfem2d\"\n", "# where WORKDIR: points to your own working directory\n", "# and SPECFEM2D: points to an existing specfem2D repository if available (if not set as '')\n", "# ^^^ USER MUST EDIT THE FOLLOWING PATHS ^^^\n", @@ -182,19 +140,21 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Existing SPECMFE2D respository found, symlinking to working directory\n" + "SPECFEM2D repository already found, you may skip this subsection\n" ] } ], "source": [ "# Download SPECFEM2D from GitHub, devel branch for latest codebase OR symlink from existing repo\n", + "if not os.path.exists(WORKDIR):\n", + " os.makedirs(WORKDIR)\n", "os.chdir(WORKDIR)\n", "\n", "if os.path.exists(\"specfem2d\"):\n", @@ -240,10 +200,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "/home/bchow/REPOSITORIES/specfem2d\n", - "xadj_seismogram\t\t xconvolve_source_timefunction xspecfem2D\n", - "xcheck_quality_external_mesh xmeshfem2D\t\t xsum_kernels\n", - "xcombine_sem\t\t xsmooth_sem\n" + "/Users/Chow/Repositories/specfem2d\n", + "\u001b[1m\u001b[34mxadj_seismogram\u001b[m\u001b[m \u001b[1m\u001b[34mxmeshfem2D\u001b[m\u001b[m\n", + "\u001b[1m\u001b[32mxadj_seismogram.dSYM\u001b[m\u001b[m \u001b[1m\u001b[32mxmeshfem2D.dSYM\u001b[m\u001b[m\n", + "\u001b[1m\u001b[34mxcheck_quality_external_mesh\u001b[m\u001b[m \u001b[1m\u001b[34mxsmooth_sem\u001b[m\u001b[m\n", + "\u001b[1m\u001b[32mxcheck_quality_external_mesh.dSYM\u001b[m\u001b[m \u001b[1m\u001b[32mxsmooth_sem.dSYM\u001b[m\u001b[m\n", + "\u001b[1m\u001b[34mxcombine_sem\u001b[m\u001b[m \u001b[1m\u001b[34mxspecfem2D\u001b[m\u001b[m\n", + "\u001b[1m\u001b[32mxcombine_sem.dSYM\u001b[m\u001b[m \u001b[1m\u001b[32mxspecfem2D.dSYM\u001b[m\u001b[m\n", + "\u001b[1m\u001b[34mxconvolve_source_timefunction\u001b[m\u001b[m \u001b[1m\u001b[34mxsum_kernels\u001b[m\u001b[m\n", + "\u001b[1m\u001b[32mxconvolve_source_timefunction.dSYM\u001b[m\u001b[m \u001b[1m\u001b[32mxsum_kernels.dSYM\u001b[m\u001b[m\n" ] } ], @@ -260,7 +225,7 @@ "source": [ "#### 1b. Create a separate SPECFEM2D working directory\n", "\n", - "Next we'll create a new SPECFEM2D working directory, separate from the original repository. The intent here is to isolate the original SPECFEM2D repository from our working state, to protect it from things like accidental file deletions or manipulations. This is not a mandatory step for using SeisFlows3, but it helps keep file structure clean in the long run, and is the SeisFlows3 dev team's preferred method of using SPECFEM. " + "Next we'll create a new SPECFEM2D working directory, separate from the original repository. The intent here is to isolate the original SPECFEM2D repository from our working state, to protect it from things like accidental file deletions or manipulations. This is not a mandatory step for using SeisFlows, but it helps keep file structure clean in the long run, and is the SeisFlows3 dev team's preferred method of using SPECFEM. " ] }, { @@ -275,20 +240,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this tutorial we will be using the [Tape2007 example problem](https://github.com/geodynamics/specfem2d/tree/devel/EXAMPLES/Tape2007) to define our __DATA/__ directory (last tested 3/9/22, cf893667)." + "In this tutorial we will be using the [Tape2007 example problem](https://github.com/geodynamics/specfem2d/tree/devel/EXAMPLES/Tape2007) to define our __DATA/__ directory (last tested 8/15/22, bdba4389)." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "/home/bchow/Work/work/sf3_specfem2d_example/specfem2d_workdir\n", - "bin DATA\n" + "/Users/Chow/Work/work/sf_specfem2d_example/specfem2d_workdir\n", + "\u001b[1m\u001b[32mDATA\u001b[m\u001b[m \u001b[1m\u001b[32mbin\u001b[m\u001b[m\n" ] } ], @@ -313,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -322,14 +287,14 @@ "text": [ " -------------------------------------------------------------------------------\r\n", " -------------------------------------------------------------------------------\r\n", - " D a t e : 29 - 04 - 2022 T i m e : 12:24:51\r\n", + " D a t e : 15 - 08 - 2022 T i m e : 10:13:31\r\n", " -------------------------------------------------------------------------------\r\n", " -------------------------------------------------------------------------------\r\n", "\r\n", "see results in directory: OUTPUT_FILES/\r\n", "\r\n", "done\r\n", - "Fri Apr 29 12:24:51 AKDT 2022\r\n" + "Mon Aug 15 10:13:31 PDT 2022\r\n" ] } ], @@ -367,22 +332,29 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "interfaces_Tape2007.dat\t\t SOURCE_003 SOURCE_012 SOURCE_021\r\n", - "model_velocity.dat_checker\t SOURCE_004 SOURCE_013 SOURCE_022\r\n", - "Par_file\t\t\t SOURCE_005 SOURCE_014 SOURCE_023\r\n", - "Par_file_Tape2007_132rec_checker SOURCE_006 SOURCE_015 SOURCE_024\r\n", - "Par_file_Tape2007_onerec\t SOURCE_007 SOURCE_016 SOURCE_025\r\n", - "proc000000_model_velocity.dat_input SOURCE_008 SOURCE_017 STATIONS\r\n", - "SOURCE\t\t\t\t SOURCE_009 SOURCE_018 STATIONS_checker\r\n", - "SOURCE_001\t\t\t SOURCE_010 SOURCE_019\r\n", - "SOURCE_002\t\t\t SOURCE_011 SOURCE_020\r\n" + "Par_file SOURCE_013\r\n", + "Par_file_Tape2007_132rec_checker SOURCE_014\r\n", + "Par_file_Tape2007_onerec SOURCE_015\r\n", + "\u001b[1m\u001b[35mSOURCE\u001b[m\u001b[m SOURCE_016\r\n", + "SOURCE_001 SOURCE_017\r\n", + "SOURCE_002 SOURCE_018\r\n", + "SOURCE_003 SOURCE_019\r\n", + "SOURCE_004 SOURCE_020\r\n", + "SOURCE_005 SOURCE_021\r\n", + "SOURCE_006 SOURCE_022\r\n", + "SOURCE_007 SOURCE_023\r\n", + "SOURCE_008 SOURCE_024\r\n", + "SOURCE_009 SOURCE_025\r\n", + "SOURCE_010 STATIONS_checker\r\n", + "SOURCE_011 interfaces_Tape2007.dat\r\n", + "SOURCE_012 model_velocity.dat_checker\r\n" ] } ], @@ -428,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -452,14 +424,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "bin DATA OUTPUT_FILES\r\n" + "\u001b[1m\u001b[32mDATA\u001b[m\u001b[m \u001b[1m\u001b[32mOUTPUT_FILES\u001b[m\u001b[m \u001b[1m\u001b[32mbin\u001b[m\u001b[m\r\n" ] } ], @@ -477,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -489,8 +461,8 @@ " **** Specfem 2-D Solver - serial version ****\n", " **********************************************\n", "\n", - " Running Git version of the code corresponding to commit cf89366717d9435985ba852ef1d41a10cee97884\n", - " dating From Date: Mon Nov 29 23:20:51 2021 -0800\n", + " Running Git version of the code corresponding to \n", + " dating From \n", "\n", "\n", " NDIM = 2\n", @@ -501,7 +473,7 @@ " Tape-Liu-Tromp (GJI 2007)\n", " -------------------------------------------------------------------------------\n", " -------------------------------------------------------------------------------\n", - " D a t e : 29 - 04 - 2022 T i m e : 12:25:24\n", + " D a t e : 15 - 08 - 2022 T i m e : 10:14:13\n", " -------------------------------------------------------------------------------\n", " -------------------------------------------------------------------------------\n" ] @@ -536,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -561,7 +533,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -573,8 +545,8 @@ " **** Specfem 2-D Solver - serial version ****\n", " **********************************************\n", "\n", - " Running Git version of the code corresponding to commit cf89366717d9435985ba852ef1d41a10cee97884\n", - " dating From Date: Mon Nov 29 23:20:51 2021 -0800\n", + " Running Git version of the code corresponding to \n", + " dating From \n", "\n", "\n", " NDIM = 2\n", @@ -585,7 +557,7 @@ " Tape-Liu-Tromp (GJI 2007)\n", " -------------------------------------------------------------------------------\n", " -------------------------------------------------------------------------------\n", - " D a t e : 29 - 04 - 2022 T i m e : 12:25:24\n", + " D a t e : 15 - 08 - 2022 T i m e : 10:14:13\n", " -------------------------------------------------------------------------------\n", " -------------------------------------------------------------------------------\n" ] @@ -614,19 +586,19 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "bin DATA OUTPUT_FILES_INIT OUTPUT_FILES_TRUE\r\n" + "\u001b[1m\u001b[32mDATA\u001b[m\u001b[m \u001b[1m\u001b[32mOUTPUT_FILES_INIT\u001b[m\u001b[m \u001b[1m\u001b[32mOUTPUT_FILES_TRUE\u001b[m\u001b[m \u001b[1m\u001b[32mbin\u001b[m\u001b[m\r\n" ] } ], "source": [ - "# Great, we have all the necessary SPECFEM files to run our SeisFlows3 inversion!\n", + "# Great, we have all the necessary SPECFEM files to run our SeisFlows inversion!\n", "! ls" ] }, @@ -634,14 +606,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 2. Initialize SeisFlows3 (SF3)\n", - "In this Section we will look at a SeisFlows3 working directory, parameter file, and working state.\n", + "### 2. Initialize SeisFlows (SF)\n", + "In this Section we will look at a SeisFlows working directory, parameter file, and working state.\n", "\n", - "#### 2a. SF3 working directory and parameter file\n", + "#### 2a. SeisFlows working directory and parameter file\n", "\n", - "As with SPECFEM, SeisFlows3 requires a parameter file (__parameters.yaml__) that controls how an automated workflow will proceed. Because SeisFlows3 is modular, there are a large number of potential parameters which may be present in SF3 parameter file, as each sub-module may have its own set of unique parameters.\n", + "As with SPECFEM, SeisFlows requires a parameter file (__parameters.yaml__) that controls how an automated workflow will proceed. Because SeisFlows is modular, there are a large number of potential parameters which may be present in a SeisFlows parameter file, as each sub-module may have its own set of unique parameters.\n", "\n", - "In contrast to SPECFEM's method of listing all available parameters and leaving it up the User to determine which ones are relevant to them, SeisFlows3 dynamically builds its parameter file based on User inputs. In this subsection we will use the built-in SeisFlows3 command line tools to generate and populate the parameter file. " + "In contrast to SPECFEM's method of listing all available parameters and leaving it up the User to determine which ones are relevant to them, SeisFlows dynamically builds its parameter file based on User inputs. In this subsection we will use the built-in SeisFlows command line tools to generate and populate the parameter file. " ] }, { @@ -649,7 +621,7 @@ "metadata": {}, "source": [ ".. note::\n", - " See the `parameter file documentation page `__ for a more in depth exploration of this central SeisFlows3 file." + " See the `parameter file documentation page `__ for a more in depth exploration of this central SeisFlows file." ] }, { @@ -661,7 +633,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -669,12 +641,12 @@ "output_type": "stream", "text": [ "usage: seisflows [-h] [-w [WORKDIR]] [-p [PARAMETER_FILE]]\r\n", - " {setup,configure,init,submit,resume,restart,clean,par,sempar,check,print,convert,reset,debug,edit,examples}\r\n", + " {setup,configure,swap,init,submit,resume,restart,clean,par,sempar,check,print,reset,debug,examples}\r\n", " ...\r\n", "\r\n", "================================================================================\r\n", "\r\n", - " SeisFlows3: Waveform Inversion Package \r\n", + " SeisFlows: Waveform Inversion Package \r\n", "\r\n", "================================================================================\r\n", "\r\n", @@ -690,19 +662,18 @@ "\r\n", " setup Setup working directory from scratch\r\n", " configure Fill parameter file with defaults\r\n", + " swap Swap module parameters in an existing parameter file\r\n", " init Initiate working environment\r\n", " submit Submit initial workflow to system\r\n", " resume Re-submit previous workflow to system\r\n", " restart Remove current environment and submit new workflow\r\n", " clean Remove files relating to an active working environment\r\n", - " par View and edit SeisFlows3 parameter file\r\n", + " par View and edit SeisFlows parameter file\r\n", " sempar View and edit SPECFEM parameter file\r\n", " check Check state of an active environment\r\n", " print Print information related to an active environment\r\n", - " convert Convert model file format\r\n", " reset Reset modules within an active state\r\n", " debug Start interactive debug environment\r\n", - " edit Open source code file in text editor\r\n", " examples Look at and run pre-configured example problems\r\n", "\r\n", "'seisflows [command] -h' for more detailed descriptions of each command.\r\n" @@ -715,20 +686,23 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 1, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "creating parameter file: parameters.yaml\n", - "parameters.yaml specfem2d specfem2d_workdir\n" + "ename": "NameError", + "evalue": "name 'os' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# The command 'setup' creates the 'parameters.yaml' file that controls all of SeisFlows\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m# the '-f' flag removes any exist 'parameters.yaml' file that might be in the directory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mWORKDIR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' seisflows setup -f'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' ls'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'os' is not defined" ] } ], "source": [ - "# The command 'setup' creates the 'parameters.yaml' file that controls all of SeisFlows3\n", + "# The command 'setup' creates the 'parameters.yaml' file that controls all of SeisFlows\n", "# the '-f' flag removes any exist 'parameters.yaml' file that might be in the directory\n", "os.chdir(WORKDIR)\n", "! seisflows setup -f\n", @@ -737,7 +711,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -746,16 +720,16 @@ "text": [ "# //////////////////////////////////////////////////////////////////////////////\r\n", "#\r\n", - "# SeisFlows3 YAML Parameter File\r\n", + "# SeisFlows YAML Parameter File\r\n", "#\r\n", "# //////////////////////////////////////////////////////////////////////////////\r\n", "#\r\n", "# Modules correspond to the structure of the source code, and determine\r\n", - "# SeisFlows3' behavior at runtime. Each module requires its own sub-parameters.\r\n", + "# SeisFlows' behavior at runtime. Each module requires its own sub-parameters.\r\n", "#\r\n", "# .. rubric::\r\n", "# - To determine available options for modules listed below, run:\r\n", - "# > seisflows print module\r\n", + "# > seisflows print modules\r\n", "# - To auto-fill with docstrings and default values (recommended), run:\r\n", "# > seisflows configure\r\n", "# - To set values as NoneType, use: null\r\n", @@ -763,19 +737,17 @@ "#\r\n", "# MODULES\r\n", "# ///////\r\n", - "# WORKFLOW (str): The method for running SeisFlows3; equivalent to main()\r\n", - "# SOLVER (str): External numerical solver to use for waveform simulations\r\n", - "# SYSTEM (str): Computer architecture of the system being used\r\n", - "# OPTIMIZE (str): Optimization algorithm for the inverse problem\r\n", - "# PREPROCESS (str): Preprocessing schema for waveform data\r\n", - "# POSTPROCESS (str): Postprocessing schema for kernels and gradients\r\n", + "# workflow (str): The types and order of functions for running SeisFlows\r\n", + "# system (str): Computer architecture of the system being used\r\n", + "# solver (str): External numerical solver to use for waveform simulations\r\n", + "# preprocess (str): Preprocessing schema for waveform data\r\n", + "# optimize (str): Optimization algorithm for the inverse problem\r\n", "# ==============================================================================\r\n", - "WORKFLOW: inversion\r\n", - "SOLVER: specfem2d\r\n", - "SYSTEM: workstation\r\n", - "OPTIMIZE: LBFGS \r\n", - "PREPROCESS: base\r\n", - "POSTPROCESS: base\r\n" + "workflow: forward\r\n", + "system: workstation\r\n", + "solver: specfem2d\r\n", + "preprocess: default\r\n", + "optimize: gradient\r\n" ] } ], @@ -786,60 +758,41 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " SEISFLOWS3 MODULES \r\n", - " ////////////////// \r\n", - "'+': package, '-': module, '*': class\r\n", + " SEISFLOWS MODULES \r\n", + " ///////////////// \r\n", + "'-': module, '*': class\r\n", "\r\n", - "+ SYSTEM\r\n", - " - seisflows\r\n", - " * base\r\n", - " * cluster\r\n", - " * lsf\r\n", - " * slurm\r\n", - " * workstation\r\n", - " - seisflows-super\r\n", - " * chinook\r\n", - " * maui\r\n", - "+ PREPROCESS\r\n", - " - seisflows\r\n", - " * base\r\n", - " * pyatoa\r\n", - " - seisflows-super\r\n", - " * pyatoa_nz\r\n", - "+ SOLVER\r\n", - " - seisflows\r\n", - " * base\r\n", - " * specfem2d\r\n", - " * specfem3d\r\n", - " * specfem3d_globe\r\n", - " - seisflows-super\r\n", - " * specfem3d_maui\r\n", - "+ POSTPROCESS\r\n", - " - seisflows\r\n", - " * base\r\n", - " - seisflows-super\r\n", - "+ OPTIMIZE\r\n", - " - seisflows\r\n", - " * LBFGS\r\n", - " * NLCG\r\n", - " * base\r\n", - " - seisflows-super\r\n", - "+ WORKFLOW\r\n", - " - seisflows\r\n", - " * base\r\n", - " * inversion\r\n", - " * migration\r\n", - " * test\r\n", - " - seisflows-super\r\n", - " * thrifty_inversion\r\n", - " * thrifty_maui\r\n" + "- workflow\r\n", + " * forward\r\n", + " * inversion\r\n", + " * migration\r\n", + "- system\r\n", + " * chinook\r\n", + " * cluster\r\n", + " * frontera\r\n", + " * lsf\r\n", + " * maui\r\n", + " * slurm\r\n", + " * workstation\r\n", + "- solver\r\n", + " * specfem\r\n", + " * specfem2d\r\n", + " * specfem3d\r\n", + " * specfem3d_globe\r\n", + "- preprocess\r\n", + " * default\r\n", + " * pyaflowa\r\n", + "- optimize\r\n", + " * LBFGS\r\n", + " * NLCG\r\n", + " * gradient\r\n" ] } ], @@ -850,26 +803,26 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "SOLVER: specfem2d -> specfem2d\n", + "workflow: forward -> inversion\n", "# //////////////////////////////////////////////////////////////////////////////\n", "#\n", - "# SeisFlows3 YAML Parameter File\n", + "# SeisFlows YAML Parameter File\n", "#\n", "# //////////////////////////////////////////////////////////////////////////////\n", "#\n", "# Modules correspond to the structure of the source code, and determine\n", - "# SeisFlows3' behavior at runtime. Each module requires its own sub-parameters.\n", + "# SeisFlows' behavior at runtime. Each module requires its own sub-parameters.\n", "#\n", "# .. rubric::\n", "# - To determine available options for modules listed below, run:\n", - "# > seisflows print module\n", + "# > seisflows print modules\n", "# - To auto-fill with docstrings and default values (recommended), run:\n", "# > seisflows configure\n", "# - To set values as NoneType, use: null\n", @@ -877,26 +830,24 @@ "#\n", "# MODULES\n", "# ///////\n", - "# WORKFLOW (str): The method for running SeisFlows3; equivalent to main()\n", - "# SOLVER (str): External numerical solver to use for waveform simulations\n", - "# SYSTEM (str): Computer architecture of the system being used\n", - "# OPTIMIZE (str): Optimization algorithm for the inverse problem\n", - "# PREPROCESS (str): Preprocessing schema for waveform data\n", - "# POSTPROCESS (str): Postprocessing schema for kernels and gradients\n", + "# workflow (str): The types and order of functions for running SeisFlows\n", + "# system (str): Computer architecture of the system being used\n", + "# solver (str): External numerical solver to use for waveform simulations\n", + "# preprocess (str): Preprocessing schema for waveform data\n", + "# optimize (str): Optimization algorithm for the inverse problem\n", "# ==============================================================================\n", - "WORKFLOW: inversion\n", - "SOLVER: specfem2d\n", - "SYSTEM: workstation\n", - "OPTIMIZE: LBFGS \n", - "PREPROCESS: base\n", - "POSTPROCESS: base\n" + "workflow: inversion\n", + "system: workstation\n", + "solver: specfem2d\n", + "preprocess: default\n", + "optimize: gradient\n" ] } ], "source": [ "# For this example, we can use most of the default modules, however we need to \n", - "# change the SOLVER module to let SeisFlows3 know we're using SPECFEM2D (as opposed to 3D)\n", - "! seisflows par solver specfem2d\n", + "# change the SOLVER module to let SeisFlows know we're using SPECFEM2D (as opposed to 3D)\n", + "! seisflows par workflow inversion\n", "! cat parameters.yaml" ] }, @@ -905,326 +856,424 @@ "metadata": {}, "source": [ "-------------------------\n", - "The `seisflows configure` command populates the parameter file based on the chosen modules. SeisFlows3 will attempt to fill in all parameters with default values when possible, but values that the User __MUST__ set will be denoted by the value:\n", + "The `seisflows configure` command populates the parameter file based on the chosen modules. SeisFlows will attempt to fill in all parameters with reasonable default values. Docstrings above each module show descriptions and available options for each of these parameters. \n", "\n", - ">__!!! REQUIRED PARAMETER !!!__\n", - "\n", - "SeisFlows3 will not work until all of these required parameters are set by the User. Docstrings above each module show descriptions and available options for each of these parameters. In the follownig cell we will use the `seisflows par` command to edit the parameters.yaml file directly, replacing each of the required parameters with a chosen value. Comments next to each evaluation describe the choice for each." + "In the follownig cell we will use the `seisflows par` command to edit the parameters.yaml file directly, replacing some default parameters with our own values. Comments next to each evaluation describe the choice for each." ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "filling parameters.yaml w/ default values\n", - "# //////////////////////////////////////////////////////////////////////////////\n", - "#\n", - "# SeisFlows3 YAML Parameter File\n", - "#\n", - "# //////////////////////////////////////////////////////////////////////////////\n", - "#\n", - "# Modules correspond to the structure of the source code, and determine\n", - "# SeisFlows3' behavior at runtime. Each module requires its own sub-parameters.\n", - "#\n", - "# .. rubric::\n", - "# - To determine available options for modules listed below, run:\n", - "# > seisflows print module\n", - "# - To auto-fill with docstrings and default values (recommended), run:\n", - "# > seisflows configure\n", - "# - To set values as NoneType, use: null\n", - "# - To set values as infinity, use: inf\n", - "#\n", - "# MODULES\n", - "# ///////\n", - "# WORKFLOW (str): The method for running SeisFlows3; equivalent to main()\n", - "# SOLVER (str): External numerical solver to use for waveform simulations\n", - "# SYSTEM (str): Computer architecture of the system being used\n", - "# OPTIMIZE (str): Optimization algorithm for the inverse problem\n", - "# PREPROCESS (str): Preprocessing schema for waveform data\n", - "# POSTPROCESS (str): Postprocessing schema for kernels and gradients\n", - "# ==============================================================================\n", - "WORKFLOW: inversion\n", - "SOLVER: specfem2d\n", - "SYSTEM: workstation\n", - "OPTIMIZE: LBFGS \n", - "PREPROCESS: base\n", - "POSTPROCESS: base\n", - "\n", - "# =============================================================================\n", - "# SYSTEM \n", - "# ////// \n", - "# TITLE (str):\n", - "# The name used to submit jobs to the system, defaults to the name of the\n", - "# working directory\n", - "# PRECHECK (list):\n", - "# A list of parameters that will be displayed to stdout before 'submit' or\n", - "# 'resume' is run. Useful for manually reviewing important parameters prior\n", - "# to system submission\n", - "# LOG_LEVEL (str):\n", - "# Verbosity output of SF3 logger. Available from least to most verbosity:\n", - "# 'CRITICAL', 'WARNING', 'INFO', 'DEBUG'; defaults to 'DEBUG'\n", - "# VERBOSE (bool):\n", - "# Level of verbosity provided to the output log. If True, log statements\n", - "# will declare what module/class/function they are being called from.\n", - "# Useful for debugging but also very noisy.\n", - "# MPIEXEC (str):\n", - "# Function used to invoke executables on the system. For example 'srun' on\n", - "# SLURM systems, or './' on a workstation. If left blank, will guess based\n", - "# on the system.\n", - "# NTASK (int):\n", - "# Number of separate, individual tasks. Also equal to the number of desired\n", - "# sources in workflow\n", - "# NPROC (int):\n", - "# Number of processor to use for each simulation\n", - "# =============================================================================\n", - "TITLE: sf3_specfem2d_example\n", - "PRECHECK:\n", - " - TITLE\n", - "LOG_LEVEL: DEBUG\n", - "VERBOSE: False\n", - "MPIEXEC:\n", - "NTASK: 1\n", - "NPROC: 1\n", - "\n", - "# =============================================================================\n", - "# PREPROCESS \n", - "# ////////// \n", - "# MISFIT (str):\n", - "# Misfit function for waveform comparisons, for available see\n", - "# seisflows.plugins.misfit\n", - "# BACKPROJECT (str):\n", - "# Backprojection function for migration, for available see\n", - "# seisflows.plugins.adjoint\n", - "# NORMALIZE (list):\n", - "# Data normalization parameters used to normalize the amplitudes of\n", - "# waveforms. Choose from two sets: ENORML1: normalize per event by L1 of\n", - "# traces; OR ENORML2: normalize per event by L2 of traces; AND TNORML1:\n", - "# normalize per trace by L1 of itself; OR TNORML2: normalize per trace by\n", - "# L2 of itself\n", - "# FILTER (str):\n", - "# Data filtering type, available options are:BANDPASS (req. MIN/MAX\n", - "# PERIOD/FREQ);LOWPASS (req. MAX_FREQ or MIN_PERIOD); HIGHPASS (req.\n", - "# MIN_FREQ or MAX_PERIOD)\n", - "# MIN_PERIOD (float):\n", - "# Minimum filter period applied to time series.See also MIN_FREQ, MAX_FREQ,\n", - "# if User defines FREQ parameters, they will overwrite PERIOD parameters.\n", - "# MAX_PERIOD (float):\n", - "# Maximum filter period applied to time series.See also MIN_FREQ, MAX_FREQ,\n", - "# if User defines FREQ parameters, they will overwrite PERIOD parameters.\n", - "# MIN_FREQ (float):\n", - "# Maximum filter frequency applied to time series.See also MIN_PERIOD,\n", - "# MAX_PERIOD, if User defines FREQ parameters, they will overwrite PERIOD\n", - "# parameters.\n", - "# MAX_FREQ (float):\n", - "# Maximum filter frequency applied to time series,See also MIN_PERIOD,\n", - "# MAX_PERIOD, if User defines FREQ parameters, they will overwrite PERIOD\n", - "# parameters.\n", - "# MUTE (list):\n", - "# Data mute parameters used to zero out early / late arrivals or offsets.\n", - "# Choose any number of: EARLY: mute early arrivals; LATE: mute late\n", - "# arrivals; SHORT: mute short source-receiver distances; LONG: mute long\n", - "# source-receiver distances\n", - "# =============================================================================\n", - "MISFIT: waveform\n", - "BACKPROJECT: null\n", - "NORMALIZE: []\n", - "FILTER: null\n", - "MIN_PERIOD:\n", - "MAX_PERIOD:\n", - "MIN_FREQ:\n", - "MAX_FREQ:\n", - "MUTE: []\n", - "\n", - "# =============================================================================\n", - "# SOLVER \n", - "# ////// \n", - "# MATERIALS (str):\n", - "# Material parameters used to define model. Available: ['ELASTIC': Vp, Vs,\n", - "# 'ACOUSTIC': Vp, 'ISOTROPIC', 'ANISOTROPIC']\n", - "# DENSITY (str):\n", - "# How to treat density during inversion. Available: ['CONSTANT': Do not\n", - "# update density, 'VARIABLE': Update density]\n", - "# ATTENUATION (str):\n", - "# If True, turn on attenuation during forward simulations, otherwise set\n", - "# attenuation off. Attenuation is always off for adjoint simulations.\n", - "# COMPONENTS (str):\n", - "# Components used to generate data, formatted as a single string, e.g. ZNE\n", - "# or NZ or E\n", - "# SOLVERIO (int):\n", - "# The format external solver files. Available: ['fortran_binary', 'adios']\n", - "# NT (float):\n", - "# Number of time steps set in the SPECFEM Par_file\n", - "# DT (float):\n", - "# Time step or delta set in the SPECFEM Par_file\n", - "# F0 (float):\n", - "# Dominant source frequency\n", - "# FORMAT (float):\n", - "# Format of synthetic waveforms used during workflow, available options:\n", - "# ['ascii', 'su']\n", - "# SOURCE_PREFIX (str):\n", - "# Prefix of SOURCE files in path SPECFEM_DATA. By default, 'SOURCE' for\n", - "# SPECFEM2D\n", - "# =============================================================================\n", - "MATERIALS: !!! REQUIRED PARAMETER !!!\n", - "DENSITY: !!! REQUIRED PARAMETER !!!\n", - "ATTENUATION: !!! REQUIRED PARAMETER !!!\n", - "COMPONENTS: ZNE\n", - "SOLVERIO: fortran_binary\n", - "NT: !!! REQUIRED PARAMETER !!!\n", - "DT: !!! REQUIRED PARAMETER !!!\n", - "F0: !!! REQUIRED PARAMETER !!!\n", - "FORMAT: !!! REQUIRED PARAMETER !!!\n", - "SOURCE_PREFIX: SOURCE\n", - "\n", - "# =============================================================================\n", - "# POSTPROCESS \n", - "# /////////// \n", - "# SMOOTH_H (float):\n", - "# Gaussian half-width for horizontal smoothing in units of meters. If 0.,\n", - "# no smoothing applied\n", - "# SMOOTH_V (float):\n", - "# Gaussian half-width for vertical smoothing in units of meters\n", - "# TASKTIME_SMOOTH (int):\n", - "# Large radii smoothing may take longer than normal tasks. Allocate\n", - "# additional smoothing task time as a multiple of TASKTIME\n", - "# =============================================================================\n", - "SMOOTH_H: 0.0\n", - "SMOOTH_V: 0.0\n", - "TASKTIME_SMOOTH: 1\n", - "\n", - "# =============================================================================\n", - "# OPTIMIZE \n", - "# //////// \n", - "# LINESEARCH (str):\n", - "# Algorithm to use for line search, see seisflows.plugins.line_search for\n", - "# available choices\n", - "# PRECOND (str):\n", - "# Algorithm to use for preconditioning gradients, see\n", - "# seisflows.plugins.preconds for available choices\n", - "# STEPCOUNTMAX (int):\n", - "# Max number of trial steps in line search before a change in line search\n", - "# behavior\n", - "# STEPLENINIT (float):\n", - "# Initial line search step length, as a fraction of current model\n", - "# parameters\n", - "# STEPLENMAX (float):\n", - "# Max allowable step length, as a fraction of current model parameters\n", - "# LBFGSMEM (int):\n", - "# Max number of previous gradients to retain in local memory\n", - "# LBFGSMAX (int):\n", - "# LBFGS periodic restart interval, between 1 and 'inf'\n", - "# LBFGSTHRESH (float):\n", - "# LBFGS angle restart threshold\n", - "# =============================================================================\n", - "LINESEARCH: Backtrack\n", - "PRECOND:\n", - "STEPCOUNTMAX: 10\n", - "STEPLENINIT: 0.05\n", - "STEPLENMAX: 0.5\n", - "LBFGSMEM: 3\n", - "LBFGSMAX: inf\n", - "LBFGSTHRESH: 0.0\n", - "\n", - "# =============================================================================\n", - "# WORKFLOW \n", - "# //////// \n", - "# CASE (str):\n", - "# Type of inversion, available: ['data': real data inversion, 'synthetic':\n", - "# synthetic-synthetic inversion]\n", - "# RESUME_FROM (str):\n", - "# Name of task to resume inversion from\n", - "# STOP_AFTER (str):\n", - "# Name of task to stop inversion after finishing\n", - "# SAVEMODEL (bool):\n", - "# Save final model files after each iteration\n", - "# SAVEGRADIENT (bool):\n", - "# Save gradient files after each iteration\n", - "# SAVEKERNELS (bool):\n", - "# Save event kernel files after each iteration\n", - "# SAVETRACES (bool):\n", - "# Save waveform traces after each iteration\n", - "# SAVERESIDUALS (bool):\n", - "# Save waveform residuals after each iteration\n", - "# SAVEAS (str):\n", - "# Format to save models, gradients, kernels. Available: ['binary': save\n", - "# files in native SPECFEM .bin format, 'vector': save files as NumPy .npy\n", - "# files, 'both': save as both binary and vectors]\n", - "# BEGIN (int):\n", - "# First iteration of workflow, 1 <= BEGIN <= inf\n", - "# END (int):\n", - "# Last iteration of workflow, BEGIN <= END <= inf\n", - "# =============================================================================\n", - "CASE: !!! REQUIRED PARAMETER !!!\n", - "RESUME_FROM:\n", - "STOP_AFTER:\n", - "SAVEMODEL: True\n", - "SAVEGRADIENT: True\n", - "SAVEKERNELS: False\n", - "SAVETRACES: False\n", - "SAVERESIDUALS: False\n", - "SAVEAS: binary\n", - "BEGIN: 1\n", - "END: !!! REQUIRED PARAMETER !!!\n", - "\n", - "# =============================================================================\n", - "# PATHS \n", - "# ///// \n", - "# SCRATCH:\n", - "# scratch path to hold temporary data during workflow\n", - "# OUTPUT:\n", - "# directory to save workflow outputs to disk\n", - "# SYSTEM:\n", - "# scratch path to hold any system related data\n", - "# LOCAL:\n", - "# path to local data to be used during workflow\n", - "# LOGFILE:\n", - "# the main output log file where all processes will track their status\n", - "# SOLVER:\n", - "# scratch path to hold solver working directories\n", - "# SPECFEM_BIN:\n", - "# path to the SPECFEM binary executables\n", - "# SPECFEM_DATA:\n", - "# path to the SPECFEM DATA/ directory containing the 'Par_file', 'STATIONS'\n", - "# file and 'CMTSOLUTION' files\n", - "# DATA:\n", - "# path to data available to workflow\n", - "# MASK:\n", - "# Directory to mask files for gradient masking\n", - "# OPTIMIZE:\n", - "# scratch path to store data related to nonlinear optimization\n", - "# MODEL_INIT:\n", - "# location of the initial model to be used for workflow\n", - "# MODEL_TRUE:\n", - "# Target model to be used for PAR.CASE == 'synthetic'\n", - "# FUNC:\n", - "# scratch path to store data related to function evaluations\n", - "# GRAD:\n", - "# scratch path to store data related to gradient evaluations\n", - "# HESS:\n", - "# scratch path to store data related to Hessian evaluations\n", - "# =============================================================================\n", - "PATHS:\n", - " SCRATCH: /home/bchow/Work/work/sf3_specfem2d_example/scratch\n", - " OUTPUT: /home/bchow/Work/work/sf3_specfem2d_example/output\n", - " SYSTEM: /home/bchow/Work/work/sf3_specfem2d_example/scratch/system\n", - " LOCAL:\n", - " LOGFILE: /home/bchow/Work/work/sf3_specfem2d_example/output_sf3.txt\n", - " SOLVER: /home/bchow/Work/work/sf3_specfem2d_example/scratch/solver\n", - " SPECFEM_BIN: !!! REQUIRED PATH !!!\n", - " SPECFEM_DATA: !!! REQUIRED PATH !!!\n", - " DATA:\n", - " MASK:\n", - " OPTIMIZE: /home/bchow/Work/work/sf3_specfem2d_example/scratch/optimize\n", - " MODEL_INIT: !!! REQUIRED PATH !!!\n", - " MODEL_TRUE:\n", - " FUNC: /home/bchow/Work/work/sf3_specfem2d_example/scratch/scratch\n", - " GRAD: /home/bchow/Work/work/sf3_specfem2d_example/scratch/evalgrad\n", - " HESS: /home/bchow/Work/work/sf3_specfem2d_example/scratch/evalhess\n" + "# //////////////////////////////////////////////////////////////////////////////\r\n", + "#\r\n", + "# SeisFlows YAML Parameter File\r\n", + "#\r\n", + "# //////////////////////////////////////////////////////////////////////////////\r\n", + "#\r\n", + "# Modules correspond to the structure of the source code, and determine\r\n", + "# SeisFlows' behavior at runtime. Each module requires its own sub-parameters.\r\n", + "#\r\n", + "# .. rubric::\r\n", + "# - To determine available options for modules listed below, run:\r\n", + "# > seisflows print modules\r\n", + "# - To auto-fill with docstrings and default values (recommended), run:\r\n", + "# > seisflows configure\r\n", + "# - To set values as NoneType, use: null\r\n", + "# - To set values as infinity, use: inf\r\n", + "#\r\n", + "# MODULES\r\n", + "# ///////\r\n", + "# workflow (str): The types and order of functions for running SeisFlows\r\n", + "# system (str): Computer architecture of the system being used\r\n", + "# solver (str): External numerical solver to use for waveform simulations\r\n", + "# preprocess (str): Preprocessing schema for waveform data\r\n", + "# optimize (str): Optimization algorithm for the inverse problem\r\n", + "# ==============================================================================\r\n", + "workflow: inversion\r\n", + "system: workstation\r\n", + "solver: specfem2d\r\n", + "preprocess: default\r\n", + "optimize: gradient\r\n", + "# =============================================================================\r\n", + "#\r\n", + "# Forward Workflow\r\n", + "# ----------------\r\n", + "# Run forward solver in parallel and (optionally) calculate\r\n", + "# data-synthetic misfit and adjoint sources.\r\n", + "#\r\n", + "# Parameters\r\n", + "# ----------\r\n", + "# :type modules: list of module\r\n", + "# :param modules: instantiated SeisFlows modules which should have been\r\n", + "# generated by the function `seisflows.config.import_seisflows` with a\r\n", + "# parameter file generated by seisflows.configure\r\n", + "# :type data_case: str\r\n", + "# :param data_case: How to address 'data' in the workflow, available options:\r\n", + "# 'data': real data will be provided by the user in\r\n", + "# `path_data/{source_name}` in the same format that the solver will\r\n", + "# produce synthetics (controlled by `solver.format`) OR\r\n", + "# synthetic': 'data' will be generated as synthetic seismograms using\r\n", + "# a target model provided in `path_model_true`. If None, workflow will\r\n", + "# not attempt to generate data.\r\n", + "# :type stop_after: str\r\n", + "# :param stop_after: optional name of task in task list (use\r\n", + "# `seisflows print tasks` to get task list for given workflow) to stop\r\n", + "# workflow after, allowing user to prematurely stop a workflow to explore\r\n", + "# intermediate results or debug.\r\n", + "# :type export_traces: bool\r\n", + "# :param export_traces: export all waveforms that are generated by the\r\n", + "# external solver to `path_output`. If False, solver traces stored in\r\n", + "# scratch may be discarded at any time in the workflow\r\n", + "# :type export_residuals: bool\r\n", + "# :param export_residuals: export all residuals (data-synthetic misfit) that\r\n", + "# are generated by the external solver to `path_output`. If False,\r\n", + "# residuals stored in scratch may be discarded at any time in the workflow\r\n", + "#\r\n", + "# \r\n", + "# Migration Workflow\r\n", + "# ------------------\r\n", + "# Run forward and adjoint solver to produce event-dependent misfit kernels.\r\n", + "# Sum and postprocess kernels to produce gradient. In seismic exploration\r\n", + "# this is 'reverse time migration'.\r\n", + "#\r\n", + "# Parameters\r\n", + "# ----------\r\n", + "# :type export_gradient: bool\r\n", + "# :param export_gradient: export the gradient after it has been generated\r\n", + "# in the scratch directory. If False, gradient can be discarded from\r\n", + "# scratch at any time in the workflow\r\n", + "# :type export_kernels: bool\r\n", + "# :param export_kernels: export each sources event kernels after they have\r\n", + "# been generated in the scratch directory. If False, gradient can be\r\n", + "# discarded from scratch at any time in the workflow\r\n", + "#\r\n", + "# \r\n", + "# Inversion Workflow\r\n", + "# ------------------\r\n", + "# Peforms iterative nonlinear inversion using the machinery of the Forward\r\n", + "# and Migration workflows, as well as a built-in optimization library.\r\n", + "#\r\n", + "# Parameters\r\n", + "# ----------\r\n", + "# :type start: int\r\n", + "# :param start: start inversion workflow at this iteration. 1 <= start <= inf\r\n", + "# :type end: int\r\n", + "# :param end: end inversion workflow at this iteration. start <= end <= inf\r\n", + "# :type iteration: int\r\n", + "# :param iteration: The current iteration of the workflow. If NoneType, takes\r\n", + "# the value of `start` (i.e., first iteration of the workflow). User can\r\n", + "# also set between `start` and `end` to resume a failed workflow.\r\n", + "# :type thrifty: bool\r\n", + "# :param thrifty: a thrifty inversion skips the costly intialization step\r\n", + "# (i.e., forward simulations and misfit quantification) if the final\r\n", + "# forward simulations from the previous iterations line search can be\r\n", + "# used in the current one. Requires L-BFGS optimization.\r\n", + "# :type export_model: bool\r\n", + "# :param export_model: export best-fitting model from the line search to disk.\r\n", + "# If False, new models can be discarded from scratch at any time.\r\n", + "#\r\n", + "# \r\n", + "# =============================================================================\r\n", + "data_case: data\r\n", + "stop_after: null\r\n", + "export_traces: False\r\n", + "export_residuals: False\r\n", + "export_gradient: False\r\n", + "export_kernels: False\r\n", + "start: 1\r\n", + "end: 1\r\n", + "export_model: True\r\n", + "thrifty: False\r\n", + "iteration: 1\r\n", + "# =============================================================================\r\n", + "#\r\n", + "# Workstation System\r\n", + "# ------------------\r\n", + "# Runs tasks in serial on a local machine.\r\n", + "#\r\n", + "# Parameters\r\n", + "# ----------\r\n", + "# :type ntask: int\r\n", + "# :param ntask: number of individual tasks/events to run during workflow.\r\n", + "# Must be <= the number of source files in `path_specfem_data`\r\n", + "# :type nproc: int\r\n", + "# :param nproc: number of processors to use for each simulation\r\n", + "# :type log_level: str\r\n", + "# :param log_level: logger level to pass to logging module.\r\n", + "# Available: 'debug', 'info', 'warning', 'critical'\r\n", + "# :type verbose: bool\r\n", + "# :param verbose: if True, formats the log messages to include the file\r\n", + "# name, line number and message type. Useful for debugging but\r\n", + "# also very verbose.\r\n", + "#\r\n", + "# \r\n", + "# =============================================================================\r\n", + "ntask: 1\r\n", + "nproc: 1\r\n", + "log_level: DEBUG\r\n", + "verbose: False\r\n", + "# =============================================================================\r\n", + "#\r\n", + "# Solver SPECFEM\r\n", + "# --------------\r\n", + "# Generalized SPECFEM interface to manipulate SPECFEM2D/3D/3D_GLOBE w/ Python\r\n", + "#\r\n", + "# Parameters\r\n", + "# ----------\r\n", + "# :type data_format: str\r\n", + "# :param data_format: data format for reading traces into memory.\r\n", + "# Available: ['SU': seismic unix format, 'ASCII': human-readable ascii]\r\n", + "# :type materials: str\r\n", + "# :param materials: Material parameters used to define model. Available:\r\n", + "# ['ELASTIC': Vp, Vs, 'ACOUSTIC': Vp, 'ISOTROPIC', 'ANISOTROPIC']\r\n", + "# :type density: bool\r\n", + "# :param density: How to treat density during inversion. If True, updates\r\n", + "# density during inversion. If False, keeps it constant.\r\n", + "# TODO allow density scaling during an inversion\r\n", + "# :type attenuation: bool\r\n", + "# :param attenuation: How to treat attenuation during inversion.\r\n", + "# if True, turns on attenuation during forward simulations only. If\r\n", + "# False, attenuation is always set to False. Requires underlying\r\n", + "# attenution (Q_mu, Q_kappa) model\r\n", + "# :type smooth_h: float\r\n", + "# :param smooth_h: Gaussian half-width for horizontal smoothing in units\r\n", + "# of meters. If 0., no smoothing applied\r\n", + "# :type smooth_h: float\r\n", + "# :param smooth_v: Gaussian half-width for vertical smoothing in units\r\n", + "# of meters.\r\n", + "# :type components: str\r\n", + "# :param components: components to consider and tag data with. Should be\r\n", + "# string of letters such as 'RTZ'\r\n", + "# :type solver_io: str\r\n", + "# :param solver_io: format of model/kernel/gradient files expected by the\r\n", + "# numerical solver. Available: ['fortran_binary': default .bin files].\r\n", + "# TODO: ['adios': ADIOS formatted files]\r\n", + "# :type source_prefix: str\r\n", + "# :param source_prefix: prefix of source/event/earthquake files. If None,\r\n", + "# will attempt to guess based on the specific solver chosen.\r\n", + "# :type mpiexec: str\r\n", + "# :param mpiexec: MPI executable used to run parallel processes. Should also\r\n", + "# be defined for the system module\r\n", + "#\r\n", + "# \r\n", + "# Solver SPECFEM2D\r\n", + "# ----------------\r\n", + "# SPECFEM2D-specific alterations to the base SPECFEM module\r\n", + "#\r\n", + "# Parameters\r\n", + "# ----------\r\n", + "# :type source_prefix: str\r\n", + "# :param source_prefix: Prefix of source files in path SPECFEM_DATA. Defaults\r\n", + "# to 'SOURCE'\r\n", + "# :type multiples: bool\r\n", + "# :param multiples: set an absorbing top-boundary condition\r\n", + "#\r\n", + "# \r\n", + "# =============================================================================\r\n", + "data_format: ascii\r\n", + "materials: acoustic\r\n", + "density: False\r\n", + "attenuation: False\r\n", + "smooth_h: 0.0\r\n", + "smooth_v: 0.0\r\n", + "components: ZNE\r\n", + "source_prefix: SOURCE\r\n", + "multiples: False\r\n", + "# =============================================================================\r\n", + "#\r\n", + "# Default Preprocess\r\n", + "# ------------------\r\n", + "# Data processing for seismic traces, with options for data misfit,\r\n", + "# filtering, normalization and muting.\r\n", + "#\r\n", + "# Parameters\r\n", + "# ----------\r\n", + "# :type data_format: str\r\n", + "# :param data_format: data format for reading traces into memory. For\r\n", + "# available see: seisflows.plugins.preprocess.readers\r\n", + "# :type misfit: str\r\n", + "# :param misfit: misfit function for waveform comparisons. For available\r\n", + "# see seisflows.plugins.preprocess.misfit\r\n", + "# :type backproject: str\r\n", + "# :param backproject: backprojection function for migration, or the\r\n", + "# objective function in FWI. For available see\r\n", + "# seisflows.plugins.preprocess.adjoint\r\n", + "# :type normalize: str\r\n", + "# :param normalize: Data normalization parameters used to normalize the\r\n", + "# amplitudes of waveforms. Choose from two sets:\r\n", + "# ENORML1: normalize per event by L1 of traces; OR\r\n", + "# ENORML2: normalize per event by L2 of traces;\r\n", + "# &\r\n", + "# TNORML1: normalize per trace by L1 of itself; OR\r\n", + "# TNORML2: normalize per trace by L2 of itself\r\n", + "# :type filter: str\r\n", + "# :param filter: Data filtering type, available options are:\r\n", + "# BANDPASS (req. MIN/MAX PERIOD/FREQ);\r\n", + "# LOWPASS (req. MAX_FREQ or MIN_PERIOD);\r\n", + "# HIGHPASS (req. MIN_FREQ or MAX_PERIOD)\r\n", + "# :type min_period: float\r\n", + "# :param min_period: Minimum filter period applied to time series.\r\n", + "# See also MIN_FREQ, MAX_FREQ, if User defines FREQ parameters, they\r\n", + "# will overwrite PERIOD parameters.\r\n", + "# :type max_period: float\r\n", + "# :param max_period: Maximum filter period applied to time series. See\r\n", + "# also MIN_FREQ, MAX_FREQ, if User defines FREQ parameters, they will\r\n", + "# overwrite PERIOD parameters.\r\n", + "# :type min_freq: float\r\n", + "# :param min_freq: Maximum filter frequency applied to time series,\r\n", + "# See also MIN_PERIOD, MAX_PERIOD, if User defines FREQ parameters,\r\n", + "# they will overwrite PERIOD parameters.\r\n", + "# :type max_freq: float\r\n", + "# :param max_freq: Maximum filter frequency applied to time series,\r\n", + "# See also MIN_PERIOD, MAX_PERIOD, if User defines FREQ parameters,\r\n", + "# they will overwrite PERIOD parameters.\r\n", + "# :type mute: list\r\n", + "# :param mute: Data mute parameters used to zero out early / late\r\n", + "# arrivals or offsets. Choose any number of:\r\n", + "# EARLY: mute early arrivals;\r\n", + "# LATE: mute late arrivals;\r\n", + "# SHORT: mute short source-receiver distances;\r\n", + "# LONG: mute long source-receiver distances\r\n", + "#\r\n", + "# \r\n", + "# =============================================================================\r\n", + "misfit: waveform\r\n", + "adjoint: waveform\r\n", + "normalize: []\r\n", + "filter: null\r\n", + "min_period: null\r\n", + "max_period: null\r\n", + "min_freq: null\r\n", + "max_freq: null\r\n", + "mute: []\r\n", + "early_slope: null\r\n", + "early_const: null\r\n", + "late_slope: null\r\n", + "late_const: null\r\n", + "short_dist: null\r\n", + "long_dist: null\r", + "\r\n", + "# =============================================================================\r\n", + "#\r\n", + "# Gradient Optimization\r\n", + "# ---------------------\r\n", + "# Gradient/steepest descent optimization algorithm.\r\n", + "#\r\n", + "# Parameters\r\n", + "# ----------\r\n", + "# :type line_search_method: str\r\n", + "# :param line_search_method: chosen line_search algorithm. Currently available\r\n", + "# are 'bracket' and 'backtrack'. See seisflows.plugins.line_search\r\n", + "# for all available options\r\n", + "# :type preconditioner: str\r\n", + "# :param preconditioner: algorithm for preconditioning gradients. Currently\r\n", + "# available: 'diagonal'. Requires `path_preconditioner` to point to a\r\n", + "# set of files that define the preconditioner, formatted the same as the\r\n", + "# input model\r\n", + "# :type step_count_max: int\r\n", + "# :param step_count_max: maximum number of trial steps to perform during\r\n", + "# the line search before a change in line search behavior is\r\n", + "# considered, or a line search is considered to have failed.\r\n", + "# :type step_len_init: float\r\n", + "# :param step_len_init: initial line search step length guess, provided\r\n", + "# as a fraction of current model parameters.\r\n", + "# :type step_len_max: float\r\n", + "# :param step_len_max: maximum allowable step length during the line\r\n", + "# search. Set as a fraction of the current model parameters\r\n", + "#\r\n", + "# \r\n", + "# =============================================================================\r\n", + "preconditioner: null\r\n", + "step_count_max: 10\r\n", + "step_len_init: 0.05\r\n", + "step_len_max: 0.5\r\n", + "line_search_method: bracket\r\n", + "# =============================================================================\r\n", + "#\r\n", + "#\t Paths\r\n", + "#\t -----\r\n", + "# :type workdir: str\r\n", + "# :param workdir: working directory in which to look for data and store\r\n", + "# results. Defaults to current working directory\r\n", + "# :type path_output: str\r\n", + "# :param path_output: path to directory used for permanent storage on disk.\r\n", + "# Results and exported scratch files are saved here.\r\n", + "# :type path_data: str\r\n", + "# :param path_data: path to any externally stored data required by the solver\r\n", + "# :type path_state_file: str\r\n", + "# :param path_state_file: path to a text file used to track the current\r\n", + "# status of a workflow (i.e., what functions have already been completed),\r\n", + "# used for checkpointing and resuming workflows\r\n", + "# :type path_model_init: str\r\n", + "# :param path_model_init: path to the starting model used to calculate the\r\n", + "# initial misfit. Must match the expected `solver_io` format.\r\n", + "# :type path_model_true: str\r\n", + "# :param path_model_true: path to a target model if `case`=='synthetic' and\r\n", + "# a set of synthetic 'observations' are required for workflow.\r\n", + "# :type path_eval_grad: str\r\n", + "# :param path_eval_grad: scratch path to store files for gradient evaluation,\r\n", + "# including models, kernels, gradient and residuals.\r\n", + "# :type path_mask: str\r\n", + "# :param path_mask: optional path to a masking function which is used to\r\n", + "# mask out or scale parts of the gradient. The user-defined mask must\r\n", + "# match the file format of the input model (e.g., .bin files).\r\n", + "# :type path_eval_func: str\r\n", + "# :param path_eval_func: scratch path to store files for line search objective\r\n", + "# function evaluations, including models, misfit and residuals\r\n", + "# \r\n", + "# :type path_output_log: str\r\n", + "# :param path_output_log: path to a text file used to store the outputs of\r\n", + "# the package wide logger, which are also written to stdout\r\n", + "# :type path_par_file: str\r\n", + "# :param path_par_file: path to parameter file which is used to instantiate\r\n", + "# the package\r\n", + "# :type path_log_files: str\r\n", + "# :param path_log_files: path to a directory where individual log files are\r\n", + "# saved whenever a number of parallel tasks are run on the system.\r\n", + "# \r\n", + "# :type path_data: str\r\n", + "# :param path_data: path to any externally stored data required by the solver\r\n", + "# :type path_specfem_bin: str\r\n", + "# :param path_specfem_bin: path to SPECFEM bin/ directory which\r\n", + "# contains binary executables for running SPECFEM\r\n", + "# :type path_specfem_data: str\r\n", + "# :param path_specfem_data: path to SPECFEM DATA/ directory which must\r\n", + "# contain the CMTSOLUTION, STATIONS and Par_file files used for\r\n", + "# running SPECFEM\r\n", + "# \r\n", + "# :type path_preprocess: str\r\n", + "# :param path_preprocess: scratch path for all preprocessing processes,\r\n", + "# including saving files\r\n", + "# \r\n", + "# :type path_preconditioner: str\r\n", + "# :param path_preconditioner: optional path to a set of preconditioner files\r\n", + "# formatted the same as the input model (or output model of solver).\r\n", + "# Required to exist and contain files if `preconditioner`==True\r\n", + "# \r\n", + "# =============================================================================\r\n", + "path_workdir: /Users/Chow/Work/work/sf_specfem2d_example\r\n", + "path_scratch: /Users/Chow/Work/work/sf_specfem2d_example/scratch\r\n", + "path_eval_grad: /Users/Chow/Work/work/sf_specfem2d_example/scratch/eval_grad\r\n", + "path_output: /Users/Chow/Work/work/sf_specfem2d_example/output\r\n", + "path_model_init: null\r\n", + "path_model_true: null\r\n", + "path_state_file: /Users/Chow/Work/work/sf_specfem2d_example/sfstate.txt\r\n", + "path_data: null\r\n", + "path_mask: null\r\n", + "path_eval_func: /Users/Chow/Work/work/sf_specfem2d_example/scratch/eval_func\r\n", + "path_par_file: /Users/Chow/Work/work/sf_specfem2d_example/parameters.yaml\r\n", + "path_log_files: /Users/Chow/Work/work/sf_specfem2d_example/logs\r\n", + "path_output_log: /Users/Chow/Work/work/sf_specfem2d_example/sflog.txt\r\n", + "path_specfem_bin: null\r\n", + "path_specfem_data: null\r\n", + "path_solver: /Users/Chow/Work/work/sf_specfem2d_example/scratch/solver\r\n", + "path_preconditioner: null\r\n" ] } ], @@ -1235,56 +1284,22 @@ }, { "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "!!! REQUIRED PARAMETER !!!\r\n", - "==========================\r\n", - "\tMATERIALS\r\n", - "\tDENSITY\r\n", - "\tATTENUATION\r\n", - "\tNT\r\n", - "\tDT\r\n", - "\tF0\r\n", - "\tFORMAT\r\n", - "\tCASE\r\n", - "\tEND\r\n", - "!!! REQUIRED PATH !!!\r\n", - "=====================\r\n", - "\tSPECFEM_BIN\r\n", - "\tSPECFEM_DATA\r\n", - "\tMODEL_INIT\r\n" - ] - } - ], - "source": [ - "# We can check which parameters we will NEED to fill out before running the workflow with the --required flag\n", - "! seisflows par --required" - ] - }, - { - "cell_type": "code", - "execution_count": 47, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "MATERIALS: !!! REQUIRED PARAMETER !!! -> elastic\n", - "DENSITY: !!! REQUIRED PARAMETER !!! -> constant\n", - "ATTENUATION: !!! REQUIRED PARAMETER !!! -> False\n", - "NT: !!! REQUIRED PARAMETER !!! -> 5000\n", - "DT: !!! REQUIRED PARAMETER !!! -> .06\n", - "F0: !!! REQUIRED PARAMETER !!! -> 0.084\n", - "FORMAT: !!! REQUIRED PARAMETER !!! -> ascii\n", - "BEGIN: 1 -> 1\n", - "END: !!! REQUIRED PARAMETER !!! -> 1\n", - "CASE: !!! REQUIRED PARAMETER !!! -> synthetic\n" + "ntask: 1 -> 3\n", + "materials: acoustic -> elastic\n", + "density: False -> False\n", + "attenuation: False -> False\n", + "start: 1 -> 1\n", + "end: 1 -> 2\n", + "data_case: data -> synthetic\n", + "components: ZNE -> Y\n", + "step_count_max: 10 -> 5\n" ] }, { @@ -1293,29 +1308,28 @@ "0" ] }, - "execution_count": 47, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# EDIT THE SEISFLOWS3 PARAMETER FILE\n", + "# EDIT THE SEISFLOWS PARAMETER FILE\n", + "! seisflows par ntask 3 # set the number of sources/events to use\n", "! seisflows par materials elastic # how the velocity model is parameterized\n", - "! seisflows par density constant # update density or keep constant\n", + "! seisflows par density False # update density or keep constant\n", "! seisflows par attenuation False\n", - "! seisflows par nt 5000 # set by SPECFEM2D Par_file\n", - "! seisflows par dt .06 # set by SPECFEM2D Par_file\n", - "! seisflows par f0 0.084 # set by SOURCE file\n", - "! seisflows par format ascii # how to output synthetic seismograms\n", - "! seisflows par begin 1 # first iteration\n", - "! seisflows par end 1 # final iteration -- we will only run 1\n", - "! seisflows par case synthetic # synthetic-synthetic means we need both INIT and TRUE models\n", + "! seisflows par start 1 # first iteration\n", + "! seisflows par end 2 # final iteration -- we will only run 1\n", + "! seisflows par data_case synthetic # synthetic-synthetic means we need both INIT and TRUE models\n", + "! seisflows par components Y # this default example creates Y-component seismograms\n", + "! seisflows par step_count_max 5 # limit the number of steps in the line search\n", "\n", "# Use Python syntax here to access path constants\n", - "os.system(f\"seisflows par specfem_bin {SPECFEM2D_BIN}\") # set path to SPECFEM2D binaries\n", - "os.system(f\"seisflows par specfem_data {SPECFEM2D_DATA}\") # set path to SEPCFEM2D DATA/\n", - "os.system(f\"seisflows par model_init {SPECFEM2D_MODEL_INIT}\") # set path to INIT model\n", - "os.system(f\"seisflows par model_true {SPECFEM2D_MODEL_TRUE}\") # set path to TRUE model" + "os.system(f\"seisflows par path_specfem_bin {SPECFEM2D_BIN}\") # set path to SPECFEM2D binaries\n", + "os.system(f\"seisflows par path_specfem_data {SPECFEM2D_DATA}\") # set path to SEPCFEM2D DATA/\n", + "os.system(f\"seisflows par path_model_init {SPECFEM2D_MODEL_INIT}\") # set path to INIT model\n", + "os.system(f\"seisflows par path_model_true {SPECFEM2D_MODEL_TRUE}\") # set path to TRUE model" ] }, { @@ -1328,7 +1342,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1348,136 +1362,45 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### 2b. Initialize SF3 working state\n", - "\n", - "The SeisFlows3 command `seisflows init` will generate the a SeisFlows3 working state without submitting any jobs to the system. This is useful for testing to see if the user has set an acceptable parameter file, and if SeisFlows3 is working as expected. \n", + "### 3. Run SeisFlows\n", "\n", - "The result of running `seisflows init` is a collection of pickle (\\*.p) and JSON files which define the active Python environment. SeisFlows3 relies directly on these files to determine where it is in a workflow. Throughout an active workflow, SeisFlows3 will checkpoint itself to these pickle and JSON files such that if a workflow finishes or crashes, the User can resume a workflow from the last checkpointed state rather than needing to restart the workflow.\n", - "\n", - ">__DEBUG MODE:__ After running `seisflows init` you can explore the SeisFlows3 working state in an interactive iPython environment by running `seisflows debug`. This will open up an iPython environment in which the active working state is loaded and accessible The debug mode is invaluable for exploring the SeisFlows3 working state, debugging errors, and performing manual manipulations to an otherwise automated tool. You can try for yourself by running debug mode and typing 'preprocess' to access the active preprocess module." - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "instantiating SeisFlows3 working state in directory: output\n", - "seisflows_optimize.p\t seisflows_postprocess.p seisflows_system.p\n", - "seisflows_parameters.json seisflows_preprocess.p seisflows_workflow.p\n", - "seisflows_paths.json\t seisflows_solver.p\n" - ] - } - ], - "source": [ - "os.chdir(WORKDIR)\n", - "! seisflows init\n", - "! ls output" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\r\n", - " \"ATTENUATION\": false,\r\n", - " \"BACKPROJECT\": null,\r\n", - " \"BEGIN\": 1,\r\n", - " \"CASE\": \"synthetic\",\r\n", - " \"COMPONENTS\": \"ZNE\",\r\n", - " \"DENSITY\": \"constant\",\r\n", - " \"DT\": 0.06,\r\n", - " \"END\": 1,\r\n", - " \"F0\": 0.084,\r\n" - ] - } - ], - "source": [ - "# All of the parameters defined in parameters.yaml are saved in this \n", - "# internally-used JSON file\n", - "! head output/seisflows_parameters.json" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\r\n", - " \"DATA\": null,\r\n", - " \"FUNC\": \"/home/bchow/Work/work/sf3_specfem2d_example/scratch/scratch\",\r\n", - " \"GRAD\": \"/home/bchow/Work/work/sf3_specfem2d_example/scratch/evalgrad\",\r\n", - " \"HESS\": \"/home/bchow/Work/work/sf3_specfem2d_example/scratch/evalhess\",\r\n", - " \"LOCAL\": null,\r\n", - " \"LOGFILE\": \"/home/bchow/Work/work/sf3_specfem2d_example/output_sf3.txt\",\r\n", - " \"MASK\": null,\r\n", - " \"MODEL_INIT\": \"/home/bchow/Work/work/sf3_specfem2d_example/specfem2d_workdir/OUTPUT_FILES_INIT\",\r\n", - " \"MODEL_TRUE\": \"/home/bchow/Work/work/sf3_specfem2d_example/specfem2d_workdir/OUTPUT_FILES_TRUE\",\r\n" - ] - } - ], - "source": [ - "# Similarly, paths that SeisFlows3 uses to navigate the system are stored\n", - "# in the seisflows_paths.json file\n", - "! head output/seisflows_paths.json" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3. Run SeisFlows3\n", - "\n", - "In this Section we will run SeisFlows3 to generate synthetic seismograms, kernels, a gradient, and an updated velocity model.\n", + "In this Section we will run SeisFlows to generate synthetic seismograms, kernels, a gradient, and an updated velocity model.\n", "\n", "#### 3a. Forward simulations\n", "\n", - "SeisFlows3 is an automated workflow tool, such that once we run `seisflows submit` we should not need to intervene in the workflow. However the package does allow the User flexibility in how they want the workflow to behave.\n", + "SeisFlows is an automated workflow tool, such that once we run `seisflows submit` we should not need to intervene in the workflow. However the package does allow the User flexibility in how they want the workflow to behave.\n", "\n", - "For example, we can run our workflow in stages by taking advantage of the `stop_after` and `resume_from` parameters. As their names suggest, these parameters allow us to stop and resume the workflow at certain stages (i.e., functions in workflow.main()). \n", + "For example, we can run our workflow in stages by taking advantage of the `stop_after` parameter. As its name suggests, `stop_after` allows us to stop a workflow prematurely so that we may stop and look at results, or debug a failing workflow.\n", "\n", - "The available arguments for `stop_after` and `resume_from` are discovered by running the command: `seisflows print flow`, which tells us what functions will be run from main(). " + "The `seisflows print flow` command tells us what functions we can use for the `stop_after` parameter. " ] }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " SEISFLOWS3 WORKFLOW MAIN \r\n", - " //////////////////////// \r\n", - "Flow arguments for \r\n", + " SEISFLOWS WORKFLOW TASK LIST \r\n", + " //////////////////////////// \r\n", + "Task list for \r\n", "\r\n", - "1: setup\r\n", - "2: initialize\r\n", - "3: evaluate_gradient\r\n", - "4: write_gradient\r\n", - "5: compute_direction\r\n", - "6: line_search\r\n", - "7: finalize\r\n", - "8: clean\r\n" + "1: evaluate_initial_misfit\r\n", + "2: run_adjoint_simulations\r\n", + "3: postprocess_event_kernels\r\n", + "4: evaluate_gradient_from_kernels\r\n", + "5: initialize_line_search\r\n", + "6: perform_line_search\r\n", + "7: finalize_iteration\r\n" ] } ], "source": [ - "! seisflows print flow" + "os.chdir(WORKDIR)\n", + "! seisflows print tasks" ] }, { @@ -1485,39 +1408,39 @@ "metadata": {}, "source": [ "-----------------------------\n", - "In an inversion (the workflow we have selected) the flow arguments are described as:\n", + "In the Inversion workflow, the tasks listed are described as follows:\n", "\n", - "0. __setup:__ Not technically listed in the flow arguments, runs setup() for all SeisFlows3 modules. If running a synthetic-synthetic workflow, solver.setup() will generate \"data\" by running the forward solver using MODEL_TRUE\n", - "1. __initialize:__ \n", - " a. Call numerical solver to run forward simulations using MODEL_INIT, generating synthetics \n", - " b. Evaluate the objective function by performing waveform comparisons \n", - " c. Prepare `evaluate gradient` step by generating adjoint sources and auxiliary files\n", - "2. __evaluate_gradient:__ Call numerical solver to run adjoint simulation, generating kernels\n", - "3. __write_gradient:__ Combine all event kernels into a misfit kernel. Optionally smooth and mask the misfit kernel\n", - "4. __compute_direction:__ Call on the optimization library to scale the misfit kernel into the gradient and compute a search direction\n", - "5. __line_search:__ Perform a line search by algorithmically scaling the gradient and evaluating the misfit function (forward simulations and misfit quantification) until misfit is acceptably reduced\n", - "6. __finalize:__ Run any finalization steps such as saving traces, kernels, gradients and models to disk, setting up SeisFlows3 for any subsequent iterations.\n", - "7. __clean:__ Clean the scratch/ directory in preparation for subsequent i\n", + "1. __evaluate_initial_misfit:__ \n", + " a. Prepare data for inversion by either copying data from disk or generating 'synthetic data' with MODEL_TRUE \n", + " b. Call numerical solver to run forward simulations using MODEL_INIT, generating synthetics \n", + " c. Evaluate the objective function by performing waveform comparisons \n", + " d. Prepare `run_adjoint_simulations` step by generating adjoint sources and auxiliary files\n", + "2. __run_adjoint_simulations:__ Call numerical solver to run adjoint simulation, generating kernels\n", + "3. __postprocess_event_kernels:__ Combine all event kernels into a misfit kernel. \n", + "4. __evaluate_gradient_from_kernels:__ Smooth and mask the misfit kernel to create the gradient\n", + "4. __initialize_line_search:__ Call on the optimization library to scale the gradient by a step length to compute the search direction. Prepare file structure for line search.\n", + "5. __perform_line_search:__ Perform a line search by algorithmically scaling the gradient and evaluating the misfit function (forward simulations and misfit quantification) until misfit is acceptably reduced.\n", + "6. __finalize_iteration:__ Run any finalization steps such as saving traces, kernels, gradients and models to disk, setting up SeisFlows3 for any subsequent iterations. Clean the scratch/ directory in preparation for subsequent iterations\n", "\n", - "Let's set the `stop_after` argument to __initialize__, this will halt the workflow after the intialization step. We'll also set the `verbose` parameter to 'False', to keep the logging format relatively simple. We will explore the `verbose`==True option in a later cell." + "Let's set the `stop_after` argument to __evaluate_initial_misfit__, this will halt the workflow after the intialization step. We'll also set the `verbose` parameter to 'False', to keep the logging format relatively simple. We will explore the `verbose`==True option in a later cell." ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "STOP_AFTER: -> initialize\n", - "VERBOSE: False -> False\n" + "stop_after: null -> evaluate_initial_misfit\n", + "verbose: False -> False\n" ] } ], "source": [ - "! seisflows par stop_after initialize\n", + "! seisflows par stop_after evaluate_initial_misfit\n", "! seisflows par verbose False" ] }, @@ -1526,139 +1449,81 @@ "metadata": {}, "source": [ "-----------------------\n", - "Now let's run SeisFlows3. There are a few ways to do this: `submit`, `resume`, and `restart`\n", + "Now let's run SeisFlows. There are two ways to do this: `submit` and `restart`\n", "\n", - "1. Since we already ran `seisflows init`, the `seisflows submit` option will not work, as SeisFlows3 considers this an active working state and `submit` can only be run on uninitialized working states.\n", - "2. To run a workflow in an active working state `resume` will load the current working state from the output/ directory and submit a workflow given the current parameter file.\n", - "3. The `restart` command is simply a convenience function that runs `clean` (to remove an active working state) and `submit` (to submit a fresh working state). \n", + "1. `seisflows submit` is used to run new workflows and resume stopped or failed workflows.\n", + "2. The `restart` command is simply a convenience function that runs `clean` (to remove an active working state) and `submit` (to submit a fresh workflow). \n", "\n", - "Since we haven't done anything in this working state, we will go with a modified version of Option 3 by running `clean` and then `submit`. We'll use the `-f` flag (stands for __'force'__) to skip over the standard input prompt that asks the User if they are sure they want to clean and submit.\n", - "\n", - "But first we'll try to run `seisflows submit` to show why Option 1 **will not work**." + "Since this is our first run, we'll use `seisflows submit`." ] }, { "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-04-29 12:32:17 | initializing SeisFlows3 in sys.modules\r\n", - "================================================================================\r\n", - " WARNING \r\n", - " /////// \r\n", - "Data from previous workflow found in working directory.\r\n", - "\r\n", - "> seisflows restart: delete data and start new workflow\r\n", - "> seisflows resume: resume existing workflow\r\n", - "================================================================================\r\n" - ] - } - ], - "source": [ - "! seisflows submit -f" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "----------------------------\n", - "__Okay, let's go!__ In the following cell we will run the SeisFlows3 Inversion workflow. In the output cell we will see the logging statements outputted by SeisFlows3, both to stdout and to the output log file (defaults to ./output_seisflows.txt) which details the progress of our inversion" - ] - }, - { - "cell_type": "code", - "execution_count": 58, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "2022-08-15 16:11:40 (I) | \n", "================================================================================\n", - " CLEAN \n", - " ///// \n", - "+ skipping over: /home/bchow/Work/work/sf3_specfem2d_example/parameters.yaml\n", - "- deleting file/folder: /home/bchow/Work/work/sf3_specfem2d_example/scratch\n", - "- deleting file/folder: /home/bchow/Work/work/sf3_specfem2d_example/stats\n", - "- deleting file/folder: /home/bchow/Work/work/sf3_specfem2d_example/output\n", - "- deleting file/folder: /home/bchow/Work/work/sf3_specfem2d_example/output_sf3.txt\n", - "- deleting file/folder: /home/bchow/Work/work/sf3_specfem2d_example/logs\n", + " SETTING UP INVERSION WORKFLOW \n", "================================================================================\n", - "2022-04-29 12:38:37 | initializing SeisFlows3 in sys.modules\n", - "2022-04-29 12:38:42 | copying par/log file to: /home/bchow/Work/work/sf3_specfem2d_example/logs/output_sf3_001.txt\n", - "2022-04-29 12:38:42 | copying par/log file to: /home/bchow/Work/work/sf3_specfem2d_example/logs/parameters_001.yaml\n", - "2022-04-29 12:38:42 | exporting current working environment to disk\n", - "2022-04-29 12:38:42 | \n", + "2022-08-15 16:11:47 (D) | running setup for module 'system.Workstation'\n", + "2022-08-15 16:11:50 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/sflog_001.txt\n", + "2022-08-15 16:11:50 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/parameters_001.yaml\n", + "2022-08-15 16:11:50 (D) | running setup for module 'solver.Specfem2D'\n", + "2022-08-15 16:11:50 (I) | initializing 3 solver directories\n", + "2022-08-15 16:11:50 (D) | initializing solver directory source: 001\n", + "2022-08-15 16:11:58 (D) | linking source '001' as 'mainsolver'\n", + "2022-08-15 16:11:58 (D) | initializing solver directory source: 002\n", + "2022-08-15 16:12:04 (D) | initializing solver directory source: 003\n", + "2022-08-15 16:12:13 (D) | running setup for module 'preprocess.Default'\n", + "2022-08-15 16:12:14 (D) | running setup for module 'optimize.Gradient'\n", + "2022-08-15 16:12:15 (I) | no optimization checkpoint found, assuming first run\n", + "2022-08-15 16:12:16 (I) | re-loading optimization module from checkpoint\n", + "2022-08-15 16:12:16 (I) | \n", "////////////////////////////////////////////////////////////////////////////////\n", - " WORKFLOW WILL STOP AFTER FUNC: 'initialize' \n", + " RUNNING ITERATION 01 \n", "////////////////////////////////////////////////////////////////////////////////\n", - "2022-04-29 12:38:42 | \n", + "2022-08-15 16:12:16 (I) | \n", "================================================================================\n", - " STARTING INVERSION WORKFLOW \n", + " RUNNING INVERSION WORKFLOW \n", "================================================================================\n", - "2022-04-29 12:38:42 | \n", - "////////////////////////////////////////////////////////////////////////////////\n", - " ITERATION 1 / 1 \n", + "2022-08-15 16:12:16 (I) | \n", "////////////////////////////////////////////////////////////////////////////////\n", - "2022-04-29 12:38:42 | \n", + " EVALUATING MISFIT FOR INITIAL MODEL \n", "////////////////////////////////////////////////////////////////////////////////\n", - " PERFORMING MODULE SETUP \n", - "////////////////////////////////////////////////////////////////////////////////\n", - "2022-04-29 12:38:42 | misfit function is: 'waveform'\n", - "2022-04-29 12:38:43 | writing line search history file:\n", - "/home/bchow/Work/work/sf3_specfem2d_example/stats/line_search.txt\n", - "2022-04-29 12:38:44 | checking poissons ratio for: 'm_new.npy'\n", - "2022-04-29 12:38:44 | model parameters (m_new.npy i01s00):\n", - "2022-04-29 12:38:44 | 5800.00 <= vp <= 5800.00\n", - "2022-04-29 12:38:44 | 3500.00 <= vs <= 3500.00\n", - "2022-04-29 12:38:44 | 0.21 <= pr <= 0.21\n", - "2022-04-29 12:38:46 | setting up solver on system...\n", - "2022-04-29 12:38:46 | checkpointing working environment to disk\n", - "2022-04-29 12:38:47 | exporting current working environment to disk\n", - "2022-04-29 12:38:48 | running task solver.setup 1 times\n", - "2022-04-29 12:38:48 | initializing 1 solver directories\n", - "2022-04-29 12:38:53 | source 001 symlinked as mainsolver\n", - "2022-04-29 12:38:53 | generating 'data' with MODEL_TRUE synthetics\n", - "2022-04-29 12:39:00 | running mesh generation for MODEL_INIT\n", - "2022-04-29 12:39:02 | \n", - "================================================================================\n", - " INITIALIZING INVERSION \n", - "================================================================================\n", - "2022-04-29 12:39:02 | \n", - "EVALUATE OBJECTIVE FUNCTION\n", - "--------------------------------------------------------------------------------\n", - "2022-04-29 12:39:02 | saving model 'm_new.npy' to:\n", - "/home/bchow/Work/work/sf3_specfem2d_example/scratch/evalgrad/model\n", - "2022-04-29 12:39:03 | evaluating objective function 1 times on system...\n", - "2022-04-29 12:39:03 | checkpointing working environment to disk\n", - "2022-04-29 12:39:05 | exporting current working environment to disk\n", - "2022-04-29 12:39:05 | running task solver.eval_func 1 times\n", - "2022-04-29 12:39:05 | running forward simulations\n", - "2022-04-29 12:39:11 | calling preprocess.prepare_eval_grad()\n", - "2022-04-29 12:39:11 | preparing files for gradient evaluation\n", - "2022-04-29 12:39:11 | exporting residuals to:\n", - "/home/bchow/Work/work/sf3_specfem2d_example/scratch/evalgrad\n", - "2022-04-29 12:39:12 | summing residuals with preprocess module\n", - "2022-04-29 12:39:12 | saving misfit 1.748E-03 to tag 'f_new.txt'\n", - "2022-04-29 12:39:12 | \n", - "================================================================================\n", - " FINISHED FLOW EXECUTION \n", - "================================================================================\n", - "2022-04-29 12:39:12 | \n", - "================================================================================\n", - " FINISHED INVERSION WORKFLOW \n", - "================================================================================\n" + "2022-08-15 16:12:16 (I) | checking initial model parameters\n", + "2022-08-15 16:12:16 (I) | 2600.00 <= rho <= 2600.00\n", + "2022-08-15 16:12:16 (I) | 3500.00 <= vs <= 3500.00\n", + "2022-08-15 16:12:16 (I) | 5800.00 <= vp <= 5800.00\n", + "2022-08-15 16:12:16 (I) | checking true/target model parameters\n", + "2022-08-15 16:12:16 (I) | 2600.00 <= rho <= 2600.00\n", + "2022-08-15 16:12:16 (I) | 3550.00 <= vs <= 3550.00\n", + "2022-08-15 16:12:16 (I) | 5900.00 <= vp <= 5900.00\n", + "2022-08-15 16:12:16 (I) | preparing observation data for source 001\n", + "2022-08-15 16:12:16 (I) | running forward simulation w/ target model for 001\n", + "2022-08-15 16:12:33 (I) | evaluating objective function for source 001\n", + "2022-08-15 16:12:33 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:12:53 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:12:53 (I) | preparing observation data for source 002\n", + "2022-08-15 16:12:53 (I) | running forward simulation w/ target model for 002\n", + "2022-08-15 16:13:09 (I) | evaluating objective function for source 002\n", + "2022-08-15 16:13:09 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:13:31 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:13:31 (I) | preparing observation data for source 003\n", + "2022-08-15 16:13:31 (I) | running forward simulation w/ target model for 003\n", + "2022-08-15 16:14:16 (I) | evaluating objective function for source 003\n", + "2022-08-15 16:14:16 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:14:33 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:14:33 (I) | stop workflow at `stop_after`: evaluate_initial_misfit\n" ] } ], "source": [ - "! seisflows clean -f\n", - "! seisflows submit -f" + "! seisflows submit " ] }, { @@ -1666,12 +1531,12 @@ "metadata": {}, "source": [ ".. note::\n", - " For a detailed exploration of a SeisFlows3 working directory, see the `working directory `__ documentation page where we explain each of the files and directories that have been generated during this workflow. Below we just look at two files which are required for our adjoint simulation, the adjoint sources (.adj) and STATIONS_ADJOINT file" + " For a detailed exploration of a SeisFlows working directory, see the `working directory `__ documentation page where we explain each of the files and directories that have been generated during this workflow. Below we just look at two files which are required for our adjoint simulation, the adjoint sources (.adj) and STATIONS_ADJOINT file" ] }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1696,25 +1561,6 @@ "! head scratch/solver/001/traces/adj/AA.S0001.BXY.adj" ] }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "S0001 AA 180081.4100000 388768.7100000 0.0 0.0\r\n" - ] - } - ], - "source": [ - "# We can also see that we have generated a STATIONS_ADJOINT file, which is required for \n", - "# running the adjoint simulations (i.e., evaluate the gradient)\n", - "! head scratch/solver/001/DATA/STATIONS_ADJOINT" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1726,123 +1572,109 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " SEISFLOWS3 WORKFLOW MAIN \r\n", - " //////////////////////// \r\n", - "Flow arguments for \r\n", + " SEISFLOWS WORKFLOW TASK LIST \r\n", + " //////////////////////////// \r\n", + "Task list for \r\n", "\r\n", - "1: setup\r\n", - "2: initialize\r\n", - "3: evaluate_gradient\r\n", - "4: write_gradient\r\n", - "5: compute_direction\r\n", - "6: line_search\r\n", - "7: finalize\r\n", - "8: clean\r\n" + "1: evaluate_initial_misfit\r\n", + "2: run_adjoint_simulations\r\n", + "3: postprocess_event_kernels\r\n", + "4: evaluate_gradient_from_kernels\r\n", + "5: initialize_line_search\r\n", + "6: perform_line_search\r\n", + "7: finalize_iteration\r\n" ] } ], "source": [ - "! seisflows print flow" + "! seisflows print tasks" ] }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RESUME_FROM: -> evaluate_gradient\n", - "STOP_AFTER: initialize -> compute_direction\n" + "stop_after: evaluate_initial_misfit -> evaluate_gradient_from_kernels\r\n" ] } ], "source": [ "# We'll stop just before the line search so that we can take a look at the files \n", "# generated during the middle tasks\n", - "! seisflows par resume_from evaluate_gradient\n", - "! seisflows par stop_after compute_direction" + "! seisflows par stop_after evaluate_gradient_from_kernels" ] }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2022-04-29 12:41:21 | copying par/log file to: /home/bchow/Work/work/sf3_specfem2d_example/logs/output_sf3_002.txt\n", - "2022-04-29 12:41:21 | copying par/log file to: /home/bchow/Work/work/sf3_specfem2d_example/logs/parameters_002.yaml\n", - "2022-04-29 12:41:21 | exporting current working environment to disk\n", - "2022-04-29 12:41:21 | \n", - "////////////////////////////////////////////////////////////////////////////////\n", - " WORKFLOW WILL RESUME FROM FUNC: 'evaluate_gradient' \n", - "////////////////////////////////////////////////////////////////////////////////\n", - "2022-04-29 12:41:21 | \n", - "////////////////////////////////////////////////////////////////////////////////\n", - " WORKFLOW WILL STOP AFTER FUNC: 'compute_direction' \n", - "////////////////////////////////////////////////////////////////////////////////\n", - "2022-04-29 12:41:21 | \n", + "2022-08-15 16:15:06 (D) | setting iteration==1 from state file\n", + "2022-08-15 16:15:06 (I) | \n", "================================================================================\n", - " STARTING INVERSION WORKFLOW \n", + " SETTING UP INVERSION WORKFLOW \n", "================================================================================\n", - "2022-04-29 12:41:21 | \n", - "////////////////////////////////////////////////////////////////////////////////\n", - " ITERATION 1 / 1 \n", - "////////////////////////////////////////////////////////////////////////////////\n", - "2022-04-29 12:41:21 | \n", + "2022-08-15 16:15:16 (D) | running setup for module 'system.Workstation'\n", + "2022-08-15 16:15:20 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/sflog_002.txt\n", + "2022-08-15 16:15:20 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/parameters_002.yaml\n", + "2022-08-15 16:15:20 (D) | running setup for module 'solver.Specfem2D'\n", + "2022-08-15 16:15:20 (I) | initializing 3 solver directories\n", + "2022-08-15 16:15:22 (D) | running setup for module 'preprocess.Default'\n", + "2022-08-15 16:15:23 (D) | running setup for module 'optimize.Gradient'\n", + "2022-08-15 16:15:25 (I) | re-loading optimization module from checkpoint\n", + "2022-08-15 16:15:27 (I) | re-loading optimization module from checkpoint\n", + "2022-08-15 16:15:27 (I) | \n", "////////////////////////////////////////////////////////////////////////////////\n", - " EVALUATING GRADIENT \n", + " RUNNING ITERATION 01 \n", "////////////////////////////////////////////////////////////////////////////////\n", - "2022-04-29 12:41:21 | evaluating gradient 1 times on system...\n", - "2022-04-29 12:41:21 | checkpointing working environment to disk\n", - "2022-04-29 12:41:22 | exporting current working environment to disk\n", - "2022-04-29 12:41:23 | running task solver.eval_grad 1 times\n", - "2022-04-29 12:41:23 | running adjoint simulations\n", - "2022-04-29 12:41:38 | exporting kernels to:\n", - "/home/bchow/Work/work/sf3_specfem2d_example/scratch/evalgrad\n", - "2022-04-29 12:41:38 | \n", + "2022-08-15 16:15:27 (I) | \n", + "================================================================================\n", + " RUNNING INVERSION WORKFLOW \n", + "================================================================================\n", + "2022-08-15 16:15:27 (I) | 'evaluate_initial_misfit' has already been run, skipping\n", + "2022-08-15 16:15:27 (I) | \n", "////////////////////////////////////////////////////////////////////////////////\n", - " POSTPROCESSING KERNELS \n", + " EVALUATING EVENT KERNELS W/ ADJOINT SIMULATIONS \n", "////////////////////////////////////////////////////////////////////////////////\n", - "2022-04-29 12:41:38 | processing kernels into gradient on system...\n", - "2022-04-29 12:41:38 | checkpointing working environment to disk\n", - "2022-04-29 12:41:39 | exporting current working environment to disk\n", - "2022-04-29 12:41:39 | running task postprocess.process_kernels 1 times\n", - "2022-04-29 12:41:39 | saving summed kernels to:\n", - "/home/bchow/Work/work/sf3_specfem2d_example/scratch/evalgrad/kernels/sum\n", - "2022-04-29 12:41:41 | \n", + "2022-08-15 16:15:27 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log'\n", + "2022-08-15 16:16:11 (D) | renaming output event kernels: 'alpha' -> 'vp'\n", + "2022-08-15 16:16:11 (D) | renaming output event kernels: 'beta' -> 'vs'\n", + "2022-08-15 16:16:12 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log'\n", + "2022-08-15 16:16:59 (D) | renaming output event kernels: 'alpha' -> 'vp'\n", + "2022-08-15 16:16:59 (D) | renaming output event kernels: 'beta' -> 'vs'\n", + "2022-08-15 16:16:59 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log'\n", + "2022-08-15 16:17:45 (D) | renaming output event kernels: 'alpha' -> 'vp'\n", + "2022-08-15 16:17:45 (D) | renaming output event kernels: 'beta' -> 'vs'\n", + "2022-08-15 16:17:45 (I) | \n", "////////////////////////////////////////////////////////////////////////////////\n", - " COMPUTING SEARCH DIRECTION \n", + " GENERATING/PROCESSING MISFIT KERNEL \n", "////////////////////////////////////////////////////////////////////////////////\n", - "2022-04-29 12:41:41 | computing search direction with L-BFGS\n", - "2022-04-29 12:41:41 | first L-BFGS iteration, setting search direction as inverse gradient\n", - "2022-04-29 12:41:41 | \n", - "================================================================================\n", - " FINISHED FLOW EXECUTION \n", - "================================================================================\n", - "2022-04-29 12:41:41 | \n", - "================================================================================\n", - " FINISHED INVERSION WORKFLOW \n", - "================================================================================\n" + "2022-08-15 16:17:45 (I) | combining event kernels into single misfit kernel\n", + "2022-08-15 16:17:47 (I) | scaling gradient to absolute model perturbations\n", + "2022-08-15 16:17:49 (I) | stop workflow at `stop_after`: evaluate_gradient_from_kernels\n" ] } ], "source": [ - "# We can use the `seisflows resume` command to continue an active workflow\n", - "# again we use the '-f' flag to skip past the user-input stage.\n", - "! seisflows resume -f" + "# We can use the `seisflows submit` command to continue an active workflow\n", + "# The state file created during the first run will tell the workflow to resume from the stopped point in the workflow\n", + "! seisflows submit " ] }, { @@ -1850,79 +1682,79 @@ "metadata": {}, "source": [ "----------------\n", - "The functions __evaluate_gradient()__ through __compute_direction()__ have run adjoint simulations to generate event kernels and sum the kernels into the misfit kernel. \n", + "The function __run_adjoint_simulations()__ has run adjoint simulations to generate event kernels. The functions __postprocess_event_kernels__ and __evaluate_gradient_from_kernels__ will have summed and (optionally) smoothed the kernels to recover the gradient, which will be used to update our starting model.\n", "\n", - "> **NOTE**: Because we only have one event, our misfit kernel is just exactly our event kernel. And since we did not specify any smoothing lenghts (PAR.SMOOTH_H and PAR.SMOOTH_V), no smoothing of the gradient has occurred. \n", + "> **NOTE**: Since we did not specify any smoothing lenghts (PAR.SMOOTH_H and PAR.SMOOTH_V), no smoothing of the gradient has occurred. \n", "\n", - "Using the L-BFGS optimization algorithm, SeisFlows3 has computed a search direction that will be used in the line search to search for a best fitting model which optimally reduces the objective function. We can take a look at where SeisFlows3 has stored the information relating to kernel generation and the optimization computation." + "Using the gradient-descent optimization algorithm, SeisFlows will now compute a search direction that will be used in the line search to search for a best fitting model which optimally reduces the objective function. We can take a look at where SeisFlows has stored the information relating to kernel generation and the optimization computation." ] }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "gradient kernels model residuals\r\n" + "\u001b[1m\u001b[32mgradient\u001b[m\u001b[m \u001b[1m\u001b[32mkernels\u001b[m\u001b[m \u001b[1m\u001b[32mmisfit_kernel\u001b[m\u001b[m \u001b[1m\u001b[32mmodel\u001b[m\u001b[m residuals.txt\r\n" ] } ], "source": [ "# Gradient evaluation files are stored here, the kernels are stored separately from the gradient incase\n", "# the user wants to manually manipulate them\n", - "! ls scratch/evalgrad" + "! ls scratch/eval_grad" ] }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "proc000000_vp_kernel.bin proc000000_vs_kernel.bin\r\n" + "proc000000_vp_kernel.bin proc000000_vs_kernel.bin\r\n" ] } ], "source": [ "# SeisFlows3 stores all kernels and gradient information as SPECFEM binary (.bin) files\n", - "! ls scratch/evalgrad/gradient" + "! ls scratch/eval_grad/gradient" ] }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "001 sum\r\n" + "\u001b[1m\u001b[32m001\u001b[m\u001b[m \u001b[1m\u001b[32m002\u001b[m\u001b[m \u001b[1m\u001b[32m003\u001b[m\u001b[m\r\n" ] } ], "source": [ "# Kernels are stored on a per-event basis, and summed together (sum/). If smoothing was performed, \n", "# we would see both smoothed and unsmoothed versions of the misfit kernel\n", - "! ls scratch/evalgrad/kernels" + "! ls scratch/eval_grad/kernels" ] }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "f_new.txt g_new.npy LBFGS m_new.npy\tp_new.npy\r\n" + "checkpoint.npz f_new.txt g_new.npz m_new.npz\r\n" ] } ], @@ -1935,21 +1767,21 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[-0.00000000e+00 -0.00000000e+00 -0.00000000e+00 ... -3.96447909e-11\n", - " -2.00156454e-11 -2.61676726e-12]\n" + "[[-1.18126331e-12 2.40273470e-12 3.97045036e-11 ... 9.62017688e-11\n", + " 4.21140102e-11 3.96825021e-12]]\n" ] } ], "source": [ - "p_new = np.load(\"scratch/optimize/p_new.npy\")\n", - "print(p_new)" + "g_new = np.load(\"scratch/optimize/g_new.npz\")\n", + "print(g_new[\"vs_kernel\"])" ] }, { @@ -1960,188 +1792,199 @@ "#### 3c. Line search and model update\n", "\n", "Let's finish off the inversion by running through the line search, which will generate new models using the\n", - "gradient, evaluate the objective function by running forward simulations, and comparing the evaluated objective function with the value obtained in __initialize__. Satisfactory reduction in the objective function will result in a termination of the line search. We are using a bracketing line search here (CITE RYANS PAPER), which requires finding models which both increase and decrease the misfit with respect to the initial evaluation. Therefore it will likely take more than two trial steps to complete the line search" + "gradient, evaluate the objective function by running forward simulations, and comparing the evaluated objective function with the value obtained in __evalaute_initial_misfit__. \n", + "\n", + "Satisfactory reduction in the objective function will result in a termination of the line search. We are using a bracketing line search here [(Modrak et al. 2018)](https://academic.oup.com/gji/article/206/3/1864/2583505), which requires finding models which both increase and decrease the misfit with respect to the initial evaluation. Therefore it takes atleast two trial steps to complete the line search." ] }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RESUME_FROM: evaluate_gradient -> line_search\n", - "STOP_AFTER: compute_direction -> finalize\n" + "stop_after: evaluate_gradient_from_kernels -> finalize_iteration\r\n" ] } ], "source": [ - "! seisflows par resume_from line_search # resume from the line search \n", - "! seisflows par stop_after finalize # We don't want to run the clean() argument so that we can explore the dir" + "! seisflows par stop_after perform_line_search # We don't want to run the finalize_iteration argument so that we can explore the dir" ] }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2022-04-29 12:42:40 | copying par/log file to: /home/bchow/Work/work/sf3_specfem2d_example/logs/output_sf3_003.txt\n", - "2022-04-29 12:42:40 | copying par/log file to: /home/bchow/Work/work/sf3_specfem2d_example/logs/parameters_003.yaml\n", - "2022-04-29 12:42:40 | exporting current working environment to disk\n", - "2022-04-29 12:42:42 | \n", - "////////////////////////////////////////////////////////////////////////////////\n", - " WORKFLOW WILL RESUME FROM FUNC: 'line_search' \n", - "////////////////////////////////////////////////////////////////////////////////\n", - "2022-04-29 12:42:42 | \n", - "////////////////////////////////////////////////////////////////////////////////\n", - " WORKFLOW WILL STOP AFTER FUNC: 'finalize' \n", - "////////////////////////////////////////////////////////////////////////////////\n", - "2022-04-29 12:42:42 | \n", + "2022-08-15 16:21:55 (D) | setting iteration==1 from state file\n", + "2022-08-15 16:21:55 (I) | \n", "================================================================================\n", - " STARTING INVERSION WORKFLOW \n", + " SETTING UP INVERSION WORKFLOW \n", "================================================================================\n", - "2022-04-29 12:42:42 | \n", + "2022-08-15 16:22:03 (D) | running setup for module 'system.Workstation'\n", + "2022-08-15 16:22:05 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/sflog_003.txt\n", + "2022-08-15 16:22:05 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/parameters_003.yaml\n", + "2022-08-15 16:22:05 (D) | running setup for module 'solver.Specfem2D'\n", + "2022-08-15 16:22:05 (I) | initializing 3 solver directories\n", + "2022-08-15 16:22:07 (D) | running setup for module 'preprocess.Default'\n", + "2022-08-15 16:22:08 (D) | running setup for module 'optimize.Gradient'\n", + "2022-08-15 16:22:09 (I) | re-loading optimization module from checkpoint\n", + "2022-08-15 16:22:11 (I) | re-loading optimization module from checkpoint\n", + "2022-08-15 16:22:11 (I) | \n", "////////////////////////////////////////////////////////////////////////////////\n", - " ITERATION 1 / 1 \n", + " RUNNING ITERATION 01 \n", "////////////////////////////////////////////////////////////////////////////////\n", - "2022-04-29 12:42:42 | \n", + "2022-08-15 16:22:11 (I) | \n", "================================================================================\n", - " CONDUCTING LINE SEARCH (i01s00) \n", + " RUNNING INVERSION WORKFLOW \n", "================================================================================\n", - "2022-04-29 12:42:42 | max step length safeguard is: 5.26E+10\n", - "2022-04-29 12:42:42 | \n", - "EVALUATE BRACKETING LINE SEARCH\n", - "--------------------------------------------------------------------------------\n", - "2022-04-29 12:42:42 | step length(s) = 0.00E+00\n", - "2022-04-29 12:42:42 | misfit val(s) = 1.75E-03\n", - "2022-04-29 12:42:42 | first iteration, guessing trial step\n", - "2022-04-29 12:42:42 | initial step length safegaurd, setting manual step length\n", - "2022-04-29 12:42:42 | manually set initial step length: 5.26E+09\n", - "2022-04-29 12:42:42 | checking poissons ratio for: 'm_try.npy'\n", - "2022-04-29 12:42:42 | model parameters (m_try.npy i01s00):\n", - "2022-04-29 12:42:42 | 5800.00 <= vp <= 5800.00\n", - "2022-04-29 12:42:42 | 3278.69 <= vs <= 3790.00\n", - "2022-04-29 12:42:42 | 0.13 <= pr <= 0.27\n", - "2022-04-29 12:42:42 | \n", - "////////////////////////////////////////////////////////////////////////////////\n", - " TRIAL STEP COUNT: i01s01 \n", - "////////////////////////////////////////////////////////////////////////////////\n", - "2022-04-29 12:42:42 | \n", - "EVALUATE OBJECTIVE FUNCTION\n", + "2022-08-15 16:22:11 (I) | 'evaluate_initial_misfit' has already been run, skipping\n", + "2022-08-15 16:22:11 (I) | 'run_adjoint_simulations' has already been run, skipping\n", + "2022-08-15 16:22:11 (I) | 'postprocess_event_kernels' has already been run, skipping\n", + "2022-08-15 16:22:11 (I) | 'evaluate_gradient_from_kernels' has already been run, skipping\n", + "2022-08-15 16:22:11 (I) | initializing 'bracket'ing line search\n", + "2022-08-15 16:22:11 (I) | enforcing max step length safeguard\n", + "2022-08-15 16:22:11 (D) | step length(s) = 0.00E+00\n", + "2022-08-15 16:22:11 (D) | misfit val(s) = 1.28E-03\n", + "2022-08-15 16:22:11 (I) | try: first evaluation, attempt guess step length, alpha=9.08E+11\n", + "2022-08-15 16:22:11 (I) | try: applying initial step length safegaurd as alpha has exceeded maximum step length, alpha_new=1.44E+10\n", + "2022-08-15 16:22:11 (D) | overwriting initial step length, alpha_new=2.32E+09\n", + "2022-08-15 16:22:11 (I) | trial model 'm_try' parameters: \n", + "2022-08-15 16:22:11 (I) | 5800.00 <= vp <= 5800.00\n", + "2022-08-15 16:22:11 (I) | 3244.51 <= vs <= 3790.00\n", + "2022-08-15 16:22:12 (I) | \n", + "LINE SEARCH STEP COUNT 01\n", "--------------------------------------------------------------------------------\n", - "2022-04-29 12:42:42 | saving model 'm_try.npy' to:\n", - "/home/bchow/Work/work/sf3_specfem2d_example/scratch/scratch/model\n", - "2022-04-29 12:42:42 | evaluating objective function 1 times on system...\n", - "2022-04-29 12:42:42 | checkpointing working environment to disk\n", - "2022-04-29 12:42:44 | exporting current working environment to disk\n", - "2022-04-29 12:42:44 | running task solver.eval_func 1 times\n", - "2022-04-29 12:42:44 | running forward simulations\n", - "2022-04-29 12:42:49 | calling preprocess.prepare_eval_grad()\n", - "2022-04-29 12:42:49 | preparing files for gradient evaluation\n", - "2022-04-29 12:42:50 | exporting residuals to:\n", - "/home/bchow/Work/work/sf3_specfem2d_example/scratch/scratch\n", - "2022-04-29 12:42:50 | summing residuals with preprocess module\n", - "2022-04-29 12:42:50 | saving misfit 9.850E-04 to tag 'f_try.txt'\n", - "2022-04-29 12:42:50 | \n", - "EVALUATE BRACKETING LINE SEARCH\n", + "2022-08-15 16:22:12 (I) | evaluating objective function for source 001\n", + "2022-08-15 16:22:12 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:22:23 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:22:23 (I) | evaluating objective function for source 002\n", + "2022-08-15 16:22:23 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:22:35 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:22:35 (I) | evaluating objective function for source 003\n", + "2022-08-15 16:22:35 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:22:48 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:22:48 (D) | misfit for trial model (f_try) == 8.65E-04\n", + "2022-08-15 16:22:48 (D) | step length(s) = 0.00E+00, 2.32E+09\n", + "2022-08-15 16:22:48 (D) | misfit val(s) = 1.28E-03, 8.65E-04\n", + "2022-08-15 16:22:48 (I) | try: misfit not bracketed, increasing step length using golden ratio, alpha=3.76E+09\n", + "2022-08-15 16:22:49 (I) | line search model 'm_try' parameters: \n", + "2022-08-15 16:22:49 (I) | 5800.00 <= vp <= 5800.00\n", + "2022-08-15 16:22:49 (I) | 3086.61 <= vs <= 3969.23\n", + "2022-08-15 16:22:49 (I) | trial step unsuccessful. re-attempting line search\n", + "2022-08-15 16:22:49 (I) | \n", + "LINE SEARCH STEP COUNT 02\n", "--------------------------------------------------------------------------------\n", - "2022-04-29 12:42:50 | step length(s) = 0.00E+00, 5.26E+09\n", - "2022-04-29 12:42:50 | misfit val(s) = 1.75E-03, 9.85E-04\n", - "2022-04-29 12:42:50 | misfit not bracketed, increasing step length\n", - "2022-04-29 12:42:50 | checking poissons ratio for: 'm_try.npy'\n", - "2022-04-29 12:42:50 | model parameters (m_try.npy i01s01):\n", - "2022-04-29 12:42:50 | 5800.00 <= vp <= 5800.00\n", - "2022-04-29 12:42:50 | 3141.92 <= vs <= 3969.23\n", - "2022-04-29 12:42:50 | 0.06 <= pr <= 0.29\n", - "2022-04-29 12:42:50 | retrying with new trial step\n", - "2022-04-29 12:42:50 | \n", - "////////////////////////////////////////////////////////////////////////////////\n", - " TRIAL STEP COUNT: i01s02 \n", - "////////////////////////////////////////////////////////////////////////////////\n", - "2022-04-29 12:42:50 | \n", - "EVALUATE OBJECTIVE FUNCTION\n", + "2022-08-15 16:22:49 (I) | evaluating objective function for source 001\n", + "2022-08-15 16:22:49 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:23:01 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:23:01 (I) | evaluating objective function for source 002\n", + "2022-08-15 16:23:01 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:23:13 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:23:13 (I) | evaluating objective function for source 003\n", + "2022-08-15 16:23:13 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:23:25 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:23:25 (D) | misfit for trial model (f_try) == 1.73E-03\n", + "2022-08-15 16:23:25 (D) | step length(s) = 0.00E+00, 2.32E+09, 3.76E+09\n", + "2022-08-15 16:23:25 (D) | misfit val(s) = 1.28E-03, 8.65E-04, 1.73E-03\n", + "2022-08-15 16:23:25 (I) | try: bracket acceptable but step length unreasonable attempting to re-adjust step length alpha=1.59E+09\n", + "2022-08-15 16:23:25 (I) | line search model 'm_try' parameters: \n", + "2022-08-15 16:23:25 (I) | 5800.00 <= vp <= 5800.00\n", + "2022-08-15 16:23:25 (I) | 3325.01 <= vs <= 3698.63\n", + "2022-08-15 16:23:25 (I) | trial step unsuccessful. re-attempting line search\n", + "2022-08-15 16:23:25 (I) | \n", + "LINE SEARCH STEP COUNT 03\n", "--------------------------------------------------------------------------------\n", - "2022-04-29 12:42:50 | saving model 'm_try.npy' to:\n", - "/home/bchow/Work/work/sf3_specfem2d_example/scratch/scratch/model\n", - "2022-04-29 12:42:51 | evaluating objective function 1 times on system...\n", - "2022-04-29 12:42:51 | checkpointing working environment to disk\n", - "2022-04-29 12:42:52 | exporting current working environment to disk\n", - "2022-04-29 12:42:53 | running task solver.eval_func 1 times\n", - "2022-04-29 12:42:53 | running forward simulations\n", - "2022-04-29 12:42:59 | calling preprocess.prepare_eval_grad()\n", - "2022-04-29 12:42:59 | preparing files for gradient evaluation\n", - "2022-04-29 12:42:59 | exporting residuals to:\n", - "/home/bchow/Work/work/sf3_specfem2d_example/scratch/scratch\n", - "2022-04-29 12:43:00 | summing residuals with preprocess module\n", - "2022-04-29 12:43:00 | saving misfit 1.227E-03 to tag 'f_try.txt'\n", - "2022-04-29 12:43:00 | \n", - "EVALUATE BRACKETING LINE SEARCH\n", + "2022-08-15 16:23:25 (I) | evaluating objective function for source 001\n", + "2022-08-15 16:23:25 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:23:37 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:23:37 (I) | evaluating objective function for source 002\n", + "2022-08-15 16:23:37 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:23:51 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:23:51 (I) | evaluating objective function for source 003\n", + "2022-08-15 16:23:51 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:24:03 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:24:04 (D) | misfit for trial model (f_try) == 2.59E-03\n", + "2022-08-15 16:24:04 (D) | step length(s) = 0.00E+00, 1.59E+09, 2.32E+09, 3.76E+09\n", + "2022-08-15 16:24:04 (D) | misfit val(s) = 1.28E-03, 2.59E-03, 8.65E-04, 1.73E-03\n", + "2022-08-15 16:24:04 (I) | try: bracket acceptable but step length unreasonable attempting to re-adjust step length alpha=2.82E+09\n", + "2022-08-15 16:24:04 (I) | line search model 'm_try' parameters: \n", + "2022-08-15 16:24:04 (I) | 5800.00 <= vp <= 5800.00\n", + "2022-08-15 16:24:04 (I) | 3189.77 <= vs <= 3852.13\n", + "2022-08-15 16:24:04 (I) | trial step unsuccessful. re-attempting line search\n", + "2022-08-15 16:24:04 (I) | \n", + "LINE SEARCH STEP COUNT 04\n", "--------------------------------------------------------------------------------\n", - "2022-04-29 12:43:00 | step length(s) = 0.00E+00, 5.26E+09, 8.51E+09\n", - "2022-04-29 12:43:00 | misfit val(s) = 1.75E-03, 9.85E-04, 1.23E-03\n", - "2022-04-29 12:43:00 | bracket okay, step length reasonable, pass\n", - "2022-04-29 12:43:00 | checking poissons ratio for: 'm_try.npy'\n", - "2022-04-29 12:43:00 | model parameters (m_try.npy i01s02):\n", - "2022-04-29 12:43:00 | 5800.00 <= vp <= 5800.00\n", - "2022-04-29 12:43:00 | 3278.69 <= vs <= 3790.00\n", - "2022-04-29 12:43:00 | 0.13 <= pr <= 0.27\n", - "2022-04-29 12:43:00 | trial step successful\n", - "2022-04-29 12:43:00 | \n", + "2022-08-15 16:24:04 (I) | evaluating objective function for source 001\n", + "2022-08-15 16:24:04 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:24:15 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:24:15 (I) | evaluating objective function for source 002\n", + "2022-08-15 16:24:15 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:24:27 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:24:27 (I) | evaluating objective function for source 003\n", + "2022-08-15 16:24:27 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-15 16:24:39 (D) | quantifying misfit with 'Default'\n", + "2022-08-15 16:24:39 (D) | misfit for trial model (f_try) == 3.46E-03\n", + "2022-08-15 16:24:39 (D) | step length(s) = 0.00E+00, 1.59E+09, 2.32E+09, 2.82E+09, 3.76E+09\n", + "2022-08-15 16:24:39 (D) | misfit val(s) = 1.28E-03, 2.59E-03, 8.65E-04, 3.46E-03, 1.73E-03\n", + "2022-08-15 16:24:39 (I) | pass: bracket acceptable and step length reasonable. returning minimum line search misfit.\n", + "2022-08-15 16:24:39 (I) | line search model 'm_try' parameters: \n", + "2022-08-15 16:24:39 (I) | 5800.00 <= vp <= 5800.00\n", + "2022-08-15 16:24:39 (I) | 3244.51 <= vs <= 3790.00\n", + "2022-08-15 16:24:39 (I) | trial step successful. finalizing line search\n", + "2022-08-15 16:24:39 (I) | \n", "FINALIZING LINE SEARCH\n", "--------------------------------------------------------------------------------\n", - "2022-04-29 12:43:00 | shifting current model (new) to previous model (old)\n", - "2022-04-29 12:43:00 | setting accepted line search model as current model\n", - "2022-04-29 12:43:00 | current misfit is f_new.txt=9.850E-04\n", - "2022-04-29 12:43:00 | writing optimization stats to: stats\n", - "2022-04-29 12:43:00 | resetting line search step count to 0\n", - "2022-04-29 12:43:00 | \n", - "================================================================================\n", - " FINALIZING ITERATION 1 \n", - "================================================================================\n", - "2022-04-29 12:43:00 | exporting current working environment to disk\n", - "2022-04-29 12:43:01 | saving model 'm_new.npy' to path:\n", - "/home/bchow/Work/work/sf3_specfem2d_example/output/model_0001\n", - "2022-04-29 12:43:02 | saving gradient to path:\n", - "/home/bchow/Work/work/sf3_specfem2d_example/output/gradient_0001\n", - "2022-04-29 12:43:02 | \n", - "================================================================================\n", - " FINISHED FLOW EXECUTION \n", - "================================================================================\n", - "2022-04-29 12:43:02 | \n", - "================================================================================\n", - " FINISHED INVERSION WORKFLOW \n", - "================================================================================\n" + "2022-08-15 16:24:39 (I) | writing optimization stats\n", + "2022-08-15 16:24:39 (I) | renaming current (new) optimization vectors as previous model (old)\n", + "2022-08-15 16:24:39 (I) | setting accepted trial model (try) as current model (new)\n", + "2022-08-15 16:24:39 (I) | misfit of accepted trial model is f=8.645E-04\n", + "2022-08-15 16:24:39 (I) | resetting line search step count to 0\n", + "2022-08-15 16:24:39 (I) | \n", + "////////////////////////////////////////////////////////////////////////////////\n", + " CLEANING WORKDIR FOR NEXT ITERATION \n", + "////////////////////////////////////////////////////////////////////////////////\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-08-15 16:24:41 (I) | thrifty inversion encountering first iteration, defaulting to standard inversion workflow\n", + "2022-08-15 16:24:42 (I) | stop workflow at `stop_after`: finalize_iteration\n" ] } ], "source": [ - "! seisflows resume -f" + "! seisflows submit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "From the log statements above, we can see that the SeisFlows3 line search required 2 trial steps, where it modified values of Vs until satisfactory reduction in the objective function was met. This was the final step in the iteration, and so the finalization step made last-minute preparations for a subsequent iteration. " + "From the log statements above, we can see that the SeisFlows line search required 4 trial steps, where it modified values of Vs (shear-wave velocity) until satisfactory reduction in the objective function was met. This was the final step in the iteration, and so the finalization of the line search made preparations for a subsequent iteration. " ] }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "alpha.npy f_old.txt g_old.npy m_new.npy p_old.npy\r\n", - "f_new.txt f_try.txt LBFGS\t m_old.npy\r\n" + "alpha.txt f_old.txt m_new.npz p_old.npz\r\n", + "checkpoint.npz f_try.txt m_old.npz\r\n", + "f_new.txt g_old.npz output_optim.txt\r\n" ] } ], @@ -2153,44 +1996,21 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "factor.txt\t line_search.txt slope.txt\ttheta.txt\r\n", - "gradient_norm_L1.txt misfit.txt step_count.txt\r\n", - "gradient_norm_L2.txt restarted.txt step_length.txt\r\n" + "step_count,step_length,gradient_norm_L1,gradient_norm_L2,misfit,if_restarted,slope,theta\r\n", + "04,2.323E+09,9.243E-05,1.049E-06,1.279E-03,0,8.263E-13,0.000E+00\r\n" ] } ], "source": [ "# The stats/ directory contains text files describing the optimization/line search\n", - "! ls stats" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " ITER STEPLEN MISFIT\r\n", - "========== ========== ==========\r\n", - " 1 0.000e+00 1.748e-03\r\n", - " 5.261e+09 9.850e-04\r\n", - " 8.512e+09 1.227e-03\r\n" - ] - } - ], - "source": [ - "# For example we can look at the step length chosen for the accepted trial step in the line search\n", - "! cat stats/line_search.txt" + "! cat scratch/optimize/output_optim.txt" ] }, { @@ -2199,15 +2019,8 @@ "source": [ "### 4. Conclusions\n", "\n", - "We've now seen how SeisFlows3 runs an __Inversion__ workflow using the __Specfem2D__ solver on a __serial__ system (local workstation). More or less, this is all you need to run SeisFlows3 with any combination of modules. The specificities of a system or numerical solver are already handled internally by SeisFlows3, so if you want to use Specmfe3D_Cartesian as your solver, you would only need to run `seisflows par solver specfem3d` at the beginning of your workflow (you will also need to setup your Specfem3D models, similar to what we did for Specfem2D here). To run on a slurm system like Chinook, you can run `seisflows par system chinook`. " + "We've now seen how SeisFlows runs an __Inversion__ workflow using the __Specfem2D__ solver on a __Workstation__ system. More or less, this is all you need to run SeisFlows with any combination of modules. The specificities of a system or numerical solver are already handled internally by SeisFlows, so if you want to use Specmfe3D_Cartesian as your solver, you would only need to run `seisflows par solver specfem3d` at the beginning of your workflow (you will also need to set up your Specfem3D models, similar to what we did for Specfem2D here). To run on a slurm system like Chinook (University of Alaska Fairbanks), you can run `seisflows par system chinook`. " ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -2226,7 +2039,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/docs/notebooks/working_directory.ipynb b/docs/notebooks/working_directory.ipynb index 57aa577e..50779626 100644 --- a/docs/notebooks/working_directory.ipynb +++ b/docs/notebooks/working_directory.ipynb @@ -6,26 +6,28 @@ "source": [ "# Working Directory Structure\n", "\n", - "SeisFlows3 hardcodes it's own working directory when executing a workflow. Below we explore the working directory set up by the SPECFEM2D-workstation example. Working directories may change slightly depending on the chosen workflow, but will more or less follow the following structure. The two specfem2d directories listed below are not part of the SeisFlows3 working directory." + "SeisFlows sets it's own working directory when executing a workflow. Below we explore the working directory set up by the SPECFEM2D-workstation example. Working directories may change slightly depending on the chosen workflow, but will more or less follow the same structure. \n", + "\n", + ">__NOTE__: The two SPECFEM2D directories listed below are not part of the standard SeisFlows working directory." ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "/home/bchow/Work/official/workshop_pyatoa_sf3/ex1_specfem2d_workstation\n", - "logs\toutput_sf3.txt\t scratch\t stats\r\n", - "output\tparameters.yaml specfem2d_workdir\r\n" + "/Users/Chow/Work/work/sf_specfem2d_example\n", + "\u001b[1m\u001b[32mlogs\u001b[m\u001b[m parameters.yaml sflog.txt \u001b[1m\u001b[35mspecfem2d\u001b[m\u001b[m\r\n", + "\u001b[1m\u001b[32moutput\u001b[m\u001b[m \u001b[1m\u001b[32mscratch\u001b[m\u001b[m sfstate.txt \u001b[1m\u001b[32mspecfem2d_workdir\u001b[m\u001b[m\r\n" ] } ], "source": [ - "%cd ~/Work/official/workshop_pyatoa_sf3/ex1_specfem2d_workstation\n", + "%cd /Users/Chow/Work/work/sf_specfem2d_example\n", "! ls" ] }, @@ -35,19 +37,19 @@ "source": [ "----------------------\n", "## scratch/\n", - "The active working directory of SeisFlows3 where all of the heavy lifting takes place. Each module in the SeisFlows3 package may have it's own sub-directory where it stores temporary work data. Additionally, we have two eval*/ directories where objective function evaluation (evalfunc) and gradient evaluation (evalgrad) files are stored." + "The active working directory of SeisFlows where all of the heavy lifting takes place. Each module in the SeisFlows package may have it's own sub-directory where it stores temporary work data. Additionally, we have two eval*/ directories where objective function evaluation (eval_func) and gradient evaluation (eval_grad) files are stored." ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "evalfunc evalgrad optimize preprocess solver system\r\n" + "\u001b[1m\u001b[32meval_func\u001b[m\u001b[m \u001b[1m\u001b[32meval_grad\u001b[m\u001b[m \u001b[1m\u001b[32moptimize\u001b[m\u001b[m \u001b[1m\u001b[32mpreprocess\u001b[m\u001b[m \u001b[1m\u001b[32msolver\u001b[m\u001b[m \u001b[1m\u001b[32msystem\u001b[m\u001b[m\r\n" ] } ], @@ -82,14 +84,14 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "001 002 003 mainsolver\r\n" + "\u001b[1m\u001b[32m001\u001b[m\u001b[m \u001b[1m\u001b[32m002\u001b[m\u001b[m \u001b[1m\u001b[32m003\u001b[m\u001b[m \u001b[1m\u001b[35mmainsolver\u001b[m\u001b[m\r\n" ] } ], @@ -99,14 +101,16 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "bin DATA kernel_paths mesher.log OUTPUT_FILES SEM\tsolver.log traces\r\n" + "\u001b[1m\u001b[32mDATA\u001b[m\u001b[m adj_solver.log fwd_solver.log solver_vs.log\r\n", + "\u001b[1m\u001b[32mOUTPUT_FILES\u001b[m\u001b[m \u001b[1m\u001b[32mbin\u001b[m\u001b[m kernel_paths \u001b[1m\u001b[32mtraces\u001b[m\u001b[m\r\n", + "\u001b[1m\u001b[35mSEM\u001b[m\u001b[m fwd_mesher.log solver_vp.log\r\n" ] } ], @@ -131,14 +135,14 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "adj obs syn\r\n" + "\u001b[1m\u001b[32madj\u001b[m\u001b[m \u001b[1m\u001b[32mobs\u001b[m\u001b[m \u001b[1m\u001b[32msyn\u001b[m\u001b[m\r\n" ] } ], @@ -148,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -165,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -219,15 +223,16 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "alpha.npy f_old.txt g_old.npy m_new.npy p_old.npy\r\n", - "f_new.txt f_try.txt LBFGS\t m_old.npy\r\n" + "alpha.txt f_old.txt m_new.npz p_old.npz\r\n", + "checkpoint.npz f_try.txt m_old.npz\r\n", + "f_new.txt g_old.npz output_optim.txt\r\n" ] } ], @@ -237,34 +242,34 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[5800. 5800. 5800. ... 3499.77655379 3499.9021825\n", - " 3499.99078301]\n" + "[[3500.0027437 3499.99441921 3499.90777902 ... 3499.77655378\n", + " 3499.9021825 3499.99078301]]\n" ] } ], "source": [ "import numpy as np\n", - "m_new = np.load(\"scratch/optimize/m_new.npy\")\n", - "print(m_new)" + "m_new = np.load(\"scratch/optimize/m_new.npz\")\n", + "print(m_new[\"vs\"])" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2.591424e-03\r\n" + "8.645199999999999153e-04\r\n" ] } ], @@ -276,30 +281,28 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### evalfunc/ & evalgrad/\n", + "### eval_func/ & eval_grad/\n", "\n", "Scratch directories containing objective function evaluation and gradient evaluation files. These include (1) the current **model** being used for misfit evaluation, and (2) **residuals** which define the misfit for each event. **evalgrad/** also contains **kernels** which define per-event kernels which are summed and manipulated with the postprocess module." ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "model residuals\n", - "\n", - "kernels model\tresiduals\n" + "\r\n" ] } ], "source": [ - "! ls scratch/evalfunc\n", + "! ls scratch/eval_func\n", "! echo\n", - "! ls scratch/evalgrad" + "! ls scratch/eval_grad" ] }, { @@ -609,9 +612,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python (docs)", + "display_name": "Python 3", "language": "python", - "name": "docs" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -623,7 +626,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.12" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/docs/parameter_file.rst b/docs/parameter_file.rst index 920b7ce6..148abdf5 100644 --- a/docs/parameter_file.rst +++ b/docs/parameter_file.rst @@ -1,35 +1,57 @@ -Parameter File -============== - -The parameter file is the central control object for a SeisFlows -workflow. Here we take a look at the anatomy of a parameter file. -Parameter files in SeisFlows are formatted in the `YAML format (YAML -Ain’t Markup Language) `__. - -Template --------- - -Each workflow starts with the module-only template parameter file which -defines the core modules which make up the package. Running -``seisflows setup`` from the command line will create this file. - -.. code:: ipython3 - - ! seisflows setup - - -.. parsed-literal:: - +Parameter File +============== + +The parameter file is the central control object for a SeisFlows +workflow. Here we take a look at the anatomy of a parameter file. +Parameter files in SeisFlows are formatted in the `YAML format (YAML +Ain’t Markup Language) `__. + +Template +-------- + +Each workflow starts with the module-only template parameter file which +defines the core modules of the package. Your choices for each of these +modules will determine which paths and parameters are included in the +full parameter file. Running ``seisflows setup`` from the command line +will create the template file. + +.. code:: ipython3 + + ! seisflows setup -h + + +.. parsed-literal:: + + usage: seisflows setup [-h] [-f] + + In the specified working directory, copy template parameter file containing + only module choices, and symlink source code for both the base and super + repositories for easy edit access. If a parameter file matching the provided + name exists in the working directory, a prompt will appear asking the user if + they want to overwrite. + + optional arguments: + -h, --help show this help message and exit + -f, --force automatically overwrites existing parameter file + + +.. code:: ipython3 + + ! seisflows setup + + +.. parsed-literal:: + creating parameter file: parameters.yaml - - -.. code:: ipython3 - - ! cat parameters.yaml - - -.. parsed-literal:: - + + +.. code:: ipython3 + + ! cat parameters.yaml + + +.. parsed-literal:: + # ////////////////////////////////////////////////////////////////////////////// # # SeisFlows YAML Parameter File @@ -49,162 +71,168 @@ defines the core modules which make up the package. Running # # MODULES # /////// - # WORKFLOW (str): The method for running SeisFlows; equivalent to main() - # SOLVER (str): External numerical solver to use for waveform simulations - # SYSTEM (str): Computer architecture of the system being used - # OPTIMIZE (str): Optimization algorithm for the inverse problem - # PREPROCESS (str): Preprocessing schema for waveform data - # POSTPROCESS (str): Postprocessing schema for kernels and gradients + # workflow (str): The types and order of functions for running SeisFlows + # system (str): Computer architecture of the system being used + # solver (str): External numerical solver to use for waveform simulations + # preprocess (str): Preprocessing schema for waveform data + # optimize (str): Optimization algorithm for the inverse problem # ============================================================================== - WORKFLOW: inversion - SOLVER: specfem2d - SYSTEM: workstation - OPTIMIZE: LBFGS - PREPROCESS: base - POSTPROCESS: base - - -How do I choose my modules? -~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -As seen above, each of the modules comes with a default value. But you -may want to run a migration, not an inversion. Or run with SPECFEM3D not -2D. As stated in the comments at the top of the file, the -``seisflows print modules`` command lists out all available options. -Don’t see an option that works for you? Learn to extend the SeisFlows -package here: **!!! docs page link here !!!** - -.. code:: ipython3 - - ! seisflows print modules - - -.. parsed-literal:: - - SEISFLOWS3 MODULES - ////////////////// - '+': package, '-': module, '*': class + workflow: forward + system: workstation + solver: specfem2d + preprocess: default + optimize: gradient + + +How do I choose modules? +~~~~~~~~~~~~~~~~~~~~~~~~ + +As seen above, each of the modules comes with a default value which +represents the base class\* for this module. + +* For an explanation of base classes and Python inheritance, see the `inheritance page.`__ + +These default values are likely not suitable for all, e.g., if you want +to run an inversion and not a forward workflow, or use SPECFEM3D not +SPECFEM2D. To see all available module options, use the +``seisflows print modules`` command. + +.. code:: ipython3 + + ! seisflows print modules + + +.. parsed-literal:: + + SEISFLOWS MODULES + ///////////////// + '-': module, '*': class - + SYSTEM - - seisflows - * base - * cluster - * lsf - * slurm - * workstation - - seisflows-super - * chinook - * maui - + PREPROCESS - - seisflows - * base - * pyatoa - - seisflows-super - * pyatoa_nz - + SOLVER - - seisflows - * base - * specfem2d - * specfem3d - * specfem3d_globe - - seisflows-super - * specfem3d_maui - + POSTPROCESS - - seisflows - * base - - seisflows-super - + OPTIMIZE - - seisflows - * LBFGS - * NLCG - * base - - seisflows-super - + WORKFLOW - - seisflows - * base - * inversion - * migration - * test - - seisflows-super - * thrifty_inversion - * thrifty_maui - - -How do I change modules? -~~~~~~~~~~~~~~~~~~~~~~~~ - -Feel free to use any old text editor to edit the YAML file, or you can -use the ``seisflows par`` command to make changes directly from the -command line. For example, say we want to use SPECFEM3D - -.. code:: ipython3 - - # Changes the current parameter to the given value - ! seisflows par solver specfem3d - - -.. parsed-literal:: - - SOLVER: specfem2d -> specfem3d - - -.. code:: ipython3 - - # Prints out the current parameter value - ! seisflows par solver - - -.. parsed-literal:: - - SOLVER: specfem3d - - -How do I get to a full parameter file? -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The module-only parameter file serves as as a template for dynamically -generating a full parameter file. Since each module requires it’s own -unique set of parameters and paths, each parameter file will look -different. We can use the ``seisflows configure`` command to complete -our parmater file, based on the chosen modules. - -.. code:: ipython3 - - ! seisflows configure - - -.. parsed-literal:: - - filling parameters.yaml w/ default values - - -Anatomy of the parameter file ------------------------------ - -As we will see below, the parameter file has now been generated. Each -module will define its own section, separated by a header of comments. -Within each header, parameter names, types and descriptions are listed. -At the bottom of the parameter file, there is a section defining paths -required by the workflow. Section headers will look something: - -.. code:: ipython3 - - # ============================================================================= - # MODULE - # ////// - # PARAMETER_NAME (type): - # Description - # ... - # ============================================================================= - PARAMETER_NAME: parameter_value - -.. code:: ipython3 - - ! head -80 parameters.yaml - - -.. parsed-literal:: - + - workflow + * forward + * inversion + * migration + - system + * chinook + * cluster + * frontera + * lsf + * maui + * slurm + * workstation + - solver + * specfem + * specfem2d + * specfem3d + * specfem3d_globe + - preprocess + * default + * pyaflowa + - optimize + * LBFGS + * NLCG + * gradient + + +How do I change modules? +~~~~~~~~~~~~~~~~~~~~~~~~ + +Feel free to use any text editor, or use the ``seisflows par`` command +to make changes directly from the command line. For example, say we want +to use SPECFEM3D as our solver module. + +This is also covered in the `command line tool page.`__ + +.. code:: ipython3 + + # Changes the current parameter to the given value + ! seisflows par solver specfem3d + + +.. parsed-literal:: + + solver: specfem2d -> specfem3d + + +.. code:: ipython3 + + # Prints out the current parameter value + ! seisflows par solver + + +.. parsed-literal:: + + solver: specfem3d + + +How do I create a full parameter file? +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The module-only parameter file serves as as a template for dynamically +generating the full parameter file. Since each module requires it’s own +unique set of parameters and paths, each parameter file will look +different. We use the ``seisflows configure`` command to complete the +file. + +.. code:: ipython3 + + ! seisflows configure -h + + +.. parsed-literal:: + + usage: seisflows configure [-h] [-a] + + SeisFlows parameter files will vary depending on chosen modules and their + respective required parameters. This function will dynamically traverse the + source code and generate a template parameter file based on module choices. + The resulting file incldues docstrings and type hints for each parameter. + Optional parameters will be set with default values and required parameters + and paths will be marked appropriately. Required parameters must be set before + a workflow can be submitted. + + optional arguments: + -h, --help show this help message and exit + -a, --absolute_paths Set default paths relative to cwd + + +.. code:: ipython3 + + ! seisflows configure + +Below we will take a look at the parameter file we just created + +Anatomy of a parameter file +--------------------------- + +Each of SeisFlows’ modules will define its own section in the parameter +file, separated by a header of comments representing the docstring. +Within each header, parameter names, types and descriptions are listed. +At the bottom of the parameter file, there is a section defining paths +required by SeisFlows. Section headers will look something: + +.. code:: ipython3 + + # ============================================================================= + # MODULE + # ------ + # Module description + # + # Parameters + # ---------- + # :type parameter: type + # :param paramter: description + # ... + # ============================================================================= + parameter: value + +.. code:: ipython3 + + ! head -80 parameters.yaml + + +.. parsed-literal:: + # ////////////////////////////////////////////////////////////////////////////// # # SeisFlows YAML Parameter File @@ -224,315 +252,112 @@ required by the workflow. Section headers will look something: # # MODULES # /////// - # WORKFLOW (str): The method for running SeisFlows; equivalent to main() - # SOLVER (str): External numerical solver to use for waveform simulations - # SYSTEM (str): Computer architecture of the system being used - # OPTIMIZE (str): Optimization algorithm for the inverse problem - # PREPROCESS (str): Preprocessing schema for waveform data - # POSTPROCESS (str): Postprocessing schema for kernels and gradients + # workflow (str): The types and order of functions for running SeisFlows + # system (str): Computer architecture of the system being used + # solver (str): External numerical solver to use for waveform simulations + # preprocess (str): Preprocessing schema for waveform data + # optimize (str): Optimization algorithm for the inverse problem # ============================================================================== - WORKFLOW: inversion - SOLVER: specfem3d - SYSTEM: workstation - OPTIMIZE: LBFGS - PREPROCESS: base - POSTPROCESS: base - - # ============================================================================= - # SYSTEM - # ////// - # TITLE (str): - # The name used to submit jobs to the system, defaults to the name of the - # working directory - # PRECHECK (list): - # A list of parameters that will be displayed to stdout before 'submit' or - # 'resume' is run. Useful for manually reviewing important parameters prior - # to system submission - # LOG_LEVEL (str): - # Verbosity output of SF3 logger. Available from least to most verbosity: - # 'CRITICAL', 'WARNING', 'INFO', 'DEBUG'; defaults to 'DEBUG' - # VERBOSE (bool): - # Level of verbosity provided to the output log. If True, log statements - # will declare what module/class/function they are being called from. - # Useful for debugging but also very noisy. - # MPIEXEC (str): - # Function used to invoke executables on the system. For example 'srun' on - # SLURM systems, or './' on a workstation. If left blank, will guess based - # on the system. - # NTASK (int): - # Number of separate, individual tasks. Also equal to the number of desired - # sources in workflow - # NPROC (int): - # Number of processor to use for each simulation + workflow: forward + system: workstation + solver: specfem3d + preprocess: default + optimize: gradient # ============================================================================= - TITLE: docs - PRECHECK: - - TITLE - LOG_LEVEL: DEBUG - VERBOSE: False - MPIEXEC: - NTASK: 1 - NPROC: 1 - - # ============================================================================= - # PREPROCESS - # ////////// - # MISFIT (str): - # Misfit function for waveform comparisons, for available see - # seisflows.plugins.misfit - # BACKPROJECT (str): - # Backprojection function for migration, for available see - # seisflows.plugins.adjoint - # NORMALIZE (list): - # Data normalization parameters used to normalize the amplitudes of - - -.. code:: ipython3 - - ! tail --lines=54 parameters.yaml - - -.. parsed-literal:: - + # + # Forward Workflow + # ---------------- + # Run forward solver in parallel and (optionally) calculate + # data-synthetic misfit and adjoint sources. + # + # Parameters + # ---------- + # :type modules: list of module + # :param modules: instantiated SeisFlows modules which should have been + # generated by the function `seisflows.config.import_seisflows` with a + # parameter file generated by seisflows.configure + # :type data_case: str + # :param data_case: How to address 'data' in the workflow, available options: + # 'data': real data will be provided by the user in + # `path_data/{source_name}` in the same format that the solver will + # produce synthetics (controlled by `solver.format`) OR + # synthetic': 'data' will be generated as synthetic seismograms using + # a target model provided in `path_model_true`. If None, workflow will + # not attempt to generate data. + # :type export_traces: bool + # :param export_traces: export all waveforms that are generated by the + # external solver to `path_output`. If False, solver traces stored in + # scratch may be discarded at any time in the workflow + # :type export_residuals: bool + # :param export_residuals: export all residuals (data-synthetic misfit) that + # are generated by the external solver to `path_output`. If False, + # residuals stored in scratch may be discarded at any time in the workflow + # + # # ============================================================================= - # PATHS - # ///// - # SCRATCH: - # scratch path to hold temporary data during workflow - # OUTPUT: - # directory to save workflow outputs to disk - # SYSTEM: - # scratch path to hold any system related data - # LOCAL: - # path to local data to be used during workflow - # LOGFILE: - # the main output log file where all processes will track their status - # SOLVER: - # scratch path to hold solver working directories - # SPECFEM_BIN: - # path to the SPECFEM binary executables - # SPECFEM_DATA: - # path to the SPECFEM DATA/ directory containing the 'Par_file', 'STATIONS' - # file and 'CMTSOLUTION' files - # DATA: - # path to data available to workflow - # MASK: - # Directory to mask files for gradient masking - # OPTIMIZE: - # scratch path to store data related to nonlinear optimization - # MODEL_INIT: - # location of the initial model to be used for workflow - # MODEL_TRUE: - # Target model to be used for PAR.CASE == 'synthetic' - # FUNC: - # scratch path to store data related to function evaluations - # GRAD: - # scratch path to store data related to gradient evaluations - # HESS: - # scratch path to store data related to Hessian evaluations + data_case: data + export_traces: False + export_residuals: False # ============================================================================= - PATHS: - SCRATCH: /home/bchow/REPOSITORIES/seisflows/seisflows/docs/scratch - OUTPUT: /home/bchow/REPOSITORIES/seisflows/seisflows/docs/output - SYSTEM: /home/bchow/REPOSITORIES/seisflows/seisflows/docs/scratch/system - LOCAL: - LOGFILE: /home/bchow/REPOSITORIES/seisflows/seisflows/docs/output_sf3.txt - SOLVER: /home/bchow/REPOSITORIES/seisflows/seisflows/docs/scratch/solver - SPECFEM_BIN: !!! REQUIRED PATH !!! - SPECFEM_DATA: !!! REQUIRED PATH !!! - DATA: - MASK: - OPTIMIZE: /home/bchow/REPOSITORIES/seisflows/seisflows/docs/scratch/optimize - MODEL_INIT: !!! REQUIRED PATH !!! - MODEL_TRUE: - FUNC: /home/bchow/REPOSITORIES/seisflows/seisflows/docs/scratch/scratch - GRAD: /home/bchow/REPOSITORIES/seisflows/seisflows/docs/scratch/evalgrad - HESS: /home/bchow/REPOSITORIES/seisflows/seisflows/docs/scratch/evalhess - - -How do I know what parameters need to be set? -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - - **NOTE**: Required parameters that can not be set to default values - will be listed as ``!!! REQUIRED PARAMETER !!!`` - -We can check the required paths and parameters manually by scrolling -through the parameter file, or we can use the -``seisflows par --required`` command to list them out all at once. - -.. code:: ipython3 - - ! seisflows par --required - - -.. parsed-literal:: - - !!! REQUIRED PARAMETER !!! - ========================== - MATERIALS - DENSITY - ATTENUATION - NT - DT - FORMAT - CASE - END - !!! REQUIRED PATH !!! - ===================== - SPECFEM_BIN - SPECFEM_DATA - MODEL_INIT - - -Checking parameter validity ---------------------------- - -You might be asking, how do I know if my parameters are set correctly? -SeisFlows modules feature check() functions which dictate correct -parameter values. You can run ``seisflows init`` to run these check() -functions. Because we have required parameters still left unset in our -parameter file, we expect the ``seisflows init`` function to throw an -error. - -.. code:: ipython3 - - ! seisflows init - - -.. parsed-literal:: - - ================================================================================ - PARAMETER FILE READ ERROR - ///////////////////////// - Please check that your parameter file is properly formatted in the YAML format. - If you have just run 'seisflows configure', you may have some required - parameters that will need to be filled out before you can proceed. The error - message is: + # + # Workstation System + # ------------------ + # Runs tasks in serial on a local machine. + # + # Parameters + # ---------- + # :type ntask: int + # :param ntask: number of individual tasks/events to run during workflow. + # Must be <= the number of source files in `path_specfem_data` + # :type nproc: int + # :param nproc: number of processors to use for each simulation + # :type log_level: str + # :param log_level: logger level to pass to logging module. + + +.. code:: ipython3 + + ! tail parameters.yaml + + +.. parsed-literal:: + + path_model_true: null + path_state_file: /Users/Chow/Repositories/seisflows/docs/notebooks/sfstate.txt + path_data: null + path_par_file: /Users/Chow/Repositories/seisflows/docs/notebooks/parameters.yaml + path_log_files: /Users/Chow/Repositories/seisflows/docs/notebooks/logs + path_output_log: /Users/Chow/Repositories/seisflows/docs/notebooks/sflog.txt + path_specfem_bin: null + path_specfem_data: null + path_solver: /Users/Chow/Repositories/seisflows/docs/notebooks/scratch/solver + path_preconditioner: null + + +How do I know how parameters need to be set? +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Most SeisFlows parameters come with reasonable default values. The +docstrings headers will also list the expected type and available +options (if any). You may also run the ``seisflows check`` command which +verifies that parameters are set correctly. + +.. code:: ipython3 + + ! seisflows check + + +.. parsed-literal:: + - could not determine a constructor for the tag 'tag:yaml.org,2002:!' - in "parameters.yaml", line 147, column 12 ================================================================================ - - -Let’s set some random variables for the required parameters with the -``seisflows par`` command and try again. - -.. code:: ipython3 - - ! seisflows par materials elastic - ! seisflows par density constant - ! seisflows par attenuation False - ! seisflows par nt 100 - ! seisflows par dt .05 - ! seisflows par format ascii - ! seisflows par case data - ! seisflows par end 1 - ! seisflows par specfem_bin ./ - ! seisflows par specfem_data ./ - ! seisflows par model_init ./ - - -.. parsed-literal:: - - MATERIALS: !!! REQUIRED PARAMETER !!! -> elastic - DENSITY: !!! REQUIRED PARAMETER !!! -> constant - ATTENUATION: !!! REQUIRED PARAMETER !!! -> False - NT: !!! REQUIRED PARAMETER !!! -> 100 - DT: !!! REQUIRED PARAMETER !!! -> .05 - FORMAT: !!! REQUIRED PARAMETER !!! -> ascii - CASE: !!! REQUIRED PARAMETER !!! -> data - END: !!! REQUIRED PARAMETER !!! -> 1 - SPECFEM_BIN: !!! REQUIRED PATH !!! -> ./ - SPECFEM_DATA: !!! REQUIRED PATH !!! -> ./ - MODEL_INIT: !!! REQUIRED PATH !!! -> ./ - - -.. code:: ipython3 - - ! seisflows init - - -.. parsed-literal:: - - instantiating SeisFlows working state in directory: output - - -Of course we knew that the above parameters were acceptable. But what if -we input an unacceptable parameter into the parameter file and try -again? - -.. code:: ipython3 - - ! rm -r output/ - ! seisflows par materials visibily_incorrect_value - ! seisflows init - - -.. parsed-literal:: - - MATERIALS: elastic -> visibily_incorrect_value + PARAMETER ERRROR + //////////////// + `path_specfem_bin` must exist and must point to directory containing SPECFEM + executables ================================================================================ - MODULE CHECK ERROR - ////////////////// - seisflows.config module check failed with: - - solver: MATERIALS must be in ['ELASTIC', 'ACOUSTIC', 'ISOTROPIC', 'ANISOTROPIC'] - ================================================================================ - - -And voila, the module check has thrown an error, and told us (the User) -how to properly set the value of the materials parameter. Hopefully a -combination of thorough explanations in the parameter file section -headers, and error catching with ``seisflows init`` makes crafting your -own parameter file a smooth process. - -.. code:: ipython3 - - ! head -155 parameters.yaml | tail --lines=38 - - -.. parsed-literal:: - - - # ============================================================================= - # SOLVER - # ////// - # MATERIALS (str): - # Material parameters used to define model. Available: ['ELASTIC': Vp, Vs, - # 'ACOUSTIC': Vp, 'ISOTROPIC', 'ANISOTROPIC'] - # DENSITY (str): - # How to treat density during inversion. Available: ['CONSTANT': Do not - # update density, 'VARIABLE': Update density] - # ATTENUATION (str): - # If True, turn on attenuation during forward simulations, otherwise set - # attenuation off. Attenuation is always off for adjoint simulations. - # COMPONENTS (str): - # Components used to generate data, formatted as a single string, e.g. ZNE - # or NZ or E - # SOLVERIO (int): - # The format external solver files. Available: ['fortran_binary', 'adios'] - # NT (float): - # Number of time steps set in the SPECFEM Par_file - # DT (float): - # Time step or delta set in the SPECFEM Par_file - # FORMAT (float): - # Format of synthetic waveforms used during workflow, available options: - # ['ascii', 'su'] - # SOURCE_PREFIX (str): - # Prefix of SOURCE files in path SPECFEM_DATA. Available ['CMTSOLUTION', - # FORCESOLUTION'] - # ============================================================================= - MATERIALS: visibily_incorrect_value - DENSITY: constant - ATTENUATION: False - COMPONENTS: ZNE - SOLVERIO: fortran_binary - NT: 100 - DT: .05 - FORMAT: ascii - SOURCE_PREFIX: CMTSOLUTION - - -.. code:: ipython3 - - ! rm parameters.yaml # to delete the created file from this working directory + + +.. code:: ipython3 + + ! rm parameters.yaml # to delete the created file from this working directory diff --git a/docs/specfem2d_example.rst b/docs/specfem2d_example.rst index 3f0ced56..6e0f01f6 100644 --- a/docs/specfem2d_example.rst +++ b/docs/specfem2d_example.rst @@ -2,10 +2,11 @@ Specfem2D workstation example ============================= To demonstrate the inversion capabilities of SeisFlows, we will run a -**Specfem2D synthetic-synthetic example** on a **local machine** (Linux -workstation running CentOS 7). Many of the setup steps here will likely -be unique to our OS and workstation, but hopefully they may serve as -templates for new Users wanting to explore SeisFlows. +**Specfem2D synthetic-synthetic example** on a **local machine** (tested +on a Linux workstation running CentOS 7, and an Apple Laptop running +macOS 10.14.6). Many of the setup steps here may be unique to our OS and +workstation, but hopefully they may serve as templates for new Users +wanting to explore SeisFlows. The numerical solver we will use is: `SPECFEM2D `__. We’ll @@ -29,49 +30,7 @@ generate initial and target models, and (3) Run a SeisFlows inversion. .. code:: ipython3 - seisflows examples run 1 - - -.. parsed-literal:: - - Run example: ex1_specfem2d_workstation_inversion - - @@@@@@@@@@ - .@@@@. .%&( %@. - @@@@ @@@@ &@@@@@@ ,%@ - @@@@ @@@, /@@ @ - @@@ @@@@ @@@ @ - @@@@ @@@@ @@@ @ @ - @@@ @@@@ ,@@@ @ @ - @@@@ @@@@ @@@@ @@ @ @ - @@@@ @@@@@ @@@@@ @@@ @@ @ - @@@@ @@@@@ @@@@@@@@@@@@@@ @@ @ - @@@@ @@@@@@ @@@& @@@ @ - @@@@@ @@@@@@@@ %@@@@# @@ - @@@@# @@@@@@@@@@@@@@@@@ @@ - &@@@@@ @@@@( @@& - @@@@@@@ /@@@@ - @@@@@@@@@@@@@@@@@ - @@@@@@@@@@ - - ================================================================================ - SEISFLOWS EXAMPLE 1 - //////////////////// - This is a [SPECFEM2D] [WORKSTATION] example, which will run 2 iterations of an - inversion to assess misfit between two homogeneous halfspace models with - slightly different velocities, 3 sources and 1 receiver. The tasks involved - include: - - 1. (optional) Download, configure, compile SPECFEM2D - 2. Set up a SPECFEM2D working directory - 3. Generate starting model from Tape2007 example - 4. Generate target model w/ perturbed starting model - 5. Set up a SeisFlows working directory - 6. Run 2 iterations of an inversion workflow - ================================================================================ - If you have already downloaded SPECMFE2D, please input its path here. If blank, - this example will pull the latest version from GitHub and attempt to configure - and make the binaries: > + ! seisflows examples run 1 -------------- @@ -98,15 +57,13 @@ tutorial: 2. `Initialize SeisFlows (SF) <#2.-Initialize-SeisFlows-(SF)>`__ - a. `SF working directory and parameter + a. `SeisFlows working directory and parameter file <#2a.-SF-working-directory-and-parameter-file>`__ - b. `Initialize SF working - state <#2b.-Initialize-SF-working-state>`__ 3. `Run SeisFlows <#2.-Run-SeisFlows>`__ a. `Forward simulations <#3a.-Forward-simulations>`__ - b. `Exploring the SF directory + b. `Exploring the SeisFlows directory structure <#3b.-Exploring-the-SF-directory-structure>`__ c. `Adjoint simulations <#3c.-Adjoint-simulations>`__ d. `Line search and model @@ -128,9 +85,9 @@ tutorial: First we’ll download and compile SPECFEM2D to generate the binaries necessary to run our simulations. We will then populate a new SPECFEM2D -working directory that will be used by SeisFlows. We’ll use to Python -OS module to do our filesystem processes just to keep everything in -Python, but this can easily be accomplished in bash. +working directory that will be used by SeisFlows. We’ll use to Python OS +module to do our filesystem processes just to keep everything in Python, +but this can easily be accomplished in bash. .. code:: ipython3 @@ -142,8 +99,12 @@ Python, but this can easily be accomplished in bash. .. code:: ipython3 # vvv USER MUST EDIT THE FOLLOWING PATHS vvv - WORKDIR = "/home/bchow/Work/work/sf_specfem2d_example" - SPECFEM2D = "/home/bchow/REPOSITORIES/specfem2d" + # MAC PATHS + WORKDIR = "/Users/Chow/Work/work/sf_specfem2d_example" + SPECFEM2D = "/Users/Chow/Repositories/specfem2d" + # LINUX PATHS + # WORKDIR = "/home/bchow/Work/work/sf_specfem2d_example" + # SPECFEM2D = "/home/bchow/REPOSITORIES/specfem2d" # where WORKDIR: points to your own working directory # and SPECFEM2D: points to an existing specfem2D repository if available (if not set as '') # ^^^ USER MUST EDIT THE FOLLOWING PATHS ^^^ @@ -168,6 +129,7 @@ Python, but this can easily be accomplished in bash. .. code:: ipython3 # Download SPECFEM2D from GitHub, devel branch for latest codebase OR symlink from existing repo + os.makedirs(WORKDIR) os.chdir(WORKDIR) if os.path.exists("specfem2d"): @@ -209,10 +171,15 @@ Python, but this can easily be accomplished in bash. .. parsed-literal:: - /home/bchow/REPOSITORIES/specfem2d - xadj_seismogram xconvolve_source_timefunction xspecfem2D - xcheck_quality_external_mesh xmeshfem2D xsum_kernels - xcombine_sem xsmooth_sem + /Users/Chow/Repositories/specfem2d + xadj_seismogram xmeshfem2D + xadj_seismogram.dSYM xmeshfem2D.dSYM + xcheck_quality_external_mesh xsmooth_sem + xcheck_quality_external_mesh.dSYM xsmooth_sem.dSYM + xcombine_sem xspecfem2D + xcombine_sem.dSYM xspecfem2D.dSYM + xconvolve_source_timefunction xsum_kernels + xconvolve_source_timefunction.dSYM xsum_kernels.dSYM 1b. Create a separate SPECFEM2D working directory @@ -223,7 +190,7 @@ original repository. The intent here is to isolate the original SPECFEM2D repository from our working state, to protect it from things like accidental file deletions or manipulations. This is not a mandatory step for using SeisFlows, but it helps keep file structure clean in the -long run, and is the SeisFlows dev team’s preferred method of using +long run, and is the SeisFlows3 dev team’s preferred method of using SPECFEM. .. note:: @@ -231,7 +198,7 @@ SPECFEM. In this tutorial we will be using the `Tape2007 example problem `__ -to define our **DATA/** directory (last tested 3/9/22, cf893667). +to define our **DATA/** directory (last tested 8/15/22, bdba4389). .. code:: ipython3 @@ -255,8 +222,8 @@ to define our **DATA/** directory (last tested 3/9/22, cf893667). .. parsed-literal:: - /home/bchow/Work/work/sf_specfem2d_example/specfem2d_workdir - bin DATA + /Users/Chow/Work/work/sf_specfem2d_example/specfem2d_workdir + DATA bin .. code:: ipython3 @@ -274,16 +241,16 @@ to define our **DATA/** directory (last tested 3/9/22, cf893667). .. parsed-literal:: - ------------------------------------------------------------------------------- - ------------------------------------------------------------------------------- - D a t e : 29 - 04 - 2022 T i m e : 12:24:51 - ------------------------------------------------------------------------------- - ------------------------------------------------------------------------------- - - see results in directory: OUTPUT_FILES/ - - done - Fri Apr 29 12:24:51 AKDT 2022 + ------------------------------------------------------------------------------- + ------------------------------------------------------------------------------- + D a t e : 15 - 08 - 2022 T i m e : 10:13:31 + ------------------------------------------------------------------------------- + ------------------------------------------------------------------------------- + + see results in directory: OUTPUT_FILES/ + + done + Mon Aug 15 10:13:31 PDT 2022 -------------- @@ -291,7 +258,7 @@ to define our **DATA/** directory (last tested 3/9/22, cf893667). Now we need to manually set up our SPECFEM2D working directory. As mentioned in the previous cell, the only required elements of this working directory are the following (these files will form the basis for -how SeisFlows operates within the SPECFEM2D framework): +how SeisFlows3 operates within the SPECFEM2D framework): 1. **bin/** directory containing SPECFEM2D binaries 2. **DATA/** directory containing SOURCE and STATION files, as well as a @@ -323,15 +290,22 @@ how SeisFlows operates within the SPECFEM2D framework): .. parsed-literal:: - interfaces_Tape2007.dat SOURCE_003 SOURCE_012 SOURCE_021 - model_velocity.dat_checker SOURCE_004 SOURCE_013 SOURCE_022 - Par_file SOURCE_005 SOURCE_014 SOURCE_023 - Par_file_Tape2007_132rec_checker SOURCE_006 SOURCE_015 SOURCE_024 - Par_file_Tape2007_onerec SOURCE_007 SOURCE_016 SOURCE_025 - proc000000_model_velocity.dat_input SOURCE_008 SOURCE_017 STATIONS - SOURCE SOURCE_009 SOURCE_018 STATIONS_checker - SOURCE_001 SOURCE_010 SOURCE_019 - SOURCE_002 SOURCE_011 SOURCE_020 + Par_file SOURCE_013 + Par_file_Tape2007_132rec_checker SOURCE_014 + Par_file_Tape2007_onerec SOURCE_015 + SOURCE SOURCE_016 + SOURCE_001 SOURCE_017 + SOURCE_002 SOURCE_018 + SOURCE_003 SOURCE_019 + SOURCE_004 SOURCE_020 + SOURCE_005 SOURCE_021 + SOURCE_006 SOURCE_022 + SOURCE_007 SOURCE_023 + SOURCE_008 SOURCE_024 + SOURCE_009 SOURCE_025 + SOURCE_010 STATIONS_checker + SOURCE_011 interfaces_Tape2007.dat + SOURCE_012 model_velocity.dat_checker 1c. Generate initial and target models @@ -354,7 +328,7 @@ We will generate our target model by slightly perturbing the parameters of the initial model. .. note:: - We can use the SeisFlows command line option `seisflows sempar` to directly edit the SPECFEM2D Par_file in the command line. This will work for the SPECFEM3D Par_file as well. + We can use the SeisFlows3 command line option `seisflows sempar` to directly edit the SPECFEM2D Par_file in the command line. This will work for the SPECFEM3D Par_file as well. .. code:: ipython3 @@ -388,7 +362,7 @@ of the initial model. .. parsed-literal:: - bin DATA OUTPUT_FILES + DATA OUTPUT_FILES bin .. code:: ipython3 @@ -400,7 +374,7 @@ of the initial model. ! ./bin/xmeshfem2D > OUTPUT_FILES/mesher_log.txt ! ./bin/xspecfem2D > OUTPUT_FILES/solver_log.txt - # Move the model files (*.bin) into the OUTPUT_FILES directory, where SeisFlows expects them + # Move the model files (*.bin) into the OUTPUT_FILES directory, where SeisFlows3 expects them ! mv DATA/*bin OUTPUT_FILES # Make sure we don't overwrite this initial model when creating our target model in the next step @@ -417,8 +391,8 @@ of the initial model. **** Specfem 2-D Solver - serial version **** ********************************************** - Running Git version of the code corresponding to commit cf89366717d9435985ba852ef1d41a10cee97884 - dating From Date: Mon Nov 29 23:20:51 2021 -0800 + Running Git version of the code corresponding to + dating From NDIM = 2 @@ -429,7 +403,7 @@ of the initial model. Tape-Liu-Tromp (GJI 2007) ------------------------------------------------------------------------------- ------------------------------------------------------------------------------- - D a t e : 29 - 04 - 2022 T i m e : 12:25:24 + D a t e : 15 - 08 - 2022 T i m e : 10:14:13 ------------------------------------------------------------------------------- ------------------------------------------------------------------------------- @@ -453,11 +427,11 @@ directly. .. parsed-literal:: - VELOCITY_MODEL: - - 1 1 2600.d0 5800.d0 3500.0d0 0 0 10.d0 10.d0 0 0 0 0 0 0 - -> - 1 1 2600.d0 5900.d0 3550.0d0 0 0 10.d0 10.d0 0 0 0 0 0 0 + VELOCITY_MODEL: + + 1 1 2600.d0 5800.d0 3500.0d0 0 0 10.d0 10.d0 0 0 0 0 0 0 + -> + 1 1 2600.d0 5900.d0 3550.0d0 0 0 10.d0 10.d0 0 0 0 0 0 0 .. code:: ipython3 @@ -489,8 +463,8 @@ directly. **** Specfem 2-D Solver - serial version **** ********************************************** - Running Git version of the code corresponding to commit cf89366717d9435985ba852ef1d41a10cee97884 - dating From Date: Mon Nov 29 23:20:51 2021 -0800 + Running Git version of the code corresponding to + dating From NDIM = 2 @@ -501,7 +475,7 @@ directly. Tape-Liu-Tromp (GJI 2007) ------------------------------------------------------------------------------- ------------------------------------------------------------------------------- - D a t e : 29 - 04 - 2022 T i m e : 12:25:24 + D a t e : 15 - 08 - 2022 T i m e : 10:14:13 ------------------------------------------------------------------------------- ------------------------------------------------------------------------------- @@ -514,29 +488,29 @@ directly. .. parsed-literal:: - bin DATA OUTPUT_FILES_INIT OUTPUT_FILES_TRUE + DATA OUTPUT_FILES_INIT OUTPUT_FILES_TRUE bin 2. Initialize SeisFlows (SF) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -In this Section we will look at a SeisFlows working directory, -parameter file, and working state. +In this Section we will look at a SeisFlows working directory, parameter +file, and working state. -2a. SF working directory and parameter file -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +2a. SeisFlows working directory and parameter file +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ As with SPECFEM, SeisFlows requires a parameter file (**parameters.yaml**) that controls how an automated workflow will proceed. Because SeisFlows is modular, there are a large number of -potential parameters which may be present in SF parameter file, as each -sub-module may have its own set of unique parameters. +potential parameters which may be present in a SeisFlows parameter file, +as each sub-module may have its own set of unique parameters. In contrast to SPECFEM’s method of listing all available parameters and leaving it up the User to determine which ones are relevant to them, -SeisFlows dynamically builds its parameter file based on User inputs. -In this subsection we will use the built-in SeisFlows command line -tools to generate and populate the parameter file. +SeisFlows dynamically builds its parameter file based on User inputs. In +this subsection we will use the built-in SeisFlows command line tools to +generate and populate the parameter file. .. note:: See the `parameter file documentation page `__ for a more in depth exploration of this central SeisFlows file. @@ -552,44 +526,43 @@ line commands. .. parsed-literal:: - usage: seisflows [-h] [-w [WORKDIR]] [-p [PARAMETER_FILE]] - {setup,configure,init,submit,resume,restart,clean,par,sempar,check,print,convert,reset,debug,edit,examples} - ... - - ================================================================================ - - SeisFlows: Waveform Inversion Package - - ================================================================================ - - optional arguments: - -h, --help show this help message and exit - -w [WORKDIR], --workdir [WORKDIR] - The SeisFlows working directory, default: cwd - -p [PARAMETER_FILE], --parameter_file [PARAMETER_FILE] - Parameters file, default: 'parameters.yaml' - - command: - Available SeisFlows arguments and their intended usages - - setup Setup working directory from scratch - configure Fill parameter file with defaults - init Initiate working environment - submit Submit initial workflow to system - resume Re-submit previous workflow to system - restart Remove current environment and submit new workflow - clean Remove files relating to an active working environment - par View and edit SeisFlows parameter file - sempar View and edit SPECFEM parameter file - check Check state of an active environment - print Print information related to an active environment - convert Convert model file format - reset Reset modules within an active state - debug Start interactive debug environment - edit Open source code file in text editor - examples Look at and run pre-configured example problems - - 'seisflows [command] -h' for more detailed descriptions of each command. + usage: seisflows [-h] [-w [WORKDIR]] [-p [PARAMETER_FILE]] + {setup,configure,swap,init,submit,resume,restart,clean,par,sempar,check,print,reset,debug,examples} + ... + + ================================================================================ + + SeisFlows: Waveform Inversion Package + + ================================================================================ + + optional arguments: + -h, --help show this help message and exit + -w [WORKDIR], --workdir [WORKDIR] + The SeisFlows working directory, default: cwd + -p [PARAMETER_FILE], --parameter_file [PARAMETER_FILE] + Parameters file, default: 'parameters.yaml' + + command: + Available SeisFlows arguments and their intended usages + + setup Setup working directory from scratch + configure Fill parameter file with defaults + swap Swap module parameters in an existing parameter file + init Initiate working environment + submit Submit initial workflow to system + resume Re-submit previous workflow to system + restart Remove current environment and submit new workflow + clean Remove files relating to an active working environment + par View and edit SeisFlows parameter file + sempar View and edit SPECFEM parameter file + check Check state of an active environment + print Print information related to an active environment + reset Reset modules within an active state + debug Start interactive debug environment + examples Look at and run pre-configured example problems + + 'seisflows [command] -h' for more detailed descriptions of each command. .. code:: ipython3 @@ -604,7 +577,7 @@ line commands. .. parsed-literal:: creating parameter file: parameters.yaml - parameters.yaml specfem2d specfem2d_workdir + parameters.yaml specfem2d specfem2d_workdir .. code:: ipython3 @@ -615,38 +588,36 @@ line commands. .. parsed-literal:: - # ////////////////////////////////////////////////////////////////////////////// - # - # SeisFlows YAML Parameter File - # - # ////////////////////////////////////////////////////////////////////////////// - # - # Modules correspond to the structure of the source code, and determine - # SeisFlows' behavior at runtime. Each module requires its own sub-parameters. - # - # .. rubric:: - # - To determine available options for modules listed below, run: - # > seisflows print module - # - To auto-fill with docstrings and default values (recommended), run: - # > seisflows configure - # - To set values as NoneType, use: null - # - To set values as infinity, use: inf - # - # MODULES - # /////// - # WORKFLOW (str): The method for running SeisFlows; equivalent to main() - # SOLVER (str): External numerical solver to use for waveform simulations - # SYSTEM (str): Computer architecture of the system being used - # OPTIMIZE (str): Optimization algorithm for the inverse problem - # PREPROCESS (str): Preprocessing schema for waveform data - # POSTPROCESS (str): Postprocessing schema for kernels and gradients - # ============================================================================== - WORKFLOW: inversion - SOLVER: specfem2d - SYSTEM: workstation - OPTIMIZE: LBFGS - PREPROCESS: base - POSTPROCESS: base + # ////////////////////////////////////////////////////////////////////////////// + # + # SeisFlows YAML Parameter File + # + # ////////////////////////////////////////////////////////////////////////////// + # + # Modules correspond to the structure of the source code, and determine + # SeisFlows' behavior at runtime. Each module requires its own sub-parameters. + # + # .. rubric:: + # - To determine available options for modules listed below, run: + # > seisflows print modules + # - To auto-fill with docstrings and default values (recommended), run: + # > seisflows configure + # - To set values as NoneType, use: null + # - To set values as infinity, use: inf + # + # MODULES + # /////// + # workflow (str): The types and order of functions for running SeisFlows + # system (str): Computer architecture of the system being used + # solver (str): External numerical solver to use for waveform simulations + # preprocess (str): Preprocessing schema for waveform data + # optimize (str): Optimization algorithm for the inverse problem + # ============================================================================== + workflow: forward + system: workstation + solver: specfem2d + preprocess: default + optimize: gradient .. code:: ipython3 @@ -657,66 +628,47 @@ line commands. .. parsed-literal:: - SEISFLOWS MODULES - ////////////////// - '+': package, '-': module, '*': class - - + SYSTEM - - seisflows - * base - * cluster - * lsf - * slurm - * workstation - - seisflows-super - * chinook - * maui - + PREPROCESS - - seisflows - * base - * pyatoa - - seisflows-super - * pyatoa_nz - + SOLVER - - seisflows - * base - * specfem2d - * specfem3d - * specfem3d_globe - - seisflows-super - * specfem3d_maui - + POSTPROCESS - - seisflows - * base - - seisflows-super - + OPTIMIZE - - seisflows - * LBFGS - * NLCG - * base - - seisflows-super - + WORKFLOW - - seisflows - * base - * inversion - * migration - * test - - seisflows-super - * thrifty_inversion - * thrifty_maui + SEISFLOWS MODULES + ///////////////// + '-': module, '*': class + + - workflow + * forward + * inversion + * migration + - system + * chinook + * cluster + * frontera + * lsf + * maui + * slurm + * workstation + - solver + * specfem + * specfem2d + * specfem3d + * specfem3d_globe + - preprocess + * default + * pyaflowa + - optimize + * LBFGS + * NLCG + * gradient .. code:: ipython3 # For this example, we can use most of the default modules, however we need to # change the SOLVER module to let SeisFlows know we're using SPECFEM2D (as opposed to 3D) - ! seisflows par solver specfem2d + ! seisflows par workflow inversion ! cat parameters.yaml .. parsed-literal:: - SOLVER: specfem2d -> specfem2d + workflow: forward -> inversion # ////////////////////////////////////////////////////////////////////////////// # # SeisFlows YAML Parameter File @@ -728,7 +680,7 @@ line commands. # # .. rubric:: # - To determine available options for modules listed below, run: - # > seisflows print module + # > seisflows print modules # - To auto-fill with docstrings and default values (recommended), run: # > seisflows configure # - To set values as NoneType, use: null @@ -736,36 +688,30 @@ line commands. # # MODULES # /////// - # WORKFLOW (str): The method for running SeisFlows; equivalent to main() - # SOLVER (str): External numerical solver to use for waveform simulations - # SYSTEM (str): Computer architecture of the system being used - # OPTIMIZE (str): Optimization algorithm for the inverse problem - # PREPROCESS (str): Preprocessing schema for waveform data - # POSTPROCESS (str): Postprocessing schema for kernels and gradients + # workflow (str): The types and order of functions for running SeisFlows + # system (str): Computer architecture of the system being used + # solver (str): External numerical solver to use for waveform simulations + # preprocess (str): Preprocessing schema for waveform data + # optimize (str): Optimization algorithm for the inverse problem # ============================================================================== - WORKFLOW: inversion - SOLVER: specfem2d - SYSTEM: workstation - OPTIMIZE: LBFGS - PREPROCESS: base - POSTPROCESS: base + workflow: inversion + system: workstation + solver: specfem2d + preprocess: default + optimize: gradient -------------- The ``seisflows configure`` command populates the parameter file based on the chosen modules. SeisFlows will attempt to fill in all parameters -with default values when possible, but values that the User **MUST** set -will be denoted by the value: +with reasonable default values. Docstrings above each module show +descriptions and available options for each of these parameters. - **!!! REQUIRED PARAMETER !!!** - -SeisFlows will not work until all of these required parameters are set -by the User. Docstrings above each module show descriptions and -available options for each of these parameters. In the follownig cell we -will use the ``seisflows par`` command to edit the parameters.yaml file -directly, replacing each of the required parameters with a chosen value. -Comments next to each evaluation describe the choice for each. +In the follownig cell we will use the ``seisflows par`` command to edit +the parameters.yaml file directly, replacing some default parameters +with our own values. Comments next to each evaluation describe the +choice for each. .. code:: ipython3 @@ -775,371 +721,442 @@ Comments next to each evaluation describe the choice for each. .. parsed-literal:: - filling parameters.yaml w/ default values - # ////////////////////////////////////////////////////////////////////////////// - # - # SeisFlows YAML Parameter File - # - # ////////////////////////////////////////////////////////////////////////////// - # - # Modules correspond to the structure of the source code, and determine - # SeisFlows' behavior at runtime. Each module requires its own sub-parameters. - # - # .. rubric:: - # - To determine available options for modules listed below, run: - # > seisflows print module - # - To auto-fill with docstrings and default values (recommended), run: - # > seisflows configure - # - To set values as NoneType, use: null - # - To set values as infinity, use: inf - # - # MODULES - # /////// - # WORKFLOW (str): The method for running SeisFlows; equivalent to main() - # SOLVER (str): External numerical solver to use for waveform simulations - # SYSTEM (str): Computer architecture of the system being used - # OPTIMIZE (str): Optimization algorithm for the inverse problem - # PREPROCESS (str): Preprocessing schema for waveform data - # POSTPROCESS (str): Postprocessing schema for kernels and gradients - # ============================================================================== - WORKFLOW: inversion - SOLVER: specfem2d - SYSTEM: workstation - OPTIMIZE: LBFGS - PREPROCESS: base - POSTPROCESS: base - - # ============================================================================= - # SYSTEM - # ////// - # TITLE (str): - # The name used to submit jobs to the system, defaults to the name of the - # working directory - # PRECHECK (list): - # A list of parameters that will be displayed to stdout before 'submit' or - # 'resume' is run. Useful for manually reviewing important parameters prior - # to system submission - # LOG_LEVEL (str): - # Verbosity output of SF logger. Available from least to most verbosity: - # 'CRITICAL', 'WARNING', 'INFO', 'DEBUG'; defaults to 'DEBUG' - # VERBOSE (bool): - # Level of verbosity provided to the output log. If True, log statements - # will declare what module/class/function they are being called from. - # Useful for debugging but also very noisy. - # MPIEXEC (str): - # Function used to invoke executables on the system. For example 'srun' on - # SLURM systems, or './' on a workstation. If left blank, will guess based - # on the system. - # NTASK (int): - # Number of separate, individual tasks. Also equal to the number of desired - # sources in workflow - # NPROC (int): - # Number of processor to use for each simulation - # ============================================================================= - TITLE: sf_specfem2d_example - PRECHECK: - - TITLE - LOG_LEVEL: DEBUG - VERBOSE: False - MPIEXEC: - NTASK: 1 - NPROC: 1 - - # ============================================================================= - # PREPROCESS - # ////////// - # MISFIT (str): - # Misfit function for waveform comparisons, for available see - # seisflows.plugins.misfit - # BACKPROJECT (str): - # Backprojection function for migration, for available see - # seisflows.plugins.adjoint - # NORMALIZE (list): - # Data normalization parameters used to normalize the amplitudes of - # waveforms. Choose from two sets: ENORML1: normalize per event by L1 of - # traces; OR ENORML2: normalize per event by L2 of traces; AND TNORML1: - # normalize per trace by L1 of itself; OR TNORML2: normalize per trace by - # L2 of itself - # FILTER (str): - # Data filtering type, available options are:BANDPASS (req. MIN/MAX - # PERIOD/FREQ);LOWPASS (req. MAX_FREQ or MIN_PERIOD); HIGHPASS (req. - # MIN_FREQ or MAX_PERIOD) - # MIN_PERIOD (float): - # Minimum filter period applied to time series.See also MIN_FREQ, MAX_FREQ, - # if User defines FREQ parameters, they will overwrite PERIOD parameters. - # MAX_PERIOD (float): - # Maximum filter period applied to time series.See also MIN_FREQ, MAX_FREQ, - # if User defines FREQ parameters, they will overwrite PERIOD parameters. - # MIN_FREQ (float): - # Maximum filter frequency applied to time series.See also MIN_PERIOD, - # MAX_PERIOD, if User defines FREQ parameters, they will overwrite PERIOD - # parameters. - # MAX_FREQ (float): - # Maximum filter frequency applied to time series,See also MIN_PERIOD, - # MAX_PERIOD, if User defines FREQ parameters, they will overwrite PERIOD - # parameters. - # MUTE (list): - # Data mute parameters used to zero out early / late arrivals or offsets. - # Choose any number of: EARLY: mute early arrivals; LATE: mute late - # arrivals; SHORT: mute short source-receiver distances; LONG: mute long - # source-receiver distances - # ============================================================================= - MISFIT: waveform - BACKPROJECT: null - NORMALIZE: [] - FILTER: null - MIN_PERIOD: - MAX_PERIOD: - MIN_FREQ: - MAX_FREQ: - MUTE: [] - - # ============================================================================= - # SOLVER - # ////// - # MATERIALS (str): - # Material parameters used to define model. Available: ['ELASTIC': Vp, Vs, - # 'ACOUSTIC': Vp, 'ISOTROPIC', 'ANISOTROPIC'] - # DENSITY (str): - # How to treat density during inversion. Available: ['CONSTANT': Do not - # update density, 'VARIABLE': Update density] - # ATTENUATION (str): - # If True, turn on attenuation during forward simulations, otherwise set - # attenuation off. Attenuation is always off for adjoint simulations. - # COMPONENTS (str): - # Components used to generate data, formatted as a single string, e.g. ZNE - # or NZ or E - # SOLVERIO (int): - # The format external solver files. Available: ['fortran_binary', 'adios'] - # NT (float): - # Number of time steps set in the SPECFEM Par_file - # DT (float): - # Time step or delta set in the SPECFEM Par_file - # F0 (float): - # Dominant source frequency - # FORMAT (float): - # Format of synthetic waveforms used during workflow, available options: - # ['ascii', 'su'] - # SOURCE_PREFIX (str): - # Prefix of SOURCE files in path SPECFEM_DATA. By default, 'SOURCE' for - # SPECFEM2D - # ============================================================================= - MATERIALS: !!! REQUIRED PARAMETER !!! - DENSITY: !!! REQUIRED PARAMETER !!! - ATTENUATION: !!! REQUIRED PARAMETER !!! - COMPONENTS: ZNE - SOLVERIO: fortran_binary - NT: !!! REQUIRED PARAMETER !!! - DT: !!! REQUIRED PARAMETER !!! - F0: !!! REQUIRED PARAMETER !!! - FORMAT: !!! REQUIRED PARAMETER !!! - SOURCE_PREFIX: SOURCE - - # ============================================================================= - # POSTPROCESS - # /////////// - # SMOOTH_H (float): - # Gaussian half-width for horizontal smoothing in units of meters. If 0., - # no smoothing applied - # SMOOTH_V (float): - # Gaussian half-width for vertical smoothing in units of meters - # TASKTIME_SMOOTH (int): - # Large radii smoothing may take longer than normal tasks. Allocate - # additional smoothing task time as a multiple of TASKTIME - # ============================================================================= - SMOOTH_H: 0.0 - SMOOTH_V: 0.0 - TASKTIME_SMOOTH: 1 - - # ============================================================================= - # OPTIMIZE - # //////// - # LINESEARCH (str): - # Algorithm to use for line search, see seisflows.plugins.line_search for - # available choices - # PRECOND (str): - # Algorithm to use for preconditioning gradients, see - # seisflows.plugins.preconds for available choices - # STEPCOUNTMAX (int): - # Max number of trial steps in line search before a change in line search - # behavior - # STEPLENINIT (float): - # Initial line search step length, as a fraction of current model - # parameters - # STEPLENMAX (float): - # Max allowable step length, as a fraction of current model parameters - # LBFGSMEM (int): - # Max number of previous gradients to retain in local memory - # LBFGSMAX (int): - # LBFGS periodic restart interval, between 1 and 'inf' - # LBFGSTHRESH (float): - # LBFGS angle restart threshold - # ============================================================================= - LINESEARCH: Backtrack - PRECOND: - STEPCOUNTMAX: 10 - STEPLENINIT: 0.05 - STEPLENMAX: 0.5 - LBFGSMEM: 3 - LBFGSMAX: inf - LBFGSTHRESH: 0.0 - - # ============================================================================= - # WORKFLOW - # //////// - # CASE (str): - # Type of inversion, available: ['data': real data inversion, 'synthetic': - # synthetic-synthetic inversion] - # RESUME_FROM (str): - # Name of task to resume inversion from - # STOP_AFTER (str): - # Name of task to stop inversion after finishing - # SAVEMODEL (bool): - # Save final model files after each iteration - # SAVEGRADIENT (bool): - # Save gradient files after each iteration - # SAVEKERNELS (bool): - # Save event kernel files after each iteration - # SAVETRACES (bool): - # Save waveform traces after each iteration - # SAVERESIDUALS (bool): - # Save waveform residuals after each iteration - # SAVEAS (str): - # Format to save models, gradients, kernels. Available: ['binary': save - # files in native SPECFEM .bin format, 'vector': save files as NumPy .npy - # files, 'both': save as both binary and vectors] - # BEGIN (int): - # First iteration of workflow, 1 <= BEGIN <= inf - # END (int): - # Last iteration of workflow, BEGIN <= END <= inf - # ============================================================================= - CASE: !!! REQUIRED PARAMETER !!! - RESUME_FROM: - STOP_AFTER: - SAVEMODEL: True - SAVEGRADIENT: True - SAVEKERNELS: False - SAVETRACES: False - SAVERESIDUALS: False - SAVEAS: binary - BEGIN: 1 - END: !!! REQUIRED PARAMETER !!! - - # ============================================================================= - # PATHS - # ///// - # SCRATCH: - # scratch path to hold temporary data during workflow - # OUTPUT: - # directory to save workflow outputs to disk - # SYSTEM: - # scratch path to hold any system related data - # LOCAL: - # path to local data to be used during workflow - # LOGFILE: - # the main output log file where all processes will track their status - # SOLVER: - # scratch path to hold solver working directories - # SPECFEM_BIN: - # path to the SPECFEM binary executables - # SPECFEM_DATA: - # path to the SPECFEM DATA/ directory containing the 'Par_file', 'STATIONS' - # file and 'CMTSOLUTION' files - # DATA: - # path to data available to workflow - # MASK: - # Directory to mask files for gradient masking - # OPTIMIZE: - # scratch path to store data related to nonlinear optimization - # MODEL_INIT: - # location of the initial model to be used for workflow - # MODEL_TRUE: - # Target model to be used for PAR.CASE == 'synthetic' - # FUNC: - # scratch path to store data related to function evaluations - # GRAD: - # scratch path to store data related to gradient evaluations - # HESS: - # scratch path to store data related to Hessian evaluations - # ============================================================================= - PATHS: - SCRATCH: /home/bchow/Work/work/sf_specfem2d_example/scratch - OUTPUT: /home/bchow/Work/work/sf_specfem2d_example/output - SYSTEM: /home/bchow/Work/work/sf_specfem2d_example/scratch/system - LOCAL: - LOGFILE: /home/bchow/Work/work/sf_specfem2d_example/output_sf.txt - SOLVER: /home/bchow/Work/work/sf_specfem2d_example/scratch/solver - SPECFEM_BIN: !!! REQUIRED PATH !!! - SPECFEM_DATA: !!! REQUIRED PATH !!! - DATA: - MASK: - OPTIMIZE: /home/bchow/Work/work/sf_specfem2d_example/scratch/optimize - MODEL_INIT: !!! REQUIRED PATH !!! - MODEL_TRUE: - FUNC: /home/bchow/Work/work/sf_specfem2d_example/scratch/scratch - GRAD: /home/bchow/Work/work/sf_specfem2d_example/scratch/evalgrad - HESS: /home/bchow/Work/work/sf_specfem2d_example/scratch/evalhess - - -.. code:: ipython3 - - # We can check which parameters we will NEED to fill out before running the workflow with the --required flag - ! seisflows par --required - - -.. parsed-literal:: - - !!! REQUIRED PARAMETER !!! - ========================== - MATERIALS - DENSITY - ATTENUATION - NT - DT - F0 - FORMAT - CASE - END - !!! REQUIRED PATH !!! - ===================== - SPECFEM_BIN - SPECFEM_DATA - MODEL_INIT + # ////////////////////////////////////////////////////////////////////////////// + # + # SeisFlows YAML Parameter File + # + # ////////////////////////////////////////////////////////////////////////////// + # + # Modules correspond to the structure of the source code, and determine + # SeisFlows' behavior at runtime. Each module requires its own sub-parameters. + # + # .. rubric:: + # - To determine available options for modules listed below, run: + # > seisflows print modules + # - To auto-fill with docstrings and default values (recommended), run: + # > seisflows configure + # - To set values as NoneType, use: null + # - To set values as infinity, use: inf + # + # MODULES + # /////// + # workflow (str): The types and order of functions for running SeisFlows + # system (str): Computer architecture of the system being used + # solver (str): External numerical solver to use for waveform simulations + # preprocess (str): Preprocessing schema for waveform data + # optimize (str): Optimization algorithm for the inverse problem + # ============================================================================== + workflow: inversion + system: workstation + solver: specfem2d + preprocess: default + optimize: gradient + # ============================================================================= + # + # Forward Workflow + # ---------------- + # Run forward solver in parallel and (optionally) calculate + # data-synthetic misfit and adjoint sources. + # + # Parameters + # ---------- + # :type modules: list of module + # :param modules: instantiated SeisFlows modules which should have been + # generated by the function `seisflows.config.import_seisflows` with a + # parameter file generated by seisflows.configure + # :type data_case: str + # :param data_case: How to address 'data' in the workflow, available options: + # 'data': real data will be provided by the user in + # `path_data/{source_name}` in the same format that the solver will + # produce synthetics (controlled by `solver.format`) OR + # synthetic': 'data' will be generated as synthetic seismograms using + # a target model provided in `path_model_true`. If None, workflow will + # not attempt to generate data. + # :type stop_after: str + # :param stop_after: optional name of task in task list (use + # `seisflows print tasks` to get task list for given workflow) to stop + # workflow after, allowing user to prematurely stop a workflow to explore + # intermediate results or debug. + # :type export_traces: bool + # :param export_traces: export all waveforms that are generated by the + # external solver to `path_output`. If False, solver traces stored in + # scratch may be discarded at any time in the workflow + # :type export_residuals: bool + # :param export_residuals: export all residuals (data-synthetic misfit) that + # are generated by the external solver to `path_output`. If False, + # residuals stored in scratch may be discarded at any time in the workflow + # + # + # Migration Workflow + # ------------------ + # Run forward and adjoint solver to produce event-dependent misfit kernels. + # Sum and postprocess kernels to produce gradient. In seismic exploration + # this is 'reverse time migration'. + # + # Parameters + # ---------- + # :type export_gradient: bool + # :param export_gradient: export the gradient after it has been generated + # in the scratch directory. If False, gradient can be discarded from + # scratch at any time in the workflow + # :type export_kernels: bool + # :param export_kernels: export each sources event kernels after they have + # been generated in the scratch directory. If False, gradient can be + # discarded from scratch at any time in the workflow + # + # + # Inversion Workflow + # ------------------ + # Peforms iterative nonlinear inversion using the machinery of the Forward + # and Migration workflows, as well as a built-in optimization library. + # + # Parameters + # ---------- + # :type start: int + # :param start: start inversion workflow at this iteration. 1 <= start <= inf + # :type end: int + # :param end: end inversion workflow at this iteration. start <= end <= inf + # :type iteration: int + # :param iteration: The current iteration of the workflow. If NoneType, takes + # the value of `start` (i.e., first iteration of the workflow). User can + # also set between `start` and `end` to resume a failed workflow. + # :type thrifty: bool + # :param thrifty: a thrifty inversion skips the costly intialization step + # (i.e., forward simulations and misfit quantification) if the final + # forward simulations from the previous iterations line search can be + # used in the current one. Requires L-BFGS optimization. + # :type export_model: bool + # :param export_model: export best-fitting model from the line search to disk. + # If False, new models can be discarded from scratch at any time. + # + # + # ============================================================================= + data_case: data + stop_after: null + export_traces: False + export_residuals: False + export_gradient: False + export_kernels: False + start: 1 + end: 1 + export_model: True + thrifty: False + iteration: 1 + # ============================================================================= + # + # Workstation System + # ------------------ + # Runs tasks in serial on a local machine. + # + # Parameters + # ---------- + # :type ntask: int + # :param ntask: number of individual tasks/events to run during workflow. + # Must be <= the number of source files in `path_specfem_data` + # :type nproc: int + # :param nproc: number of processors to use for each simulation + # :type log_level: str + # :param log_level: logger level to pass to logging module. + # Available: 'debug', 'info', 'warning', 'critical' + # :type verbose: bool + # :param verbose: if True, formats the log messages to include the file + # name, line number and message type. Useful for debugging but + # also very verbose. + # + # + # ============================================================================= + ntask: 1 + nproc: 1 + log_level: DEBUG + verbose: False + # ============================================================================= + # + # Solver SPECFEM + # -------------- + # Generalized SPECFEM interface to manipulate SPECFEM2D/3D/3D_GLOBE w/ Python + # + # Parameters + # ---------- + # :type data_format: str + # :param data_format: data format for reading traces into memory. + # Available: ['SU': seismic unix format, 'ASCII': human-readable ascii] + # :type materials: str + # :param materials: Material parameters used to define model. Available: + # ['ELASTIC': Vp, Vs, 'ACOUSTIC': Vp, 'ISOTROPIC', 'ANISOTROPIC'] + # :type density: bool + # :param density: How to treat density during inversion. If True, updates + # density during inversion. If False, keeps it constant. + # TODO allow density scaling during an inversion + # :type attenuation: bool + # :param attenuation: How to treat attenuation during inversion. + # if True, turns on attenuation during forward simulations only. If + # False, attenuation is always set to False. Requires underlying + # attenution (Q_mu, Q_kappa) model + # :type smooth_h: float + # :param smooth_h: Gaussian half-width for horizontal smoothing in units + # of meters. If 0., no smoothing applied + # :type smooth_h: float + # :param smooth_v: Gaussian half-width for vertical smoothing in units + # of meters. + # :type components: str + # :param components: components to consider and tag data with. Should be + # string of letters such as 'RTZ' + # :type solver_io: str + # :param solver_io: format of model/kernel/gradient files expected by the + # numerical solver. Available: ['fortran_binary': default .bin files]. + # TODO: ['adios': ADIOS formatted files] + # :type source_prefix: str + # :param source_prefix: prefix of source/event/earthquake files. If None, + # will attempt to guess based on the specific solver chosen. + # :type mpiexec: str + # :param mpiexec: MPI executable used to run parallel processes. Should also + # be defined for the system module + # + # + # Solver SPECFEM2D + # ---------------- + # SPECFEM2D-specific alterations to the base SPECFEM module + # + # Parameters + # ---------- + # :type source_prefix: str + # :param source_prefix: Prefix of source files in path SPECFEM_DATA. Defaults + # to 'SOURCE' + # :type multiples: bool + # :param multiples: set an absorbing top-boundary condition + # + # + # ============================================================================= + data_format: ascii + materials: acoustic + density: False + attenuation: False + smooth_h: 0.0 + smooth_v: 0.0 + components: ZNE + source_prefix: SOURCE + multiples: False + # ============================================================================= + # + # Default Preprocess + # ------------------ + # Data processing for seismic traces, with options for data misfit, + # filtering, normalization and muting. + # + # Parameters + # ---------- + # :type data_format: str + # :param data_format: data format for reading traces into memory. For + # available see: seisflows.plugins.preprocess.readers + # :type misfit: str + # :param misfit: misfit function for waveform comparisons. For available + # see seisflows.plugins.preprocess.misfit + # :type backproject: str + # :param backproject: backprojection function for migration, or the + # objective function in FWI. For available see + # seisflows.plugins.preprocess.adjoint + # :type normalize: str + # :param normalize: Data normalization parameters used to normalize the + # amplitudes of waveforms. Choose from two sets: + # ENORML1: normalize per event by L1 of traces; OR + # ENORML2: normalize per event by L2 of traces; + # & + # TNORML1: normalize per trace by L1 of itself; OR + # TNORML2: normalize per trace by L2 of itself + # :type filter: str + # :param filter: Data filtering type, available options are: + # BANDPASS (req. MIN/MAX PERIOD/FREQ); + # LOWPASS (req. MAX_FREQ or MIN_PERIOD); + # HIGHPASS (req. MIN_FREQ or MAX_PERIOD) + # :type min_period: float + # :param min_period: Minimum filter period applied to time series. + # See also MIN_FREQ, MAX_FREQ, if User defines FREQ parameters, they + # will overwrite PERIOD parameters. + # :type max_period: float + # :param max_period: Maximum filter period applied to time series. See + # also MIN_FREQ, MAX_FREQ, if User defines FREQ parameters, they will + # overwrite PERIOD parameters. + # :type min_freq: float + # :param min_freq: Maximum filter frequency applied to time series, + # See also MIN_PERIOD, MAX_PERIOD, if User defines FREQ parameters, + # they will overwrite PERIOD parameters. + # :type max_freq: float + # :param max_freq: Maximum filter frequency applied to time series, + # See also MIN_PERIOD, MAX_PERIOD, if User defines FREQ parameters, + # they will overwrite PERIOD parameters. + # :type mute: list + # :param mute: Data mute parameters used to zero out early / late + # arrivals or offsets. Choose any number of: + # EARLY: mute early arrivals; + # LATE: mute late arrivals; + # SHORT: mute short source-receiver distances; + # LONG: mute long source-receiver distances + # + # + # ============================================================================= + misfit: waveform + adjoint: waveform + normalize: [] + filter: null + min_period: null + max_period: null + min_freq: null + max_freq: null + mute: [] + early_slope: null + early_const: null + late_slope: null + late_const: null + short_dist: null + + # ============================================================================= + # + # Gradient Optimization + # --------------------- + # Gradient/steepest descent optimization algorithm. + # + # Parameters + # ---------- + # :type line_search_method: str + # :param line_search_method: chosen line_search algorithm. Currently available + # are 'bracket' and 'backtrack'. See seisflows.plugins.line_search + # for all available options + # :type preconditioner: str + # :param preconditioner: algorithm for preconditioning gradients. Currently + # available: 'diagonal'. Requires `path_preconditioner` to point to a + # set of files that define the preconditioner, formatted the same as the + # input model + # :type step_count_max: int + # :param step_count_max: maximum number of trial steps to perform during + # the line search before a change in line search behavior is + # considered, or a line search is considered to have failed. + # :type step_len_init: float + # :param step_len_init: initial line search step length guess, provided + # as a fraction of current model parameters. + # :type step_len_max: float + # :param step_len_max: maximum allowable step length during the line + # search. Set as a fraction of the current model parameters + # + # + # ============================================================================= + preconditioner: null + step_count_max: 10 + step_len_init: 0.05 + step_len_max: 0.5 + line_search_method: bracket + # ============================================================================= + # + # Paths + # ----- + # :type workdir: str + # :param workdir: working directory in which to look for data and store + # results. Defaults to current working directory + # :type path_output: str + # :param path_output: path to directory used for permanent storage on disk. + # Results and exported scratch files are saved here. + # :type path_data: str + # :param path_data: path to any externally stored data required by the solver + # :type path_state_file: str + # :param path_state_file: path to a text file used to track the current + # status of a workflow (i.e., what functions have already been completed), + # used for checkpointing and resuming workflows + # :type path_model_init: str + # :param path_model_init: path to the starting model used to calculate the + # initial misfit. Must match the expected `solver_io` format. + # :type path_model_true: str + # :param path_model_true: path to a target model if `case`=='synthetic' and + # a set of synthetic 'observations' are required for workflow. + # :type path_eval_grad: str + # :param path_eval_grad: scratch path to store files for gradient evaluation, + # including models, kernels, gradient and residuals. + # :type path_mask: str + # :param path_mask: optional path to a masking function which is used to + # mask out or scale parts of the gradient. The user-defined mask must + # match the file format of the input model (e.g., .bin files). + # :type path_eval_func: str + # :param path_eval_func: scratch path to store files for line search objective + # function evaluations, including models, misfit and residuals + # + # :type path_output_log: str + # :param path_output_log: path to a text file used to store the outputs of + # the package wide logger, which are also written to stdout + # :type path_par_file: str + # :param path_par_file: path to parameter file which is used to instantiate + # the package + # :type path_log_files: str + # :param path_log_files: path to a directory where individual log files are + # saved whenever a number of parallel tasks are run on the system. + # + # :type path_data: str + # :param path_data: path to any externally stored data required by the solver + # :type path_specfem_bin: str + # :param path_specfem_bin: path to SPECFEM bin/ directory which + # contains binary executables for running SPECFEM + # :type path_specfem_data: str + # :param path_specfem_data: path to SPECFEM DATA/ directory which must + # contain the CMTSOLUTION, STATIONS and Par_file files used for + # running SPECFEM + # + # :type path_preprocess: str + # :param path_preprocess: scratch path for all preprocessing processes, + # including saving files + # + # :type path_preconditioner: str + # :param path_preconditioner: optional path to a set of preconditioner files + # formatted the same as the input model (or output model of solver). + # Required to exist and contain files if `preconditioner`==True + # + # ============================================================================= + path_workdir: /Users/Chow/Work/work/sf_specfem2d_example + path_scratch: /Users/Chow/Work/work/sf_specfem2d_example/scratch + path_eval_grad: /Users/Chow/Work/work/sf_specfem2d_example/scratch/eval_grad + path_output: /Users/Chow/Work/work/sf_specfem2d_example/output + path_model_init: null + path_model_true: null + path_state_file: /Users/Chow/Work/work/sf_specfem2d_example/sfstate.txt + path_data: null + path_mask: null + path_eval_func: /Users/Chow/Work/work/sf_specfem2d_example/scratch/eval_func + path_par_file: /Users/Chow/Work/work/sf_specfem2d_example/parameters.yaml + path_log_files: /Users/Chow/Work/work/sf_specfem2d_example/logs + path_output_log: /Users/Chow/Work/work/sf_specfem2d_example/sflog.txt + path_specfem_bin: null + path_specfem_data: null + path_solver: /Users/Chow/Work/work/sf_specfem2d_example/scratch/solver + path_preconditioner: null .. code:: ipython3 # EDIT THE SEISFLOWS PARAMETER FILE + ! seisflows par ntask 3 # set the number of sources/events to use ! seisflows par materials elastic # how the velocity model is parameterized - ! seisflows par density constant # update density or keep constant + ! seisflows par density False # update density or keep constant ! seisflows par attenuation False - ! seisflows par nt 5000 # set by SPECFEM2D Par_file - ! seisflows par dt .06 # set by SPECFEM2D Par_file - ! seisflows par f0 0.084 # set by SOURCE file - ! seisflows par format ascii # how to output synthetic seismograms - ! seisflows par begin 1 # first iteration - ! seisflows par end 1 # final iteration -- we will only run 1 - ! seisflows par case synthetic # synthetic-synthetic means we need both INIT and TRUE models + ! seisflows par start 1 # first iteration + ! seisflows par end 2 # final iteration -- we will only run 1 + ! seisflows par data_case synthetic # synthetic-synthetic means we need both INIT and TRUE models + ! seisflows par components Y # this default example creates Y-component seismograms + ! seisflows par step_count_max 5 # limit the number of steps in the line search # Use Python syntax here to access path constants - os.system(f"seisflows par specfem_bin {SPECFEM2D_BIN}") # set path to SPECFEM2D binaries - os.system(f"seisflows par specfem_data {SPECFEM2D_DATA}") # set path to SEPCFEM2D DATA/ - os.system(f"seisflows par model_init {SPECFEM2D_MODEL_INIT}") # set path to INIT model - os.system(f"seisflows par model_true {SPECFEM2D_MODEL_TRUE}") # set path to TRUE model + os.system(f"seisflows par path_specfem_bin {SPECFEM2D_BIN}") # set path to SPECFEM2D binaries + os.system(f"seisflows par path_specfem_data {SPECFEM2D_DATA}") # set path to SEPCFEM2D DATA/ + os.system(f"seisflows par path_model_init {SPECFEM2D_MODEL_INIT}") # set path to INIT model + os.system(f"seisflows par path_model_true {SPECFEM2D_MODEL_TRUE}") # set path to TRUE model .. parsed-literal:: - MATERIALS: !!! REQUIRED PARAMETER !!! -> elastic - DENSITY: !!! REQUIRED PARAMETER !!! -> constant - ATTENUATION: !!! REQUIRED PARAMETER !!! -> False - NT: !!! REQUIRED PARAMETER !!! -> 5000 - DT: !!! REQUIRED PARAMETER !!! -> .06 - F0: !!! REQUIRED PARAMETER !!! -> 0.084 - FORMAT: !!! REQUIRED PARAMETER !!! -> ascii - BEGIN: 1 -> 1 - END: !!! REQUIRED PARAMETER !!! -> 1 - CASE: !!! REQUIRED PARAMETER !!! -> synthetic + ntask: 1 -> 3 + materials: acoustic -> elastic + density: False -> False + attenuation: False -> False + start: 1 -> 1 + end: 1 -> 2 + data_case: data -> synthetic + components: ZNE -> Y + step_count_max: 10 -> 5 @@ -1165,96 +1182,14 @@ Par_file. .. parsed-literal:: - MODEL: default -> gll - - -2b. Initialize SF working state -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -The SeisFlows command ``seisflows init`` will generate the a SeisFlows -working state without submitting any jobs to the system. This is useful -for testing to see if the user has set an acceptable parameter file, and -if SeisFlows is working as expected. - -The result of running ``seisflows init`` is a collection of pickle (*.p) -and JSON files which define the active Python environment. SeisFlows -relies directly on these files to determine where it is in a workflow. -Throughout an active workflow, SeisFlows will checkpoint itself to -these pickle and JSON files such that if a workflow finishes or crashes, -the User can resume a workflow from the last checkpointed state rather -than needing to restart the workflow. - - **DEBUG MODE:** After running ``seisflows init`` you can explore the - SeisFlows working state in an interactive iPython environment by - running ``seisflows debug``. This will open up an iPython environment - in which the active working state is loaded and accessible The debug - mode is invaluable for exploring the SeisFlows working state, - debugging errors, and performing manual manipulations to an otherwise - automated tool. You can try for yourself by running debug mode and - typing ‘preprocess’ to access the active preprocess module. - -.. code:: ipython3 - - os.chdir(WORKDIR) - ! seisflows init - ! ls output - - -.. parsed-literal:: - - instantiating SeisFlows working state in directory: output - seisflows_optimize.p seisflows_postprocess.p seisflows_system.p - seisflows_parameters.json seisflows_preprocess.p seisflows_workflow.p - seisflows_paths.json seisflows_solver.p - - -.. code:: ipython3 - - # All of the parameters defined in parameters.yaml are saved in this - # internally-used JSON file - ! head output/seisflows_parameters.json - - -.. parsed-literal:: - - { - "ATTENUATION": false, - "BACKPROJECT": null, - "BEGIN": 1, - "CASE": "synthetic", - "COMPONENTS": "ZNE", - "DENSITY": "constant", - "DT": 0.06, - "END": 1, - "F0": 0.084, - - -.. code:: ipython3 - - # Similarly, paths that SeisFlows uses to navigate the system are stored - # in the seisflows_paths.json file - ! head output/seisflows_paths.json - - -.. parsed-literal:: - - { - "DATA": null, - "FUNC": "/home/bchow/Work/work/sf_specfem2d_example/scratch/scratch", - "GRAD": "/home/bchow/Work/work/sf_specfem2d_example/scratch/evalgrad", - "HESS": "/home/bchow/Work/work/sf_specfem2d_example/scratch/evalhess", - "LOCAL": null, - "LOGFILE": "/home/bchow/Work/work/sf_specfem2d_example/output_sf.txt", - "MASK": null, - "MODEL_INIT": "/home/bchow/Work/work/sf_specfem2d_example/specfem2d_workdir/OUTPUT_FILES_INIT", - "MODEL_TRUE": "/home/bchow/Work/work/sf_specfem2d_example/specfem2d_workdir/OUTPUT_FILES_TRUE", + MODEL: default -> gll 3. Run SeisFlows -~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~ -In this Section we will run SeisFlows to generate synthetic -seismograms, kernels, a gradient, and an updated velocity model. +In this Section we will run SeisFlows to generate synthetic seismograms, +kernels, a gradient, and an updated velocity model. 3a. Forward simulations ^^^^^^^^^^^^^^^^^^^^^^^ @@ -1265,216 +1200,157 @@ However the package does allow the User flexibility in how they want the workflow to behave. For example, we can run our workflow in stages by taking advantage of -the ``stop_after`` and ``resume_from`` parameters. As their names -suggest, these parameters allow us to stop and resume the workflow at -certain stages (i.e., functions in workflow.main()). +the ``stop_after`` parameter. As its name suggests, ``stop_after`` +allows us to stop a workflow prematurely so that we may stop and look at +results, or debug a failing workflow. -The available arguments for ``stop_after`` and ``resume_from`` are -discovered by running the command: ``seisflows print flow``, which tells -us what functions will be run from main(). +The ``seisflows print flow`` command tells us what functions we can use +for the ``stop_after`` parameter. .. code:: ipython3 - ! seisflows print flow + os.chdir(WORKDIR) + ! seisflows print tasks .. parsed-literal:: - SEISFLOWS WORKFLOW MAIN - //////////////////////// - Flow arguments for - - 1: setup - 2: initialize - 3: evaluate_gradient - 4: write_gradient - 5: compute_direction - 6: line_search - 7: finalize - 8: clean + SEISFLOWS WORKFLOW TASK LIST + //////////////////////////// + Task list for + + 1: evaluate_initial_misfit + 2: run_adjoint_simulations + 3: postprocess_event_kernels + 4: evaluate_gradient_from_kernels + 5: initialize_line_search + 6: perform_line_search + 7: finalize_iteration -------------- -In an inversion (the workflow we have selected) the flow arguments are -described as: +In the Inversion workflow, the tasks listed are described as follows: -0. **setup:** Not technically listed in the flow arguments, runs setup() - for all SeisFlows modules. If running a synthetic-synthetic - workflow, solver.setup() will generate “data” by running the forward - solver using MODEL_TRUE -1. **initialize:** +1. **evaluate_initial_misfit:** - a. Call numerical solver to run forward simulations using MODEL_INIT, + a. Prepare data for inversion by either copying data from disk or + generating ‘synthetic data’ with MODEL_TRUE + b. Call numerical solver to run forward simulations using MODEL_INIT, generating synthetics - b. Evaluate the objective function by performing waveform comparisons - c. Prepare ``evaluate gradient`` step by generating adjoint sources - and auxiliary files + c. Evaluate the objective function by performing waveform comparisons + d. Prepare ``run_adjoint_simulations`` step by generating adjoint + sources and auxiliary files -2. **evaluate_gradient:** Call numerical solver to run adjoint +2. **run_adjoint_simulations:** Call numerical solver to run adjoint simulation, generating kernels -3. **write_gradient:** Combine all event kernels into a misfit kernel. - Optionally smooth and mask the misfit kernel -4. **compute_direction:** Call on the optimization library to scale the - misfit kernel into the gradient and compute a search direction -5. **line_search:** Perform a line search by algorithmically scaling the - gradient and evaluating the misfit function (forward simulations and - misfit quantification) until misfit is acceptably reduced -6. **finalize:** Run any finalization steps such as saving traces, - kernels, gradients and models to disk, setting up SeisFlows for any - subsequent iterations. -7. **clean:** Clean the scratch/ directory in preparation for subsequent - i - -Let’s set the ``stop_after`` argument to **initialize**, this will halt -the workflow after the intialization step. We’ll also set the -``verbose`` parameter to ‘False’, to keep the logging format relatively -simple. We will explore the ``verbose``\ ==True option in a later cell. +3. **postprocess_event_kernels:** Combine all event kernels into a + misfit kernel. +4. **evaluate_gradient_from_kernels:** Smooth and mask the misfit kernel + to create the gradient +5. **initialize_line_search:** Call on the optimization library to scale + the gradient by a step length to compute the search direction. + Prepare file structure for line search. +6. **perform_line_search:** Perform a line search by algorithmically + scaling the gradient and evaluating the misfit function (forward + simulations and misfit quantification) until misfit is acceptably + reduced. +7. **finalize_iteration:** Run any finalization steps such as saving + traces, kernels, gradients and models to disk, setting up SeisFlows3 + for any subsequent iterations. Clean the scratch/ directory in + preparation for subsequent iterations + +Let’s set the ``stop_after`` argument to **evaluate_initial_misfit**, +this will halt the workflow after the intialization step. We’ll also set +the ``verbose`` parameter to ‘False’, to keep the logging format +relatively simple. We will explore the ``verbose``\ ==True option in a +later cell. .. code:: ipython3 - ! seisflows par stop_after initialize + ! seisflows par stop_after evaluate_initial_misfit ! seisflows par verbose False .. parsed-literal:: - STOP_AFTER: -> initialize - VERBOSE: False -> False + stop_after: null -> evaluate_initial_misfit + verbose: False -> False -------------- -Now let’s run SeisFlows. There are a few ways to do this: ``submit``, -``resume``, and ``restart`` +Now let’s run SeisFlows. There are two ways to do this: ``submit`` and +``restart`` -1. Since we already ran ``seisflows init``, the ``seisflows submit`` - option will not work, as SeisFlows considers this an active working - state and ``submit`` can only be run on uninitialized working states. -2. To run a workflow in an active working state ``resume`` will load the - current working state from the output/ directory and submit a - workflow given the current parameter file. -3. The ``restart`` command is simply a convenience function that runs +1. ``seisflows submit`` is used to run new workflows and resume stopped + or failed workflows. +2. The ``restart`` command is simply a convenience function that runs ``clean`` (to remove an active working state) and ``submit`` (to - submit a fresh working state). - -Since we haven’t done anything in this working state, we will go with a -modified version of Option 3 by running ``clean`` and then ``submit``. -We’ll use the ``-f`` flag (stands for **‘force’**) to skip over the -standard input prompt that asks the User if they are sure they want to -clean and submit. - -But first we’ll try to run ``seisflows submit`` to show why Option 1 -**will not work**. - -.. code:: ipython3 + submit a fresh workflow). - ! seisflows submit -f - - -.. parsed-literal:: - - 2022-04-29 12:32:17 | initializing SeisFlows in sys.modules - ================================================================================ - WARNING - /////// - Data from previous workflow found in working directory. - - > seisflows restart: delete data and start new workflow - > seisflows resume: resume existing workflow - ================================================================================ - - --------------- - -**Okay, let’s go!** In the following cell we will run the SeisFlows -Inversion workflow. In the output cell we will see the logging -statements outputted by SeisFlows, both to stdout and to the output log -file (defaults to ./output_seisflows.txt) which details the progress of -our inversion +Since this is our first run, we’ll use ``seisflows submit``. .. code:: ipython3 - ! seisflows clean -f - ! seisflows submit -f + ! seisflows submit .. parsed-literal:: + 2022-08-15 16:11:40 (I) | ================================================================================ - CLEAN - ///// - + skipping over: /home/bchow/Work/work/sf_specfem2d_example/parameters.yaml - - deleting file/folder: /home/bchow/Work/work/sf_specfem2d_example/scratch - - deleting file/folder: /home/bchow/Work/work/sf_specfem2d_example/stats - - deleting file/folder: /home/bchow/Work/work/sf_specfem2d_example/output - - deleting file/folder: /home/bchow/Work/work/sf_specfem2d_example/output_sf.txt - - deleting file/folder: /home/bchow/Work/work/sf_specfem2d_example/logs + SETTING UP INVERSION WORKFLOW ================================================================================ - 2022-04-29 12:38:37 | initializing SeisFlows in sys.modules - 2022-04-29 12:38:42 | copying par/log file to: /home/bchow/Work/work/sf_specfem2d_example/logs/output_sf_001.txt - 2022-04-29 12:38:42 | copying par/log file to: /home/bchow/Work/work/sf_specfem2d_example/logs/parameters_001.yaml - 2022-04-29 12:38:42 | exporting current working environment to disk - 2022-04-29 12:38:42 | + 2022-08-15 16:11:47 (D) | running setup for module 'system.Workstation' + 2022-08-15 16:11:50 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/sflog_001.txt + 2022-08-15 16:11:50 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/parameters_001.yaml + 2022-08-15 16:11:50 (D) | running setup for module 'solver.Specfem2D' + 2022-08-15 16:11:50 (I) | initializing 3 solver directories + 2022-08-15 16:11:50 (D) | initializing solver directory source: 001 + 2022-08-15 16:11:58 (D) | linking source '001' as 'mainsolver' + 2022-08-15 16:11:58 (D) | initializing solver directory source: 002 + 2022-08-15 16:12:04 (D) | initializing solver directory source: 003 + 2022-08-15 16:12:13 (D) | running setup for module 'preprocess.Default' + 2022-08-15 16:12:14 (D) | running setup for module 'optimize.Gradient' + 2022-08-15 16:12:15 (I) | no optimization checkpoint found, assuming first run + 2022-08-15 16:12:16 (I) | re-loading optimization module from checkpoint + 2022-08-15 16:12:16 (I) | //////////////////////////////////////////////////////////////////////////////// - WORKFLOW WILL STOP AFTER FUNC: 'initialize' + RUNNING ITERATION 01 //////////////////////////////////////////////////////////////////////////////// - 2022-04-29 12:38:42 | + 2022-08-15 16:12:16 (I) | ================================================================================ - STARTING INVERSION WORKFLOW + RUNNING INVERSION WORKFLOW ================================================================================ - 2022-04-29 12:38:42 | - //////////////////////////////////////////////////////////////////////////////// - ITERATION 1 / 1 + 2022-08-15 16:12:16 (I) | //////////////////////////////////////////////////////////////////////////////// - 2022-04-29 12:38:42 | + EVALUATING MISFIT FOR INITIAL MODEL //////////////////////////////////////////////////////////////////////////////// - PERFORMING MODULE SETUP - //////////////////////////////////////////////////////////////////////////////// - 2022-04-29 12:38:42 | misfit function is: 'waveform' - 2022-04-29 12:38:43 | writing line search history file: - /home/bchow/Work/work/sf_specfem2d_example/stats/line_search.txt - 2022-04-29 12:38:44 | checking poissons ratio for: 'm_new.npy' - 2022-04-29 12:38:44 | model parameters (m_new.npy i01s00): - 2022-04-29 12:38:44 | 5800.00 <= vp <= 5800.00 - 2022-04-29 12:38:44 | 3500.00 <= vs <= 3500.00 - 2022-04-29 12:38:44 | 0.21 <= pr <= 0.21 - 2022-04-29 12:38:46 | setting up solver on system... - 2022-04-29 12:38:46 | checkpointing working environment to disk - 2022-04-29 12:38:47 | exporting current working environment to disk - 2022-04-29 12:38:48 | running task solver.setup 1 times - 2022-04-29 12:38:48 | initializing 1 solver directories - 2022-04-29 12:38:53 | source 001 symlinked as mainsolver - 2022-04-29 12:38:53 | generating 'data' with MODEL_TRUE synthetics - 2022-04-29 12:39:00 | running mesh generation for MODEL_INIT - 2022-04-29 12:39:02 | - ================================================================================ - INITIALIZING INVERSION - ================================================================================ - 2022-04-29 12:39:02 | - EVALUATE OBJECTIVE FUNCTION - -------------------------------------------------------------------------------- - 2022-04-29 12:39:02 | saving model 'm_new.npy' to: - /home/bchow/Work/work/sf_specfem2d_example/scratch/evalgrad/model - 2022-04-29 12:39:03 | evaluating objective function 1 times on system... - 2022-04-29 12:39:03 | checkpointing working environment to disk - 2022-04-29 12:39:05 | exporting current working environment to disk - 2022-04-29 12:39:05 | running task solver.eval_func 1 times - 2022-04-29 12:39:05 | running forward simulations - 2022-04-29 12:39:11 | calling preprocess.prepare_eval_grad() - 2022-04-29 12:39:11 | preparing files for gradient evaluation - 2022-04-29 12:39:11 | exporting residuals to: - /home/bchow/Work/work/sf_specfem2d_example/scratch/evalgrad - 2022-04-29 12:39:12 | summing residuals with preprocess module - 2022-04-29 12:39:12 | saving misfit 1.748E-03 to tag 'f_new.txt' - 2022-04-29 12:39:12 | - ================================================================================ - FINISHED FLOW EXECUTION - ================================================================================ - 2022-04-29 12:39:12 | - ================================================================================ - FINISHED INVERSION WORKFLOW - ================================================================================ + 2022-08-15 16:12:16 (I) | checking initial model parameters + 2022-08-15 16:12:16 (I) | 2600.00 <= rho <= 2600.00 + 2022-08-15 16:12:16 (I) | 3500.00 <= vs <= 3500.00 + 2022-08-15 16:12:16 (I) | 5800.00 <= vp <= 5800.00 + 2022-08-15 16:12:16 (I) | checking true/target model parameters + 2022-08-15 16:12:16 (I) | 2600.00 <= rho <= 2600.00 + 2022-08-15 16:12:16 (I) | 3550.00 <= vs <= 3550.00 + 2022-08-15 16:12:16 (I) | 5900.00 <= vp <= 5900.00 + 2022-08-15 16:12:16 (I) | preparing observation data for source 001 + 2022-08-15 16:12:16 (I) | running forward simulation w/ target model for 001 + 2022-08-15 16:12:33 (I) | evaluating objective function for source 001 + 2022-08-15 16:12:33 (D) | running forward simulation with 'Specfem2D' + 2022-08-15 16:12:53 (D) | quantifying misfit with 'Default' + 2022-08-15 16:12:53 (I) | preparing observation data for source 002 + 2022-08-15 16:12:53 (I) | running forward simulation w/ target model for 002 + 2022-08-15 16:13:09 (I) | evaluating objective function for source 002 + 2022-08-15 16:13:09 (D) | running forward simulation with 'Specfem2D' + 2022-08-15 16:13:31 (D) | quantifying misfit with 'Default' + 2022-08-15 16:13:31 (I) | preparing observation data for source 003 + 2022-08-15 16:13:31 (I) | running forward simulation w/ target model for 003 + 2022-08-15 16:14:16 (I) | evaluating objective function for source 003 + 2022-08-15 16:14:16 (D) | running forward simulation with 'Specfem2D' + 2022-08-15 16:14:33 (D) | quantifying misfit with 'Default' + 2022-08-15 16:14:33 (I) | stop workflow at `stop_after`: evaluate_initial_misfit .. note:: @@ -1488,28 +1364,16 @@ our inversion .. parsed-literal:: - -48.0000000 0.0000000 - -47.9400000 0.0000000 - -47.8800000 0.0000000 - -47.8200000 0.0000000 - -47.7600000 0.0000000 - -47.7000000 0.0000000 - -47.6400000 0.0000000 - -47.5800000 0.0000000 - -47.5200000 0.0000000 - -47.4600000 0.0000000 - - -.. code:: ipython3 - - # We can also see that we have generated a STATIONS_ADJOINT file, which is required for - # running the adjoint simulations (i.e., evaluate the gradient) - ! head scratch/solver/001/DATA/STATIONS_ADJOINT - - -.. parsed-literal:: - - S0001 AA 180081.4100000 388768.7100000 0.0 0.0 + -48.0000000 0.0000000 + -47.9400000 0.0000000 + -47.8800000 0.0000000 + -47.8200000 0.0000000 + -47.7600000 0.0000000 + -47.7000000 0.0000000 + -47.6400000 0.0000000 + -47.5800000 0.0000000 + -47.5200000 0.0000000 + -47.4600000 0.0000000 3b. Adjoint simulations @@ -1517,162 +1381,150 @@ our inversion Now that we have all the required files for running an adjoint simulation (*.adj waveforms and STATIONS_ADJOINT file), we can continue -with the SeisFlows Inversion workflow. No need to edit the Par_file or -anything like that, SeisFlows will take care of that under the hood. We +with the SeisFlows3 Inversion workflow. No need to edit the Par_file or +anything like that, SeisFlows3 will take care of that under the hood. We simply need to tell the workflow (via the parameters.yaml file) to ``resume_from`` the correct function. We can have a look at these functions again: .. code:: ipython3 - ! seisflows print flow + ! seisflows print tasks .. parsed-literal:: - SEISFLOWS WORKFLOW MAIN - //////////////////////// - Flow arguments for - - 1: setup - 2: initialize - 3: evaluate_gradient - 4: write_gradient - 5: compute_direction - 6: line_search - 7: finalize - 8: clean + SEISFLOWS WORKFLOW TASK LIST + //////////////////////////// + Task list for + + 1: evaluate_initial_misfit + 2: run_adjoint_simulations + 3: postprocess_event_kernels + 4: evaluate_gradient_from_kernels + 5: initialize_line_search + 6: perform_line_search + 7: finalize_iteration .. code:: ipython3 # We'll stop just before the line search so that we can take a look at the files # generated during the middle tasks - ! seisflows par resume_from evaluate_gradient - ! seisflows par stop_after compute_direction + ! seisflows par stop_after evaluate_gradient_from_kernels .. parsed-literal:: - RESUME_FROM: -> evaluate_gradient - STOP_AFTER: initialize -> compute_direction + stop_after: evaluate_initial_misfit -> evaluate_gradient_from_kernels .. code:: ipython3 - # We can use the `seisflows resume` command to continue an active workflow - # again we use the '-f' flag to skip past the user-input stage. - ! seisflows resume -f + # We can use the `seisflows submit` command to continue an active workflow + # The state file created during the first run will tell the workflow to resume from the stopped point in the workflow + ! seisflows submit .. parsed-literal:: - 2022-04-29 12:41:21 | copying par/log file to: /home/bchow/Work/work/sf_specfem2d_example/logs/output_sf_002.txt - 2022-04-29 12:41:21 | copying par/log file to: /home/bchow/Work/work/sf_specfem2d_example/logs/parameters_002.yaml - 2022-04-29 12:41:21 | exporting current working environment to disk - 2022-04-29 12:41:21 | - //////////////////////////////////////////////////////////////////////////////// - WORKFLOW WILL RESUME FROM FUNC: 'evaluate_gradient' - //////////////////////////////////////////////////////////////////////////////// - 2022-04-29 12:41:21 | - //////////////////////////////////////////////////////////////////////////////// - WORKFLOW WILL STOP AFTER FUNC: 'compute_direction' - //////////////////////////////////////////////////////////////////////////////// - 2022-04-29 12:41:21 | + 2022-08-15 16:15:06 (D) | setting iteration==1 from state file + 2022-08-15 16:15:06 (I) | ================================================================================ - STARTING INVERSION WORKFLOW + SETTING UP INVERSION WORKFLOW ================================================================================ - 2022-04-29 12:41:21 | - //////////////////////////////////////////////////////////////////////////////// - ITERATION 1 / 1 - //////////////////////////////////////////////////////////////////////////////// - 2022-04-29 12:41:21 | + 2022-08-15 16:15:16 (D) | running setup for module 'system.Workstation' + 2022-08-15 16:15:20 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/sflog_002.txt + 2022-08-15 16:15:20 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/parameters_002.yaml + 2022-08-15 16:15:20 (D) | running setup for module 'solver.Specfem2D' + 2022-08-15 16:15:20 (I) | initializing 3 solver directories + 2022-08-15 16:15:22 (D) | running setup for module 'preprocess.Default' + 2022-08-15 16:15:23 (D) | running setup for module 'optimize.Gradient' + 2022-08-15 16:15:25 (I) | re-loading optimization module from checkpoint + 2022-08-15 16:15:27 (I) | re-loading optimization module from checkpoint + 2022-08-15 16:15:27 (I) | //////////////////////////////////////////////////////////////////////////////// - EVALUATING GRADIENT + RUNNING ITERATION 01 //////////////////////////////////////////////////////////////////////////////// - 2022-04-29 12:41:21 | evaluating gradient 1 times on system... - 2022-04-29 12:41:21 | checkpointing working environment to disk - 2022-04-29 12:41:22 | exporting current working environment to disk - 2022-04-29 12:41:23 | running task solver.eval_grad 1 times - 2022-04-29 12:41:23 | running adjoint simulations - 2022-04-29 12:41:38 | exporting kernels to: - /home/bchow/Work/work/sf_specfem2d_example/scratch/evalgrad - 2022-04-29 12:41:38 | + 2022-08-15 16:15:27 (I) | + ================================================================================ + RUNNING INVERSION WORKFLOW + ================================================================================ + 2022-08-15 16:15:27 (I) | 'evaluate_initial_misfit' has already been run, skipping + 2022-08-15 16:15:27 (I) | //////////////////////////////////////////////////////////////////////////////// - POSTPROCESSING KERNELS + EVALUATING EVENT KERNELS W/ ADJOINT SIMULATIONS //////////////////////////////////////////////////////////////////////////////// - 2022-04-29 12:41:38 | processing kernels into gradient on system... - 2022-04-29 12:41:38 | checkpointing working environment to disk - 2022-04-29 12:41:39 | exporting current working environment to disk - 2022-04-29 12:41:39 | running task postprocess.process_kernels 1 times - 2022-04-29 12:41:39 | saving summed kernels to: - /home/bchow/Work/work/sf_specfem2d_example/scratch/evalgrad/kernels/sum - 2022-04-29 12:41:41 | + 2022-08-15 16:15:27 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log' + 2022-08-15 16:16:11 (D) | renaming output event kernels: 'alpha' -> 'vp' + 2022-08-15 16:16:11 (D) | renaming output event kernels: 'beta' -> 'vs' + 2022-08-15 16:16:12 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log' + 2022-08-15 16:16:59 (D) | renaming output event kernels: 'alpha' -> 'vp' + 2022-08-15 16:16:59 (D) | renaming output event kernels: 'beta' -> 'vs' + 2022-08-15 16:16:59 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log' + 2022-08-15 16:17:45 (D) | renaming output event kernels: 'alpha' -> 'vp' + 2022-08-15 16:17:45 (D) | renaming output event kernels: 'beta' -> 'vs' + 2022-08-15 16:17:45 (I) | //////////////////////////////////////////////////////////////////////////////// - COMPUTING SEARCH DIRECTION + GENERATING/PROCESSING MISFIT KERNEL //////////////////////////////////////////////////////////////////////////////// - 2022-04-29 12:41:41 | computing search direction with L-BFGS - 2022-04-29 12:41:41 | first L-BFGS iteration, setting search direction as inverse gradient - 2022-04-29 12:41:41 | - ================================================================================ - FINISHED FLOW EXECUTION - ================================================================================ - 2022-04-29 12:41:41 | - ================================================================================ - FINISHED INVERSION WORKFLOW - ================================================================================ + 2022-08-15 16:17:45 (I) | combining event kernels into single misfit kernel + 2022-08-15 16:17:47 (I) | scaling gradient to absolute model perturbations + 2022-08-15 16:17:49 (I) | stop workflow at `stop_after`: evaluate_gradient_from_kernels -------------- -The functions **evaluate_gradient()** through **compute_direction()** -have run adjoint simulations to generate event kernels and sum the -kernels into the misfit kernel. +The function **run_adjoint_simulations()** has run adjoint simulations +to generate event kernels. The functions **postprocess_event_kernels** +and **evaluate_gradient_from_kernels** will have summed and (optionally) +smoothed the kernels to recover the gradient, which will be used to +update our starting model. - **NOTE**: Because we only have one event, our misfit kernel is just - exactly our event kernel. And since we did not specify any smoothing - lenghts (PAR.SMOOTH_H and PAR.SMOOTH_V), no smoothing of the gradient - has occurred. + **NOTE**: Since we did not specify any smoothing lenghts + (PAR.SMOOTH_H and PAR.SMOOTH_V), no smoothing of the gradient has + occurred. -Using the L-BFGS optimization algorithm, SeisFlows has computed a -search direction that will be used in the line search to search for a -best fitting model which optimally reduces the objective function. We -can take a look at where SeisFlows has stored the information relating -to kernel generation and the optimization computation. +Using the gradient-descent optimization algorithm, SeisFlows will now +compute a search direction that will be used in the line search to +search for a best fitting model which optimally reduces the objective +function. We can take a look at where SeisFlows has stored the +information relating to kernel generation and the optimization +computation. .. code:: ipython3 # Gradient evaluation files are stored here, the kernels are stored separately from the gradient incase # the user wants to manually manipulate them - ! ls scratch/evalgrad + ! ls scratch/eval_grad .. parsed-literal:: - gradient kernels model residuals + gradient kernels misfit_kernel model residuals.txt .. code:: ipython3 - # SeisFlows stores all kernels and gradient information as SPECFEM binary (.bin) files - ! ls scratch/evalgrad/gradient + # SeisFlows3 stores all kernels and gradient information as SPECFEM binary (.bin) files + ! ls scratch/eval_grad/gradient .. parsed-literal:: - proc000000_vp_kernel.bin proc000000_vs_kernel.bin + proc000000_vp_kernel.bin proc000000_vs_kernel.bin .. code:: ipython3 # Kernels are stored on a per-event basis, and summed together (sum/). If smoothing was performed, # we would see both smoothed and unsmoothed versions of the misfit kernel - ! ls scratch/evalgrad/kernels + ! ls scratch/eval_grad/kernels .. parsed-literal:: - 001 sum + 001 002 003 .. code:: ipython3 @@ -1685,19 +1537,19 @@ to kernel generation and the optimization computation. .. parsed-literal:: - f_new.txt g_new.npy LBFGS m_new.npy p_new.npy + checkpoint.npz f_new.txt g_new.npz m_new.npz .. code:: ipython3 - p_new = np.load("scratch/optimize/p_new.npy") - print(p_new) + g_new = np.load("scratch/optimize/g_new.npz") + print(g_new["vs_kernel"]) .. parsed-literal:: - [-0.00000000e+00 -0.00000000e+00 -0.00000000e+00 ... -3.96447909e-11 - -2.00156454e-11 -2.61676726e-12] + [[-1.18126331e-12 2.40273470e-12 3.97045036e-11 ... 9.62017688e-11 + 4.21140102e-11 3.96825021e-12]] -------------- @@ -1708,166 +1560,171 @@ to kernel generation and the optimization computation. Let’s finish off the inversion by running through the line search, which will generate new models using the gradient, evaluate the objective function by running forward simulations, and comparing the evaluated -objective function with the value obtained in **initialize**. +objective function with the value obtained in +**evalaute_initial_misfit**. + Satisfactory reduction in the objective function will result in a termination of the line search. We are using a bracketing line search -here (CITE RYANS PAPER), which requires finding models which both -increase and decrease the misfit with respect to the initial evaluation. -Therefore it will likely take more than two trial steps to complete the -line search +here `(Modrak et +al. 2018) `__, +which requires finding models which both increase and decrease the +misfit with respect to the initial evaluation. Therefore it takes +atleast two trial steps to complete the line search. .. code:: ipython3 - ! seisflows par resume_from line_search # resume from the line search - ! seisflows par stop_after finalize # We don't want to run the clean() argument so that we can explore the dir + ! seisflows par stop_after finalize_iteration # We don't want to run the finalize() argument so that we can explore the dir .. parsed-literal:: - RESUME_FROM: evaluate_gradient -> line_search - STOP_AFTER: compute_direction -> finalize + stop_after: evaluate_gradient_from_kernels -> finalize_iteration .. code:: ipython3 - ! seisflows resume -f + ! seisflows submit .. parsed-literal:: - 2022-04-29 12:42:40 | copying par/log file to: /home/bchow/Work/work/sf_specfem2d_example/logs/output_sf_003.txt - 2022-04-29 12:42:40 | copying par/log file to: /home/bchow/Work/work/sf_specfem2d_example/logs/parameters_003.yaml - 2022-04-29 12:42:40 | exporting current working environment to disk - 2022-04-29 12:42:42 | - //////////////////////////////////////////////////////////////////////////////// - WORKFLOW WILL RESUME FROM FUNC: 'line_search' - //////////////////////////////////////////////////////////////////////////////// - 2022-04-29 12:42:42 | - //////////////////////////////////////////////////////////////////////////////// - WORKFLOW WILL STOP AFTER FUNC: 'finalize' - //////////////////////////////////////////////////////////////////////////////// - 2022-04-29 12:42:42 | + 2022-08-15 16:21:55 (D) | setting iteration==1 from state file + 2022-08-15 16:21:55 (I) | ================================================================================ - STARTING INVERSION WORKFLOW + SETTING UP INVERSION WORKFLOW ================================================================================ - 2022-04-29 12:42:42 | + 2022-08-15 16:22:03 (D) | running setup for module 'system.Workstation' + 2022-08-15 16:22:05 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/sflog_003.txt + 2022-08-15 16:22:05 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/parameters_003.yaml + 2022-08-15 16:22:05 (D) | running setup for module 'solver.Specfem2D' + 2022-08-15 16:22:05 (I) | initializing 3 solver directories + 2022-08-15 16:22:07 (D) | running setup for module 'preprocess.Default' + 2022-08-15 16:22:08 (D) | running setup for module 'optimize.Gradient' + 2022-08-15 16:22:09 (I) | re-loading optimization module from checkpoint + 2022-08-15 16:22:11 (I) | re-loading optimization module from checkpoint + 2022-08-15 16:22:11 (I) | //////////////////////////////////////////////////////////////////////////////// - ITERATION 1 / 1 + RUNNING ITERATION 01 //////////////////////////////////////////////////////////////////////////////// - 2022-04-29 12:42:42 | + 2022-08-15 16:22:11 (I) | ================================================================================ - CONDUCTING LINE SEARCH (i01s00) + RUNNING INVERSION WORKFLOW ================================================================================ - 2022-04-29 12:42:42 | max step length safeguard is: 5.26E+10 - 2022-04-29 12:42:42 | - EVALUATE BRACKETING LINE SEARCH + 2022-08-15 16:22:11 (I) | 'evaluate_initial_misfit' has already been run, skipping + 2022-08-15 16:22:11 (I) | 'run_adjoint_simulations' has already been run, skipping + 2022-08-15 16:22:11 (I) | 'postprocess_event_kernels' has already been run, skipping + 2022-08-15 16:22:11 (I) | 'evaluate_gradient_from_kernels' has already been run, skipping + 2022-08-15 16:22:11 (I) | initializing 'bracket'ing line search + 2022-08-15 16:22:11 (I) | enforcing max step length safeguard + 2022-08-15 16:22:11 (D) | step length(s) = 0.00E+00 + 2022-08-15 16:22:11 (D) | misfit val(s) = 1.28E-03 + 2022-08-15 16:22:11 (I) | try: first evaluation, attempt guess step length, alpha=9.08E+11 + 2022-08-15 16:22:11 (I) | try: applying initial step length safegaurd as alpha has exceeded maximum step length, alpha_new=1.44E+10 + 2022-08-15 16:22:11 (D) | overwriting initial step length, alpha_new=2.32E+09 + 2022-08-15 16:22:11 (I) | trial model 'm_try' parameters: + 2022-08-15 16:22:11 (I) | 5800.00 <= vp <= 5800.00 + 2022-08-15 16:22:11 (I) | 3244.51 <= vs <= 3790.00 + 2022-08-15 16:22:12 (I) | + LINE SEARCH STEP COUNT 01 -------------------------------------------------------------------------------- - 2022-04-29 12:42:42 | step length(s) = 0.00E+00 - 2022-04-29 12:42:42 | misfit val(s) = 1.75E-03 - 2022-04-29 12:42:42 | first iteration, guessing trial step - 2022-04-29 12:42:42 | initial step length safegaurd, setting manual step length - 2022-04-29 12:42:42 | manually set initial step length: 5.26E+09 - 2022-04-29 12:42:42 | checking poissons ratio for: 'm_try.npy' - 2022-04-29 12:42:42 | model parameters (m_try.npy i01s00): - 2022-04-29 12:42:42 | 5800.00 <= vp <= 5800.00 - 2022-04-29 12:42:42 | 3278.69 <= vs <= 3790.00 - 2022-04-29 12:42:42 | 0.13 <= pr <= 0.27 - 2022-04-29 12:42:42 | - //////////////////////////////////////////////////////////////////////////////// - TRIAL STEP COUNT: i01s01 - //////////////////////////////////////////////////////////////////////////////// - 2022-04-29 12:42:42 | - EVALUATE OBJECTIVE FUNCTION - -------------------------------------------------------------------------------- - 2022-04-29 12:42:42 | saving model 'm_try.npy' to: - /home/bchow/Work/work/sf_specfem2d_example/scratch/scratch/model - 2022-04-29 12:42:42 | evaluating objective function 1 times on system... - 2022-04-29 12:42:42 | checkpointing working environment to disk - 2022-04-29 12:42:44 | exporting current working environment to disk - 2022-04-29 12:42:44 | running task solver.eval_func 1 times - 2022-04-29 12:42:44 | running forward simulations - 2022-04-29 12:42:49 | calling preprocess.prepare_eval_grad() - 2022-04-29 12:42:49 | preparing files for gradient evaluation - 2022-04-29 12:42:50 | exporting residuals to: - /home/bchow/Work/work/sf_specfem2d_example/scratch/scratch - 2022-04-29 12:42:50 | summing residuals with preprocess module - 2022-04-29 12:42:50 | saving misfit 9.850E-04 to tag 'f_try.txt' - 2022-04-29 12:42:50 | - EVALUATE BRACKETING LINE SEARCH + 2022-08-15 16:22:12 (I) | evaluating objective function for source 001 + 2022-08-15 16:22:12 (D) | running forward simulation with 'Specfem2D' + 2022-08-15 16:22:23 (D) | quantifying misfit with 'Default' + 2022-08-15 16:22:23 (I) | evaluating objective function for source 002 + 2022-08-15 16:22:23 (D) | running forward simulation with 'Specfem2D' + 2022-08-15 16:22:35 (D) | quantifying misfit with 'Default' + 2022-08-15 16:22:35 (I) | evaluating objective function for source 003 + 2022-08-15 16:22:35 (D) | running forward simulation with 'Specfem2D' + 2022-08-15 16:22:48 (D) | quantifying misfit with 'Default' + 2022-08-15 16:22:48 (D) | misfit for trial model (f_try) == 8.65E-04 + 2022-08-15 16:22:48 (D) | step length(s) = 0.00E+00, 2.32E+09 + 2022-08-15 16:22:48 (D) | misfit val(s) = 1.28E-03, 8.65E-04 + 2022-08-15 16:22:48 (I) | try: misfit not bracketed, increasing step length using golden ratio, alpha=3.76E+09 + 2022-08-15 16:22:49 (I) | line search model 'm_try' parameters: + 2022-08-15 16:22:49 (I) | 5800.00 <= vp <= 5800.00 + 2022-08-15 16:22:49 (I) | 3086.61 <= vs <= 3969.23 + 2022-08-15 16:22:49 (I) | trial step unsuccessful. re-attempting line search + 2022-08-15 16:22:49 (I) | + LINE SEARCH STEP COUNT 02 -------------------------------------------------------------------------------- - 2022-04-29 12:42:50 | step length(s) = 0.00E+00, 5.26E+09 - 2022-04-29 12:42:50 | misfit val(s) = 1.75E-03, 9.85E-04 - 2022-04-29 12:42:50 | misfit not bracketed, increasing step length - 2022-04-29 12:42:50 | checking poissons ratio for: 'm_try.npy' - 2022-04-29 12:42:50 | model parameters (m_try.npy i01s01): - 2022-04-29 12:42:50 | 5800.00 <= vp <= 5800.00 - 2022-04-29 12:42:50 | 3141.92 <= vs <= 3969.23 - 2022-04-29 12:42:50 | 0.06 <= pr <= 0.29 - 2022-04-29 12:42:50 | retrying with new trial step - 2022-04-29 12:42:50 | - //////////////////////////////////////////////////////////////////////////////// - TRIAL STEP COUNT: i01s02 - //////////////////////////////////////////////////////////////////////////////// - 2022-04-29 12:42:50 | - EVALUATE OBJECTIVE FUNCTION + 2022-08-15 16:22:49 (I) | evaluating objective function for source 001 + 2022-08-15 16:22:49 (D) | running forward simulation with 'Specfem2D' + 2022-08-15 16:23:01 (D) | quantifying misfit with 'Default' + 2022-08-15 16:23:01 (I) | evaluating objective function for source 002 + 2022-08-15 16:23:01 (D) | running forward simulation with 'Specfem2D' + 2022-08-15 16:23:13 (D) | quantifying misfit with 'Default' + 2022-08-15 16:23:13 (I) | evaluating objective function for source 003 + 2022-08-15 16:23:13 (D) | running forward simulation with 'Specfem2D' + 2022-08-15 16:23:25 (D) | quantifying misfit with 'Default' + 2022-08-15 16:23:25 (D) | misfit for trial model (f_try) == 1.73E-03 + 2022-08-15 16:23:25 (D) | step length(s) = 0.00E+00, 2.32E+09, 3.76E+09 + 2022-08-15 16:23:25 (D) | misfit val(s) = 1.28E-03, 8.65E-04, 1.73E-03 + 2022-08-15 16:23:25 (I) | try: bracket acceptable but step length unreasonable attempting to re-adjust step length alpha=1.59E+09 + 2022-08-15 16:23:25 (I) | line search model 'm_try' parameters: + 2022-08-15 16:23:25 (I) | 5800.00 <= vp <= 5800.00 + 2022-08-15 16:23:25 (I) | 3325.01 <= vs <= 3698.63 + 2022-08-15 16:23:25 (I) | trial step unsuccessful. re-attempting line search + 2022-08-15 16:23:25 (I) | + LINE SEARCH STEP COUNT 03 -------------------------------------------------------------------------------- - 2022-04-29 12:42:50 | saving model 'm_try.npy' to: - /home/bchow/Work/work/sf_specfem2d_example/scratch/scratch/model - 2022-04-29 12:42:51 | evaluating objective function 1 times on system... - 2022-04-29 12:42:51 | checkpointing working environment to disk - 2022-04-29 12:42:52 | exporting current working environment to disk - 2022-04-29 12:42:53 | running task solver.eval_func 1 times - 2022-04-29 12:42:53 | running forward simulations - 2022-04-29 12:42:59 | calling preprocess.prepare_eval_grad() - 2022-04-29 12:42:59 | preparing files for gradient evaluation - 2022-04-29 12:42:59 | exporting residuals to: - /home/bchow/Work/work/sf_specfem2d_example/scratch/scratch - 2022-04-29 12:43:00 | summing residuals with preprocess module - 2022-04-29 12:43:00 | saving misfit 1.227E-03 to tag 'f_try.txt' - 2022-04-29 12:43:00 | - EVALUATE BRACKETING LINE SEARCH + 2022-08-15 16:23:25 (I) | evaluating objective function for source 001 + 2022-08-15 16:23:25 (D) | running forward simulation with 'Specfem2D' + 2022-08-15 16:23:37 (D) | quantifying misfit with 'Default' + 2022-08-15 16:23:37 (I) | evaluating objective function for source 002 + 2022-08-15 16:23:37 (D) | running forward simulation with 'Specfem2D' + 2022-08-15 16:23:51 (D) | quantifying misfit with 'Default' + 2022-08-15 16:23:51 (I) | evaluating objective function for source 003 + 2022-08-15 16:23:51 (D) | running forward simulation with 'Specfem2D' + 2022-08-15 16:24:03 (D) | quantifying misfit with 'Default' + 2022-08-15 16:24:04 (D) | misfit for trial model (f_try) == 2.59E-03 + 2022-08-15 16:24:04 (D) | step length(s) = 0.00E+00, 1.59E+09, 2.32E+09, 3.76E+09 + 2022-08-15 16:24:04 (D) | misfit val(s) = 1.28E-03, 2.59E-03, 8.65E-04, 1.73E-03 + 2022-08-15 16:24:04 (I) | try: bracket acceptable but step length unreasonable attempting to re-adjust step length alpha=2.82E+09 + 2022-08-15 16:24:04 (I) | line search model 'm_try' parameters: + 2022-08-15 16:24:04 (I) | 5800.00 <= vp <= 5800.00 + 2022-08-15 16:24:04 (I) | 3189.77 <= vs <= 3852.13 + 2022-08-15 16:24:04 (I) | trial step unsuccessful. re-attempting line search + 2022-08-15 16:24:04 (I) | + LINE SEARCH STEP COUNT 04 -------------------------------------------------------------------------------- - 2022-04-29 12:43:00 | step length(s) = 0.00E+00, 5.26E+09, 8.51E+09 - 2022-04-29 12:43:00 | misfit val(s) = 1.75E-03, 9.85E-04, 1.23E-03 - 2022-04-29 12:43:00 | bracket okay, step length reasonable, pass - 2022-04-29 12:43:00 | checking poissons ratio for: 'm_try.npy' - 2022-04-29 12:43:00 | model parameters (m_try.npy i01s02): - 2022-04-29 12:43:00 | 5800.00 <= vp <= 5800.00 - 2022-04-29 12:43:00 | 3278.69 <= vs <= 3790.00 - 2022-04-29 12:43:00 | 0.13 <= pr <= 0.27 - 2022-04-29 12:43:00 | trial step successful - 2022-04-29 12:43:00 | + 2022-08-15 16:24:04 (I) | evaluating objective function for source 001 + 2022-08-15 16:24:04 (D) | running forward simulation with 'Specfem2D' + 2022-08-15 16:24:15 (D) | quantifying misfit with 'Default' + 2022-08-15 16:24:15 (I) | evaluating objective function for source 002 + 2022-08-15 16:24:15 (D) | running forward simulation with 'Specfem2D' + 2022-08-15 16:24:27 (D) | quantifying misfit with 'Default' + 2022-08-15 16:24:27 (I) | evaluating objective function for source 003 + 2022-08-15 16:24:27 (D) | running forward simulation with 'Specfem2D' + 2022-08-15 16:24:39 (D) | quantifying misfit with 'Default' + 2022-08-15 16:24:39 (D) | misfit for trial model (f_try) == 3.46E-03 + 2022-08-15 16:24:39 (D) | step length(s) = 0.00E+00, 1.59E+09, 2.32E+09, 2.82E+09, 3.76E+09 + 2022-08-15 16:24:39 (D) | misfit val(s) = 1.28E-03, 2.59E-03, 8.65E-04, 3.46E-03, 1.73E-03 + 2022-08-15 16:24:39 (I) | pass: bracket acceptable and step length reasonable. returning minimum line search misfit. + 2022-08-15 16:24:39 (I) | line search model 'm_try' parameters: + 2022-08-15 16:24:39 (I) | 5800.00 <= vp <= 5800.00 + 2022-08-15 16:24:39 (I) | 3244.51 <= vs <= 3790.00 + 2022-08-15 16:24:39 (I) | trial step successful. finalizing line search + 2022-08-15 16:24:39 (I) | FINALIZING LINE SEARCH -------------------------------------------------------------------------------- - 2022-04-29 12:43:00 | shifting current model (new) to previous model (old) - 2022-04-29 12:43:00 | setting accepted line search model as current model - 2022-04-29 12:43:00 | current misfit is f_new.txt=9.850E-04 - 2022-04-29 12:43:00 | writing optimization stats to: stats - 2022-04-29 12:43:00 | resetting line search step count to 0 - 2022-04-29 12:43:00 | - ================================================================================ - FINALIZING ITERATION 1 - ================================================================================ - 2022-04-29 12:43:00 | exporting current working environment to disk - 2022-04-29 12:43:01 | saving model 'm_new.npy' to path: - /home/bchow/Work/work/sf_specfem2d_example/output/model_0001 - 2022-04-29 12:43:02 | saving gradient to path: - /home/bchow/Work/work/sf_specfem2d_example/output/gradient_0001 - 2022-04-29 12:43:02 | - ================================================================================ - FINISHED FLOW EXECUTION - ================================================================================ - 2022-04-29 12:43:02 | - ================================================================================ - FINISHED INVERSION WORKFLOW - ================================================================================ + 2022-08-15 16:24:39 (I) | writing optimization stats + 2022-08-15 16:24:39 (I) | renaming current (new) optimization vectors as previous model (old) + 2022-08-15 16:24:39 (I) | setting accepted trial model (try) as current model (new) + 2022-08-15 16:24:39 (I) | misfit of accepted trial model is f=8.645E-04 + 2022-08-15 16:24:39 (I) | resetting line search step count to 0 + 2022-08-15 16:24:39 (I) | + //////////////////////////////////////////////////////////////////////////////// + CLEANING WORKDIR FOR NEXT ITERATION + //////////////////////////////////////////////////////////////////////////////// + 2022-08-15 16:24:41 (I) | thrifty inversion encountering first iteration, defaulting to standard inversion workflow + 2022-08-15 16:24:42 (I) | stop workflow at `stop_after`: finalize_iteration -From the log statements above, we can see that the SeisFlows line -search required 2 trial steps, where it modified values of Vs until -satisfactory reduction in the objective function was met. This was the -final step in the iteration, and so the finalization step made -last-minute preparations for a subsequent iteration. +From the log statements above, we can see that the SeisFlows line search +required 4 trial steps, where it modified values of Vs (shear-wave +velocity) until satisfactory reduction in the objective function was +met. This was the final step in the iteration, and so the finalization +of the line search made preparations for a subsequent iteration. .. code:: ipython3 @@ -1878,49 +1735,34 @@ last-minute preparations for a subsequent iteration. .. parsed-literal:: - alpha.npy f_old.txt g_old.npy m_new.npy p_old.npy - f_new.txt f_try.txt LBFGS m_old.npy + alpha.txt f_old.txt m_new.npz p_old.npz + checkpoint.npz f_try.txt m_old.npz + f_new.txt g_old.npz output_optim.txt .. code:: ipython3 # The stats/ directory contains text files describing the optimization/line search - ! ls stats + ! cat scratch/optimize/output_optim.txt .. parsed-literal:: - factor.txt line_search.txt slope.txt theta.txt - gradient_norm_L1.txt misfit.txt step_count.txt - gradient_norm_L2.txt restarted.txt step_length.txt - - -.. code:: ipython3 - - # For example we can look at the step length chosen for the accepted trial step in the line search - ! cat stats/line_search.txt - - -.. parsed-literal:: - - ITER STEPLEN MISFIT - ========== ========== ========== - 1 0.000e+00 1.748e-03 - 5.261e+09 9.850e-04 - 8.512e+09 1.227e-03 + step_count,step_length,gradient_norm_L1,gradient_norm_L2,misfit,if_restarted,slope,theta + 04,2.323E+09,9.243E-05,1.049E-06,1.279E-03,0,8.263E-13,0.000E+00 4. Conclusions ~~~~~~~~~~~~~~ We’ve now seen how SeisFlows runs an **Inversion** workflow using the -**Specfem2D** solver on a **serial** system (local workstation). More or -less, this is all you need to run SeisFlows with any combination of -modules. The specificities of a system or numerical solver are already -handled internally by SeisFlows, so if you want to use -Specmfe3D_Cartesian as your solver, you would only need to run +**Specfem2D** solver on a **Workstation** system. More or less, this is +all you need to run SeisFlows with any combination of modules. The +specificities of a system or numerical solver are already handled +internally by SeisFlows, so if you want to use Specmfe3D_Cartesian as +your solver, you would only need to run ``seisflows par solver specfem3d`` at the beginning of your workflow -(you will also need to setup your Specfem3D models, similar to what we -did for Specfem2D here). To run on a slurm system like Chinook, you can -run ``seisflows par system chinook``. - +(you will also need to set up your Specfem3D models, similar to what we +did for Specfem2D here). To run on a slurm system like Chinook +(University of Alaska Fairbanks), you can run +``seisflows par system chinook``. diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index a5fefecd..b2fb0f9c 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -45,6 +45,7 @@ class Specfem: :type density: bool :param density: How to treat density during inversion. If True, updates density during inversion. If False, keeps it constant. + TODO allow density scaling during an inversion :type attenuation: bool :param attenuation: How to treat attenuation during inversion. if True, turns on attenuation during forward simulations only. If @@ -232,6 +233,9 @@ def check(self): source_prefix=self.source_prefix, ntask=self.ntask ) + assert(isinstance(self.density, bool)), \ + f"solver `density` must be True (variable) or False (constant)" + @property def source_names(self): """ diff --git a/seisflows/tools/specfem.py b/seisflows/tools/specfem.py index 992571ff..f285baf8 100644 --- a/seisflows/tools/specfem.py +++ b/seisflows/tools/specfem.py @@ -49,7 +49,7 @@ def __init__(self, path=None, fmt="", parameters=None): self.path = path self.fmt = fmt self.model = None - self._parameters = None + self._parameters = parameters self._ngll = None self._nproc = None diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index 219eba2d..069023fc 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -197,8 +197,18 @@ def checkpoint(self): Add an additional line in the state file to keep track of iteration, """ super().checkpoint() - with open(self.path.state_file, "a") as f: - f.write(f"iteration: {self.iteration}") + with open(self.path.state_file, "r") as f: + lines = f.readlines() + # Clear out the previous 'iteration' line and add in new + for i, line in enumerate(lines[:]): + if "iteration:" in line: + lines.pop(i) + break + lines.append(f"iteration: {self.iteration}") + + # Rewrite checkpoint file with new iteration line + with open(self.path.state_file, "w") as f: + f.writelines(lines) def evaluate_objective_function(self, save_residuals=False, **kwargs): """ @@ -244,7 +254,7 @@ def evaluate_initial_misfit(self): # solvers path_model = os.path.join(self.path.eval_grad, "model") m_new = self.optimize.load_vector("m_new") - m_new.write(path=path_model, parameters=self.solver._parameters) + m_new.write(path=path_model) # Run forward simulation/misfit quantification with previous # model @@ -270,7 +280,8 @@ def evaluate_gradient_from_kernels(self): """ super().evaluate_gradient_from_kernels() - model = Model(os.path.join(self.path.eval_grad, "model")) + model = Model(os.path.join(self.path.eval_grad, "model"), + parameters=self.solver._parameters) self.optimize.save_vector(name="m_new", m=model) gradient = Model(path=os.path.join(self.path.eval_grad, "gradient")) @@ -309,8 +320,7 @@ def initialize_line_search(self): self.optimize.checkpoint() # Expose model `m_try` to the solver by placing it in eval_func dir. - m_try.write(path=os.path.join(self.path.eval_func, "model"), - parameters=self.solver._parameters) + m_try.write(path=os.path.join(self.path.eval_func, "model")) def perform_line_search(self): """ @@ -407,7 +417,6 @@ def finalize_iteration(self): model = self.optimize.load_vector("m_new") model.write(path=os.path.join(self.path.output, f"M{self.iteration:0>2}"), - parameters=self.solver._parameters ) # Update optimization From 4988dbde3051317a380fc8d717fd4da6f56a07b5 Mon Sep 17 00:00:00 2001 From: bch0w Date: Tue, 16 Aug 2022 15:14:32 -0800 Subject: [PATCH 112/195] finished updating docs notebooks and fixed weird ls coloring issue and replaced all mentions of seisflows3 w/ seisflows. Put under construction tags on docs pages to be created --- docs/design.rst | 17 +- docs/extending.rst | 4 + docs/notebooks/command_line_tool.ipynb | 4 +- docs/notebooks/parameter_file.ipynb | 4 +- .../notebooks/scratch/specfem2d_example.ipynb | 2047 ----------------- docs/notebooks/specfem2d_example.ipynb | 930 +++----- docs/notebooks/working_directory.ipynb | 350 +-- docs/specfem2d_example.rst | 825 ++----- docs/working_directory.rst | 355 +-- 9 files changed, 874 insertions(+), 3662 deletions(-) delete mode 100644 docs/notebooks/scratch/specfem2d_example.ipynb diff --git a/docs/design.rst b/docs/design.rst index 2978e0bd..adac60d2 100644 --- a/docs/design.rst +++ b/docs/design.rst @@ -1,10 +1,13 @@ Design Philosophy ================================= -Although SeisFlows was written with 'Pythonic' style in mind, there are some design -choices within the internal structure of the package that may be unique to SeisFlows. -Throughout this documentation page we point out some of these choices, and provide -detail on why and how they were implemented. This page is intended for developers and -super users who want to explore the source code deeply or edit/extend SeisFlows for other -purposes. -`Page under construction` +.. note:: + Page Under Construction + +Although SeisFlows was written with 'Pythonic' style in mind, there are some +design choices within the internal structure of the package that may be unique +to SeisFlows. Throughout this documentation page we point out some of these +choices, and provide detail on why and how they were implemented. This page is +intended for developers and super users who want to explore the source code +deeply or edit/extend SeisFlows for other purposes. + diff --git a/docs/extending.rst b/docs/extending.rst index 48ece5d8..5e92034d 100644 --- a/docs/extending.rst +++ b/docs/extending.rst @@ -1,5 +1,9 @@ Extending SeisFlows ================================== + +.. note:: + Page Under Construction + The design philosophy of SeisFlows is such that being a user of the codebase more-than-likely requires also becoming a developer, as custom-made subclasses are often required to tailor the functionalities of SeisFlows to a specific diff --git a/docs/notebooks/command_line_tool.ipynb b/docs/notebooks/command_line_tool.ipynb index 558ebe56..4123c3a8 100644 --- a/docs/notebooks/command_line_tool.ipynb +++ b/docs/notebooks/command_line_tool.ipynb @@ -520,7 +520,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -534,7 +534,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.12" } }, "nbformat": 4, diff --git a/docs/notebooks/parameter_file.ipynb b/docs/notebooks/parameter_file.ipynb index 5e249fd6..fe727007 100644 --- a/docs/notebooks/parameter_file.ipynb +++ b/docs/notebooks/parameter_file.ipynb @@ -471,7 +471,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -485,7 +485,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.12" } }, "nbformat": 4, diff --git a/docs/notebooks/scratch/specfem2d_example.ipynb b/docs/notebooks/scratch/specfem2d_example.ipynb deleted file mode 100644 index 2778a6a8..00000000 --- a/docs/notebooks/scratch/specfem2d_example.ipynb +++ /dev/null @@ -1,2047 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Specfem2D workstation example\n", - "\n", - "To demonstrate the inversion capabilities of SeisFlows, we will run a __Specfem2D synthetic-synthetic example__ on a __local machine__ (tested on a Linux workstation running CentOS 7, and an Apple Laptop running macOS 10.14.6). Many of the setup steps here may be unique to our OS and workstation, but hopefully they may serve as templates for new Users wanting to explore SeisFlows. \n", - "\n", - "The numerical solver we will use is: [SPECFEM2D](https://geodynamics.org/cig/software/specfem2d/). We'll also be working in our `seisflows` [Conda](https://docs.conda.io/en/latest/) environment, see the installation documentation page for instructions on how to install and activate the required Conda environment.\n", - "\n", - "-----------------------------------" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Option 1: Automated run\n", - "\n", - "We have set up this example to run using a single command line argument. The following command will run an example script which will (1) download and compile SPECFEM2D, (2) setup a SPECFEM2D working directory to generate initial and target models, and (3) Run a SeisFlows inversion. " - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - ".. warning:: \n", - " This example attempts to automatically download and compile SPECFEM2D. This step may fail if you are software required by SPECFEM2D, there are issues with the SPECFEM2D repository itself, or the configuration and compiling steps fail. If you run any issues, it is recommended that you manually install and compile SPECFEM2D, and directly provide its path to this example problem when prompted." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# ! seisflows examples run 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "--------------\n", - "## Option 2: Manual run\n", - "\n", - "The notebook below details a walkthrough of the automated run shown above. This is meant for those who want to understand what is going on under the hood. You are welcome to follow along on your workstation. The following Table of Contents outlines the steps we will take in this tutorial:\n", - "\n" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - ".. warning:: \n", - " Navigation links will not work outside of Jupyter. Please use the navigation bar to the left." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "1. __[Setup SPECFEM2D](#1.-Setup-SPECFEM2D)__ \n", - " a. [Download and compile codebase](#1a.-Download-and-compile-codebase*) \n", - " b. [Create a separate SPECFEM2D working directory](#1b.-Create-a-separate-SPECFEM2D-working-directory) \n", - " c. [Generate initial and target models](#1c.-Generate-initial-and-target-models) \n", - "\n", - "2. __[Initialize SeisFlows (SF)](#2.-Initialize-SeisFlows-(SF))__ \n", - " a. [SeisFlows working directory and parameter file](#2a.-SF-working-directory-and-parameter-file) \n", - "\n", - "3. __[Run SeisFlows](#2.-Run-SeisFlows)__ \n", - " a. [Forward simulations](#3a.-Forward-simulations) \n", - " b. [Exploring the SeisFlows directory structure](#3b.-Exploring-the-SF-directory-structure) \n", - " c. [Adjoint simulations](#3c.-Adjoint-simulations) \n", - " d. [Line search and model update](#3d.-Line-search-and-model-update) \n", - "\n", - "4. __[Conclusions](#4.-Conclusions)__ " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Setup SPECFEM2D \n", - "#### 1a. Download and compile codebase (optional)\n", - "\n", - "> **NOTE**: If you have already downloaded and compiled SPECFEM2D, you can skip most of this subsection (1a). However you will need to edit the first two paths in the following cell (WORKDIR and SPECFEM2D_ORIGINAL), and execute the path structure defined in the cell.\n", - "\n", - "First we'll download and compile SPECFEM2D to generate the binaries necessary to run our simulations. We will then populate a new SPECFEM2D working directory that will be used by SeisFlows. We'll use to Python OS module to do our filesystem processes just to keep everything in Python, but this can easily be accomplished in bash." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import glob\n", - "import shutil\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# vvv USER MUST EDIT THE FOLLOWING PATHS vvv\n", - "# MAC PATHS\n", - "WORKDIR = \"/Users/Chow/Work/work/sf_specfem2d_example\" \n", - "SPECFEM2D = \"/Users/Chow/Repositories/specfem2d\"\n", - "# LINUX PATHS\n", - "# WORKDIR = \"/home/bchow/Work/work/sf_specfem2d_example\" \n", - "# SPECFEM2D = \"/home/bchow/REPOSITORIES/specfem2d\"\n", - "# where WORKDIR: points to your own working directory\n", - "# and SPECFEM2D: points to an existing specfem2D repository if available (if not set as '')\n", - "# ^^^ USER MUST EDIT THE FOLLOWING PATHS ^^^\n", - "# ======================================================================================================\n", - "\n", - "# Distribute the necessary file structure of the SPECFEM2D repository that we will downloaded/reference\n", - "SPECFEM2D_ORIGINAL = os.path.join(WORKDIR, \"specfem2d\")\n", - "SPECFEM2D_BIN_ORIGINAL = os.path.join(SPECFEM2D_ORIGINAL, \"bin\")\n", - "SPECFEM2D_DATA_ORIGINAL = os.path.join(SPECFEM2D_ORIGINAL, \"DATA\")\n", - "TAPE_2007_EXAMPLE = os.path.join(SPECFEM2D_ORIGINAL, \"EXAMPLES\", \"Tape2007\")\n", - "\n", - "# The SPECFEM2D working directory that we will create separate from the downloaded repo\n", - "SPECFEM2D_WORKDIR = os.path.join(WORKDIR, \"specfem2d_workdir\")\n", - "SPECFEM2D_BIN = os.path.join(SPECFEM2D_WORKDIR, \"bin\")\n", - "SPECFEM2D_DATA = os.path.join(SPECFEM2D_WORKDIR, \"DATA\")\n", - "SPECFEM2D_OUTPUT = os.path.join(SPECFEM2D_WORKDIR, \"OUTPUT_FILES\")\n", - "\n", - "# Pre-defined locations of velocity models we will generate using the solver\n", - "SPECFEM2D_MODEL_INIT = os.path.join(SPECFEM2D_WORKDIR, \"OUTPUT_FILES_INIT\")\n", - "SPECFEM2D_MODEL_TRUE = os.path.join(SPECFEM2D_WORKDIR, \"OUTPUT_FILES_TRUE\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SPECFEM2D repository already found, you may skip this subsection\n" - ] - } - ], - "source": [ - "# Download SPECFEM2D from GitHub, devel branch for latest codebase OR symlink from existing repo\n", - "if not os.path.exists(WORKDIR):\n", - " os.makedirs(WORKDIR)\n", - "os.chdir(WORKDIR)\n", - "\n", - "if os.path.exists(\"specfem2d\"):\n", - " print(\"SPECFEM2D repository already found, you may skip this subsection\")\n", - " pass\n", - "elif os.path.exists(SPECFEM2D):\n", - " print(\"Existing SPECMFE2D respository found, symlinking to working directory\")\n", - " os.symlink(SPECFEM2D, \"./specfem2d\")\n", - "else:\n", - " print(\"Cloning respository from GitHub\")\n", - " ! git clone --recursive --branch devel https://github.com/geodynamics/specfem2d.git" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Compile SPECFEM2D to generate the Makefile\n", - "os.chdir(SPECFEM2D_ORIGINAL)\n", - "if not os.path.exists(\"./config.log\"):\n", - " os.system(\"./configure\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Run make to generate SPECFEM2D binaries\n", - "if not os.path.exists(\"bin\"):\n", - " os.system(\"make all\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/Users/Chow/Repositories/specfem2d\n", - "\u001b[1m\u001b[34mxadj_seismogram\u001b[m\u001b[m \u001b[1m\u001b[34mxmeshfem2D\u001b[m\u001b[m\n", - "\u001b[1m\u001b[32mxadj_seismogram.dSYM\u001b[m\u001b[m \u001b[1m\u001b[32mxmeshfem2D.dSYM\u001b[m\u001b[m\n", - "\u001b[1m\u001b[34mxcheck_quality_external_mesh\u001b[m\u001b[m \u001b[1m\u001b[34mxsmooth_sem\u001b[m\u001b[m\n", - "\u001b[1m\u001b[32mxcheck_quality_external_mesh.dSYM\u001b[m\u001b[m \u001b[1m\u001b[32mxsmooth_sem.dSYM\u001b[m\u001b[m\n", - "\u001b[1m\u001b[34mxcombine_sem\u001b[m\u001b[m \u001b[1m\u001b[34mxspecfem2D\u001b[m\u001b[m\n", - "\u001b[1m\u001b[32mxcombine_sem.dSYM\u001b[m\u001b[m \u001b[1m\u001b[32mxspecfem2D.dSYM\u001b[m\u001b[m\n", - "\u001b[1m\u001b[34mxconvolve_source_timefunction\u001b[m\u001b[m \u001b[1m\u001b[34mxsum_kernels\u001b[m\u001b[m\n", - "\u001b[1m\u001b[32mxconvolve_source_timefunction.dSYM\u001b[m\u001b[m \u001b[1m\u001b[32mxsum_kernels.dSYM\u001b[m\u001b[m\n" - ] - } - ], - "source": [ - "# Check out the binary files that have been created\n", - "os.chdir(SPECFEM2D_ORIGINAL)\n", - "! pwd\n", - "! ls bin/" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 1b. Create a separate SPECFEM2D working directory\n", - "\n", - "Next we'll create a new SPECFEM2D working directory, separate from the original repository. The intent here is to isolate the original SPECFEM2D repository from our working state, to protect it from things like accidental file deletions or manipulations. This is not a mandatory step for using SeisFlows, but it helps keep file structure clean in the long run, and is the SeisFlows3 dev team's preferred method of using SPECFEM. " - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - ".. note::\n", - " All SPECFEM2D/3D/3D_GLOBE need to run successfully are the bin/, DATA/, and OUTPUT_FILES/ directories. Everything else in the repository is not mandatory for running binaries." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this tutorial we will be using the [Tape2007 example problem](https://github.com/geodynamics/specfem2d/tree/devel/EXAMPLES/Tape2007) to define our __DATA/__ directory (last tested 8/15/22, bdba4389)." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/Users/Chow/Work/work/sf_specfem2d_example/specfem2d_workdir\n", - "\u001b[1m\u001b[32mDATA\u001b[m\u001b[m \u001b[1m\u001b[32mbin\u001b[m\u001b[m\n" - ] - } - ], - "source": [ - "# Incase we've run this docs page before, delete the working directory before remaking\n", - "if os.path.exists(SPECFEM2D_WORKDIR):\n", - " shutil.rmtree(SPECFEM2D_WORKDIR)\n", - "\n", - "os.mkdir(SPECFEM2D_WORKDIR)\n", - "os.chdir(SPECFEM2D_WORKDIR)\n", - "\n", - "# Copy the binary files incase we update the source code. These can also be symlinked.\n", - "shutil.copytree(SPECFEM2D_BIN_ORIGINAL, \"bin\")\n", - "\n", - "# Copy the DATA/ directory because we will be making edits here frequently and it's useful to\n", - "# retain the original files for reference. We will be running one of the example problems: Tape2007\n", - "shutil.copytree(os.path.join(TAPE_2007_EXAMPLE, \"DATA\"), \"DATA\")\n", - "\n", - "! pwd\n", - "! ls" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " -------------------------------------------------------------------------------\r\n", - " -------------------------------------------------------------------------------\r\n", - " D a t e : 15 - 08 - 2022 T i m e : 10:13:31\r\n", - " -------------------------------------------------------------------------------\r\n", - " -------------------------------------------------------------------------------\r\n", - "\r\n", - "see results in directory: OUTPUT_FILES/\r\n", - "\r\n", - "done\r\n", - "Mon Aug 15 10:13:31 PDT 2022\r\n" - ] - } - ], - "source": [ - "# Run the Tape2007 example to make sure SPECFEM2D is working as expected\n", - "os.chdir(TAPE_2007_EXAMPLE)\n", - "! ./run_this_example.sh > output_log.txt\n", - "\n", - "assert(os.path.exists(\"OUTPUT_FILES/forward_image000004800.jpg\")), \\\n", - " (f\"Example did not run, the remainder of this docs page will likely not work.\"\n", - " f\"Please check the following directory: {TAPE_2007_EXAMPLE}\")\n", - "\n", - "! tail output_log.txt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "------------------------------------\n", - "Now we need to manually set up our SPECFEM2D working directory. As mentioned in the previous cell, the only required elements of this working directory are the following (these files will form the basis for how SeisFlows3 operates within the SPECFEM2D framework):\n", - "\n", - "1. __bin/__ directory containing SPECFEM2D binaries\n", - "2. __DATA/__ directory containing SOURCE and STATION files, as well as a SPECFEM2D Par_file\n", - "3. __OUTPUT_FILES/proc??????_*.bin__ files which define the starting (and target) models" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - ".. note:: \n", - " This file structure is the same for all versions of SPECFEM (2D/3D/3D_GLOBE)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Par_file SOURCE_013\r\n", - "Par_file_Tape2007_132rec_checker SOURCE_014\r\n", - "Par_file_Tape2007_onerec SOURCE_015\r\n", - "\u001b[1m\u001b[35mSOURCE\u001b[m\u001b[m SOURCE_016\r\n", - "SOURCE_001 SOURCE_017\r\n", - "SOURCE_002 SOURCE_018\r\n", - "SOURCE_003 SOURCE_019\r\n", - "SOURCE_004 SOURCE_020\r\n", - "SOURCE_005 SOURCE_021\r\n", - "SOURCE_006 SOURCE_022\r\n", - "SOURCE_007 SOURCE_023\r\n", - "SOURCE_008 SOURCE_024\r\n", - "SOURCE_009 SOURCE_025\r\n", - "SOURCE_010 STATIONS_checker\r\n", - "SOURCE_011 interfaces_Tape2007.dat\r\n", - "SOURCE_012 model_velocity.dat_checker\r\n" - ] - } - ], - "source": [ - "# First we will set the correct SOURCE and STATION files.\n", - "# This is the same task as shown in ./run_this_example.sh\n", - "os.chdir(SPECFEM2D_DATA)\n", - "\n", - "# Symlink source 001 as our main source\n", - "if os.path.exists(\"SOURCE\"):\n", - " os.remove(\"SOURCE\")\n", - "os.symlink(\"SOURCE_001\", \"SOURCE\")\n", - "\n", - "# Copy the correct Par_file so that edits do not affect the original file\n", - "if os.path.exists(\"Par_file\"):\n", - " os.remove(\"Par_file\")\n", - "shutil.copy(\"Par_file_Tape2007_onerec\", \"Par_file\")\n", - "\n", - "! ls" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 1c. Generate initial and target models\n", - "\n", - "Since we're doing a synthetic-synthetic inversion, we need to manually set up the velocity models with which we generate our synthetic waveforms. The naming conventions for these models are:\n", - "\n", - "1. __MODEL_INIT:__ The initial or starting model. Used to generate the actual synthetic seismograms. This is considered M00.\n", - "2. __MODEL_TRUE:__ The target or true model. Used to generate 'data' (also synthetic). This is the reference model that our inversion is trying to resolve.\n", - "\n", - "The starting model is defined as a homogeneous halfspace uin the Tape2007 example problem. We will need to run both `xmeshfem2D` and `xspecfem2D` to generate the required velocity model database files. We will generate our target model by slightly perturbing the parameters of the initial model." - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - ".. note::\n", - " We can use the SeisFlows3 command line option `seisflows sempar` to directly edit the SPECFEM2D Par_file in the command line. This will work for the SPECFEM3D Par_file as well." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "setup_with_binary_database: 0 -> 1\n", - "SAVE_MODEL: default -> binary\n", - "save_ASCII_kernels: .true. -> .false.\n" - ] - } - ], - "source": [ - "os.chdir(SPECFEM2D_DATA)\n", - "\n", - "# Ensure that SPECFEM2D outputs the velocity model in the expected binary format\n", - "! seisflows sempar setup_with_binary_database 1 # allow creation of .bin files\n", - "! seisflows sempar save_model binary # output model in .bin database format\n", - "! seisflows sempar save_ascii_kernels .false. # output kernels in .bin format, not ASCII" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m\u001b[32mDATA\u001b[m\u001b[m \u001b[1m\u001b[32mOUTPUT_FILES\u001b[m\u001b[m \u001b[1m\u001b[32mbin\u001b[m\u001b[m\r\n" - ] - } - ], - "source": [ - "# SPECFEM requires that we create the OUTPUT_FILES directory before running\n", - "os.chdir(SPECFEM2D_WORKDIR)\n", - "\n", - "if os.path.exists(SPECFEM2D_OUTPUT):\n", - " shutil.rmtree(SPECFEM2D_OUTPUT)\n", - " \n", - "os.mkdir(SPECFEM2D_OUTPUT)\n", - "\n", - "! ls" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " **********************************************\n", - " **** Specfem 2-D Solver - serial version ****\n", - " **********************************************\n", - "\n", - " Running Git version of the code corresponding to \n", - " dating From \n", - "\n", - "\n", - " NDIM = 2\n", - " -------------------------------------------------------------------------------\n", - " Program SPECFEM2D: \n", - " -------------------------------------------------------------------------------\n", - " -------------------------------------------------------------------------------\n", - " Tape-Liu-Tromp (GJI 2007)\n", - " -------------------------------------------------------------------------------\n", - " -------------------------------------------------------------------------------\n", - " D a t e : 15 - 08 - 2022 T i m e : 10:14:13\n", - " -------------------------------------------------------------------------------\n", - " -------------------------------------------------------------------------------\n" - ] - } - ], - "source": [ - "# GENERATE MODEL_INIT\n", - "os.chdir(SPECFEM2D_WORKDIR)\n", - "\n", - "# Run the mesher and solver to generate our initial model\n", - "! ./bin/xmeshfem2D > OUTPUT_FILES/mesher_log.txt\n", - "! ./bin/xspecfem2D > OUTPUT_FILES/solver_log.txt\n", - "\n", - "# Move the model files (*.bin) into the OUTPUT_FILES directory, where SeisFlows3 expects them\n", - "! mv DATA/*bin OUTPUT_FILES\n", - "\n", - "# Make sure we don't overwrite this initial model when creating our target model in the next step\n", - "! mv OUTPUT_FILES OUTPUT_FILES_INIT\n", - "\n", - "! head OUTPUT_FILES_INIT/solver_log.txt\n", - "! tail OUTPUT_FILES_INIT/solver_log.txt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "-----------------------------\n", - "\n", - "Now we want to perturb the initial model to create our target model (__MODEL_TRUE__). The seisflows command line subargument `seisflows sempar velocity_model` will let us view and edit the velocity model. You can also do this manually by editing the Par_file directly. " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "VELOCITY_MODEL:\r\n", - "\r\n", - "1 1 2600.d0 5800.d0 3500.0d0 0 0 10.d0 10.d0 0 0 0 0 0 0\r\n", - "->\r\n", - "1 1 2600.d0 5900.d0 3550.0d0 0 0 10.d0 10.d0 0 0 0 0 0 0\r\n" - ] - } - ], - "source": [ - "# GENERATE MODEL_TRUE\n", - "os.chdir(SPECFEM2D_DATA)\n", - "\n", - "# Edit the Par_file by increasing velocities by ~10% \n", - "! seisflows sempar velocity_model '1 1 2600.d0 5900.d0 3550.0d0 0 0 10.d0 10.d0 0 0 0 0 0 0'" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " **********************************************\n", - " **** Specfem 2-D Solver - serial version ****\n", - " **********************************************\n", - "\n", - " Running Git version of the code corresponding to \n", - " dating From \n", - "\n", - "\n", - " NDIM = 2\n", - " -------------------------------------------------------------------------------\n", - " Program SPECFEM2D: \n", - " -------------------------------------------------------------------------------\n", - " -------------------------------------------------------------------------------\n", - " Tape-Liu-Tromp (GJI 2007)\n", - " -------------------------------------------------------------------------------\n", - " -------------------------------------------------------------------------------\n", - " D a t e : 15 - 08 - 2022 T i m e : 10:14:13\n", - " -------------------------------------------------------------------------------\n", - " -------------------------------------------------------------------------------\n" - ] - } - ], - "source": [ - "# Re-run the mesher and solver to generate our target velocity model\n", - "os.chdir(SPECFEM2D_WORKDIR)\n", - "\n", - "# Make sure the ./OUTPUT_FILES directory exists since we moved the old one\n", - "if os.path.exists(SPECFEM2D_OUTPUT):\n", - " shutil.rmtree(SPECFEM2D_OUTPUT)\n", - "os.mkdir(SPECFEM2D_OUTPUT)\n", - "\n", - "# Run the binaries to generate MODEL_TRUE\n", - "! ./bin/xmeshfem2D > OUTPUT_FILES/mesher_log.txt\n", - "! ./bin/xspecfem2D > OUTPUT_FILES/solver_log.txt\n", - "\n", - "# Move all the relevant files into OUTPUT_FILES \n", - "! mv ./DATA/*bin OUTPUT_FILES\n", - "! mv OUTPUT_FILES OUTPUT_FILES_TRUE\n", - "\n", - "! head OUTPUT_FILES_INIT/solver_log.txt\n", - "! tail OUTPUT_FILES_INIT/solver_log.txt" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m\u001b[32mDATA\u001b[m\u001b[m \u001b[1m\u001b[32mOUTPUT_FILES_INIT\u001b[m\u001b[m \u001b[1m\u001b[32mOUTPUT_FILES_TRUE\u001b[m\u001b[m \u001b[1m\u001b[32mbin\u001b[m\u001b[m\r\n" - ] - } - ], - "source": [ - "# Great, we have all the necessary SPECFEM files to run our SeisFlows inversion!\n", - "! ls" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2. Initialize SeisFlows (SF)\n", - "In this Section we will look at a SeisFlows working directory, parameter file, and working state.\n", - "\n", - "#### 2a. SeisFlows working directory and parameter file\n", - "\n", - "As with SPECFEM, SeisFlows requires a parameter file (__parameters.yaml__) that controls how an automated workflow will proceed. Because SeisFlows is modular, there are a large number of potential parameters which may be present in a SeisFlows parameter file, as each sub-module may have its own set of unique parameters.\n", - "\n", - "In contrast to SPECFEM's method of listing all available parameters and leaving it up the User to determine which ones are relevant to them, SeisFlows dynamically builds its parameter file based on User inputs. In this subsection we will use the built-in SeisFlows command line tools to generate and populate the parameter file. " - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - ".. note::\n", - " See the `parameter file documentation page `__ for a more in depth exploration of this central SeisFlows file." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the previous section we saw the `sempar` command in action. We can use the `-h` or help flag to list all available SiesFlows3 command line commands." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "usage: seisflows [-h] [-w [WORKDIR]] [-p [PARAMETER_FILE]]\r\n", - " {setup,configure,swap,init,submit,resume,restart,clean,par,sempar,check,print,reset,debug,examples}\r\n", - " ...\r\n", - "\r\n", - "================================================================================\r\n", - "\r\n", - " SeisFlows: Waveform Inversion Package \r\n", - "\r\n", - "================================================================================\r\n", - "\r\n", - "optional arguments:\r\n", - " -h, --help show this help message and exit\r\n", - " -w [WORKDIR], --workdir [WORKDIR]\r\n", - " The SeisFlows working directory, default: cwd\r\n", - " -p [PARAMETER_FILE], --parameter_file [PARAMETER_FILE]\r\n", - " Parameters file, default: 'parameters.yaml'\r\n", - "\r\n", - "command:\r\n", - " Available SeisFlows arguments and their intended usages\r\n", - "\r\n", - " setup Setup working directory from scratch\r\n", - " configure Fill parameter file with defaults\r\n", - " swap Swap module parameters in an existing parameter file\r\n", - " init Initiate working environment\r\n", - " submit Submit initial workflow to system\r\n", - " resume Re-submit previous workflow to system\r\n", - " restart Remove current environment and submit new workflow\r\n", - " clean Remove files relating to an active working environment\r\n", - " par View and edit SeisFlows parameter file\r\n", - " sempar View and edit SPECFEM parameter file\r\n", - " check Check state of an active environment\r\n", - " print Print information related to an active environment\r\n", - " reset Reset modules within an active state\r\n", - " debug Start interactive debug environment\r\n", - " examples Look at and run pre-configured example problems\r\n", - "\r\n", - "'seisflows [command] -h' for more detailed descriptions of each command.\r\n" - ] - } - ], - "source": [ - "! seisflows -h" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'os' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# The command 'setup' creates the 'parameters.yaml' file that controls all of SeisFlows\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m# the '-f' flag removes any exist 'parameters.yaml' file that might be in the directory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mWORKDIR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' seisflows setup -f'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' ls'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'os' is not defined" - ] - } - ], - "source": [ - "# The command 'setup' creates the 'parameters.yaml' file that controls all of SeisFlows\n", - "# the '-f' flag removes any exist 'parameters.yaml' file that might be in the directory\n", - "os.chdir(WORKDIR)\n", - "! seisflows setup -f\n", - "! ls" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# //////////////////////////////////////////////////////////////////////////////\r\n", - "#\r\n", - "# SeisFlows YAML Parameter File\r\n", - "#\r\n", - "# //////////////////////////////////////////////////////////////////////////////\r\n", - "#\r\n", - "# Modules correspond to the structure of the source code, and determine\r\n", - "# SeisFlows' behavior at runtime. Each module requires its own sub-parameters.\r\n", - "#\r\n", - "# .. rubric::\r\n", - "# - To determine available options for modules listed below, run:\r\n", - "# > seisflows print modules\r\n", - "# - To auto-fill with docstrings and default values (recommended), run:\r\n", - "# > seisflows configure\r\n", - "# - To set values as NoneType, use: null\r\n", - "# - To set values as infinity, use: inf\r\n", - "#\r\n", - "# MODULES\r\n", - "# ///////\r\n", - "# workflow (str): The types and order of functions for running SeisFlows\r\n", - "# system (str): Computer architecture of the system being used\r\n", - "# solver (str): External numerical solver to use for waveform simulations\r\n", - "# preprocess (str): Preprocessing schema for waveform data\r\n", - "# optimize (str): Optimization algorithm for the inverse problem\r\n", - "# ==============================================================================\r\n", - "workflow: forward\r\n", - "system: workstation\r\n", - "solver: specfem2d\r\n", - "preprocess: default\r\n", - "optimize: gradient\r\n" - ] - } - ], - "source": [ - "# Let's have a look at this file, which has not yet been populated\n", - "! cat parameters.yaml" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " SEISFLOWS MODULES \r\n", - " ///////////////// \r\n", - "'-': module, '*': class\r\n", - "\r\n", - "- workflow\r\n", - " * forward\r\n", - " * inversion\r\n", - " * migration\r\n", - "- system\r\n", - " * chinook\r\n", - " * cluster\r\n", - " * frontera\r\n", - " * lsf\r\n", - " * maui\r\n", - " * slurm\r\n", - " * workstation\r\n", - "- solver\r\n", - " * specfem\r\n", - " * specfem2d\r\n", - " * specfem3d\r\n", - " * specfem3d_globe\r\n", - "- preprocess\r\n", - " * default\r\n", - " * pyaflowa\r\n", - "- optimize\r\n", - " * LBFGS\r\n", - " * NLCG\r\n", - " * gradient\r\n" - ] - } - ], - "source": [ - "# We can use the `seisflows print modules` command to list out the available options \n", - "! seisflows print modules" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "workflow: forward -> inversion\n", - "# //////////////////////////////////////////////////////////////////////////////\n", - "#\n", - "# SeisFlows YAML Parameter File\n", - "#\n", - "# //////////////////////////////////////////////////////////////////////////////\n", - "#\n", - "# Modules correspond to the structure of the source code, and determine\n", - "# SeisFlows' behavior at runtime. Each module requires its own sub-parameters.\n", - "#\n", - "# .. rubric::\n", - "# - To determine available options for modules listed below, run:\n", - "# > seisflows print modules\n", - "# - To auto-fill with docstrings and default values (recommended), run:\n", - "# > seisflows configure\n", - "# - To set values as NoneType, use: null\n", - "# - To set values as infinity, use: inf\n", - "#\n", - "# MODULES\n", - "# ///////\n", - "# workflow (str): The types and order of functions for running SeisFlows\n", - "# system (str): Computer architecture of the system being used\n", - "# solver (str): External numerical solver to use for waveform simulations\n", - "# preprocess (str): Preprocessing schema for waveform data\n", - "# optimize (str): Optimization algorithm for the inverse problem\n", - "# ==============================================================================\n", - "workflow: inversion\n", - "system: workstation\n", - "solver: specfem2d\n", - "preprocess: default\n", - "optimize: gradient\n" - ] - } - ], - "source": [ - "# For this example, we can use most of the default modules, however we need to \n", - "# change the SOLVER module to let SeisFlows know we're using SPECFEM2D (as opposed to 3D)\n", - "! seisflows par workflow inversion\n", - "! cat parameters.yaml" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "-------------------------\n", - "The `seisflows configure` command populates the parameter file based on the chosen modules. SeisFlows will attempt to fill in all parameters with reasonable default values. Docstrings above each module show descriptions and available options for each of these parameters. \n", - "\n", - "In the follownig cell we will use the `seisflows par` command to edit the parameters.yaml file directly, replacing some default parameters with our own values. Comments next to each evaluation describe the choice for each." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# //////////////////////////////////////////////////////////////////////////////\r\n", - "#\r\n", - "# SeisFlows YAML Parameter File\r\n", - "#\r\n", - "# //////////////////////////////////////////////////////////////////////////////\r\n", - "#\r\n", - "# Modules correspond to the structure of the source code, and determine\r\n", - "# SeisFlows' behavior at runtime. Each module requires its own sub-parameters.\r\n", - "#\r\n", - "# .. rubric::\r\n", - "# - To determine available options for modules listed below, run:\r\n", - "# > seisflows print modules\r\n", - "# - To auto-fill with docstrings and default values (recommended), run:\r\n", - "# > seisflows configure\r\n", - "# - To set values as NoneType, use: null\r\n", - "# - To set values as infinity, use: inf\r\n", - "#\r\n", - "# MODULES\r\n", - "# ///////\r\n", - "# workflow (str): The types and order of functions for running SeisFlows\r\n", - "# system (str): Computer architecture of the system being used\r\n", - "# solver (str): External numerical solver to use for waveform simulations\r\n", - "# preprocess (str): Preprocessing schema for waveform data\r\n", - "# optimize (str): Optimization algorithm for the inverse problem\r\n", - "# ==============================================================================\r\n", - "workflow: inversion\r\n", - "system: workstation\r\n", - "solver: specfem2d\r\n", - "preprocess: default\r\n", - "optimize: gradient\r\n", - "# =============================================================================\r\n", - "#\r\n", - "# Forward Workflow\r\n", - "# ----------------\r\n", - "# Run forward solver in parallel and (optionally) calculate\r\n", - "# data-synthetic misfit and adjoint sources.\r\n", - "#\r\n", - "# Parameters\r\n", - "# ----------\r\n", - "# :type modules: list of module\r\n", - "# :param modules: instantiated SeisFlows modules which should have been\r\n", - "# generated by the function `seisflows.config.import_seisflows` with a\r\n", - "# parameter file generated by seisflows.configure\r\n", - "# :type data_case: str\r\n", - "# :param data_case: How to address 'data' in the workflow, available options:\r\n", - "# 'data': real data will be provided by the user in\r\n", - "# `path_data/{source_name}` in the same format that the solver will\r\n", - "# produce synthetics (controlled by `solver.format`) OR\r\n", - "# synthetic': 'data' will be generated as synthetic seismograms using\r\n", - "# a target model provided in `path_model_true`. If None, workflow will\r\n", - "# not attempt to generate data.\r\n", - "# :type stop_after: str\r\n", - "# :param stop_after: optional name of task in task list (use\r\n", - "# `seisflows print tasks` to get task list for given workflow) to stop\r\n", - "# workflow after, allowing user to prematurely stop a workflow to explore\r\n", - "# intermediate results or debug.\r\n", - "# :type export_traces: bool\r\n", - "# :param export_traces: export all waveforms that are generated by the\r\n", - "# external solver to `path_output`. If False, solver traces stored in\r\n", - "# scratch may be discarded at any time in the workflow\r\n", - "# :type export_residuals: bool\r\n", - "# :param export_residuals: export all residuals (data-synthetic misfit) that\r\n", - "# are generated by the external solver to `path_output`. If False,\r\n", - "# residuals stored in scratch may be discarded at any time in the workflow\r\n", - "#\r\n", - "# \r\n", - "# Migration Workflow\r\n", - "# ------------------\r\n", - "# Run forward and adjoint solver to produce event-dependent misfit kernels.\r\n", - "# Sum and postprocess kernels to produce gradient. In seismic exploration\r\n", - "# this is 'reverse time migration'.\r\n", - "#\r\n", - "# Parameters\r\n", - "# ----------\r\n", - "# :type export_gradient: bool\r\n", - "# :param export_gradient: export the gradient after it has been generated\r\n", - "# in the scratch directory. If False, gradient can be discarded from\r\n", - "# scratch at any time in the workflow\r\n", - "# :type export_kernels: bool\r\n", - "# :param export_kernels: export each sources event kernels after they have\r\n", - "# been generated in the scratch directory. If False, gradient can be\r\n", - "# discarded from scratch at any time in the workflow\r\n", - "#\r\n", - "# \r\n", - "# Inversion Workflow\r\n", - "# ------------------\r\n", - "# Peforms iterative nonlinear inversion using the machinery of the Forward\r\n", - "# and Migration workflows, as well as a built-in optimization library.\r\n", - "#\r\n", - "# Parameters\r\n", - "# ----------\r\n", - "# :type start: int\r\n", - "# :param start: start inversion workflow at this iteration. 1 <= start <= inf\r\n", - "# :type end: int\r\n", - "# :param end: end inversion workflow at this iteration. start <= end <= inf\r\n", - "# :type iteration: int\r\n", - "# :param iteration: The current iteration of the workflow. If NoneType, takes\r\n", - "# the value of `start` (i.e., first iteration of the workflow). User can\r\n", - "# also set between `start` and `end` to resume a failed workflow.\r\n", - "# :type thrifty: bool\r\n", - "# :param thrifty: a thrifty inversion skips the costly intialization step\r\n", - "# (i.e., forward simulations and misfit quantification) if the final\r\n", - "# forward simulations from the previous iterations line search can be\r\n", - "# used in the current one. Requires L-BFGS optimization.\r\n", - "# :type export_model: bool\r\n", - "# :param export_model: export best-fitting model from the line search to disk.\r\n", - "# If False, new models can be discarded from scratch at any time.\r\n", - "#\r\n", - "# \r\n", - "# =============================================================================\r\n", - "data_case: data\r\n", - "stop_after: null\r\n", - "export_traces: False\r\n", - "export_residuals: False\r\n", - "export_gradient: False\r\n", - "export_kernels: False\r\n", - "start: 1\r\n", - "end: 1\r\n", - "export_model: True\r\n", - "thrifty: False\r\n", - "iteration: 1\r\n", - "# =============================================================================\r\n", - "#\r\n", - "# Workstation System\r\n", - "# ------------------\r\n", - "# Runs tasks in serial on a local machine.\r\n", - "#\r\n", - "# Parameters\r\n", - "# ----------\r\n", - "# :type ntask: int\r\n", - "# :param ntask: number of individual tasks/events to run during workflow.\r\n", - "# Must be <= the number of source files in `path_specfem_data`\r\n", - "# :type nproc: int\r\n", - "# :param nproc: number of processors to use for each simulation\r\n", - "# :type log_level: str\r\n", - "# :param log_level: logger level to pass to logging module.\r\n", - "# Available: 'debug', 'info', 'warning', 'critical'\r\n", - "# :type verbose: bool\r\n", - "# :param verbose: if True, formats the log messages to include the file\r\n", - "# name, line number and message type. Useful for debugging but\r\n", - "# also very verbose.\r\n", - "#\r\n", - "# \r\n", - "# =============================================================================\r\n", - "ntask: 1\r\n", - "nproc: 1\r\n", - "log_level: DEBUG\r\n", - "verbose: False\r\n", - "# =============================================================================\r\n", - "#\r\n", - "# Solver SPECFEM\r\n", - "# --------------\r\n", - "# Generalized SPECFEM interface to manipulate SPECFEM2D/3D/3D_GLOBE w/ Python\r\n", - "#\r\n", - "# Parameters\r\n", - "# ----------\r\n", - "# :type data_format: str\r\n", - "# :param data_format: data format for reading traces into memory.\r\n", - "# Available: ['SU': seismic unix format, 'ASCII': human-readable ascii]\r\n", - "# :type materials: str\r\n", - "# :param materials: Material parameters used to define model. Available:\r\n", - "# ['ELASTIC': Vp, Vs, 'ACOUSTIC': Vp, 'ISOTROPIC', 'ANISOTROPIC']\r\n", - "# :type density: bool\r\n", - "# :param density: How to treat density during inversion. If True, updates\r\n", - "# density during inversion. If False, keeps it constant.\r\n", - "# TODO allow density scaling during an inversion\r\n", - "# :type attenuation: bool\r\n", - "# :param attenuation: How to treat attenuation during inversion.\r\n", - "# if True, turns on attenuation during forward simulations only. If\r\n", - "# False, attenuation is always set to False. Requires underlying\r\n", - "# attenution (Q_mu, Q_kappa) model\r\n", - "# :type smooth_h: float\r\n", - "# :param smooth_h: Gaussian half-width for horizontal smoothing in units\r\n", - "# of meters. If 0., no smoothing applied\r\n", - "# :type smooth_h: float\r\n", - "# :param smooth_v: Gaussian half-width for vertical smoothing in units\r\n", - "# of meters.\r\n", - "# :type components: str\r\n", - "# :param components: components to consider and tag data with. Should be\r\n", - "# string of letters such as 'RTZ'\r\n", - "# :type solver_io: str\r\n", - "# :param solver_io: format of model/kernel/gradient files expected by the\r\n", - "# numerical solver. Available: ['fortran_binary': default .bin files].\r\n", - "# TODO: ['adios': ADIOS formatted files]\r\n", - "# :type source_prefix: str\r\n", - "# :param source_prefix: prefix of source/event/earthquake files. If None,\r\n", - "# will attempt to guess based on the specific solver chosen.\r\n", - "# :type mpiexec: str\r\n", - "# :param mpiexec: MPI executable used to run parallel processes. Should also\r\n", - "# be defined for the system module\r\n", - "#\r\n", - "# \r\n", - "# Solver SPECFEM2D\r\n", - "# ----------------\r\n", - "# SPECFEM2D-specific alterations to the base SPECFEM module\r\n", - "#\r\n", - "# Parameters\r\n", - "# ----------\r\n", - "# :type source_prefix: str\r\n", - "# :param source_prefix: Prefix of source files in path SPECFEM_DATA. Defaults\r\n", - "# to 'SOURCE'\r\n", - "# :type multiples: bool\r\n", - "# :param multiples: set an absorbing top-boundary condition\r\n", - "#\r\n", - "# \r\n", - "# =============================================================================\r\n", - "data_format: ascii\r\n", - "materials: acoustic\r\n", - "density: False\r\n", - "attenuation: False\r\n", - "smooth_h: 0.0\r\n", - "smooth_v: 0.0\r\n", - "components: ZNE\r\n", - "source_prefix: SOURCE\r\n", - "multiples: False\r\n", - "# =============================================================================\r\n", - "#\r\n", - "# Default Preprocess\r\n", - "# ------------------\r\n", - "# Data processing for seismic traces, with options for data misfit,\r\n", - "# filtering, normalization and muting.\r\n", - "#\r\n", - "# Parameters\r\n", - "# ----------\r\n", - "# :type data_format: str\r\n", - "# :param data_format: data format for reading traces into memory. For\r\n", - "# available see: seisflows.plugins.preprocess.readers\r\n", - "# :type misfit: str\r\n", - "# :param misfit: misfit function for waveform comparisons. For available\r\n", - "# see seisflows.plugins.preprocess.misfit\r\n", - "# :type backproject: str\r\n", - "# :param backproject: backprojection function for migration, or the\r\n", - "# objective function in FWI. For available see\r\n", - "# seisflows.plugins.preprocess.adjoint\r\n", - "# :type normalize: str\r\n", - "# :param normalize: Data normalization parameters used to normalize the\r\n", - "# amplitudes of waveforms. Choose from two sets:\r\n", - "# ENORML1: normalize per event by L1 of traces; OR\r\n", - "# ENORML2: normalize per event by L2 of traces;\r\n", - "# &\r\n", - "# TNORML1: normalize per trace by L1 of itself; OR\r\n", - "# TNORML2: normalize per trace by L2 of itself\r\n", - "# :type filter: str\r\n", - "# :param filter: Data filtering type, available options are:\r\n", - "# BANDPASS (req. MIN/MAX PERIOD/FREQ);\r\n", - "# LOWPASS (req. MAX_FREQ or MIN_PERIOD);\r\n", - "# HIGHPASS (req. MIN_FREQ or MAX_PERIOD)\r\n", - "# :type min_period: float\r\n", - "# :param min_period: Minimum filter period applied to time series.\r\n", - "# See also MIN_FREQ, MAX_FREQ, if User defines FREQ parameters, they\r\n", - "# will overwrite PERIOD parameters.\r\n", - "# :type max_period: float\r\n", - "# :param max_period: Maximum filter period applied to time series. See\r\n", - "# also MIN_FREQ, MAX_FREQ, if User defines FREQ parameters, they will\r\n", - "# overwrite PERIOD parameters.\r\n", - "# :type min_freq: float\r\n", - "# :param min_freq: Maximum filter frequency applied to time series,\r\n", - "# See also MIN_PERIOD, MAX_PERIOD, if User defines FREQ parameters,\r\n", - "# they will overwrite PERIOD parameters.\r\n", - "# :type max_freq: float\r\n", - "# :param max_freq: Maximum filter frequency applied to time series,\r\n", - "# See also MIN_PERIOD, MAX_PERIOD, if User defines FREQ parameters,\r\n", - "# they will overwrite PERIOD parameters.\r\n", - "# :type mute: list\r\n", - "# :param mute: Data mute parameters used to zero out early / late\r\n", - "# arrivals or offsets. Choose any number of:\r\n", - "# EARLY: mute early arrivals;\r\n", - "# LATE: mute late arrivals;\r\n", - "# SHORT: mute short source-receiver distances;\r\n", - "# LONG: mute long source-receiver distances\r\n", - "#\r\n", - "# \r\n", - "# =============================================================================\r\n", - "misfit: waveform\r\n", - "adjoint: waveform\r\n", - "normalize: []\r\n", - "filter: null\r\n", - "min_period: null\r\n", - "max_period: null\r\n", - "min_freq: null\r\n", - "max_freq: null\r\n", - "mute: []\r\n", - "early_slope: null\r\n", - "early_const: null\r\n", - "late_slope: null\r\n", - "late_const: null\r\n", - "short_dist: null\r\n", - "long_dist: null\r", - "\r\n", - "# =============================================================================\r\n", - "#\r\n", - "# Gradient Optimization\r\n", - "# ---------------------\r\n", - "# Gradient/steepest descent optimization algorithm.\r\n", - "#\r\n", - "# Parameters\r\n", - "# ----------\r\n", - "# :type line_search_method: str\r\n", - "# :param line_search_method: chosen line_search algorithm. Currently available\r\n", - "# are 'bracket' and 'backtrack'. See seisflows.plugins.line_search\r\n", - "# for all available options\r\n", - "# :type preconditioner: str\r\n", - "# :param preconditioner: algorithm for preconditioning gradients. Currently\r\n", - "# available: 'diagonal'. Requires `path_preconditioner` to point to a\r\n", - "# set of files that define the preconditioner, formatted the same as the\r\n", - "# input model\r\n", - "# :type step_count_max: int\r\n", - "# :param step_count_max: maximum number of trial steps to perform during\r\n", - "# the line search before a change in line search behavior is\r\n", - "# considered, or a line search is considered to have failed.\r\n", - "# :type step_len_init: float\r\n", - "# :param step_len_init: initial line search step length guess, provided\r\n", - "# as a fraction of current model parameters.\r\n", - "# :type step_len_max: float\r\n", - "# :param step_len_max: maximum allowable step length during the line\r\n", - "# search. Set as a fraction of the current model parameters\r\n", - "#\r\n", - "# \r\n", - "# =============================================================================\r\n", - "preconditioner: null\r\n", - "step_count_max: 10\r\n", - "step_len_init: 0.05\r\n", - "step_len_max: 0.5\r\n", - "line_search_method: bracket\r\n", - "# =============================================================================\r\n", - "#\r\n", - "#\t Paths\r\n", - "#\t -----\r\n", - "# :type workdir: str\r\n", - "# :param workdir: working directory in which to look for data and store\r\n", - "# results. Defaults to current working directory\r\n", - "# :type path_output: str\r\n", - "# :param path_output: path to directory used for permanent storage on disk.\r\n", - "# Results and exported scratch files are saved here.\r\n", - "# :type path_data: str\r\n", - "# :param path_data: path to any externally stored data required by the solver\r\n", - "# :type path_state_file: str\r\n", - "# :param path_state_file: path to a text file used to track the current\r\n", - "# status of a workflow (i.e., what functions have already been completed),\r\n", - "# used for checkpointing and resuming workflows\r\n", - "# :type path_model_init: str\r\n", - "# :param path_model_init: path to the starting model used to calculate the\r\n", - "# initial misfit. Must match the expected `solver_io` format.\r\n", - "# :type path_model_true: str\r\n", - "# :param path_model_true: path to a target model if `case`=='synthetic' and\r\n", - "# a set of synthetic 'observations' are required for workflow.\r\n", - "# :type path_eval_grad: str\r\n", - "# :param path_eval_grad: scratch path to store files for gradient evaluation,\r\n", - "# including models, kernels, gradient and residuals.\r\n", - "# :type path_mask: str\r\n", - "# :param path_mask: optional path to a masking function which is used to\r\n", - "# mask out or scale parts of the gradient. The user-defined mask must\r\n", - "# match the file format of the input model (e.g., .bin files).\r\n", - "# :type path_eval_func: str\r\n", - "# :param path_eval_func: scratch path to store files for line search objective\r\n", - "# function evaluations, including models, misfit and residuals\r\n", - "# \r\n", - "# :type path_output_log: str\r\n", - "# :param path_output_log: path to a text file used to store the outputs of\r\n", - "# the package wide logger, which are also written to stdout\r\n", - "# :type path_par_file: str\r\n", - "# :param path_par_file: path to parameter file which is used to instantiate\r\n", - "# the package\r\n", - "# :type path_log_files: str\r\n", - "# :param path_log_files: path to a directory where individual log files are\r\n", - "# saved whenever a number of parallel tasks are run on the system.\r\n", - "# \r\n", - "# :type path_data: str\r\n", - "# :param path_data: path to any externally stored data required by the solver\r\n", - "# :type path_specfem_bin: str\r\n", - "# :param path_specfem_bin: path to SPECFEM bin/ directory which\r\n", - "# contains binary executables for running SPECFEM\r\n", - "# :type path_specfem_data: str\r\n", - "# :param path_specfem_data: path to SPECFEM DATA/ directory which must\r\n", - "# contain the CMTSOLUTION, STATIONS and Par_file files used for\r\n", - "# running SPECFEM\r\n", - "# \r\n", - "# :type path_preprocess: str\r\n", - "# :param path_preprocess: scratch path for all preprocessing processes,\r\n", - "# including saving files\r\n", - "# \r\n", - "# :type path_preconditioner: str\r\n", - "# :param path_preconditioner: optional path to a set of preconditioner files\r\n", - "# formatted the same as the input model (or output model of solver).\r\n", - "# Required to exist and contain files if `preconditioner`==True\r\n", - "# \r\n", - "# =============================================================================\r\n", - "path_workdir: /Users/Chow/Work/work/sf_specfem2d_example\r\n", - "path_scratch: /Users/Chow/Work/work/sf_specfem2d_example/scratch\r\n", - "path_eval_grad: /Users/Chow/Work/work/sf_specfem2d_example/scratch/eval_grad\r\n", - "path_output: /Users/Chow/Work/work/sf_specfem2d_example/output\r\n", - "path_model_init: null\r\n", - "path_model_true: null\r\n", - "path_state_file: /Users/Chow/Work/work/sf_specfem2d_example/sfstate.txt\r\n", - "path_data: null\r\n", - "path_mask: null\r\n", - "path_eval_func: /Users/Chow/Work/work/sf_specfem2d_example/scratch/eval_func\r\n", - "path_par_file: /Users/Chow/Work/work/sf_specfem2d_example/parameters.yaml\r\n", - "path_log_files: /Users/Chow/Work/work/sf_specfem2d_example/logs\r\n", - "path_output_log: /Users/Chow/Work/work/sf_specfem2d_example/sflog.txt\r\n", - "path_specfem_bin: null\r\n", - "path_specfem_data: null\r\n", - "path_solver: /Users/Chow/Work/work/sf_specfem2d_example/scratch/solver\r\n", - "path_preconditioner: null\r\n" - ] - } - ], - "source": [ - "! seisflows configure\n", - "! cat parameters.yaml" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ntask: 1 -> 3\n", - "materials: acoustic -> elastic\n", - "density: False -> False\n", - "attenuation: False -> False\n", - "start: 1 -> 1\n", - "end: 1 -> 2\n", - "data_case: data -> synthetic\n", - "components: ZNE -> Y\n", - "step_count_max: 10 -> 5\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# EDIT THE SEISFLOWS PARAMETER FILE\n", - "! seisflows par ntask 3 # set the number of sources/events to use\n", - "! seisflows par materials elastic # how the velocity model is parameterized\n", - "! seisflows par density False # update density or keep constant\n", - "! seisflows par attenuation False\n", - "! seisflows par start 1 # first iteration\n", - "! seisflows par end 2 # final iteration -- we will only run 1\n", - "! seisflows par data_case synthetic # synthetic-synthetic means we need both INIT and TRUE models\n", - "! seisflows par components Y # this default example creates Y-component seismograms\n", - "! seisflows par step_count_max 5 # limit the number of steps in the line search\n", - "\n", - "# Use Python syntax here to access path constants\n", - "os.system(f\"seisflows par path_specfem_bin {SPECFEM2D_BIN}\") # set path to SPECFEM2D binaries\n", - "os.system(f\"seisflows par path_specfem_data {SPECFEM2D_DATA}\") # set path to SEPCFEM2D DATA/\n", - "os.system(f\"seisflows par path_model_init {SPECFEM2D_MODEL_INIT}\") # set path to INIT model\n", - "os.system(f\"seisflows par path_model_true {SPECFEM2D_MODEL_TRUE}\") # set path to TRUE model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "------------------------------\n", - "One last thing, we will need to edit the SPECFEM2D Par_file parameter `MODEL` such that `xmeshfem2d` reads our pre-built velocity models (\\*.bin files) rather than the meshing parameters defined in the Par_file." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MODEL: default -> gll\r\n" - ] - } - ], - "source": [ - "os.chdir(SPECFEM2D_DATA)\n", - "! seisflows sempar model gll" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3. Run SeisFlows\n", - "\n", - "In this Section we will run SeisFlows to generate synthetic seismograms, kernels, a gradient, and an updated velocity model.\n", - "\n", - "#### 3a. Forward simulations\n", - "\n", - "SeisFlows is an automated workflow tool, such that once we run `seisflows submit` we should not need to intervene in the workflow. However the package does allow the User flexibility in how they want the workflow to behave.\n", - "\n", - "For example, we can run our workflow in stages by taking advantage of the `stop_after` parameter. As its name suggests, `stop_after` allows us to stop a workflow prematurely so that we may stop and look at results, or debug a failing workflow.\n", - "\n", - "The `seisflows print flow` command tells us what functions we can use for the `stop_after` parameter. " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " SEISFLOWS WORKFLOW TASK LIST \r\n", - " //////////////////////////// \r\n", - "Task list for \r\n", - "\r\n", - "1: evaluate_initial_misfit\r\n", - "2: run_adjoint_simulations\r\n", - "3: postprocess_event_kernels\r\n", - "4: evaluate_gradient_from_kernels\r\n", - "5: initialize_line_search\r\n", - "6: perform_line_search\r\n", - "7: finalize_iteration\r\n" - ] - } - ], - "source": [ - "os.chdir(WORKDIR)\n", - "! seisflows print tasks" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "-----------------------------\n", - "In the Inversion workflow, the tasks listed are described as follows:\n", - "\n", - "1. __evaluate_initial_misfit:__ \n", - " a. Prepare data for inversion by either copying data from disk or generating 'synthetic data' with MODEL_TRUE \n", - " b. Call numerical solver to run forward simulations using MODEL_INIT, generating synthetics \n", - " c. Evaluate the objective function by performing waveform comparisons \n", - " d. Prepare `run_adjoint_simulations` step by generating adjoint sources and auxiliary files\n", - "2. __run_adjoint_simulations:__ Call numerical solver to run adjoint simulation, generating kernels\n", - "3. __postprocess_event_kernels:__ Combine all event kernels into a misfit kernel. \n", - "4. __evaluate_gradient_from_kernels:__ Smooth and mask the misfit kernel to create the gradient\n", - "4. __initialize_line_search:__ Call on the optimization library to scale the gradient by a step length to compute the search direction. Prepare file structure for line search.\n", - "5. __perform_line_search:__ Perform a line search by algorithmically scaling the gradient and evaluating the misfit function (forward simulations and misfit quantification) until misfit is acceptably reduced.\n", - "6. __finalize_iteration:__ Run any finalization steps such as saving traces, kernels, gradients and models to disk, setting up SeisFlows3 for any subsequent iterations. Clean the scratch/ directory in preparation for subsequent iterations\n", - "\n", - "Let's set the `stop_after` argument to __evaluate_initial_misfit__, this will halt the workflow after the intialization step. We'll also set the `verbose` parameter to 'False', to keep the logging format relatively simple. We will explore the `verbose`==True option in a later cell." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "stop_after: null -> evaluate_initial_misfit\n", - "verbose: False -> False\n" - ] - } - ], - "source": [ - "! seisflows par stop_after evaluate_initial_misfit\n", - "! seisflows par verbose False" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "-----------------------\n", - "Now let's run SeisFlows. There are two ways to do this: `submit` and `restart`\n", - "\n", - "1. `seisflows submit` is used to run new workflows and resume stopped or failed workflows.\n", - "2. The `restart` command is simply a convenience function that runs `clean` (to remove an active working state) and `submit` (to submit a fresh workflow). \n", - "\n", - "Since this is our first run, we'll use `seisflows submit`." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-08-15 16:11:40 (I) | \n", - "================================================================================\n", - " SETTING UP INVERSION WORKFLOW \n", - "================================================================================\n", - "2022-08-15 16:11:47 (D) | running setup for module 'system.Workstation'\n", - "2022-08-15 16:11:50 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/sflog_001.txt\n", - "2022-08-15 16:11:50 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/parameters_001.yaml\n", - "2022-08-15 16:11:50 (D) | running setup for module 'solver.Specfem2D'\n", - "2022-08-15 16:11:50 (I) | initializing 3 solver directories\n", - "2022-08-15 16:11:50 (D) | initializing solver directory source: 001\n", - "2022-08-15 16:11:58 (D) | linking source '001' as 'mainsolver'\n", - "2022-08-15 16:11:58 (D) | initializing solver directory source: 002\n", - "2022-08-15 16:12:04 (D) | initializing solver directory source: 003\n", - "2022-08-15 16:12:13 (D) | running setup for module 'preprocess.Default'\n", - "2022-08-15 16:12:14 (D) | running setup for module 'optimize.Gradient'\n", - "2022-08-15 16:12:15 (I) | no optimization checkpoint found, assuming first run\n", - "2022-08-15 16:12:16 (I) | re-loading optimization module from checkpoint\n", - "2022-08-15 16:12:16 (I) | \n", - "////////////////////////////////////////////////////////////////////////////////\n", - " RUNNING ITERATION 01 \n", - "////////////////////////////////////////////////////////////////////////////////\n", - "2022-08-15 16:12:16 (I) | \n", - "================================================================================\n", - " RUNNING INVERSION WORKFLOW \n", - "================================================================================\n", - "2022-08-15 16:12:16 (I) | \n", - "////////////////////////////////////////////////////////////////////////////////\n", - " EVALUATING MISFIT FOR INITIAL MODEL \n", - "////////////////////////////////////////////////////////////////////////////////\n", - "2022-08-15 16:12:16 (I) | checking initial model parameters\n", - "2022-08-15 16:12:16 (I) | 2600.00 <= rho <= 2600.00\n", - "2022-08-15 16:12:16 (I) | 3500.00 <= vs <= 3500.00\n", - "2022-08-15 16:12:16 (I) | 5800.00 <= vp <= 5800.00\n", - "2022-08-15 16:12:16 (I) | checking true/target model parameters\n", - "2022-08-15 16:12:16 (I) | 2600.00 <= rho <= 2600.00\n", - "2022-08-15 16:12:16 (I) | 3550.00 <= vs <= 3550.00\n", - "2022-08-15 16:12:16 (I) | 5900.00 <= vp <= 5900.00\n", - "2022-08-15 16:12:16 (I) | preparing observation data for source 001\n", - "2022-08-15 16:12:16 (I) | running forward simulation w/ target model for 001\n", - "2022-08-15 16:12:33 (I) | evaluating objective function for source 001\n", - "2022-08-15 16:12:33 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:12:53 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:12:53 (I) | preparing observation data for source 002\n", - "2022-08-15 16:12:53 (I) | running forward simulation w/ target model for 002\n", - "2022-08-15 16:13:09 (I) | evaluating objective function for source 002\n", - "2022-08-15 16:13:09 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:13:31 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:13:31 (I) | preparing observation data for source 003\n", - "2022-08-15 16:13:31 (I) | running forward simulation w/ target model for 003\n", - "2022-08-15 16:14:16 (I) | evaluating objective function for source 003\n", - "2022-08-15 16:14:16 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:14:33 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:14:33 (I) | stop workflow at `stop_after`: evaluate_initial_misfit\n" - ] - } - ], - "source": [ - "! seisflows submit " - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - ".. note::\n", - " For a detailed exploration of a SeisFlows working directory, see the `working directory `__ documentation page where we explain each of the files and directories that have been generated during this workflow. Below we just look at two files which are required for our adjoint simulation, the adjoint sources (.adj) and STATIONS_ADJOINT file" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " -48.0000000 0.0000000\r\n", - " -47.9400000 0.0000000\r\n", - " -47.8800000 0.0000000\r\n", - " -47.8200000 0.0000000\r\n", - " -47.7600000 0.0000000\r\n", - " -47.7000000 0.0000000\r\n", - " -47.6400000 0.0000000\r\n", - " -47.5800000 0.0000000\r\n", - " -47.5200000 0.0000000\r\n", - " -47.4600000 0.0000000\r\n" - ] - } - ], - "source": [ - "# The adjoint source is created in the same format as the synthetics (two-column ASCII) \n", - "! head scratch/solver/001/traces/adj/AA.S0001.BXY.adj" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 3b. Adjoint simulations\n", - "\n", - "Now that we have all the required files for running an adjoint simulation (\\*.adj waveforms and STATIONS_ADJOINT file), we can continue with the SeisFlows3 Inversion workflow. No need to edit the Par_file or anything like that, SeisFlows3 will take care of that under the hood. We simply need to tell the workflow (via the parameters.yaml file) to `resume_from` the correct function. We can have a look at these functions again:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " SEISFLOWS WORKFLOW TASK LIST \r\n", - " //////////////////////////// \r\n", - "Task list for \r\n", - "\r\n", - "1: evaluate_initial_misfit\r\n", - "2: run_adjoint_simulations\r\n", - "3: postprocess_event_kernels\r\n", - "4: evaluate_gradient_from_kernels\r\n", - "5: initialize_line_search\r\n", - "6: perform_line_search\r\n", - "7: finalize_iteration\r\n" - ] - } - ], - "source": [ - "! seisflows print tasks" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "stop_after: evaluate_initial_misfit -> evaluate_gradient_from_kernels\r\n" - ] - } - ], - "source": [ - "# We'll stop just before the line search so that we can take a look at the files \n", - "# generated during the middle tasks\n", - "! seisflows par stop_after evaluate_gradient_from_kernels" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-08-15 16:15:06 (D) | setting iteration==1 from state file\n", - "2022-08-15 16:15:06 (I) | \n", - "================================================================================\n", - " SETTING UP INVERSION WORKFLOW \n", - "================================================================================\n", - "2022-08-15 16:15:16 (D) | running setup for module 'system.Workstation'\n", - "2022-08-15 16:15:20 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/sflog_002.txt\n", - "2022-08-15 16:15:20 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/parameters_002.yaml\n", - "2022-08-15 16:15:20 (D) | running setup for module 'solver.Specfem2D'\n", - "2022-08-15 16:15:20 (I) | initializing 3 solver directories\n", - "2022-08-15 16:15:22 (D) | running setup for module 'preprocess.Default'\n", - "2022-08-15 16:15:23 (D) | running setup for module 'optimize.Gradient'\n", - "2022-08-15 16:15:25 (I) | re-loading optimization module from checkpoint\n", - "2022-08-15 16:15:27 (I) | re-loading optimization module from checkpoint\n", - "2022-08-15 16:15:27 (I) | \n", - "////////////////////////////////////////////////////////////////////////////////\n", - " RUNNING ITERATION 01 \n", - "////////////////////////////////////////////////////////////////////////////////\n", - "2022-08-15 16:15:27 (I) | \n", - "================================================================================\n", - " RUNNING INVERSION WORKFLOW \n", - "================================================================================\n", - "2022-08-15 16:15:27 (I) | 'evaluate_initial_misfit' has already been run, skipping\n", - "2022-08-15 16:15:27 (I) | \n", - "////////////////////////////////////////////////////////////////////////////////\n", - " EVALUATING EVENT KERNELS W/ ADJOINT SIMULATIONS \n", - "////////////////////////////////////////////////////////////////////////////////\n", - "2022-08-15 16:15:27 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log'\n", - "2022-08-15 16:16:11 (D) | renaming output event kernels: 'alpha' -> 'vp'\n", - "2022-08-15 16:16:11 (D) | renaming output event kernels: 'beta' -> 'vs'\n", - "2022-08-15 16:16:12 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log'\n", - "2022-08-15 16:16:59 (D) | renaming output event kernels: 'alpha' -> 'vp'\n", - "2022-08-15 16:16:59 (D) | renaming output event kernels: 'beta' -> 'vs'\n", - "2022-08-15 16:16:59 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log'\n", - "2022-08-15 16:17:45 (D) | renaming output event kernels: 'alpha' -> 'vp'\n", - "2022-08-15 16:17:45 (D) | renaming output event kernels: 'beta' -> 'vs'\n", - "2022-08-15 16:17:45 (I) | \n", - "////////////////////////////////////////////////////////////////////////////////\n", - " GENERATING/PROCESSING MISFIT KERNEL \n", - "////////////////////////////////////////////////////////////////////////////////\n", - "2022-08-15 16:17:45 (I) | combining event kernels into single misfit kernel\n", - "2022-08-15 16:17:47 (I) | scaling gradient to absolute model perturbations\n", - "2022-08-15 16:17:49 (I) | stop workflow at `stop_after`: evaluate_gradient_from_kernels\n" - ] - } - ], - "source": [ - "# We can use the `seisflows submit` command to continue an active workflow\n", - "# The state file created during the first run will tell the workflow to resume from the stopped point in the workflow\n", - "! seisflows submit " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "----------------\n", - "The function __run_adjoint_simulations()__ has run adjoint simulations to generate event kernels. The functions __postprocess_event_kernels__ and __evaluate_gradient_from_kernels__ will have summed and (optionally) smoothed the kernels to recover the gradient, which will be used to update our starting model.\n", - "\n", - "> **NOTE**: Since we did not specify any smoothing lenghts (PAR.SMOOTH_H and PAR.SMOOTH_V), no smoothing of the gradient has occurred. \n", - "\n", - "Using the gradient-descent optimization algorithm, SeisFlows will now compute a search direction that will be used in the line search to search for a best fitting model which optimally reduces the objective function. We can take a look at where SeisFlows has stored the information relating to kernel generation and the optimization computation." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m\u001b[32mgradient\u001b[m\u001b[m \u001b[1m\u001b[32mkernels\u001b[m\u001b[m \u001b[1m\u001b[32mmisfit_kernel\u001b[m\u001b[m \u001b[1m\u001b[32mmodel\u001b[m\u001b[m residuals.txt\r\n" - ] - } - ], - "source": [ - "# Gradient evaluation files are stored here, the kernels are stored separately from the gradient incase\n", - "# the user wants to manually manipulate them\n", - "! ls scratch/eval_grad" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "proc000000_vp_kernel.bin proc000000_vs_kernel.bin\r\n" - ] - } - ], - "source": [ - "# SeisFlows3 stores all kernels and gradient information as SPECFEM binary (.bin) files\n", - "! ls scratch/eval_grad/gradient" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m\u001b[32m001\u001b[m\u001b[m \u001b[1m\u001b[32m002\u001b[m\u001b[m \u001b[1m\u001b[32m003\u001b[m\u001b[m\r\n" - ] - } - ], - "source": [ - "# Kernels are stored on a per-event basis, and summed together (sum/). If smoothing was performed, \n", - "# we would see both smoothed and unsmoothed versions of the misfit kernel\n", - "! ls scratch/eval_grad/kernels" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "checkpoint.npz f_new.txt g_new.npz m_new.npz\r\n" - ] - } - ], - "source": [ - "# We can see that some new values have been stored in prepartion for the line search,\n", - "# including g_new (current gradient) and p_new (current search direction). These are also\n", - "# stored as vector NumPy arrays (.npy files)\n", - "! ls scratch/optimize" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-1.18126331e-12 2.40273470e-12 3.97045036e-11 ... 9.62017688e-11\n", - " 4.21140102e-11 3.96825021e-12]]\n" - ] - } - ], - "source": [ - "g_new = np.load(\"scratch/optimize/g_new.npz\")\n", - "print(g_new[\"vs_kernel\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "--------------------\n", - "#### 3c. Line search and model update\n", - "\n", - "Let's finish off the inversion by running through the line search, which will generate new models using the\n", - "gradient, evaluate the objective function by running forward simulations, and comparing the evaluated objective function with the value obtained in __evalaute_initial_misfit__. \n", - "\n", - "Satisfactory reduction in the objective function will result in a termination of the line search. We are using a bracketing line search here [(Modrak et al. 2018)](https://academic.oup.com/gji/article/206/3/1864/2583505), which requires finding models which both increase and decrease the misfit with respect to the initial evaluation. Therefore it takes atleast two trial steps to complete the line search." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "stop_after: evaluate_gradient_from_kernels -> finalize_iteration\r\n" - ] - } - ], - "source": [ - "! seisflows par stop_after perform_line_search # We don't want to run the finalize_iteration argument so that we can explore the dir" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-08-15 16:21:55 (D) | setting iteration==1 from state file\n", - "2022-08-15 16:21:55 (I) | \n", - "================================================================================\n", - " SETTING UP INVERSION WORKFLOW \n", - "================================================================================\n", - "2022-08-15 16:22:03 (D) | running setup for module 'system.Workstation'\n", - "2022-08-15 16:22:05 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/sflog_003.txt\n", - "2022-08-15 16:22:05 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/parameters_003.yaml\n", - "2022-08-15 16:22:05 (D) | running setup for module 'solver.Specfem2D'\n", - "2022-08-15 16:22:05 (I) | initializing 3 solver directories\n", - "2022-08-15 16:22:07 (D) | running setup for module 'preprocess.Default'\n", - "2022-08-15 16:22:08 (D) | running setup for module 'optimize.Gradient'\n", - "2022-08-15 16:22:09 (I) | re-loading optimization module from checkpoint\n", - "2022-08-15 16:22:11 (I) | re-loading optimization module from checkpoint\n", - "2022-08-15 16:22:11 (I) | \n", - "////////////////////////////////////////////////////////////////////////////////\n", - " RUNNING ITERATION 01 \n", - "////////////////////////////////////////////////////////////////////////////////\n", - "2022-08-15 16:22:11 (I) | \n", - "================================================================================\n", - " RUNNING INVERSION WORKFLOW \n", - "================================================================================\n", - "2022-08-15 16:22:11 (I) | 'evaluate_initial_misfit' has already been run, skipping\n", - "2022-08-15 16:22:11 (I) | 'run_adjoint_simulations' has already been run, skipping\n", - "2022-08-15 16:22:11 (I) | 'postprocess_event_kernels' has already been run, skipping\n", - "2022-08-15 16:22:11 (I) | 'evaluate_gradient_from_kernels' has already been run, skipping\n", - "2022-08-15 16:22:11 (I) | initializing 'bracket'ing line search\n", - "2022-08-15 16:22:11 (I) | enforcing max step length safeguard\n", - "2022-08-15 16:22:11 (D) | step length(s) = 0.00E+00\n", - "2022-08-15 16:22:11 (D) | misfit val(s) = 1.28E-03\n", - "2022-08-15 16:22:11 (I) | try: first evaluation, attempt guess step length, alpha=9.08E+11\n", - "2022-08-15 16:22:11 (I) | try: applying initial step length safegaurd as alpha has exceeded maximum step length, alpha_new=1.44E+10\n", - "2022-08-15 16:22:11 (D) | overwriting initial step length, alpha_new=2.32E+09\n", - "2022-08-15 16:22:11 (I) | trial model 'm_try' parameters: \n", - "2022-08-15 16:22:11 (I) | 5800.00 <= vp <= 5800.00\n", - "2022-08-15 16:22:11 (I) | 3244.51 <= vs <= 3790.00\n", - "2022-08-15 16:22:12 (I) | \n", - "LINE SEARCH STEP COUNT 01\n", - "--------------------------------------------------------------------------------\n", - "2022-08-15 16:22:12 (I) | evaluating objective function for source 001\n", - "2022-08-15 16:22:12 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:22:23 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:22:23 (I) | evaluating objective function for source 002\n", - "2022-08-15 16:22:23 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:22:35 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:22:35 (I) | evaluating objective function for source 003\n", - "2022-08-15 16:22:35 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:22:48 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:22:48 (D) | misfit for trial model (f_try) == 8.65E-04\n", - "2022-08-15 16:22:48 (D) | step length(s) = 0.00E+00, 2.32E+09\n", - "2022-08-15 16:22:48 (D) | misfit val(s) = 1.28E-03, 8.65E-04\n", - "2022-08-15 16:22:48 (I) | try: misfit not bracketed, increasing step length using golden ratio, alpha=3.76E+09\n", - "2022-08-15 16:22:49 (I) | line search model 'm_try' parameters: \n", - "2022-08-15 16:22:49 (I) | 5800.00 <= vp <= 5800.00\n", - "2022-08-15 16:22:49 (I) | 3086.61 <= vs <= 3969.23\n", - "2022-08-15 16:22:49 (I) | trial step unsuccessful. re-attempting line search\n", - "2022-08-15 16:22:49 (I) | \n", - "LINE SEARCH STEP COUNT 02\n", - "--------------------------------------------------------------------------------\n", - "2022-08-15 16:22:49 (I) | evaluating objective function for source 001\n", - "2022-08-15 16:22:49 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:23:01 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:23:01 (I) | evaluating objective function for source 002\n", - "2022-08-15 16:23:01 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:23:13 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:23:13 (I) | evaluating objective function for source 003\n", - "2022-08-15 16:23:13 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:23:25 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:23:25 (D) | misfit for trial model (f_try) == 1.73E-03\n", - "2022-08-15 16:23:25 (D) | step length(s) = 0.00E+00, 2.32E+09, 3.76E+09\n", - "2022-08-15 16:23:25 (D) | misfit val(s) = 1.28E-03, 8.65E-04, 1.73E-03\n", - "2022-08-15 16:23:25 (I) | try: bracket acceptable but step length unreasonable attempting to re-adjust step length alpha=1.59E+09\n", - "2022-08-15 16:23:25 (I) | line search model 'm_try' parameters: \n", - "2022-08-15 16:23:25 (I) | 5800.00 <= vp <= 5800.00\n", - "2022-08-15 16:23:25 (I) | 3325.01 <= vs <= 3698.63\n", - "2022-08-15 16:23:25 (I) | trial step unsuccessful. re-attempting line search\n", - "2022-08-15 16:23:25 (I) | \n", - "LINE SEARCH STEP COUNT 03\n", - "--------------------------------------------------------------------------------\n", - "2022-08-15 16:23:25 (I) | evaluating objective function for source 001\n", - "2022-08-15 16:23:25 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:23:37 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:23:37 (I) | evaluating objective function for source 002\n", - "2022-08-15 16:23:37 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:23:51 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:23:51 (I) | evaluating objective function for source 003\n", - "2022-08-15 16:23:51 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:24:03 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:24:04 (D) | misfit for trial model (f_try) == 2.59E-03\n", - "2022-08-15 16:24:04 (D) | step length(s) = 0.00E+00, 1.59E+09, 2.32E+09, 3.76E+09\n", - "2022-08-15 16:24:04 (D) | misfit val(s) = 1.28E-03, 2.59E-03, 8.65E-04, 1.73E-03\n", - "2022-08-15 16:24:04 (I) | try: bracket acceptable but step length unreasonable attempting to re-adjust step length alpha=2.82E+09\n", - "2022-08-15 16:24:04 (I) | line search model 'm_try' parameters: \n", - "2022-08-15 16:24:04 (I) | 5800.00 <= vp <= 5800.00\n", - "2022-08-15 16:24:04 (I) | 3189.77 <= vs <= 3852.13\n", - "2022-08-15 16:24:04 (I) | trial step unsuccessful. re-attempting line search\n", - "2022-08-15 16:24:04 (I) | \n", - "LINE SEARCH STEP COUNT 04\n", - "--------------------------------------------------------------------------------\n", - "2022-08-15 16:24:04 (I) | evaluating objective function for source 001\n", - "2022-08-15 16:24:04 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:24:15 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:24:15 (I) | evaluating objective function for source 002\n", - "2022-08-15 16:24:15 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:24:27 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:24:27 (I) | evaluating objective function for source 003\n", - "2022-08-15 16:24:27 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:24:39 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:24:39 (D) | misfit for trial model (f_try) == 3.46E-03\n", - "2022-08-15 16:24:39 (D) | step length(s) = 0.00E+00, 1.59E+09, 2.32E+09, 2.82E+09, 3.76E+09\n", - "2022-08-15 16:24:39 (D) | misfit val(s) = 1.28E-03, 2.59E-03, 8.65E-04, 3.46E-03, 1.73E-03\n", - "2022-08-15 16:24:39 (I) | pass: bracket acceptable and step length reasonable. returning minimum line search misfit.\n", - "2022-08-15 16:24:39 (I) | line search model 'm_try' parameters: \n", - "2022-08-15 16:24:39 (I) | 5800.00 <= vp <= 5800.00\n", - "2022-08-15 16:24:39 (I) | 3244.51 <= vs <= 3790.00\n", - "2022-08-15 16:24:39 (I) | trial step successful. finalizing line search\n", - "2022-08-15 16:24:39 (I) | \n", - "FINALIZING LINE SEARCH\n", - "--------------------------------------------------------------------------------\n", - "2022-08-15 16:24:39 (I) | writing optimization stats\n", - "2022-08-15 16:24:39 (I) | renaming current (new) optimization vectors as previous model (old)\n", - "2022-08-15 16:24:39 (I) | setting accepted trial model (try) as current model (new)\n", - "2022-08-15 16:24:39 (I) | misfit of accepted trial model is f=8.645E-04\n", - "2022-08-15 16:24:39 (I) | resetting line search step count to 0\n", - "2022-08-15 16:24:39 (I) | \n", - "////////////////////////////////////////////////////////////////////////////////\n", - " CLEANING WORKDIR FOR NEXT ITERATION \n", - "////////////////////////////////////////////////////////////////////////////////\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-08-15 16:24:41 (I) | thrifty inversion encountering first iteration, defaulting to standard inversion workflow\n", - "2022-08-15 16:24:42 (I) | stop workflow at `stop_after`: finalize_iteration\n" - ] - } - ], - "source": [ - "! seisflows submit" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the log statements above, we can see that the SeisFlows line search required 4 trial steps, where it modified values of Vs (shear-wave velocity) until satisfactory reduction in the objective function was met. This was the final step in the iteration, and so the finalization of the line search made preparations for a subsequent iteration. " - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "alpha.txt f_old.txt m_new.npz p_old.npz\r\n", - "checkpoint.npz f_try.txt m_old.npz\r\n", - "f_new.txt g_old.npz output_optim.txt\r\n" - ] - } - ], - "source": [ - "# We can see that we have 'new' and 'old' values for each of the optimization values,\n", - "# representing the previous model (M00) and the current model (M01).\n", - "! ls scratch/optimize" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step_count,step_length,gradient_norm_L1,gradient_norm_L2,misfit,if_restarted,slope,theta\r\n", - "04,2.323E+09,9.243E-05,1.049E-06,1.279E-03,0,8.263E-13,0.000E+00\r\n" - ] - } - ], - "source": [ - "# The stats/ directory contains text files describing the optimization/line search\n", - "! cat scratch/optimize/output_optim.txt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4. Conclusions\n", - "\n", - "We've now seen how SeisFlows runs an __Inversion__ workflow using the __Specfem2D__ solver on a __Workstation__ system. More or less, this is all you need to run SeisFlows with any combination of modules. The specificities of a system or numerical solver are already handled internally by SeisFlows, so if you want to use Specmfe3D_Cartesian as your solver, you would only need to run `seisflows par solver specfem3d` at the beginning of your workflow (you will also need to set up your Specfem3D models, similar to what we did for Specfem2D here). To run on a slurm system like Chinook (University of Alaska Fairbanks), you can run `seisflows par system chinook`. " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/notebooks/specfem2d_example.ipynb b/docs/notebooks/specfem2d_example.ipynb index 6dac591a..5f5457ca 100644 --- a/docs/notebooks/specfem2d_example.ipynb +++ b/docs/notebooks/specfem2d_example.ipynb @@ -66,16 +66,13 @@ " a. [Download and compile codebase](#1a.-Download-and-compile-codebase*) \n", " b. [Create a separate SPECFEM2D working directory](#1b.-Create-a-separate-SPECFEM2D-working-directory) \n", " c. [Generate initial and target models](#1c.-Generate-initial-and-target-models) \n", - "\n", "2. __[Initialize SeisFlows (SF)](#2.-Initialize-SeisFlows-(SF))__ \n", " a. [SeisFlows working directory and parameter file](#2a.-SF-working-directory-and-parameter-file) \n", - "\n", "3. __[Run SeisFlows](#2.-Run-SeisFlows)__ \n", " a. [Forward simulations](#3a.-Forward-simulations) \n", " b. [Exploring the SeisFlows directory structure](#3b.-Exploring-the-SF-directory-structure) \n", " c. [Adjoint simulations](#3c.-Adjoint-simulations) \n", " d. [Line search and model update](#3d.-Line-search-and-model-update) \n", - "\n", "4. __[Conclusions](#4.-Conclusions)__ " ] }, @@ -93,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -105,17 +102,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# vvv USER MUST EDIT THE FOLLOWING PATHS vvv\n", - "# MAC PATHS\n", - "WORKDIR = \"/Users/Chow/Work/work/sf_specfem2d_example\" \n", - "SPECFEM2D = \"/Users/Chow/Repositories/specfem2d\"\n", - "# LINUX PATHS\n", - "# WORKDIR = \"/home/bchow/Work/work/sf_specfem2d_example\" \n", - "# SPECFEM2D = \"/home/bchow/REPOSITORIES/specfem2d\"\n", + "WORKDIR = \"/home/bchow/Work/scratch\" \n", + "SPECFEM2D = \"/home/bchow/REPOSITORIES/specfem2d\"\n", "# where WORKDIR: points to your own working directory\n", "# and SPECFEM2D: points to an existing specfem2D repository if available (if not set as '')\n", "# ^^^ USER MUST EDIT THE FOLLOWING PATHS ^^^\n", @@ -140,14 +133,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "SPECFEM2D repository already found, you may skip this subsection\n" + "Existing SPECMFE2D respository found, symlinking to working directory\n" ] } ], @@ -170,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -182,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -193,22 +186,17 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "/Users/Chow/Repositories/specfem2d\n", - "\u001b[1m\u001b[34mxadj_seismogram\u001b[m\u001b[m \u001b[1m\u001b[34mxmeshfem2D\u001b[m\u001b[m\n", - "\u001b[1m\u001b[32mxadj_seismogram.dSYM\u001b[m\u001b[m \u001b[1m\u001b[32mxmeshfem2D.dSYM\u001b[m\u001b[m\n", - "\u001b[1m\u001b[34mxcheck_quality_external_mesh\u001b[m\u001b[m \u001b[1m\u001b[34mxsmooth_sem\u001b[m\u001b[m\n", - "\u001b[1m\u001b[32mxcheck_quality_external_mesh.dSYM\u001b[m\u001b[m \u001b[1m\u001b[32mxsmooth_sem.dSYM\u001b[m\u001b[m\n", - "\u001b[1m\u001b[34mxcombine_sem\u001b[m\u001b[m \u001b[1m\u001b[34mxspecfem2D\u001b[m\u001b[m\n", - "\u001b[1m\u001b[32mxcombine_sem.dSYM\u001b[m\u001b[m \u001b[1m\u001b[32mxspecfem2D.dSYM\u001b[m\u001b[m\n", - "\u001b[1m\u001b[34mxconvolve_source_timefunction\u001b[m\u001b[m \u001b[1m\u001b[34mxsum_kernels\u001b[m\u001b[m\n", - "\u001b[1m\u001b[32mxconvolve_source_timefunction.dSYM\u001b[m\u001b[m \u001b[1m\u001b[32mxsum_kernels.dSYM\u001b[m\u001b[m\n" + "/home/bchow/REPOSITORIES/specfem2d\n", + "xadj_seismogram\t\t xconvolve_source_timefunction xspecfem2D\n", + "xcheck_quality_external_mesh xmeshfem2D\t\t xsum_kernels\n", + "xcombine_sem\t\t xsmooth_sem\n" ] } ], @@ -245,15 +233,15 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "/Users/Chow/Work/work/sf_specfem2d_example/specfem2d_workdir\n", - "\u001b[1m\u001b[32mDATA\u001b[m\u001b[m \u001b[1m\u001b[32mbin\u001b[m\u001b[m\n" + "/home/bchow/Work/scratch/specfem2d_workdir\n", + "bin DATA\n" ] } ], @@ -278,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -287,14 +275,14 @@ "text": [ " -------------------------------------------------------------------------------\r\n", " -------------------------------------------------------------------------------\r\n", - " D a t e : 15 - 08 - 2022 T i m e : 10:13:31\r\n", + " D a t e : 16 - 08 - 2022 T i m e : 14:26:37\r\n", " -------------------------------------------------------------------------------\r\n", " -------------------------------------------------------------------------------\r\n", "\r\n", "see results in directory: OUTPUT_FILES/\r\n", "\r\n", "done\r\n", - "Mon Aug 15 10:13:31 PDT 2022\r\n" + "Tue Aug 16 02:26:37 PM AKDT 2022\r\n" ] } ], @@ -332,29 +320,22 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Par_file SOURCE_013\r\n", - "Par_file_Tape2007_132rec_checker SOURCE_014\r\n", - "Par_file_Tape2007_onerec SOURCE_015\r\n", - "\u001b[1m\u001b[35mSOURCE\u001b[m\u001b[m SOURCE_016\r\n", - "SOURCE_001 SOURCE_017\r\n", - "SOURCE_002 SOURCE_018\r\n", - "SOURCE_003 SOURCE_019\r\n", - "SOURCE_004 SOURCE_020\r\n", - "SOURCE_005 SOURCE_021\r\n", - "SOURCE_006 SOURCE_022\r\n", - "SOURCE_007 SOURCE_023\r\n", - "SOURCE_008 SOURCE_024\r\n", - "SOURCE_009 SOURCE_025\r\n", - "SOURCE_010 STATIONS_checker\r\n", - "SOURCE_011 interfaces_Tape2007.dat\r\n", - "SOURCE_012 model_velocity.dat_checker\r\n" + "interfaces_Tape2007.dat\t\t SOURCE_003 SOURCE_012 SOURCE_021\r\n", + "model_velocity.dat_checker\t SOURCE_004 SOURCE_013 SOURCE_022\r\n", + "Par_file\t\t\t SOURCE_005 SOURCE_014 SOURCE_023\r\n", + "Par_file_Tape2007_132rec_checker SOURCE_006 SOURCE_015 SOURCE_024\r\n", + "Par_file_Tape2007_onerec\t SOURCE_007 SOURCE_016 SOURCE_025\r\n", + "proc000000_model_velocity.dat_input SOURCE_008 SOURCE_017 STATIONS\r\n", + "SOURCE\t\t\t\t SOURCE_009 SOURCE_018 STATIONS_checker\r\n", + "SOURCE_001\t\t\t SOURCE_010 SOURCE_019\r\n", + "SOURCE_002\t\t\t SOURCE_011 SOURCE_020\r\n" ] } ], @@ -400,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -424,14 +405,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m\u001b[32mDATA\u001b[m\u001b[m \u001b[1m\u001b[32mOUTPUT_FILES\u001b[m\u001b[m \u001b[1m\u001b[32mbin\u001b[m\u001b[m\r\n" + "bin DATA OUTPUT_FILES\r\n" ] } ], @@ -449,7 +430,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -461,8 +442,8 @@ " **** Specfem 2-D Solver - serial version ****\n", " **********************************************\n", "\n", - " Running Git version of the code corresponding to \n", - " dating From \n", + " Running Git version of the code corresponding to commit cf89366717d9435985ba852ef1d41a10cee97884\n", + " dating From Date: Mon Nov 29 23:20:51 2021 -0800\n", "\n", "\n", " NDIM = 2\n", @@ -473,7 +454,7 @@ " Tape-Liu-Tromp (GJI 2007)\n", " -------------------------------------------------------------------------------\n", " -------------------------------------------------------------------------------\n", - " D a t e : 15 - 08 - 2022 T i m e : 10:14:13\n", + " D a t e : 16 - 08 - 2022 T i m e : 14:26:52\n", " -------------------------------------------------------------------------------\n", " -------------------------------------------------------------------------------\n" ] @@ -508,7 +489,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -533,7 +514,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -545,8 +526,8 @@ " **** Specfem 2-D Solver - serial version ****\n", " **********************************************\n", "\n", - " Running Git version of the code corresponding to \n", - " dating From \n", + " Running Git version of the code corresponding to commit cf89366717d9435985ba852ef1d41a10cee97884\n", + " dating From Date: Mon Nov 29 23:20:51 2021 -0800\n", "\n", "\n", " NDIM = 2\n", @@ -557,7 +538,7 @@ " Tape-Liu-Tromp (GJI 2007)\n", " -------------------------------------------------------------------------------\n", " -------------------------------------------------------------------------------\n", - " D a t e : 15 - 08 - 2022 T i m e : 10:14:13\n", + " D a t e : 16 - 08 - 2022 T i m e : 14:26:52\n", " -------------------------------------------------------------------------------\n", " -------------------------------------------------------------------------------\n" ] @@ -586,14 +567,14 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m\u001b[32mDATA\u001b[m\u001b[m \u001b[1m\u001b[32mOUTPUT_FILES_INIT\u001b[m\u001b[m \u001b[1m\u001b[32mOUTPUT_FILES_TRUE\u001b[m\u001b[m \u001b[1m\u001b[32mbin\u001b[m\u001b[m\r\n" + "bin DATA OUTPUT_FILES_INIT OUTPUT_FILES_TRUE\r\n" ] } ], @@ -633,7 +614,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -686,18 +667,15 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 35, "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'os' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# The command 'setup' creates the 'parameters.yaml' file that controls all of SeisFlows\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m# the '-f' flag removes any exist 'parameters.yaml' file that might be in the directory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mWORKDIR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' seisflows setup -f'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' ls'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'os' is not defined" + "name": "stdout", + "output_type": "stream", + "text": [ + "creating parameter file: parameters.yaml\n", + "parameters.yaml sflog.txt specfem2d specfem2d_workdir\n" ] } ], @@ -711,7 +689,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -758,7 +736,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -803,7 +781,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -863,7 +841,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -919,372 +897,18 @@ "# `path_data/{source_name}` in the same format that the solver will\r\n", "# produce synthetics (controlled by `solver.format`) OR\r\n", "# synthetic': 'data' will be generated as synthetic seismograms using\r\n", - "# a target model provided in `path_model_true`. If None, workflow will\r\n", - "# not attempt to generate data.\r\n", - "# :type stop_after: str\r\n", - "# :param stop_after: optional name of task in task list (use\r\n", - "# `seisflows print tasks` to get task list for given workflow) to stop\r\n", - "# workflow after, allowing user to prematurely stop a workflow to explore\r\n", - "# intermediate results or debug.\r\n", - "# :type export_traces: bool\r\n", - "# :param export_traces: export all waveforms that are generated by the\r\n", - "# external solver to `path_output`. If False, solver traces stored in\r\n", - "# scratch may be discarded at any time in the workflow\r\n", - "# :type export_residuals: bool\r\n", - "# :param export_residuals: export all residuals (data-synthetic misfit) that\r\n", - "# are generated by the external solver to `path_output`. If False,\r\n", - "# residuals stored in scratch may be discarded at any time in the workflow\r\n", - "#\r\n", - "# \r\n", - "# Migration Workflow\r\n", - "# ------------------\r\n", - "# Run forward and adjoint solver to produce event-dependent misfit kernels.\r\n", - "# Sum and postprocess kernels to produce gradient. In seismic exploration\r\n", - "# this is 'reverse time migration'.\r\n", - "#\r\n", - "# Parameters\r\n", - "# ----------\r\n", - "# :type export_gradient: bool\r\n", - "# :param export_gradient: export the gradient after it has been generated\r\n", - "# in the scratch directory. If False, gradient can be discarded from\r\n", - "# scratch at any time in the workflow\r\n", - "# :type export_kernels: bool\r\n", - "# :param export_kernels: export each sources event kernels after they have\r\n", - "# been generated in the scratch directory. If False, gradient can be\r\n", - "# discarded from scratch at any time in the workflow\r\n", - "#\r\n", - "# \r\n", - "# Inversion Workflow\r\n", - "# ------------------\r\n", - "# Peforms iterative nonlinear inversion using the machinery of the Forward\r\n", - "# and Migration workflows, as well as a built-in optimization library.\r\n", - "#\r\n", - "# Parameters\r\n", - "# ----------\r\n", - "# :type start: int\r\n", - "# :param start: start inversion workflow at this iteration. 1 <= start <= inf\r\n", - "# :type end: int\r\n", - "# :param end: end inversion workflow at this iteration. start <= end <= inf\r\n", - "# :type iteration: int\r\n", - "# :param iteration: The current iteration of the workflow. If NoneType, takes\r\n", - "# the value of `start` (i.e., first iteration of the workflow). User can\r\n", - "# also set between `start` and `end` to resume a failed workflow.\r\n", - "# :type thrifty: bool\r\n", - "# :param thrifty: a thrifty inversion skips the costly intialization step\r\n", - "# (i.e., forward simulations and misfit quantification) if the final\r\n", - "# forward simulations from the previous iterations line search can be\r\n", - "# used in the current one. Requires L-BFGS optimization.\r\n", - "# :type export_model: bool\r\n", - "# :param export_model: export best-fitting model from the line search to disk.\r\n", - "# If False, new models can be discarded from scratch at any time.\r\n", - "#\r\n", - "# \r\n", - "# =============================================================================\r\n", - "data_case: data\r\n", - "stop_after: null\r\n", - "export_traces: False\r\n", - "export_residuals: False\r\n", - "export_gradient: False\r\n", - "export_kernels: False\r\n", - "start: 1\r\n", - "end: 1\r\n", - "export_model: True\r\n", - "thrifty: False\r\n", - "iteration: 1\r\n", - "# =============================================================================\r\n", - "#\r\n", - "# Workstation System\r\n", - "# ------------------\r\n", - "# Runs tasks in serial on a local machine.\r\n", - "#\r\n", - "# Parameters\r\n", - "# ----------\r\n", - "# :type ntask: int\r\n", - "# :param ntask: number of individual tasks/events to run during workflow.\r\n", - "# Must be <= the number of source files in `path_specfem_data`\r\n", - "# :type nproc: int\r\n", - "# :param nproc: number of processors to use for each simulation\r\n", - "# :type log_level: str\r\n", - "# :param log_level: logger level to pass to logging module.\r\n", - "# Available: 'debug', 'info', 'warning', 'critical'\r\n", - "# :type verbose: bool\r\n", - "# :param verbose: if True, formats the log messages to include the file\r\n", - "# name, line number and message type. Useful for debugging but\r\n", - "# also very verbose.\r\n", - "#\r\n", - "# \r\n", - "# =============================================================================\r\n", - "ntask: 1\r\n", - "nproc: 1\r\n", - "log_level: DEBUG\r\n", - "verbose: False\r\n", - "# =============================================================================\r\n", - "#\r\n", - "# Solver SPECFEM\r\n", - "# --------------\r\n", - "# Generalized SPECFEM interface to manipulate SPECFEM2D/3D/3D_GLOBE w/ Python\r\n", - "#\r\n", - "# Parameters\r\n", - "# ----------\r\n", - "# :type data_format: str\r\n", - "# :param data_format: data format for reading traces into memory.\r\n", - "# Available: ['SU': seismic unix format, 'ASCII': human-readable ascii]\r\n", - "# :type materials: str\r\n", - "# :param materials: Material parameters used to define model. Available:\r\n", - "# ['ELASTIC': Vp, Vs, 'ACOUSTIC': Vp, 'ISOTROPIC', 'ANISOTROPIC']\r\n", - "# :type density: bool\r\n", - "# :param density: How to treat density during inversion. If True, updates\r\n", - "# density during inversion. If False, keeps it constant.\r\n", - "# TODO allow density scaling during an inversion\r\n", - "# :type attenuation: bool\r\n", - "# :param attenuation: How to treat attenuation during inversion.\r\n", - "# if True, turns on attenuation during forward simulations only. If\r\n", - "# False, attenuation is always set to False. Requires underlying\r\n", - "# attenution (Q_mu, Q_kappa) model\r\n", - "# :type smooth_h: float\r\n", - "# :param smooth_h: Gaussian half-width for horizontal smoothing in units\r\n", - "# of meters. If 0., no smoothing applied\r\n", - "# :type smooth_h: float\r\n", - "# :param smooth_v: Gaussian half-width for vertical smoothing in units\r\n", - "# of meters.\r\n", - "# :type components: str\r\n", - "# :param components: components to consider and tag data with. Should be\r\n", - "# string of letters such as 'RTZ'\r\n", - "# :type solver_io: str\r\n", - "# :param solver_io: format of model/kernel/gradient files expected by the\r\n", - "# numerical solver. Available: ['fortran_binary': default .bin files].\r\n", - "# TODO: ['adios': ADIOS formatted files]\r\n", - "# :type source_prefix: str\r\n", - "# :param source_prefix: prefix of source/event/earthquake files. If None,\r\n", - "# will attempt to guess based on the specific solver chosen.\r\n", - "# :type mpiexec: str\r\n", - "# :param mpiexec: MPI executable used to run parallel processes. Should also\r\n", - "# be defined for the system module\r\n", - "#\r\n", - "# \r\n", - "# Solver SPECFEM2D\r\n", - "# ----------------\r\n", - "# SPECFEM2D-specific alterations to the base SPECFEM module\r\n", - "#\r\n", - "# Parameters\r\n", - "# ----------\r\n", - "# :type source_prefix: str\r\n", - "# :param source_prefix: Prefix of source files in path SPECFEM_DATA. Defaults\r\n", - "# to 'SOURCE'\r\n", - "# :type multiples: bool\r\n", - "# :param multiples: set an absorbing top-boundary condition\r\n", - "#\r\n", - "# \r\n", - "# =============================================================================\r\n", - "data_format: ascii\r\n", - "materials: acoustic\r\n", - "density: False\r\n", - "attenuation: False\r\n", - "smooth_h: 0.0\r\n", - "smooth_v: 0.0\r\n", - "components: ZNE\r\n", - "source_prefix: SOURCE\r\n", - "multiples: False\r\n", - "# =============================================================================\r\n", - "#\r\n", - "# Default Preprocess\r\n", - "# ------------------\r\n", - "# Data processing for seismic traces, with options for data misfit,\r\n", - "# filtering, normalization and muting.\r\n", - "#\r\n", - "# Parameters\r\n", - "# ----------\r\n", - "# :type data_format: str\r\n", - "# :param data_format: data format for reading traces into memory. For\r\n", - "# available see: seisflows.plugins.preprocess.readers\r\n", - "# :type misfit: str\r\n", - "# :param misfit: misfit function for waveform comparisons. For available\r\n", - "# see seisflows.plugins.preprocess.misfit\r\n", - "# :type backproject: str\r\n", - "# :param backproject: backprojection function for migration, or the\r\n", - "# objective function in FWI. For available see\r\n", - "# seisflows.plugins.preprocess.adjoint\r\n", - "# :type normalize: str\r\n", - "# :param normalize: Data normalization parameters used to normalize the\r\n", - "# amplitudes of waveforms. Choose from two sets:\r\n", - "# ENORML1: normalize per event by L1 of traces; OR\r\n", - "# ENORML2: normalize per event by L2 of traces;\r\n", - "# &\r\n", - "# TNORML1: normalize per trace by L1 of itself; OR\r\n", - "# TNORML2: normalize per trace by L2 of itself\r\n", - "# :type filter: str\r\n", - "# :param filter: Data filtering type, available options are:\r\n", - "# BANDPASS (req. MIN/MAX PERIOD/FREQ);\r\n", - "# LOWPASS (req. MAX_FREQ or MIN_PERIOD);\r\n", - "# HIGHPASS (req. MIN_FREQ or MAX_PERIOD)\r\n", - "# :type min_period: float\r\n", - "# :param min_period: Minimum filter period applied to time series.\r\n", - "# See also MIN_FREQ, MAX_FREQ, if User defines FREQ parameters, they\r\n", - "# will overwrite PERIOD parameters.\r\n", - "# :type max_period: float\r\n", - "# :param max_period: Maximum filter period applied to time series. See\r\n", - "# also MIN_FREQ, MAX_FREQ, if User defines FREQ parameters, they will\r\n", - "# overwrite PERIOD parameters.\r\n", - "# :type min_freq: float\r\n", - "# :param min_freq: Maximum filter frequency applied to time series,\r\n", - "# See also MIN_PERIOD, MAX_PERIOD, if User defines FREQ parameters,\r\n", - "# they will overwrite PERIOD parameters.\r\n", - "# :type max_freq: float\r\n", - "# :param max_freq: Maximum filter frequency applied to time series,\r\n", - "# See also MIN_PERIOD, MAX_PERIOD, if User defines FREQ parameters,\r\n", - "# they will overwrite PERIOD parameters.\r\n", - "# :type mute: list\r\n", - "# :param mute: Data mute parameters used to zero out early / late\r\n", - "# arrivals or offsets. Choose any number of:\r\n", - "# EARLY: mute early arrivals;\r\n", - "# LATE: mute late arrivals;\r\n", - "# SHORT: mute short source-receiver distances;\r\n", - "# LONG: mute long source-receiver distances\r\n", - "#\r\n", - "# \r\n", - "# =============================================================================\r\n", - "misfit: waveform\r\n", - "adjoint: waveform\r\n", - "normalize: []\r\n", - "filter: null\r\n", - "min_period: null\r\n", - "max_period: null\r\n", - "min_freq: null\r\n", - "max_freq: null\r\n", - "mute: []\r\n", - "early_slope: null\r\n", - "early_const: null\r\n", - "late_slope: null\r\n", - "late_const: null\r\n", - "short_dist: null\r\n", - "long_dist: null\r", - "\r\n", - "# =============================================================================\r\n", - "#\r\n", - "# Gradient Optimization\r\n", - "# ---------------------\r\n", - "# Gradient/steepest descent optimization algorithm.\r\n", - "#\r\n", - "# Parameters\r\n", - "# ----------\r\n", - "# :type line_search_method: str\r\n", - "# :param line_search_method: chosen line_search algorithm. Currently available\r\n", - "# are 'bracket' and 'backtrack'. See seisflows.plugins.line_search\r\n", - "# for all available options\r\n", - "# :type preconditioner: str\r\n", - "# :param preconditioner: algorithm for preconditioning gradients. Currently\r\n", - "# available: 'diagonal'. Requires `path_preconditioner` to point to a\r\n", - "# set of files that define the preconditioner, formatted the same as the\r\n", - "# input model\r\n", - "# :type step_count_max: int\r\n", - "# :param step_count_max: maximum number of trial steps to perform during\r\n", - "# the line search before a change in line search behavior is\r\n", - "# considered, or a line search is considered to have failed.\r\n", - "# :type step_len_init: float\r\n", - "# :param step_len_init: initial line search step length guess, provided\r\n", - "# as a fraction of current model parameters.\r\n", - "# :type step_len_max: float\r\n", - "# :param step_len_max: maximum allowable step length during the line\r\n", - "# search. Set as a fraction of the current model parameters\r\n", - "#\r\n", - "# \r\n", - "# =============================================================================\r\n", - "preconditioner: null\r\n", - "step_count_max: 10\r\n", - "step_len_init: 0.05\r\n", - "step_len_max: 0.5\r\n", - "line_search_method: bracket\r\n", - "# =============================================================================\r\n", - "#\r\n", - "#\t Paths\r\n", - "#\t -----\r\n", - "# :type workdir: str\r\n", - "# :param workdir: working directory in which to look for data and store\r\n", - "# results. Defaults to current working directory\r\n", - "# :type path_output: str\r\n", - "# :param path_output: path to directory used for permanent storage on disk.\r\n", - "# Results and exported scratch files are saved here.\r\n", - "# :type path_data: str\r\n", - "# :param path_data: path to any externally stored data required by the solver\r\n", - "# :type path_state_file: str\r\n", - "# :param path_state_file: path to a text file used to track the current\r\n", - "# status of a workflow (i.e., what functions have already been completed),\r\n", - "# used for checkpointing and resuming workflows\r\n", - "# :type path_model_init: str\r\n", - "# :param path_model_init: path to the starting model used to calculate the\r\n", - "# initial misfit. Must match the expected `solver_io` format.\r\n", - "# :type path_model_true: str\r\n", - "# :param path_model_true: path to a target model if `case`=='synthetic' and\r\n", - "# a set of synthetic 'observations' are required for workflow.\r\n", - "# :type path_eval_grad: str\r\n", - "# :param path_eval_grad: scratch path to store files for gradient evaluation,\r\n", - "# including models, kernels, gradient and residuals.\r\n", - "# :type path_mask: str\r\n", - "# :param path_mask: optional path to a masking function which is used to\r\n", - "# mask out or scale parts of the gradient. The user-defined mask must\r\n", - "# match the file format of the input model (e.g., .bin files).\r\n", - "# :type path_eval_func: str\r\n", - "# :param path_eval_func: scratch path to store files for line search objective\r\n", - "# function evaluations, including models, misfit and residuals\r\n", - "# \r\n", - "# :type path_output_log: str\r\n", - "# :param path_output_log: path to a text file used to store the outputs of\r\n", - "# the package wide logger, which are also written to stdout\r\n", - "# :type path_par_file: str\r\n", - "# :param path_par_file: path to parameter file which is used to instantiate\r\n", - "# the package\r\n", - "# :type path_log_files: str\r\n", - "# :param path_log_files: path to a directory where individual log files are\r\n", - "# saved whenever a number of parallel tasks are run on the system.\r\n", - "# \r\n", - "# :type path_data: str\r\n", - "# :param path_data: path to any externally stored data required by the solver\r\n", - "# :type path_specfem_bin: str\r\n", - "# :param path_specfem_bin: path to SPECFEM bin/ directory which\r\n", - "# contains binary executables for running SPECFEM\r\n", - "# :type path_specfem_data: str\r\n", - "# :param path_specfem_data: path to SPECFEM DATA/ directory which must\r\n", - "# contain the CMTSOLUTION, STATIONS and Par_file files used for\r\n", - "# running SPECFEM\r\n", - "# \r\n", - "# :type path_preprocess: str\r\n", - "# :param path_preprocess: scratch path for all preprocessing processes,\r\n", - "# including saving files\r\n", - "# \r\n", - "# :type path_preconditioner: str\r\n", - "# :param path_preconditioner: optional path to a set of preconditioner files\r\n", - "# formatted the same as the input model (or output model of solver).\r\n", - "# Required to exist and contain files if `preconditioner`==True\r\n", - "# \r\n", - "# =============================================================================\r\n", - "path_workdir: /Users/Chow/Work/work/sf_specfem2d_example\r\n", - "path_scratch: /Users/Chow/Work/work/sf_specfem2d_example/scratch\r\n", - "path_eval_grad: /Users/Chow/Work/work/sf_specfem2d_example/scratch/eval_grad\r\n", - "path_output: /Users/Chow/Work/work/sf_specfem2d_example/output\r\n", - "path_model_init: null\r\n", - "path_model_true: null\r\n", - "path_state_file: /Users/Chow/Work/work/sf_specfem2d_example/sfstate.txt\r\n", - "path_data: null\r\n", - "path_mask: null\r\n", - "path_eval_func: /Users/Chow/Work/work/sf_specfem2d_example/scratch/eval_func\r\n", - "path_par_file: /Users/Chow/Work/work/sf_specfem2d_example/parameters.yaml\r\n", - "path_log_files: /Users/Chow/Work/work/sf_specfem2d_example/logs\r\n", - "path_output_log: /Users/Chow/Work/work/sf_specfem2d_example/sflog.txt\r\n", - "path_specfem_bin: null\r\n", - "path_specfem_data: null\r\n", - "path_solver: /Users/Chow/Work/work/sf_specfem2d_example/scratch/solver\r\n", - "path_preconditioner: null\r\n" + "# a target model provided in `path_model_true`. If None, workflow will\r\n" ] } ], "source": [ "! seisflows configure\n", - "! cat parameters.yaml" + "! head --lines=50 parameters.yaml" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -1293,13 +917,16 @@ "text": [ "ntask: 1 -> 3\n", "materials: acoustic -> elastic\n", - "density: False -> False\n", - "attenuation: False -> False\n", - "start: 1 -> 1\n", "end: 1 -> 2\n", "data_case: data -> synthetic\n", "components: ZNE -> Y\n", - "step_count_max: 10 -> 5\n" + "step_count_max: 10 -> 5\n", + "path_specfem_bin: null -> /home/bchow/Work/scratch/specfem2d_workdir/bin\n", + "path_specfem_data: null -> /home/bchow/Work/scratch/specfem2d_workdir/DATA\n", + "path_model_init: null ->\n", + "/home/bchow/Work/scratch/specfem2d_workdir/OUTPUT_FILES_INIT\n", + "path_model_true: null ->\n", + "/home/bchow/Work/scratch/specfem2d_workdir/OUTPUT_FILES_TRUE\n" ] }, { @@ -1308,7 +935,7 @@ "0" ] }, - "execution_count": 8, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -1316,10 +943,7 @@ "source": [ "# EDIT THE SEISFLOWS PARAMETER FILE\n", "! seisflows par ntask 3 # set the number of sources/events to use\n", - "! seisflows par materials elastic # how the velocity model is parameterized\n", - "! seisflows par density False # update density or keep constant\n", - "! seisflows par attenuation False\n", - "! seisflows par start 1 # first iteration\n", + "! seisflows par materials elastic # update Vp and Vs during inversion\n", "! seisflows par end 2 # final iteration -- we will only run 1\n", "! seisflows par data_case synthetic # synthetic-synthetic means we need both INIT and TRUE models\n", "! seisflows par components Y # this default example creates Y-component seismograms\n", @@ -1342,7 +966,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1377,7 +1001,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1422,26 +1046,24 @@ "5. __perform_line_search:__ Perform a line search by algorithmically scaling the gradient and evaluating the misfit function (forward simulations and misfit quantification) until misfit is acceptably reduced.\n", "6. __finalize_iteration:__ Run any finalization steps such as saving traces, kernels, gradients and models to disk, setting up SeisFlows3 for any subsequent iterations. Clean the scratch/ directory in preparation for subsequent iterations\n", "\n", - "Let's set the `stop_after` argument to __evaluate_initial_misfit__, this will halt the workflow after the intialization step. We'll also set the `verbose` parameter to 'False', to keep the logging format relatively simple. We will explore the `verbose`==True option in a later cell." + "Let's set the `stop_after` argument to __evaluate_initial_misfit__, this will halt the workflow after the intialization step. " ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "stop_after: null -> evaluate_initial_misfit\n", - "verbose: False -> False\n" + "stop_after: null -> evaluate_initial_misfit\r\n" ] } ], "source": [ - "! seisflows par stop_after evaluate_initial_misfit\n", - "! seisflows par verbose False" + "! seisflows par stop_after evaluate_initial_misfit" ] }, { @@ -1459,66 +1081,66 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2022-08-15 16:11:40 (I) | \n", + "2022-08-16 14:32:48 (I) | \n", "================================================================================\n", " SETTING UP INVERSION WORKFLOW \n", "================================================================================\n", - "2022-08-15 16:11:47 (D) | running setup for module 'system.Workstation'\n", - "2022-08-15 16:11:50 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/sflog_001.txt\n", - "2022-08-15 16:11:50 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/parameters_001.yaml\n", - "2022-08-15 16:11:50 (D) | running setup for module 'solver.Specfem2D'\n", - "2022-08-15 16:11:50 (I) | initializing 3 solver directories\n", - "2022-08-15 16:11:50 (D) | initializing solver directory source: 001\n", - "2022-08-15 16:11:58 (D) | linking source '001' as 'mainsolver'\n", - "2022-08-15 16:11:58 (D) | initializing solver directory source: 002\n", - "2022-08-15 16:12:04 (D) | initializing solver directory source: 003\n", - "2022-08-15 16:12:13 (D) | running setup for module 'preprocess.Default'\n", - "2022-08-15 16:12:14 (D) | running setup for module 'optimize.Gradient'\n", - "2022-08-15 16:12:15 (I) | no optimization checkpoint found, assuming first run\n", - "2022-08-15 16:12:16 (I) | re-loading optimization module from checkpoint\n", - "2022-08-15 16:12:16 (I) | \n", + "2022-08-16 14:32:55 (D) | running setup for module 'system.Workstation'\n", + "2022-08-16 14:32:57 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/sflog_001.txt\n", + "2022-08-16 14:32:57 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/parameters_001.yaml\n", + "2022-08-16 14:32:57 (D) | running setup for module 'solver.Specfem2D'\n", + "2022-08-16 14:32:57 (I) | initializing 3 solver directories\n", + "2022-08-16 14:32:57 (D) | initializing solver directory source: 001\n", + "2022-08-16 14:33:04 (D) | linking source '001' as 'mainsolver'\n", + "2022-08-16 14:33:04 (D) | initializing solver directory source: 002\n", + "2022-08-16 14:33:09 (D) | initializing solver directory source: 003\n", + "2022-08-16 14:33:16 (D) | running setup for module 'preprocess.Default'\n", + "2022-08-16 14:33:16 (D) | running setup for module 'optimize.Gradient'\n", + "2022-08-16 14:33:17 (I) | no optimization checkpoint found, assuming first run\n", + "2022-08-16 14:33:17 (I) | re-loading optimization module from checkpoint\n", + "2022-08-16 14:33:17 (I) | \n", "////////////////////////////////////////////////////////////////////////////////\n", " RUNNING ITERATION 01 \n", "////////////////////////////////////////////////////////////////////////////////\n", - "2022-08-15 16:12:16 (I) | \n", + "2022-08-16 14:33:17 (I) | \n", "================================================================================\n", " RUNNING INVERSION WORKFLOW \n", "================================================================================\n", - "2022-08-15 16:12:16 (I) | \n", + "2022-08-16 14:33:17 (I) | \n", "////////////////////////////////////////////////////////////////////////////////\n", " EVALUATING MISFIT FOR INITIAL MODEL \n", "////////////////////////////////////////////////////////////////////////////////\n", - "2022-08-15 16:12:16 (I) | checking initial model parameters\n", - "2022-08-15 16:12:16 (I) | 2600.00 <= rho <= 2600.00\n", - "2022-08-15 16:12:16 (I) | 3500.00 <= vs <= 3500.00\n", - "2022-08-15 16:12:16 (I) | 5800.00 <= vp <= 5800.00\n", - "2022-08-15 16:12:16 (I) | checking true/target model parameters\n", - "2022-08-15 16:12:16 (I) | 2600.00 <= rho <= 2600.00\n", - "2022-08-15 16:12:16 (I) | 3550.00 <= vs <= 3550.00\n", - "2022-08-15 16:12:16 (I) | 5900.00 <= vp <= 5900.00\n", - "2022-08-15 16:12:16 (I) | preparing observation data for source 001\n", - "2022-08-15 16:12:16 (I) | running forward simulation w/ target model for 001\n", - "2022-08-15 16:12:33 (I) | evaluating objective function for source 001\n", - "2022-08-15 16:12:33 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:12:53 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:12:53 (I) | preparing observation data for source 002\n", - "2022-08-15 16:12:53 (I) | running forward simulation w/ target model for 002\n", - "2022-08-15 16:13:09 (I) | evaluating objective function for source 002\n", - "2022-08-15 16:13:09 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:13:31 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:13:31 (I) | preparing observation data for source 003\n", - "2022-08-15 16:13:31 (I) | running forward simulation w/ target model for 003\n", - "2022-08-15 16:14:16 (I) | evaluating objective function for source 003\n", - "2022-08-15 16:14:16 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:14:33 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:14:33 (I) | stop workflow at `stop_after`: evaluate_initial_misfit\n" + "2022-08-16 14:33:17 (I) | checking initial model parameters\n", + "2022-08-16 14:33:17 (I) | 5800.00 <= vp <= 5800.00\n", + "2022-08-16 14:33:17 (I) | 2600.00 <= rho <= 2600.00\n", + "2022-08-16 14:33:17 (I) | 3500.00 <= vs <= 3500.00\n", + "2022-08-16 14:33:17 (I) | checking true/target model parameters\n", + "2022-08-16 14:33:17 (I) | 5900.00 <= vp <= 5900.00\n", + "2022-08-16 14:33:17 (I) | 2600.00 <= rho <= 2600.00\n", + "2022-08-16 14:33:17 (I) | 3550.00 <= vs <= 3550.00\n", + "2022-08-16 14:33:17 (I) | preparing observation data for source 001\n", + "2022-08-16 14:33:17 (I) | running forward simulation w/ target model for 001\n", + "2022-08-16 14:33:21 (I) | evaluating objective function for source 001\n", + "2022-08-16 14:33:21 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:33:25 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:33:25 (I) | preparing observation data for source 002\n", + "2022-08-16 14:33:25 (I) | running forward simulation w/ target model for 002\n", + "2022-08-16 14:33:29 (I) | evaluating objective function for source 002\n", + "2022-08-16 14:33:29 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:33:33 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:33:33 (I) | preparing observation data for source 003\n", + "2022-08-16 14:33:33 (I) | running forward simulation w/ target model for 003\n", + "2022-08-16 14:33:36 (I) | evaluating objective function for source 003\n", + "2022-08-16 14:33:36 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:33:40 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:33:40 (I) | stop workflow at `stop_after`: evaluate_initial_misfit\n" ] } ], @@ -1536,7 +1158,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -1572,7 +1194,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -1599,7 +1221,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1618,56 +1240,56 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2022-08-15 16:15:06 (D) | setting iteration==1 from state file\n", - "2022-08-15 16:15:06 (I) | \n", + "2022-08-16 14:36:42 (D) | setting iteration==1 from state file\n", + "2022-08-16 14:36:42 (I) | \n", "================================================================================\n", " SETTING UP INVERSION WORKFLOW \n", "================================================================================\n", - "2022-08-15 16:15:16 (D) | running setup for module 'system.Workstation'\n", - "2022-08-15 16:15:20 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/sflog_002.txt\n", - "2022-08-15 16:15:20 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/parameters_002.yaml\n", - "2022-08-15 16:15:20 (D) | running setup for module 'solver.Specfem2D'\n", - "2022-08-15 16:15:20 (I) | initializing 3 solver directories\n", - "2022-08-15 16:15:22 (D) | running setup for module 'preprocess.Default'\n", - "2022-08-15 16:15:23 (D) | running setup for module 'optimize.Gradient'\n", - "2022-08-15 16:15:25 (I) | re-loading optimization module from checkpoint\n", - "2022-08-15 16:15:27 (I) | re-loading optimization module from checkpoint\n", - "2022-08-15 16:15:27 (I) | \n", + "2022-08-16 14:36:48 (D) | running setup for module 'system.Workstation'\n", + "2022-08-16 14:36:51 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/sflog_002.txt\n", + "2022-08-16 14:36:51 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/parameters_002.yaml\n", + "2022-08-16 14:36:51 (D) | running setup for module 'solver.Specfem2D'\n", + "2022-08-16 14:36:51 (I) | initializing 3 solver directories\n", + "2022-08-16 14:36:51 (D) | running setup for module 'preprocess.Default'\n", + "2022-08-16 14:36:52 (D) | running setup for module 'optimize.Gradient'\n", + "2022-08-16 14:36:53 (I) | re-loading optimization module from checkpoint\n", + "2022-08-16 14:36:54 (I) | re-loading optimization module from checkpoint\n", + "2022-08-16 14:36:54 (I) | \n", "////////////////////////////////////////////////////////////////////////////////\n", " RUNNING ITERATION 01 \n", "////////////////////////////////////////////////////////////////////////////////\n", - "2022-08-15 16:15:27 (I) | \n", + "2022-08-16 14:36:54 (I) | \n", "================================================================================\n", " RUNNING INVERSION WORKFLOW \n", "================================================================================\n", - "2022-08-15 16:15:27 (I) | 'evaluate_initial_misfit' has already been run, skipping\n", - "2022-08-15 16:15:27 (I) | \n", + "2022-08-16 14:36:54 (I) | 'evaluate_initial_misfit' has already been run, skipping\n", + "2022-08-16 14:36:54 (I) | \n", "////////////////////////////////////////////////////////////////////////////////\n", " EVALUATING EVENT KERNELS W/ ADJOINT SIMULATIONS \n", "////////////////////////////////////////////////////////////////////////////////\n", - "2022-08-15 16:15:27 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log'\n", - "2022-08-15 16:16:11 (D) | renaming output event kernels: 'alpha' -> 'vp'\n", - "2022-08-15 16:16:11 (D) | renaming output event kernels: 'beta' -> 'vs'\n", - "2022-08-15 16:16:12 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log'\n", - "2022-08-15 16:16:59 (D) | renaming output event kernels: 'alpha' -> 'vp'\n", - "2022-08-15 16:16:59 (D) | renaming output event kernels: 'beta' -> 'vs'\n", - "2022-08-15 16:16:59 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log'\n", - "2022-08-15 16:17:45 (D) | renaming output event kernels: 'alpha' -> 'vp'\n", - "2022-08-15 16:17:45 (D) | renaming output event kernels: 'beta' -> 'vs'\n", - "2022-08-15 16:17:45 (I) | \n", + "2022-08-16 14:36:54 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log'\n", + "2022-08-16 14:37:05 (D) | renaming output event kernels: 'alpha' -> 'vp'\n", + "2022-08-16 14:37:05 (D) | renaming output event kernels: 'beta' -> 'vs'\n", + "2022-08-16 14:37:05 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log'\n", + "2022-08-16 14:37:16 (D) | renaming output event kernels: 'alpha' -> 'vp'\n", + "2022-08-16 14:37:16 (D) | renaming output event kernels: 'beta' -> 'vs'\n", + "2022-08-16 14:37:18 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log'\n", + "2022-08-16 14:37:29 (D) | renaming output event kernels: 'alpha' -> 'vp'\n", + "2022-08-16 14:37:29 (D) | renaming output event kernels: 'beta' -> 'vs'\n", + "2022-08-16 14:37:30 (I) | \n", "////////////////////////////////////////////////////////////////////////////////\n", " GENERATING/PROCESSING MISFIT KERNEL \n", "////////////////////////////////////////////////////////////////////////////////\n", - "2022-08-15 16:17:45 (I) | combining event kernels into single misfit kernel\n", - "2022-08-15 16:17:47 (I) | scaling gradient to absolute model perturbations\n", - "2022-08-15 16:17:49 (I) | stop workflow at `stop_after`: evaluate_gradient_from_kernels\n" + "2022-08-16 14:37:30 (I) | combining event kernels into single misfit kernel\n", + "2022-08-16 14:37:31 (I) | scaling gradient to absolute model perturbations\n", + "2022-08-16 14:37:32 (I) | stop workflow at `stop_after`: evaluate_gradient_from_kernels\n" ] } ], @@ -1691,14 +1313,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m\u001b[32mgradient\u001b[m\u001b[m \u001b[1m\u001b[32mkernels\u001b[m\u001b[m \u001b[1m\u001b[32mmisfit_kernel\u001b[m\u001b[m \u001b[1m\u001b[32mmodel\u001b[m\u001b[m residuals.txt\r\n" + "gradient kernels misfit_kernel model residuals.txt\r\n" ] } ], @@ -1710,14 +1332,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "proc000000_vp_kernel.bin proc000000_vs_kernel.bin\r\n" + "proc000000_vp_kernel.bin proc000000_vs_kernel.bin\r\n" ] } ], @@ -1728,14 +1350,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m\u001b[32m001\u001b[m\u001b[m \u001b[1m\u001b[32m002\u001b[m\u001b[m \u001b[1m\u001b[32m003\u001b[m\u001b[m\r\n" + "001 002 003\r\n" ] } ], @@ -1747,14 +1369,14 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "checkpoint.npz f_new.txt g_new.npz m_new.npz\r\n" + "checkpoint.npz\tf_new.txt g_new.npz m_new.npz\r\n" ] } ], @@ -1767,7 +1389,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -1799,14 +1421,14 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "stop_after: evaluate_gradient_from_kernels -> finalize_iteration\r\n" + "stop_after: evaluate_gradient_from_kernels -> perform_line_search\r\n" ] } ], @@ -1816,149 +1438,138 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2022-08-15 16:21:55 (D) | setting iteration==1 from state file\n", - "2022-08-15 16:21:55 (I) | \n", + "2022-08-16 14:41:12 (D) | setting iteration==1 from state file\n", + "2022-08-16 14:41:12 (I) | \n", "================================================================================\n", " SETTING UP INVERSION WORKFLOW \n", "================================================================================\n", - "2022-08-15 16:22:03 (D) | running setup for module 'system.Workstation'\n", - "2022-08-15 16:22:05 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/sflog_003.txt\n", - "2022-08-15 16:22:05 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/parameters_003.yaml\n", - "2022-08-15 16:22:05 (D) | running setup for module 'solver.Specfem2D'\n", - "2022-08-15 16:22:05 (I) | initializing 3 solver directories\n", - "2022-08-15 16:22:07 (D) | running setup for module 'preprocess.Default'\n", - "2022-08-15 16:22:08 (D) | running setup for module 'optimize.Gradient'\n", - "2022-08-15 16:22:09 (I) | re-loading optimization module from checkpoint\n", - "2022-08-15 16:22:11 (I) | re-loading optimization module from checkpoint\n", - "2022-08-15 16:22:11 (I) | \n", + "2022-08-16 14:41:18 (D) | running setup for module 'system.Workstation'\n", + "2022-08-16 14:41:21 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/sflog_003.txt\n", + "2022-08-16 14:41:21 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/parameters_003.yaml\n", + "2022-08-16 14:41:21 (D) | running setup for module 'solver.Specfem2D'\n", + "2022-08-16 14:41:21 (I) | initializing 3 solver directories\n", + "2022-08-16 14:41:22 (D) | running setup for module 'preprocess.Default'\n", + "2022-08-16 14:41:24 (D) | running setup for module 'optimize.Gradient'\n", + "2022-08-16 14:41:26 (I) | re-loading optimization module from checkpoint\n", + "2022-08-16 14:41:28 (I) | re-loading optimization module from checkpoint\n", + "2022-08-16 14:41:28 (I) | \n", "////////////////////////////////////////////////////////////////////////////////\n", " RUNNING ITERATION 01 \n", "////////////////////////////////////////////////////////////////////////////////\n", - "2022-08-15 16:22:11 (I) | \n", + "2022-08-16 14:41:28 (I) | \n", "================================================================================\n", " RUNNING INVERSION WORKFLOW \n", "================================================================================\n", - "2022-08-15 16:22:11 (I) | 'evaluate_initial_misfit' has already been run, skipping\n", - "2022-08-15 16:22:11 (I) | 'run_adjoint_simulations' has already been run, skipping\n", - "2022-08-15 16:22:11 (I) | 'postprocess_event_kernels' has already been run, skipping\n", - "2022-08-15 16:22:11 (I) | 'evaluate_gradient_from_kernels' has already been run, skipping\n", - "2022-08-15 16:22:11 (I) | initializing 'bracket'ing line search\n", - "2022-08-15 16:22:11 (I) | enforcing max step length safeguard\n", - "2022-08-15 16:22:11 (D) | step length(s) = 0.00E+00\n", - "2022-08-15 16:22:11 (D) | misfit val(s) = 1.28E-03\n", - "2022-08-15 16:22:11 (I) | try: first evaluation, attempt guess step length, alpha=9.08E+11\n", - "2022-08-15 16:22:11 (I) | try: applying initial step length safegaurd as alpha has exceeded maximum step length, alpha_new=1.44E+10\n", - "2022-08-15 16:22:11 (D) | overwriting initial step length, alpha_new=2.32E+09\n", - "2022-08-15 16:22:11 (I) | trial model 'm_try' parameters: \n", - "2022-08-15 16:22:11 (I) | 5800.00 <= vp <= 5800.00\n", - "2022-08-15 16:22:11 (I) | 3244.51 <= vs <= 3790.00\n", - "2022-08-15 16:22:12 (I) | \n", + "2022-08-16 14:41:28 (I) | 'evaluate_initial_misfit' has already been run, skipping\n", + "2022-08-16 14:41:28 (I) | 'run_adjoint_simulations' has already been run, skipping\n", + "2022-08-16 14:41:28 (I) | 'postprocess_event_kernels' has already been run, skipping\n", + "2022-08-16 14:41:28 (I) | 'evaluate_gradient_from_kernels' has already been run, skipping\n", + "2022-08-16 14:41:28 (I) | initializing 'bracket'ing line search\n", + "2022-08-16 14:41:28 (I) | enforcing max step length safeguard\n", + "2022-08-16 14:41:28 (D) | step length(s) = 0.00E+00\n", + "2022-08-16 14:41:28 (D) | misfit val(s) = 1.28E-03\n", + "2022-08-16 14:41:28 (I) | try: first evaluation, attempt guess step length, alpha=9.08E+11\n", + "2022-08-16 14:41:28 (I) | try: applying initial step length safegaurd as alpha has exceeded maximum step length, alpha_new=1.44E+10\n", + "2022-08-16 14:41:28 (D) | overwriting initial step length, alpha_new=2.32E+09\n", + "2022-08-16 14:41:28 (I) | trial model 'm_try' parameters: \n", + "2022-08-16 14:41:28 (I) | 5800.00 <= vp <= 5800.00\n", + "2022-08-16 14:41:28 (I) | 3244.51 <= vs <= 3790.00\n", + "2022-08-16 14:41:29 (I) | \n", "LINE SEARCH STEP COUNT 01\n", "--------------------------------------------------------------------------------\n", - "2022-08-15 16:22:12 (I) | evaluating objective function for source 001\n", - "2022-08-15 16:22:12 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:22:23 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:22:23 (I) | evaluating objective function for source 002\n", - "2022-08-15 16:22:23 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:22:35 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:22:35 (I) | evaluating objective function for source 003\n", - "2022-08-15 16:22:35 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:22:48 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:22:48 (D) | misfit for trial model (f_try) == 8.65E-04\n", - "2022-08-15 16:22:48 (D) | step length(s) = 0.00E+00, 2.32E+09\n", - "2022-08-15 16:22:48 (D) | misfit val(s) = 1.28E-03, 8.65E-04\n", - "2022-08-15 16:22:48 (I) | try: misfit not bracketed, increasing step length using golden ratio, alpha=3.76E+09\n", - "2022-08-15 16:22:49 (I) | line search model 'm_try' parameters: \n", - "2022-08-15 16:22:49 (I) | 5800.00 <= vp <= 5800.00\n", - "2022-08-15 16:22:49 (I) | 3086.61 <= vs <= 3969.23\n", - "2022-08-15 16:22:49 (I) | trial step unsuccessful. re-attempting line search\n", - "2022-08-15 16:22:49 (I) | \n", + "2022-08-16 14:41:29 (I) | evaluating objective function for source 001\n", + "2022-08-16 14:41:29 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:41:33 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:41:33 (I) | evaluating objective function for source 002\n", + "2022-08-16 14:41:33 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:41:36 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:41:36 (I) | evaluating objective function for source 003\n", + "2022-08-16 14:41:36 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:41:40 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:41:40 (D) | misfit for trial model (f_try) == 8.65E-04\n", + "2022-08-16 14:41:40 (D) | step length(s) = 0.00E+00, 2.32E+09\n", + "2022-08-16 14:41:40 (D) | misfit val(s) = 1.28E-03, 8.65E-04\n", + "2022-08-16 14:41:40 (I) | try: misfit not bracketed, increasing step length using golden ratio, alpha=3.76E+09\n", + "2022-08-16 14:41:40 (I) | line search model 'm_try' parameters: \n", + "2022-08-16 14:41:40 (I) | 5800.00 <= vp <= 5800.00\n", + "2022-08-16 14:41:40 (I) | 3086.61 <= vs <= 3969.23\n", + "2022-08-16 14:41:40 (I) | trial step unsuccessful. re-attempting line search\n", + "2022-08-16 14:41:40 (I) | \n", "LINE SEARCH STEP COUNT 02\n", "--------------------------------------------------------------------------------\n", - "2022-08-15 16:22:49 (I) | evaluating objective function for source 001\n", - "2022-08-15 16:22:49 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:23:01 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:23:01 (I) | evaluating objective function for source 002\n", - "2022-08-15 16:23:01 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:23:13 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:23:13 (I) | evaluating objective function for source 003\n", - "2022-08-15 16:23:13 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:23:25 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:23:25 (D) | misfit for trial model (f_try) == 1.73E-03\n", - "2022-08-15 16:23:25 (D) | step length(s) = 0.00E+00, 2.32E+09, 3.76E+09\n", - "2022-08-15 16:23:25 (D) | misfit val(s) = 1.28E-03, 8.65E-04, 1.73E-03\n", - "2022-08-15 16:23:25 (I) | try: bracket acceptable but step length unreasonable attempting to re-adjust step length alpha=1.59E+09\n", - "2022-08-15 16:23:25 (I) | line search model 'm_try' parameters: \n", - "2022-08-15 16:23:25 (I) | 5800.00 <= vp <= 5800.00\n", - "2022-08-15 16:23:25 (I) | 3325.01 <= vs <= 3698.63\n", - "2022-08-15 16:23:25 (I) | trial step unsuccessful. re-attempting line search\n", - "2022-08-15 16:23:25 (I) | \n", + "2022-08-16 14:41:40 (I) | evaluating objective function for source 001\n", + "2022-08-16 14:41:40 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:41:44 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:41:44 (I) | evaluating objective function for source 002\n", + "2022-08-16 14:41:44 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:41:48 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:41:48 (I) | evaluating objective function for source 003\n", + "2022-08-16 14:41:48 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:41:52 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:41:52 (D) | misfit for trial model (f_try) == 1.73E-03\n", + "2022-08-16 14:41:52 (D) | step length(s) = 0.00E+00, 2.32E+09, 3.76E+09\n", + "2022-08-16 14:41:52 (D) | misfit val(s) = 1.28E-03, 8.65E-04, 1.73E-03\n", + "2022-08-16 14:41:52 (I) | try: bracket acceptable but step length unreasonable attempting to re-adjust step length alpha=1.59E+09\n", + "2022-08-16 14:41:52 (I) | line search model 'm_try' parameters: \n", + "2022-08-16 14:41:52 (I) | 5800.00 <= vp <= 5800.00\n", + "2022-08-16 14:41:52 (I) | 3325.01 <= vs <= 3698.63\n", + "2022-08-16 14:41:52 (I) | trial step unsuccessful. re-attempting line search\n", + "2022-08-16 14:41:52 (I) | \n", "LINE SEARCH STEP COUNT 03\n", "--------------------------------------------------------------------------------\n", - "2022-08-15 16:23:25 (I) | evaluating objective function for source 001\n", - "2022-08-15 16:23:25 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:23:37 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:23:37 (I) | evaluating objective function for source 002\n", - "2022-08-15 16:23:37 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:23:51 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:23:51 (I) | evaluating objective function for source 003\n", - "2022-08-15 16:23:51 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:24:03 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:24:04 (D) | misfit for trial model (f_try) == 2.59E-03\n", - "2022-08-15 16:24:04 (D) | step length(s) = 0.00E+00, 1.59E+09, 2.32E+09, 3.76E+09\n", - "2022-08-15 16:24:04 (D) | misfit val(s) = 1.28E-03, 2.59E-03, 8.65E-04, 1.73E-03\n", - "2022-08-15 16:24:04 (I) | try: bracket acceptable but step length unreasonable attempting to re-adjust step length alpha=2.82E+09\n", - "2022-08-15 16:24:04 (I) | line search model 'm_try' parameters: \n", - "2022-08-15 16:24:04 (I) | 5800.00 <= vp <= 5800.00\n", - "2022-08-15 16:24:04 (I) | 3189.77 <= vs <= 3852.13\n", - "2022-08-15 16:24:04 (I) | trial step unsuccessful. re-attempting line search\n", - "2022-08-15 16:24:04 (I) | \n", + "2022-08-16 14:41:52 (I) | evaluating objective function for source 001\n", + "2022-08-16 14:41:52 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:41:56 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:41:56 (I) | evaluating objective function for source 002\n", + "2022-08-16 14:41:56 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:42:00 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:42:00 (I) | evaluating objective function for source 003\n", + "2022-08-16 14:42:00 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:42:03 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:42:03 (D) | misfit for trial model (f_try) == 2.59E-03\n", + "2022-08-16 14:42:03 (D) | step length(s) = 0.00E+00, 1.59E+09, 2.32E+09, 3.76E+09\n", + "2022-08-16 14:42:03 (D) | misfit val(s) = 1.28E-03, 2.59E-03, 8.65E-04, 1.73E-03\n", + "2022-08-16 14:42:03 (I) | try: bracket acceptable but step length unreasonable attempting to re-adjust step length alpha=2.82E+09\n", + "2022-08-16 14:42:03 (I) | line search model 'm_try' parameters: \n", + "2022-08-16 14:42:03 (I) | 5800.00 <= vp <= 5800.00\n", + "2022-08-16 14:42:03 (I) | 3189.77 <= vs <= 3852.13\n", + "2022-08-16 14:42:03 (I) | trial step unsuccessful. re-attempting line search\n", + "2022-08-16 14:42:03 (I) | \n", "LINE SEARCH STEP COUNT 04\n", "--------------------------------------------------------------------------------\n", - "2022-08-15 16:24:04 (I) | evaluating objective function for source 001\n", - "2022-08-15 16:24:04 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:24:15 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:24:15 (I) | evaluating objective function for source 002\n", - "2022-08-15 16:24:15 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:24:27 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:24:27 (I) | evaluating objective function for source 003\n", - "2022-08-15 16:24:27 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-15 16:24:39 (D) | quantifying misfit with 'Default'\n", - "2022-08-15 16:24:39 (D) | misfit for trial model (f_try) == 3.46E-03\n", - "2022-08-15 16:24:39 (D) | step length(s) = 0.00E+00, 1.59E+09, 2.32E+09, 2.82E+09, 3.76E+09\n", - "2022-08-15 16:24:39 (D) | misfit val(s) = 1.28E-03, 2.59E-03, 8.65E-04, 3.46E-03, 1.73E-03\n", - "2022-08-15 16:24:39 (I) | pass: bracket acceptable and step length reasonable. returning minimum line search misfit.\n", - "2022-08-15 16:24:39 (I) | line search model 'm_try' parameters: \n", - "2022-08-15 16:24:39 (I) | 5800.00 <= vp <= 5800.00\n", - "2022-08-15 16:24:39 (I) | 3244.51 <= vs <= 3790.00\n", - "2022-08-15 16:24:39 (I) | trial step successful. finalizing line search\n", - "2022-08-15 16:24:39 (I) | \n", + "2022-08-16 14:42:03 (I) | evaluating objective function for source 001\n", + "2022-08-16 14:42:03 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:42:07 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:42:07 (I) | evaluating objective function for source 002\n", + "2022-08-16 14:42:07 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:42:11 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:42:11 (I) | evaluating objective function for source 003\n", + "2022-08-16 14:42:11 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:42:15 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:42:15 (D) | misfit for trial model (f_try) == 3.46E-03\n", + "2022-08-16 14:42:15 (D) | step length(s) = 0.00E+00, 1.59E+09, 2.32E+09, 2.82E+09, 3.76E+09\n", + "2022-08-16 14:42:15 (D) | misfit val(s) = 1.28E-03, 2.59E-03, 8.65E-04, 3.46E-03, 1.73E-03\n", + "2022-08-16 14:42:15 (I) | pass: bracket acceptable and step length reasonable. returning minimum line search misfit.\n", + "2022-08-16 14:42:15 (I) | line search model 'm_try' parameters: \n", + "2022-08-16 14:42:15 (I) | 5800.00 <= vp <= 5800.00\n", + "2022-08-16 14:42:15 (I) | 3244.51 <= vs <= 3790.00\n", + "2022-08-16 14:42:15 (I) | trial step successful. finalizing line search\n", + "2022-08-16 14:42:15 (I) | \n", "FINALIZING LINE SEARCH\n", "--------------------------------------------------------------------------------\n", - "2022-08-15 16:24:39 (I) | writing optimization stats\n", - "2022-08-15 16:24:39 (I) | renaming current (new) optimization vectors as previous model (old)\n", - "2022-08-15 16:24:39 (I) | setting accepted trial model (try) as current model (new)\n", - "2022-08-15 16:24:39 (I) | misfit of accepted trial model is f=8.645E-04\n", - "2022-08-15 16:24:39 (I) | resetting line search step count to 0\n", - "2022-08-15 16:24:39 (I) | \n", - "////////////////////////////////////////////////////////////////////////////////\n", - " CLEANING WORKDIR FOR NEXT ITERATION \n", - "////////////////////////////////////////////////////////////////////////////////\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-08-15 16:24:41 (I) | thrifty inversion encountering first iteration, defaulting to standard inversion workflow\n", - "2022-08-15 16:24:42 (I) | stop workflow at `stop_after`: finalize_iteration\n" + "2022-08-16 14:42:15 (I) | writing optimization stats\n", + "2022-08-16 14:42:15 (I) | renaming current (new) optimization vectors as previous model (old)\n", + "2022-08-16 14:42:15 (I) | setting accepted trial model (try) as current model (new)\n", + "2022-08-16 14:42:15 (I) | misfit of accepted trial model is f=8.645E-04\n", + "2022-08-16 14:42:15 (I) | resetting line search step count to 0\n", + "2022-08-16 14:42:15 (I) | stop workflow at `stop_after`: perform_line_search\n" ] } ], @@ -1975,16 +1586,15 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "alpha.txt f_old.txt m_new.npz p_old.npz\r\n", - "checkpoint.npz f_try.txt m_old.npz\r\n", - "f_new.txt g_old.npz output_optim.txt\r\n" + "alpha.txt\tf_new.txt f_try.txt m_new.npz output_optim.txt\r\n", + "checkpoint.npz\tf_old.txt g_old.npz m_old.npz p_old.npz\r\n" ] } ], @@ -1996,7 +1606,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -2025,7 +1635,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -2039,7 +1649,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.12" } }, "nbformat": 4, diff --git a/docs/notebooks/working_directory.ipynb b/docs/notebooks/working_directory.ipynb index 50779626..ec727e6a 100644 --- a/docs/notebooks/working_directory.ipynb +++ b/docs/notebooks/working_directory.ipynb @@ -4,30 +4,40 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Working Directory Structure\n", - "\n", - "SeisFlows sets it's own working directory when executing a workflow. Below we explore the working directory set up by the SPECFEM2D-workstation example. Working directories may change slightly depending on the chosen workflow, but will more or less follow the same structure. \n", - "\n", - ">__NOTE__: The two SPECFEM2D directories listed below are not part of the standard SeisFlows working directory." + "# Working Directory Structure" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "SeisFlows sets it's own working directory when executing a workflow. Below we explore the working directory set up by the `SPECFEM2D-workstation example `__. Working directories may change slightly depending on the chosen workflow, but will more or less follow the same structure." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ">__NOTE__: The two SPECFEM2D directories listed below (specfem2d/ & specfem2d_workdir/) are not part of a standard SeisFlows working directory." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "/Users/Chow/Work/work/sf_specfem2d_example\n", - "\u001b[1m\u001b[32mlogs\u001b[m\u001b[m parameters.yaml sflog.txt \u001b[1m\u001b[35mspecfem2d\u001b[m\u001b[m\r\n", - "\u001b[1m\u001b[32moutput\u001b[m\u001b[m \u001b[1m\u001b[32mscratch\u001b[m\u001b[m sfstate.txt \u001b[1m\u001b[32mspecfem2d_workdir\u001b[m\u001b[m\r\n" + "/home/bchow/Work/scratch\n", + "logs\tparameters.yaml sflog.txt specfem2d\r\n", + "output\tscratch\t\t sfstate.txt specfem2d_workdir\r\n" ] } ], "source": [ - "%cd /Users/Chow/Work/work/sf_specfem2d_example\n", + "%cd /home/bchow/Work/scratch\n", "! ls" ] }, @@ -42,14 +52,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m\u001b[32meval_func\u001b[m\u001b[m \u001b[1m\u001b[32meval_grad\u001b[m\u001b[m \u001b[1m\u001b[32moptimize\u001b[m\u001b[m \u001b[1m\u001b[32mpreprocess\u001b[m\u001b[m \u001b[1m\u001b[32msolver\u001b[m\u001b[m \u001b[1m\u001b[32msystem\u001b[m\u001b[m\r\n" + "eval_func eval_grad optimize\tpreprocess solver system\r\n" ] } ], @@ -84,14 +94,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m\u001b[32m001\u001b[m\u001b[m \u001b[1m\u001b[32m002\u001b[m\u001b[m \u001b[1m\u001b[32m003\u001b[m\u001b[m \u001b[1m\u001b[35mmainsolver\u001b[m\u001b[m\r\n" + "001 002 003 mainsolver\r\n" ] } ], @@ -101,16 +111,16 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m\u001b[32mDATA\u001b[m\u001b[m adj_solver.log fwd_solver.log solver_vs.log\r\n", - "\u001b[1m\u001b[32mOUTPUT_FILES\u001b[m\u001b[m \u001b[1m\u001b[32mbin\u001b[m\u001b[m kernel_paths \u001b[1m\u001b[32mtraces\u001b[m\u001b[m\r\n", - "\u001b[1m\u001b[35mSEM\u001b[m\u001b[m fwd_mesher.log solver_vp.log\r\n" + "adj_solver.log\tcombine_vs.log\tfwd_solver.log\tSEM\r\n", + "bin\t\tDATA\t\tkernel_paths\ttraces\r\n", + "combine_vp.log\tfwd_mesher.log\tOUTPUT_FILES\r\n" ] } ], @@ -135,14 +145,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m\u001b[32madj\u001b[m\u001b[m \u001b[1m\u001b[32mobs\u001b[m\u001b[m \u001b[1m\u001b[32msyn\u001b[m\u001b[m\r\n" + "adj obs syn\r\n" ] } ], @@ -152,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -169,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -223,16 +233,15 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "alpha.txt f_old.txt m_new.npz p_old.npz\r\n", - "checkpoint.npz f_try.txt m_old.npz\r\n", - "f_new.txt g_old.npz output_optim.txt\r\n" + "alpha.txt\tf_new.txt f_try.txt m_new.npz output_optim.txt\r\n", + "checkpoint.npz\tf_old.txt g_old.npz m_old.npz p_old.npz\r\n" ] } ], @@ -242,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -262,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -281,98 +290,121 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### eval_func/ & eval_grad/\n", - "\n", - "Scratch directories containing objective function evaluation and gradient evaluation files. These include (1) the current **model** being used for misfit evaluation, and (2) **residuals** which define the misfit for each event. **evalgrad/** also contains **kernels** which define per-event kernels which are summed and manipulated with the postprocess module." + "The 'checkpoint.npz' file contains information about the state of the line search (controlled by the Optimization module). It is used to resume failed or stopped line searches with minimal redundant use of computational resources." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\r\n" + "['restarted', 'func_vals', 'step_lens', 'gtg', 'gtp', 'step_count']\n", + "step count: 0\n", + "step lengths: [0.00000000e+00 2.32268310e+09 3.75818023e+09 1.59087505e+09\n", + " 2.82031810e+09]\n", + "misfit: [0.00127902 0.00086452 0.00172904 0.00259356 0.00345808]\n" ] } ], "source": [ - "! ls scratch/eval_func\n", - "! echo\n", - "! ls scratch/eval_grad" + "line_search = np.load(\"scratch/optimize/checkpoint.npz\")\n", + "\n", + "print(vars(line_search)[\"files\"])\n", + "\n", + "print(\"step count: \", line_search[\"step_count\"])\n", + "print(\"step lengths: \", line_search[\"step_lens\"])\n", + "print(\"misfit: \", line_search[\"func_vals\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### eval_func/ & eval_grad/\n", + "\n", + "Scratch directories containing objective function evaluation and gradient evaluation files. These include (1) the current **model** being used for misfit evaluation, and (2) a **residual** file which defines the misfit for each event. **eval_grad/** also contains **kernels** which define per-event kernels which are summed and manipulated with the postprocess module." ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "001 002 003\r\n" + "model residuals.txt\n", + "\n", + "gradient kernels misfit_kernel model residuals.txt\n" ] } ], "source": [ - "! ls scratch/evalgrad/residuals" + "! ls scratch/eval_func\n", + "! echo\n", + "! ls scratch/eval_grad" ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2.413801941841247842e-02\r\n", - "2.413801941841247842e-02\r\n", - "2.413801941841247842e-02\r\n" + "2.41E-02\r\n", + "2.14E-02\r\n", + "1.55E-02\r\n" ] } ], "source": [ - "! cat scratch/evalgrad/residuals/001" + "! cat scratch/eval_grad/residuals.txt" ] }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "001 002 003 sum\r\n" + "001 002 003\r\n" ] } ], "source": [ - "! ls scratch/evalgrad/kernels" + "! ls scratch/eval_grad/kernels" ] }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "proc000000_vp_kernel.bin proc000000_vs_kernel.bin\r\n" + "proc000000_bulk_beta_kernel.bin proc000000_rhop_kernel.bin\r\n", + "proc000000_bulk_c_kernel.bin\t proc000000_vp_kernel.bin\r\n", + "proc000000_kappa_kernel.bin\t proc000000_vs_kernel.bin\r\n", + "proc000000_mu_kernel.bin\t proc000000_weights_kernel.bin\r\n", + "proc000000_rho_kernel.bin\r\n" ] } ], "source": [ - "! ls scratch/evalgrad/kernels/sum" + "! ls scratch/eval_grad/kernels/001" ] }, { @@ -381,17 +413,9 @@ "source": [ "### system & preprocess\n", "\n", - "These two directories are empty in our example problem, but are catch-all directories where module-specific files can be output. If you are extending SeisFlows3 with other base or subclasses, it is preferable to adhere to this structure where each module only interacts with it's own directory" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], - "source": [ - "! ls scratch/system\n", - "! ls scratch/preprocess" + "These two directories are empty in our example problem, but are catch-all directories where module-specific files can be output. If you are extending SeisFlows with other base or subclasses, it is preferable to adhere to this structure where each module only interacts with it's own directory.\n", + "\n", + "When `Pyaflowa` is chosen as the preprocess module, it stores figures, log files, and data (in ASDFDataSets) within its scratch directory. It also specifies parameters for exporting these scratch files to disk for more permanent storage." ] }, { @@ -400,23 +424,19 @@ "source": [ "---------------------\n", "## output/\n", - "The current active state of SeisFlows3, containing pickle (.p) and JSON files which describe a Python environment of a current workflow. Additionally files to be permanently saved (e.g., models, graidents, traces) can be located here. These are tagged in ascending order, e.g., model_0001 refers to the updated model derived during the first iteration." + "Output files to be permanently saved (e.g., models, graidents, traces) can be located in this directory. These are tagged in ascending order. Because we did not run the finalization task in our SPECFEM2D problem, the output directory only contains our initial model." ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "gradient_0001 seisflows_optimize.p\t seisflows_solver.p\r\n", - "kwargs\t seisflows_parameters.json seisflows_system.p\r\n", - "model_0001 seisflows_paths.json\t seisflows_workflow.p\r\n", - "model_init seisflows_postprocess.p\r\n", - "model_true seisflows_preprocess.p\r\n" + "MODEL_INIT\r\n" ] } ], @@ -426,7 +446,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -438,24 +458,7 @@ } ], "source": [ - "! ls output/model_0001" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "proc000000_vp_kernel.bin proc000000_vs_kernel.bin\r\n" - ] - } - ], - "source": [ - "! ls output/gradient_0001" + "! ls output/MODEL_INIT" ] }, { @@ -469,14 +472,18 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "output_sf3_001.txt parameters_001.yaml\r\n" + "0001_00.log 0002_02.log 0004_01.log 0006_00.log\t parameters_002.yaml\r\n", + "0001_01.log 0003_00.log 0004_02.log 0006_01.log\t parameters_003.yaml\r\n", + "0001_02.log 0003_01.log 0005_00.log 0006_02.log\t sflog_001.txt\r\n", + "0002_00.log 0003_02.log 0005_01.log 0007_00.log\t sflog_002.txt\r\n", + "0002_01.log 0004_00.log 0005_02.log parameters_001.yaml sflog_003.txt\r\n" ] } ], @@ -488,49 +495,76 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "------------------------------\n", - "## stats/\n", + "----------------------------\n", + "## sflog.txt\n", "\n", - "Text files describing the optimization statistics of the current workflow. This directory is only relevant if you are running an inversion workflow. " + "The main log file for SeisFlows, where all log statements written to stdout are recorded during a workflow. Allows a user to come back to a workflow and understand the tasks completed and any important information collected during the workflow" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "factor.txt\t line_search.txt slope.txt\ttheta.txt\r\n", - "gradient_norm_L1.txt misfit.txt step_count.txt\r\n", - "gradient_norm_L2.txt restarted.txt step_length.txt\r\n" - ] - } - ], - "source": [ - "! ls stats" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ITER STEP_COUNT\r\n", - "==== ==================\r\n", - " 1 0.000000E+00\r\n", - " 1 2.000000E+00\r\n" + "2022-08-16 14:32:48 (I) | \r\n", + "================================================================================\r\n", + " SETTING UP INVERSION WORKFLOW \r\n", + "================================================================================\r\n", + "2022-08-16 14:32:55 (D) | running setup for module 'system.Workstation'\r\n", + "2022-08-16 14:32:57 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/sflog_001.txt\r\n", + "2022-08-16 14:32:57 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/parameters_001.yaml\r\n", + "2022-08-16 14:32:57 (D) | running setup for module 'solver.Specfem2D'\r\n", + "2022-08-16 14:32:57 (I) | initializing 3 solver directories\r\n", + "2022-08-16 14:32:57 (D) | initializing solver directory source: 001\r\n", + "2022-08-16 14:33:04 (D) | linking source '001' as 'mainsolver'\r\n", + "2022-08-16 14:33:04 (D) | initializing solver directory source: 002\r\n", + "2022-08-16 14:33:09 (D) | initializing solver directory source: 003\r\n", + "2022-08-16 14:33:16 (D) | running setup for module 'preprocess.Default'\r\n", + "2022-08-16 14:33:16 (D) | running setup for module 'optimize.Gradient'\r\n", + "2022-08-16 14:33:17 (I) | no optimization checkpoint found, assuming first run\r\n", + "2022-08-16 14:33:17 (I) | re-loading optimization module from checkpoint\r\n", + "2022-08-16 14:33:17 (I) | \r\n", + "////////////////////////////////////////////////////////////////////////////////\r\n", + " RUNNING ITERATION 01 \r\n", + "////////////////////////////////////////////////////////////////////////////////\r\n", + "2022-08-16 14:33:17 (I) | \r\n", + "================================================================================\r\n", + " RUNNING INVERSION WORKFLOW \r\n", + "================================================================================\r\n", + "2022-08-16 14:33:17 (I) | \r\n", + "////////////////////////////////////////////////////////////////////////////////\r\n", + " EVALUATING MISFIT FOR INITIAL MODEL \r\n", + "////////////////////////////////////////////////////////////////////////////////\r\n", + "2022-08-16 14:33:17 (I) | checking initial model parameters\r\n", + "2022-08-16 14:33:17 (I) | 5800.00 <= vp <= 5800.00\r\n", + "2022-08-16 14:33:17 (I) | 2600.00 <= rho <= 2600.00\r\n", + "2022-08-16 14:33:17 (I) | 3500.00 <= vs <= 3500.00\r\n", + "2022-08-16 14:33:17 (I) | checking true/target model parameters\r\n", + "2022-08-16 14:33:17 (I) | 5900.00 <= vp <= 5900.00\r\n", + "2022-08-16 14:33:17 (I) | 2600.00 <= rho <= 2600.00\r\n", + "2022-08-16 14:33:17 (I) | 3550.00 <= vs <= 3550.00\r\n", + "2022-08-16 14:33:17 (I) | preparing observation data for source 001\r\n", + "2022-08-16 14:33:17 (I) | running forward simulation w/ target model for 001\r\n", + "2022-08-16 14:33:21 (I) | evaluating objective function for source 001\r\n", + "2022-08-16 14:33:21 (D) | running forward simulation with 'Specfem2D'\r\n", + "2022-08-16 14:33:25 (D) | quantifying misfit with 'Default'\r\n", + "2022-08-16 14:33:25 (I) | preparing observation data for source 002\r\n", + "2022-08-16 14:33:25 (I) | running forward simulation w/ target model for 002\r\n", + "2022-08-16 14:33:29 (I) | evaluating objective function for source 002\r\n", + "2022-08-16 14:33:29 (D) | running forward simulation with 'Specfem2D'\r\n", + "2022-08-16 14:33:33 (D) | quantifying misfit with 'Default'\r\n", + "2022-08-16 14:33:33 (I) | preparing observation data for source 003\r\n", + "2022-08-16 14:33:33 (I) | running forward simulation w/ target model for 003\r\n", + "2022-08-16 14:33:36 (I) | evaluating objective function for source 003\r\n" ] } ], "source": [ - "! cat stats/step_count.txt" + "! head -50 sflog.txt" ] }, { @@ -538,81 +572,51 @@ "metadata": {}, "source": [ "----------------------------\n", - "## output_sf3.txt\n", + "## sfstate.txt\n", "\n", - "The main log file for SeisFlows3, where all log statements written to stdout are recorded during a workflow." + "A state file which tracks the progress of a workflow, allowing the User to quickly resumed stopped or failed workflows without wasting computational resources. The State file simply contains the names of functions contained in the Workflow task list, as well as their respective status, which can be 'completed', 'failed', or not available." ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2022-04-29 16:45:35 | initializing SeisFlows3 in sys.modules\r\n", - "2022-04-29 16:45:39 | copying par/log file to: /home/bchow/Work/official/workshop_pyatoa_sf3/ex1_specfem2d_workstation/logs/output_sf3_001.txt\r\n", - "2022-04-29 16:45:39 | copying par/log file to: /home/bchow/Work/official/workshop_pyatoa_sf3/ex1_specfem2d_workstation/logs/parameters_001.yaml\r\n", - "2022-04-29 16:45:39 | exporting current working environment to disk\r\n", - "2022-04-29 16:45:39 | \r\n", - "////////////////////////////////////////////////////////////////////////////////\r\n", - " WORKFLOW WILL STOP AFTER FUNC: 'finalize' \r\n", - "////////////////////////////////////////////////////////////////////////////////\r\n", - "2022-04-29 16:45:39 | \r\n", - "================================================================================\r\n", - " STARTING INVERSION WORKFLOW \r\n", - "================================================================================\r\n", - "2022-04-29 16:45:39 | \r\n", - "////////////////////////////////////////////////////////////////////////////////\r\n", - " ITERATION 1 / 1 \r\n", - "////////////////////////////////////////////////////////////////////////////////\r\n", - "2022-04-29 16:45:39 | \r\n", - "////////////////////////////////////////////////////////////////////////////////\r\n", - " PERFORMING MODULE SETUP \r\n", - "////////////////////////////////////////////////////////////////////////////////\r\n", - "2022-04-29 16:45:39 | misfit function is: 'waveform'\r\n", - "2022-04-29 16:45:40 | writing line search history file:\r\n", - "/home/bchow/Work/official/workshop_pyatoa_sf3/ex1_specfem2d_workstation/stats/line_search.txt\r\n", - "2022-04-29 16:45:40 | checking poissons ratio for: 'm_new.npy'\r\n", - "2022-04-29 16:45:40 | model parameters (m_new.npy i01s00):\r\n", - "2022-04-29 16:45:40 | 5800.00 <= vp <= 5800.00\r\n", - "2022-04-29 16:45:40 | 3500.00 <= vs <= 3500.00\r\n", - "2022-04-29 16:45:40 | 0.21 <= pr <= 0.21\r\n", - "2022-04-29 16:45:41 | setting up solver on system...\r\n", - "2022-04-29 16:45:41 | checkpointing working environment to disk\r\n", - "2022-04-29 16:45:42 | exporting current working environment to disk\r\n", - "2022-04-29 16:45:43 | running task solver.setup 3 times\r\n", - "2022-04-29 16:45:43 | initializing 3 solver directories\r\n", - "2022-04-29 16:45:50 | source 001 symlinked as mainsolver\r\n", - "2022-04-29 16:45:50 | generating 'data' with MODEL_TRUE synthetics\r\n", - "2022-04-29 16:45:57 | running mesh generation for MODEL_INIT\r\n", - "2022-04-29 16:46:27 | \r\n", - "================================================================================\r\n", - " INITIALIZING INVERSION \r\n", - "================================================================================\r\n", - "2022-04-29 16:46:27 | \r\n", - "EVALUATE OBJECTIVE FUNCTION\r\n", - "--------------------------------------------------------------------------------\r\n", - "2022-04-29 16:46:27 | saving model 'm_new.npy' to:\r\n", - "/home/bchow/Work/official/workshop_pyatoa_sf3/ex1_specfem2d_workstation/scratch/evalgrad/model\r\n", - "2022-04-29 16:46:28 | evaluating objective function 3 times on system...\r\n", - "2022-04-29 16:46:28 | checkpointing working environment to disk\r\n", - "2022-04-29 16:46:29 | exporting current working environment to disk\r\n", - "2022-04-29 16:46:30 | running task solver.eval_func 3 times\r\n", - "2022-04-29 16:46:30 | running forward simulations\r\n" + "# SeisFlows State File\r\n", + "# Tue Aug 16 14:33:17 2022\r\n", + "# Acceptable states: 'completed', 'failed'\r\n", + "# =======================================\r\n", + "evaluate_initial_misfit: completed\r\n", + "run_adjoint_simulations: completed\r\n", + "postprocess_event_kernels: completed\r\n", + "evaluate_gradient_from_kernels: completed\r\n", + "initialize_line_search: completed\r\n", + "perform_line_search: completed\r\n", + "iteration: 1" ] } ], "source": [ - "! head -50 output_sf3.txt" + "! cat sfstate.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When submitting a workflow with an existing state file, the workflow will check the status of each function. 'Completed' functions will be skipped over. 'Failed' functions will be re-run. Users can delete lines from the state file or change status' manually to re-run tasks within the list, taking care about the current configuration of the working directory, which is intrinsically tied to the task list.\n", + "\n", + "For 'Inversion' workflows, the current 'Iteration' is also saved, meaning re-submitted workflows will start at the previously checkpointed iteration. " ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -626,7 +630,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.12" } }, "nbformat": 4, diff --git a/docs/specfem2d_example.rst b/docs/specfem2d_example.rst index 6e0f01f6..81bc7477 100644 --- a/docs/specfem2d_example.rst +++ b/docs/specfem2d_example.rst @@ -99,12 +99,8 @@ but this can easily be accomplished in bash. .. code:: ipython3 # vvv USER MUST EDIT THE FOLLOWING PATHS vvv - # MAC PATHS - WORKDIR = "/Users/Chow/Work/work/sf_specfem2d_example" - SPECFEM2D = "/Users/Chow/Repositories/specfem2d" - # LINUX PATHS - # WORKDIR = "/home/bchow/Work/work/sf_specfem2d_example" - # SPECFEM2D = "/home/bchow/REPOSITORIES/specfem2d" + WORKDIR = "/home/bchow/Work/scratch" + SPECFEM2D = "/home/bchow/REPOSITORIES/specfem2d" # where WORKDIR: points to your own working directory # and SPECFEM2D: points to an existing specfem2D repository if available (if not set as '') # ^^^ USER MUST EDIT THE FOLLOWING PATHS ^^^ @@ -129,7 +125,8 @@ but this can easily be accomplished in bash. .. code:: ipython3 # Download SPECFEM2D from GitHub, devel branch for latest codebase OR symlink from existing repo - os.makedirs(WORKDIR) + if not os.path.exists(WORKDIR): + os.makedirs(WORKDIR) os.chdir(WORKDIR) if os.path.exists("specfem2d"): @@ -171,15 +168,10 @@ but this can easily be accomplished in bash. .. parsed-literal:: - /Users/Chow/Repositories/specfem2d - xadj_seismogram xmeshfem2D - xadj_seismogram.dSYM xmeshfem2D.dSYM - xcheck_quality_external_mesh xsmooth_sem - xcheck_quality_external_mesh.dSYM xsmooth_sem.dSYM - xcombine_sem xspecfem2D - xcombine_sem.dSYM xspecfem2D.dSYM - xconvolve_source_timefunction xsum_kernels - xconvolve_source_timefunction.dSYM xsum_kernels.dSYM + /home/bchow/REPOSITORIES/specfem2d + xadj_seismogram xconvolve_source_timefunction xspecfem2D + xcheck_quality_external_mesh xmeshfem2D xsum_kernels + xcombine_sem xsmooth_sem 1b. Create a separate SPECFEM2D working directory @@ -222,8 +214,8 @@ to define our **DATA/** directory (last tested 8/15/22, bdba4389). .. parsed-literal:: - /Users/Chow/Work/work/sf_specfem2d_example/specfem2d_workdir - DATA bin + /home/bchow/Work/scratch/specfem2d_workdir + bin DATA .. code:: ipython3 @@ -243,14 +235,14 @@ to define our **DATA/** directory (last tested 8/15/22, bdba4389). ------------------------------------------------------------------------------- ------------------------------------------------------------------------------- - D a t e : 15 - 08 - 2022 T i m e : 10:13:31 + D a t e : 16 - 08 - 2022 T i m e : 14:26:37 ------------------------------------------------------------------------------- ------------------------------------------------------------------------------- see results in directory: OUTPUT_FILES/ done - Mon Aug 15 10:13:31 PDT 2022 + Tue Aug 16 02:26:37 PM AKDT 2022 -------------- @@ -290,22 +282,15 @@ how SeisFlows3 operates within the SPECFEM2D framework): .. parsed-literal:: - Par_file SOURCE_013 - Par_file_Tape2007_132rec_checker SOURCE_014 - Par_file_Tape2007_onerec SOURCE_015 - SOURCE SOURCE_016 - SOURCE_001 SOURCE_017 - SOURCE_002 SOURCE_018 - SOURCE_003 SOURCE_019 - SOURCE_004 SOURCE_020 - SOURCE_005 SOURCE_021 - SOURCE_006 SOURCE_022 - SOURCE_007 SOURCE_023 - SOURCE_008 SOURCE_024 - SOURCE_009 SOURCE_025 - SOURCE_010 STATIONS_checker - SOURCE_011 interfaces_Tape2007.dat - SOURCE_012 model_velocity.dat_checker + interfaces_Tape2007.dat SOURCE_003 SOURCE_012 SOURCE_021 + model_velocity.dat_checker SOURCE_004 SOURCE_013 SOURCE_022 + Par_file SOURCE_005 SOURCE_014 SOURCE_023 + Par_file_Tape2007_132rec_checker SOURCE_006 SOURCE_015 SOURCE_024 + Par_file_Tape2007_onerec SOURCE_007 SOURCE_016 SOURCE_025 + proc000000_model_velocity.dat_input SOURCE_008 SOURCE_017 STATIONS + SOURCE SOURCE_009 SOURCE_018 STATIONS_checker + SOURCE_001 SOURCE_010 SOURCE_019 + SOURCE_002 SOURCE_011 SOURCE_020 1c. Generate initial and target models @@ -362,7 +347,7 @@ of the initial model. .. parsed-literal:: - DATA OUTPUT_FILES bin + bin DATA OUTPUT_FILES .. code:: ipython3 @@ -391,8 +376,8 @@ of the initial model. **** Specfem 2-D Solver - serial version **** ********************************************** - Running Git version of the code corresponding to - dating From + Running Git version of the code corresponding to commit cf89366717d9435985ba852ef1d41a10cee97884 + dating From Date: Mon Nov 29 23:20:51 2021 -0800 NDIM = 2 @@ -403,7 +388,7 @@ of the initial model. Tape-Liu-Tromp (GJI 2007) ------------------------------------------------------------------------------- ------------------------------------------------------------------------------- - D a t e : 15 - 08 - 2022 T i m e : 10:14:13 + D a t e : 16 - 08 - 2022 T i m e : 14:26:52 ------------------------------------------------------------------------------- ------------------------------------------------------------------------------- @@ -463,8 +448,8 @@ directly. **** Specfem 2-D Solver - serial version **** ********************************************** - Running Git version of the code corresponding to - dating From + Running Git version of the code corresponding to commit cf89366717d9435985ba852ef1d41a10cee97884 + dating From Date: Mon Nov 29 23:20:51 2021 -0800 NDIM = 2 @@ -475,7 +460,7 @@ directly. Tape-Liu-Tromp (GJI 2007) ------------------------------------------------------------------------------- ------------------------------------------------------------------------------- - D a t e : 15 - 08 - 2022 T i m e : 10:14:13 + D a t e : 16 - 08 - 2022 T i m e : 14:26:52 ------------------------------------------------------------------------------- ------------------------------------------------------------------------------- @@ -488,7 +473,7 @@ directly. .. parsed-literal:: - DATA OUTPUT_FILES_INIT OUTPUT_FILES_TRUE bin + bin DATA OUTPUT_FILES_INIT OUTPUT_FILES_TRUE 2. Initialize SeisFlows (SF) @@ -577,7 +562,7 @@ line commands. .. parsed-literal:: creating parameter file: parameters.yaml - parameters.yaml specfem2d specfem2d_workdir + parameters.yaml sflog.txt specfem2d specfem2d_workdir .. code:: ipython3 @@ -716,7 +701,7 @@ choice for each. .. code:: ipython3 ! seisflows configure - ! cat parameters.yaml + ! head --lines=50 parameters.yaml .. parsed-literal:: @@ -771,369 +756,13 @@ choice for each. # produce synthetics (controlled by `solver.format`) OR # synthetic': 'data' will be generated as synthetic seismograms using # a target model provided in `path_model_true`. If None, workflow will - # not attempt to generate data. - # :type stop_after: str - # :param stop_after: optional name of task in task list (use - # `seisflows print tasks` to get task list for given workflow) to stop - # workflow after, allowing user to prematurely stop a workflow to explore - # intermediate results or debug. - # :type export_traces: bool - # :param export_traces: export all waveforms that are generated by the - # external solver to `path_output`. If False, solver traces stored in - # scratch may be discarded at any time in the workflow - # :type export_residuals: bool - # :param export_residuals: export all residuals (data-synthetic misfit) that - # are generated by the external solver to `path_output`. If False, - # residuals stored in scratch may be discarded at any time in the workflow - # - # - # Migration Workflow - # ------------------ - # Run forward and adjoint solver to produce event-dependent misfit kernels. - # Sum and postprocess kernels to produce gradient. In seismic exploration - # this is 'reverse time migration'. - # - # Parameters - # ---------- - # :type export_gradient: bool - # :param export_gradient: export the gradient after it has been generated - # in the scratch directory. If False, gradient can be discarded from - # scratch at any time in the workflow - # :type export_kernels: bool - # :param export_kernels: export each sources event kernels after they have - # been generated in the scratch directory. If False, gradient can be - # discarded from scratch at any time in the workflow - # - # - # Inversion Workflow - # ------------------ - # Peforms iterative nonlinear inversion using the machinery of the Forward - # and Migration workflows, as well as a built-in optimization library. - # - # Parameters - # ---------- - # :type start: int - # :param start: start inversion workflow at this iteration. 1 <= start <= inf - # :type end: int - # :param end: end inversion workflow at this iteration. start <= end <= inf - # :type iteration: int - # :param iteration: The current iteration of the workflow. If NoneType, takes - # the value of `start` (i.e., first iteration of the workflow). User can - # also set between `start` and `end` to resume a failed workflow. - # :type thrifty: bool - # :param thrifty: a thrifty inversion skips the costly intialization step - # (i.e., forward simulations and misfit quantification) if the final - # forward simulations from the previous iterations line search can be - # used in the current one. Requires L-BFGS optimization. - # :type export_model: bool - # :param export_model: export best-fitting model from the line search to disk. - # If False, new models can be discarded from scratch at any time. - # - # - # ============================================================================= - data_case: data - stop_after: null - export_traces: False - export_residuals: False - export_gradient: False - export_kernels: False - start: 1 - end: 1 - export_model: True - thrifty: False - iteration: 1 - # ============================================================================= - # - # Workstation System - # ------------------ - # Runs tasks in serial on a local machine. - # - # Parameters - # ---------- - # :type ntask: int - # :param ntask: number of individual tasks/events to run during workflow. - # Must be <= the number of source files in `path_specfem_data` - # :type nproc: int - # :param nproc: number of processors to use for each simulation - # :type log_level: str - # :param log_level: logger level to pass to logging module. - # Available: 'debug', 'info', 'warning', 'critical' - # :type verbose: bool - # :param verbose: if True, formats the log messages to include the file - # name, line number and message type. Useful for debugging but - # also very verbose. - # - # - # ============================================================================= - ntask: 1 - nproc: 1 - log_level: DEBUG - verbose: False - # ============================================================================= - # - # Solver SPECFEM - # -------------- - # Generalized SPECFEM interface to manipulate SPECFEM2D/3D/3D_GLOBE w/ Python - # - # Parameters - # ---------- - # :type data_format: str - # :param data_format: data format for reading traces into memory. - # Available: ['SU': seismic unix format, 'ASCII': human-readable ascii] - # :type materials: str - # :param materials: Material parameters used to define model. Available: - # ['ELASTIC': Vp, Vs, 'ACOUSTIC': Vp, 'ISOTROPIC', 'ANISOTROPIC'] - # :type density: bool - # :param density: How to treat density during inversion. If True, updates - # density during inversion. If False, keeps it constant. - # TODO allow density scaling during an inversion - # :type attenuation: bool - # :param attenuation: How to treat attenuation during inversion. - # if True, turns on attenuation during forward simulations only. If - # False, attenuation is always set to False. Requires underlying - # attenution (Q_mu, Q_kappa) model - # :type smooth_h: float - # :param smooth_h: Gaussian half-width for horizontal smoothing in units - # of meters. If 0., no smoothing applied - # :type smooth_h: float - # :param smooth_v: Gaussian half-width for vertical smoothing in units - # of meters. - # :type components: str - # :param components: components to consider and tag data with. Should be - # string of letters such as 'RTZ' - # :type solver_io: str - # :param solver_io: format of model/kernel/gradient files expected by the - # numerical solver. Available: ['fortran_binary': default .bin files]. - # TODO: ['adios': ADIOS formatted files] - # :type source_prefix: str - # :param source_prefix: prefix of source/event/earthquake files. If None, - # will attempt to guess based on the specific solver chosen. - # :type mpiexec: str - # :param mpiexec: MPI executable used to run parallel processes. Should also - # be defined for the system module - # - # - # Solver SPECFEM2D - # ---------------- - # SPECFEM2D-specific alterations to the base SPECFEM module - # - # Parameters - # ---------- - # :type source_prefix: str - # :param source_prefix: Prefix of source files in path SPECFEM_DATA. Defaults - # to 'SOURCE' - # :type multiples: bool - # :param multiples: set an absorbing top-boundary condition - # - # - # ============================================================================= - data_format: ascii - materials: acoustic - density: False - attenuation: False - smooth_h: 0.0 - smooth_v: 0.0 - components: ZNE - source_prefix: SOURCE - multiples: False - # ============================================================================= - # - # Default Preprocess - # ------------------ - # Data processing for seismic traces, with options for data misfit, - # filtering, normalization and muting. - # - # Parameters - # ---------- - # :type data_format: str - # :param data_format: data format for reading traces into memory. For - # available see: seisflows.plugins.preprocess.readers - # :type misfit: str - # :param misfit: misfit function for waveform comparisons. For available - # see seisflows.plugins.preprocess.misfit - # :type backproject: str - # :param backproject: backprojection function for migration, or the - # objective function in FWI. For available see - # seisflows.plugins.preprocess.adjoint - # :type normalize: str - # :param normalize: Data normalization parameters used to normalize the - # amplitudes of waveforms. Choose from two sets: - # ENORML1: normalize per event by L1 of traces; OR - # ENORML2: normalize per event by L2 of traces; - # & - # TNORML1: normalize per trace by L1 of itself; OR - # TNORML2: normalize per trace by L2 of itself - # :type filter: str - # :param filter: Data filtering type, available options are: - # BANDPASS (req. MIN/MAX PERIOD/FREQ); - # LOWPASS (req. MAX_FREQ or MIN_PERIOD); - # HIGHPASS (req. MIN_FREQ or MAX_PERIOD) - # :type min_period: float - # :param min_period: Minimum filter period applied to time series. - # See also MIN_FREQ, MAX_FREQ, if User defines FREQ parameters, they - # will overwrite PERIOD parameters. - # :type max_period: float - # :param max_period: Maximum filter period applied to time series. See - # also MIN_FREQ, MAX_FREQ, if User defines FREQ parameters, they will - # overwrite PERIOD parameters. - # :type min_freq: float - # :param min_freq: Maximum filter frequency applied to time series, - # See also MIN_PERIOD, MAX_PERIOD, if User defines FREQ parameters, - # they will overwrite PERIOD parameters. - # :type max_freq: float - # :param max_freq: Maximum filter frequency applied to time series, - # See also MIN_PERIOD, MAX_PERIOD, if User defines FREQ parameters, - # they will overwrite PERIOD parameters. - # :type mute: list - # :param mute: Data mute parameters used to zero out early / late - # arrivals or offsets. Choose any number of: - # EARLY: mute early arrivals; - # LATE: mute late arrivals; - # SHORT: mute short source-receiver distances; - # LONG: mute long source-receiver distances - # - # - # ============================================================================= - misfit: waveform - adjoint: waveform - normalize: [] - filter: null - min_period: null - max_period: null - min_freq: null - max_freq: null - mute: [] - early_slope: null - early_const: null - late_slope: null - late_const: null - short_dist: null - - # ============================================================================= - # - # Gradient Optimization - # --------------------- - # Gradient/steepest descent optimization algorithm. - # - # Parameters - # ---------- - # :type line_search_method: str - # :param line_search_method: chosen line_search algorithm. Currently available - # are 'bracket' and 'backtrack'. See seisflows.plugins.line_search - # for all available options - # :type preconditioner: str - # :param preconditioner: algorithm for preconditioning gradients. Currently - # available: 'diagonal'. Requires `path_preconditioner` to point to a - # set of files that define the preconditioner, formatted the same as the - # input model - # :type step_count_max: int - # :param step_count_max: maximum number of trial steps to perform during - # the line search before a change in line search behavior is - # considered, or a line search is considered to have failed. - # :type step_len_init: float - # :param step_len_init: initial line search step length guess, provided - # as a fraction of current model parameters. - # :type step_len_max: float - # :param step_len_max: maximum allowable step length during the line - # search. Set as a fraction of the current model parameters - # - # - # ============================================================================= - preconditioner: null - step_count_max: 10 - step_len_init: 0.05 - step_len_max: 0.5 - line_search_method: bracket - # ============================================================================= - # - # Paths - # ----- - # :type workdir: str - # :param workdir: working directory in which to look for data and store - # results. Defaults to current working directory - # :type path_output: str - # :param path_output: path to directory used for permanent storage on disk. - # Results and exported scratch files are saved here. - # :type path_data: str - # :param path_data: path to any externally stored data required by the solver - # :type path_state_file: str - # :param path_state_file: path to a text file used to track the current - # status of a workflow (i.e., what functions have already been completed), - # used for checkpointing and resuming workflows - # :type path_model_init: str - # :param path_model_init: path to the starting model used to calculate the - # initial misfit. Must match the expected `solver_io` format. - # :type path_model_true: str - # :param path_model_true: path to a target model if `case`=='synthetic' and - # a set of synthetic 'observations' are required for workflow. - # :type path_eval_grad: str - # :param path_eval_grad: scratch path to store files for gradient evaluation, - # including models, kernels, gradient and residuals. - # :type path_mask: str - # :param path_mask: optional path to a masking function which is used to - # mask out or scale parts of the gradient. The user-defined mask must - # match the file format of the input model (e.g., .bin files). - # :type path_eval_func: str - # :param path_eval_func: scratch path to store files for line search objective - # function evaluations, including models, misfit and residuals - # - # :type path_output_log: str - # :param path_output_log: path to a text file used to store the outputs of - # the package wide logger, which are also written to stdout - # :type path_par_file: str - # :param path_par_file: path to parameter file which is used to instantiate - # the package - # :type path_log_files: str - # :param path_log_files: path to a directory where individual log files are - # saved whenever a number of parallel tasks are run on the system. - # - # :type path_data: str - # :param path_data: path to any externally stored data required by the solver - # :type path_specfem_bin: str - # :param path_specfem_bin: path to SPECFEM bin/ directory which - # contains binary executables for running SPECFEM - # :type path_specfem_data: str - # :param path_specfem_data: path to SPECFEM DATA/ directory which must - # contain the CMTSOLUTION, STATIONS and Par_file files used for - # running SPECFEM - # - # :type path_preprocess: str - # :param path_preprocess: scratch path for all preprocessing processes, - # including saving files - # - # :type path_preconditioner: str - # :param path_preconditioner: optional path to a set of preconditioner files - # formatted the same as the input model (or output model of solver). - # Required to exist and contain files if `preconditioner`==True - # - # ============================================================================= - path_workdir: /Users/Chow/Work/work/sf_specfem2d_example - path_scratch: /Users/Chow/Work/work/sf_specfem2d_example/scratch - path_eval_grad: /Users/Chow/Work/work/sf_specfem2d_example/scratch/eval_grad - path_output: /Users/Chow/Work/work/sf_specfem2d_example/output - path_model_init: null - path_model_true: null - path_state_file: /Users/Chow/Work/work/sf_specfem2d_example/sfstate.txt - path_data: null - path_mask: null - path_eval_func: /Users/Chow/Work/work/sf_specfem2d_example/scratch/eval_func - path_par_file: /Users/Chow/Work/work/sf_specfem2d_example/parameters.yaml - path_log_files: /Users/Chow/Work/work/sf_specfem2d_example/logs - path_output_log: /Users/Chow/Work/work/sf_specfem2d_example/sflog.txt - path_specfem_bin: null - path_specfem_data: null - path_solver: /Users/Chow/Work/work/sf_specfem2d_example/scratch/solver - path_preconditioner: null .. code:: ipython3 # EDIT THE SEISFLOWS PARAMETER FILE ! seisflows par ntask 3 # set the number of sources/events to use - ! seisflows par materials elastic # how the velocity model is parameterized - ! seisflows par density False # update density or keep constant - ! seisflows par attenuation False - ! seisflows par start 1 # first iteration + ! seisflows par materials elastic # update Vp and Vs during inversion ! seisflows par end 2 # final iteration -- we will only run 1 ! seisflows par data_case synthetic # synthetic-synthetic means we need both INIT and TRUE models ! seisflows par components Y # this default example creates Y-component seismograms @@ -1150,13 +779,16 @@ choice for each. ntask: 1 -> 3 materials: acoustic -> elastic - density: False -> False - attenuation: False -> False - start: 1 -> 1 end: 1 -> 2 data_case: data -> synthetic components: ZNE -> Y step_count_max: 10 -> 5 + path_specfem_bin: null -> /home/bchow/Work/scratch/specfem2d_workdir/bin + path_specfem_data: null -> /home/bchow/Work/scratch/specfem2d_workdir/DATA + path_model_init: null -> + /home/bchow/Work/scratch/specfem2d_workdir/OUTPUT_FILES_INIT + path_model_true: null -> + /home/bchow/Work/scratch/specfem2d_workdir/OUTPUT_FILES_TRUE @@ -1261,21 +893,16 @@ In the Inversion workflow, the tasks listed are described as follows: preparation for subsequent iterations Let’s set the ``stop_after`` argument to **evaluate_initial_misfit**, -this will halt the workflow after the intialization step. We’ll also set -the ``verbose`` parameter to ‘False’, to keep the logging format -relatively simple. We will explore the ``verbose``\ ==True option in a -later cell. +this will halt the workflow after the intialization step. .. code:: ipython3 ! seisflows par stop_after evaluate_initial_misfit - ! seisflows par verbose False .. parsed-literal:: - stop_after: null -> evaluate_initial_misfit - verbose: False -> False + stop_after: null -> evaluate_initial_misfit -------------- @@ -1298,59 +925,59 @@ Since this is our first run, we’ll use ``seisflows submit``. .. parsed-literal:: - 2022-08-15 16:11:40 (I) | + 2022-08-16 14:32:48 (I) | ================================================================================ SETTING UP INVERSION WORKFLOW ================================================================================ - 2022-08-15 16:11:47 (D) | running setup for module 'system.Workstation' - 2022-08-15 16:11:50 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/sflog_001.txt - 2022-08-15 16:11:50 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/parameters_001.yaml - 2022-08-15 16:11:50 (D) | running setup for module 'solver.Specfem2D' - 2022-08-15 16:11:50 (I) | initializing 3 solver directories - 2022-08-15 16:11:50 (D) | initializing solver directory source: 001 - 2022-08-15 16:11:58 (D) | linking source '001' as 'mainsolver' - 2022-08-15 16:11:58 (D) | initializing solver directory source: 002 - 2022-08-15 16:12:04 (D) | initializing solver directory source: 003 - 2022-08-15 16:12:13 (D) | running setup for module 'preprocess.Default' - 2022-08-15 16:12:14 (D) | running setup for module 'optimize.Gradient' - 2022-08-15 16:12:15 (I) | no optimization checkpoint found, assuming first run - 2022-08-15 16:12:16 (I) | re-loading optimization module from checkpoint - 2022-08-15 16:12:16 (I) | + 2022-08-16 14:32:55 (D) | running setup for module 'system.Workstation' + 2022-08-16 14:32:57 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/sflog_001.txt + 2022-08-16 14:32:57 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/parameters_001.yaml + 2022-08-16 14:32:57 (D) | running setup for module 'solver.Specfem2D' + 2022-08-16 14:32:57 (I) | initializing 3 solver directories + 2022-08-16 14:32:57 (D) | initializing solver directory source: 001 + 2022-08-16 14:33:04 (D) | linking source '001' as 'mainsolver' + 2022-08-16 14:33:04 (D) | initializing solver directory source: 002 + 2022-08-16 14:33:09 (D) | initializing solver directory source: 003 + 2022-08-16 14:33:16 (D) | running setup for module 'preprocess.Default' + 2022-08-16 14:33:16 (D) | running setup for module 'optimize.Gradient' + 2022-08-16 14:33:17 (I) | no optimization checkpoint found, assuming first run + 2022-08-16 14:33:17 (I) | re-loading optimization module from checkpoint + 2022-08-16 14:33:17 (I) | //////////////////////////////////////////////////////////////////////////////// RUNNING ITERATION 01 //////////////////////////////////////////////////////////////////////////////// - 2022-08-15 16:12:16 (I) | + 2022-08-16 14:33:17 (I) | ================================================================================ RUNNING INVERSION WORKFLOW ================================================================================ - 2022-08-15 16:12:16 (I) | + 2022-08-16 14:33:17 (I) | //////////////////////////////////////////////////////////////////////////////// EVALUATING MISFIT FOR INITIAL MODEL //////////////////////////////////////////////////////////////////////////////// - 2022-08-15 16:12:16 (I) | checking initial model parameters - 2022-08-15 16:12:16 (I) | 2600.00 <= rho <= 2600.00 - 2022-08-15 16:12:16 (I) | 3500.00 <= vs <= 3500.00 - 2022-08-15 16:12:16 (I) | 5800.00 <= vp <= 5800.00 - 2022-08-15 16:12:16 (I) | checking true/target model parameters - 2022-08-15 16:12:16 (I) | 2600.00 <= rho <= 2600.00 - 2022-08-15 16:12:16 (I) | 3550.00 <= vs <= 3550.00 - 2022-08-15 16:12:16 (I) | 5900.00 <= vp <= 5900.00 - 2022-08-15 16:12:16 (I) | preparing observation data for source 001 - 2022-08-15 16:12:16 (I) | running forward simulation w/ target model for 001 - 2022-08-15 16:12:33 (I) | evaluating objective function for source 001 - 2022-08-15 16:12:33 (D) | running forward simulation with 'Specfem2D' - 2022-08-15 16:12:53 (D) | quantifying misfit with 'Default' - 2022-08-15 16:12:53 (I) | preparing observation data for source 002 - 2022-08-15 16:12:53 (I) | running forward simulation w/ target model for 002 - 2022-08-15 16:13:09 (I) | evaluating objective function for source 002 - 2022-08-15 16:13:09 (D) | running forward simulation with 'Specfem2D' - 2022-08-15 16:13:31 (D) | quantifying misfit with 'Default' - 2022-08-15 16:13:31 (I) | preparing observation data for source 003 - 2022-08-15 16:13:31 (I) | running forward simulation w/ target model for 003 - 2022-08-15 16:14:16 (I) | evaluating objective function for source 003 - 2022-08-15 16:14:16 (D) | running forward simulation with 'Specfem2D' - 2022-08-15 16:14:33 (D) | quantifying misfit with 'Default' - 2022-08-15 16:14:33 (I) | stop workflow at `stop_after`: evaluate_initial_misfit + 2022-08-16 14:33:17 (I) | checking initial model parameters + 2022-08-16 14:33:17 (I) | 5800.00 <= vp <= 5800.00 + 2022-08-16 14:33:17 (I) | 2600.00 <= rho <= 2600.00 + 2022-08-16 14:33:17 (I) | 3500.00 <= vs <= 3500.00 + 2022-08-16 14:33:17 (I) | checking true/target model parameters + 2022-08-16 14:33:17 (I) | 5900.00 <= vp <= 5900.00 + 2022-08-16 14:33:17 (I) | 2600.00 <= rho <= 2600.00 + 2022-08-16 14:33:17 (I) | 3550.00 <= vs <= 3550.00 + 2022-08-16 14:33:17 (I) | preparing observation data for source 001 + 2022-08-16 14:33:17 (I) | running forward simulation w/ target model for 001 + 2022-08-16 14:33:21 (I) | evaluating objective function for source 001 + 2022-08-16 14:33:21 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:33:25 (D) | quantifying misfit with 'Default' + 2022-08-16 14:33:25 (I) | preparing observation data for source 002 + 2022-08-16 14:33:25 (I) | running forward simulation w/ target model for 002 + 2022-08-16 14:33:29 (I) | evaluating objective function for source 002 + 2022-08-16 14:33:29 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:33:33 (D) | quantifying misfit with 'Default' + 2022-08-16 14:33:33 (I) | preparing observation data for source 003 + 2022-08-16 14:33:33 (I) | running forward simulation w/ target model for 003 + 2022-08-16 14:33:36 (I) | evaluating objective function for source 003 + 2022-08-16 14:33:36 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:33:40 (D) | quantifying misfit with 'Default' + 2022-08-16 14:33:40 (I) | stop workflow at `stop_after`: evaluate_initial_misfit .. note:: @@ -1428,49 +1055,49 @@ functions again: .. parsed-literal:: - 2022-08-15 16:15:06 (D) | setting iteration==1 from state file - 2022-08-15 16:15:06 (I) | + 2022-08-16 14:36:42 (D) | setting iteration==1 from state file + 2022-08-16 14:36:42 (I) | ================================================================================ SETTING UP INVERSION WORKFLOW ================================================================================ - 2022-08-15 16:15:16 (D) | running setup for module 'system.Workstation' - 2022-08-15 16:15:20 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/sflog_002.txt - 2022-08-15 16:15:20 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/parameters_002.yaml - 2022-08-15 16:15:20 (D) | running setup for module 'solver.Specfem2D' - 2022-08-15 16:15:20 (I) | initializing 3 solver directories - 2022-08-15 16:15:22 (D) | running setup for module 'preprocess.Default' - 2022-08-15 16:15:23 (D) | running setup for module 'optimize.Gradient' - 2022-08-15 16:15:25 (I) | re-loading optimization module from checkpoint - 2022-08-15 16:15:27 (I) | re-loading optimization module from checkpoint - 2022-08-15 16:15:27 (I) | + 2022-08-16 14:36:48 (D) | running setup for module 'system.Workstation' + 2022-08-16 14:36:51 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/sflog_002.txt + 2022-08-16 14:36:51 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/parameters_002.yaml + 2022-08-16 14:36:51 (D) | running setup for module 'solver.Specfem2D' + 2022-08-16 14:36:51 (I) | initializing 3 solver directories + 2022-08-16 14:36:51 (D) | running setup for module 'preprocess.Default' + 2022-08-16 14:36:52 (D) | running setup for module 'optimize.Gradient' + 2022-08-16 14:36:53 (I) | re-loading optimization module from checkpoint + 2022-08-16 14:36:54 (I) | re-loading optimization module from checkpoint + 2022-08-16 14:36:54 (I) | //////////////////////////////////////////////////////////////////////////////// RUNNING ITERATION 01 //////////////////////////////////////////////////////////////////////////////// - 2022-08-15 16:15:27 (I) | + 2022-08-16 14:36:54 (I) | ================================================================================ RUNNING INVERSION WORKFLOW ================================================================================ - 2022-08-15 16:15:27 (I) | 'evaluate_initial_misfit' has already been run, skipping - 2022-08-15 16:15:27 (I) | + 2022-08-16 14:36:54 (I) | 'evaluate_initial_misfit' has already been run, skipping + 2022-08-16 14:36:54 (I) | //////////////////////////////////////////////////////////////////////////////// EVALUATING EVENT KERNELS W/ ADJOINT SIMULATIONS //////////////////////////////////////////////////////////////////////////////// - 2022-08-15 16:15:27 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log' - 2022-08-15 16:16:11 (D) | renaming output event kernels: 'alpha' -> 'vp' - 2022-08-15 16:16:11 (D) | renaming output event kernels: 'beta' -> 'vs' - 2022-08-15 16:16:12 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log' - 2022-08-15 16:16:59 (D) | renaming output event kernels: 'alpha' -> 'vp' - 2022-08-15 16:16:59 (D) | renaming output event kernels: 'beta' -> 'vs' - 2022-08-15 16:16:59 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log' - 2022-08-15 16:17:45 (D) | renaming output event kernels: 'alpha' -> 'vp' - 2022-08-15 16:17:45 (D) | renaming output event kernels: 'beta' -> 'vs' - 2022-08-15 16:17:45 (I) | + 2022-08-16 14:36:54 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log' + 2022-08-16 14:37:05 (D) | renaming output event kernels: 'alpha' -> 'vp' + 2022-08-16 14:37:05 (D) | renaming output event kernels: 'beta' -> 'vs' + 2022-08-16 14:37:05 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log' + 2022-08-16 14:37:16 (D) | renaming output event kernels: 'alpha' -> 'vp' + 2022-08-16 14:37:16 (D) | renaming output event kernels: 'beta' -> 'vs' + 2022-08-16 14:37:18 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log' + 2022-08-16 14:37:29 (D) | renaming output event kernels: 'alpha' -> 'vp' + 2022-08-16 14:37:29 (D) | renaming output event kernels: 'beta' -> 'vs' + 2022-08-16 14:37:30 (I) | //////////////////////////////////////////////////////////////////////////////// GENERATING/PROCESSING MISFIT KERNEL //////////////////////////////////////////////////////////////////////////////// - 2022-08-15 16:17:45 (I) | combining event kernels into single misfit kernel - 2022-08-15 16:17:47 (I) | scaling gradient to absolute model perturbations - 2022-08-15 16:17:49 (I) | stop workflow at `stop_after`: evaluate_gradient_from_kernels + 2022-08-16 14:37:30 (I) | combining event kernels into single misfit kernel + 2022-08-16 14:37:31 (I) | scaling gradient to absolute model perturbations + 2022-08-16 14:37:32 (I) | stop workflow at `stop_after`: evaluate_gradient_from_kernels -------------- @@ -1501,7 +1128,7 @@ computation. .. parsed-literal:: - gradient kernels misfit_kernel model residuals.txt + gradient kernels misfit_kernel model residuals.txt .. code:: ipython3 @@ -1512,7 +1139,7 @@ computation. .. parsed-literal:: - proc000000_vp_kernel.bin proc000000_vs_kernel.bin + proc000000_vp_kernel.bin proc000000_vs_kernel.bin .. code:: ipython3 @@ -1524,7 +1151,7 @@ computation. .. parsed-literal:: - 001 002 003 + 001 002 003 .. code:: ipython3 @@ -1537,7 +1164,7 @@ computation. .. parsed-literal:: - checkpoint.npz f_new.txt g_new.npz m_new.npz + checkpoint.npz f_new.txt g_new.npz m_new.npz .. code:: ipython3 @@ -1573,12 +1200,12 @@ atleast two trial steps to complete the line search. .. code:: ipython3 - ! seisflows par stop_after finalize_iteration # We don't want to run the finalize() argument so that we can explore the dir + ! seisflows par stop_after perform_line_search # We don't want to run the finalize_iteration argument so that we can explore the dir .. parsed-literal:: - stop_after: evaluate_gradient_from_kernels -> finalize_iteration + stop_after: evaluate_gradient_from_kernels -> perform_line_search .. code:: ipython3 @@ -1588,136 +1215,131 @@ atleast two trial steps to complete the line search. .. parsed-literal:: - 2022-08-15 16:21:55 (D) | setting iteration==1 from state file - 2022-08-15 16:21:55 (I) | + 2022-08-16 14:41:12 (D) | setting iteration==1 from state file + 2022-08-16 14:41:12 (I) | ================================================================================ SETTING UP INVERSION WORKFLOW ================================================================================ - 2022-08-15 16:22:03 (D) | running setup for module 'system.Workstation' - 2022-08-15 16:22:05 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/sflog_003.txt - 2022-08-15 16:22:05 (D) | copying par/log file to: /Users/Chow/Work/work/sf_specfem2d_example/logs/parameters_003.yaml - 2022-08-15 16:22:05 (D) | running setup for module 'solver.Specfem2D' - 2022-08-15 16:22:05 (I) | initializing 3 solver directories - 2022-08-15 16:22:07 (D) | running setup for module 'preprocess.Default' - 2022-08-15 16:22:08 (D) | running setup for module 'optimize.Gradient' - 2022-08-15 16:22:09 (I) | re-loading optimization module from checkpoint - 2022-08-15 16:22:11 (I) | re-loading optimization module from checkpoint - 2022-08-15 16:22:11 (I) | + 2022-08-16 14:41:18 (D) | running setup for module 'system.Workstation' + 2022-08-16 14:41:21 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/sflog_003.txt + 2022-08-16 14:41:21 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/parameters_003.yaml + 2022-08-16 14:41:21 (D) | running setup for module 'solver.Specfem2D' + 2022-08-16 14:41:21 (I) | initializing 3 solver directories + 2022-08-16 14:41:22 (D) | running setup for module 'preprocess.Default' + 2022-08-16 14:41:24 (D) | running setup for module 'optimize.Gradient' + 2022-08-16 14:41:26 (I) | re-loading optimization module from checkpoint + 2022-08-16 14:41:28 (I) | re-loading optimization module from checkpoint + 2022-08-16 14:41:28 (I) | //////////////////////////////////////////////////////////////////////////////// RUNNING ITERATION 01 //////////////////////////////////////////////////////////////////////////////// - 2022-08-15 16:22:11 (I) | + 2022-08-16 14:41:28 (I) | ================================================================================ RUNNING INVERSION WORKFLOW ================================================================================ - 2022-08-15 16:22:11 (I) | 'evaluate_initial_misfit' has already been run, skipping - 2022-08-15 16:22:11 (I) | 'run_adjoint_simulations' has already been run, skipping - 2022-08-15 16:22:11 (I) | 'postprocess_event_kernels' has already been run, skipping - 2022-08-15 16:22:11 (I) | 'evaluate_gradient_from_kernels' has already been run, skipping - 2022-08-15 16:22:11 (I) | initializing 'bracket'ing line search - 2022-08-15 16:22:11 (I) | enforcing max step length safeguard - 2022-08-15 16:22:11 (D) | step length(s) = 0.00E+00 - 2022-08-15 16:22:11 (D) | misfit val(s) = 1.28E-03 - 2022-08-15 16:22:11 (I) | try: first evaluation, attempt guess step length, alpha=9.08E+11 - 2022-08-15 16:22:11 (I) | try: applying initial step length safegaurd as alpha has exceeded maximum step length, alpha_new=1.44E+10 - 2022-08-15 16:22:11 (D) | overwriting initial step length, alpha_new=2.32E+09 - 2022-08-15 16:22:11 (I) | trial model 'm_try' parameters: - 2022-08-15 16:22:11 (I) | 5800.00 <= vp <= 5800.00 - 2022-08-15 16:22:11 (I) | 3244.51 <= vs <= 3790.00 - 2022-08-15 16:22:12 (I) | + 2022-08-16 14:41:28 (I) | 'evaluate_initial_misfit' has already been run, skipping + 2022-08-16 14:41:28 (I) | 'run_adjoint_simulations' has already been run, skipping + 2022-08-16 14:41:28 (I) | 'postprocess_event_kernels' has already been run, skipping + 2022-08-16 14:41:28 (I) | 'evaluate_gradient_from_kernels' has already been run, skipping + 2022-08-16 14:41:28 (I) | initializing 'bracket'ing line search + 2022-08-16 14:41:28 (I) | enforcing max step length safeguard + 2022-08-16 14:41:28 (D) | step length(s) = 0.00E+00 + 2022-08-16 14:41:28 (D) | misfit val(s) = 1.28E-03 + 2022-08-16 14:41:28 (I) | try: first evaluation, attempt guess step length, alpha=9.08E+11 + 2022-08-16 14:41:28 (I) | try: applying initial step length safegaurd as alpha has exceeded maximum step length, alpha_new=1.44E+10 + 2022-08-16 14:41:28 (D) | overwriting initial step length, alpha_new=2.32E+09 + 2022-08-16 14:41:28 (I) | trial model 'm_try' parameters: + 2022-08-16 14:41:28 (I) | 5800.00 <= vp <= 5800.00 + 2022-08-16 14:41:28 (I) | 3244.51 <= vs <= 3790.00 + 2022-08-16 14:41:29 (I) | LINE SEARCH STEP COUNT 01 -------------------------------------------------------------------------------- - 2022-08-15 16:22:12 (I) | evaluating objective function for source 001 - 2022-08-15 16:22:12 (D) | running forward simulation with 'Specfem2D' - 2022-08-15 16:22:23 (D) | quantifying misfit with 'Default' - 2022-08-15 16:22:23 (I) | evaluating objective function for source 002 - 2022-08-15 16:22:23 (D) | running forward simulation with 'Specfem2D' - 2022-08-15 16:22:35 (D) | quantifying misfit with 'Default' - 2022-08-15 16:22:35 (I) | evaluating objective function for source 003 - 2022-08-15 16:22:35 (D) | running forward simulation with 'Specfem2D' - 2022-08-15 16:22:48 (D) | quantifying misfit with 'Default' - 2022-08-15 16:22:48 (D) | misfit for trial model (f_try) == 8.65E-04 - 2022-08-15 16:22:48 (D) | step length(s) = 0.00E+00, 2.32E+09 - 2022-08-15 16:22:48 (D) | misfit val(s) = 1.28E-03, 8.65E-04 - 2022-08-15 16:22:48 (I) | try: misfit not bracketed, increasing step length using golden ratio, alpha=3.76E+09 - 2022-08-15 16:22:49 (I) | line search model 'm_try' parameters: - 2022-08-15 16:22:49 (I) | 5800.00 <= vp <= 5800.00 - 2022-08-15 16:22:49 (I) | 3086.61 <= vs <= 3969.23 - 2022-08-15 16:22:49 (I) | trial step unsuccessful. re-attempting line search - 2022-08-15 16:22:49 (I) | + 2022-08-16 14:41:29 (I) | evaluating objective function for source 001 + 2022-08-16 14:41:29 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:41:33 (D) | quantifying misfit with 'Default' + 2022-08-16 14:41:33 (I) | evaluating objective function for source 002 + 2022-08-16 14:41:33 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:41:36 (D) | quantifying misfit with 'Default' + 2022-08-16 14:41:36 (I) | evaluating objective function for source 003 + 2022-08-16 14:41:36 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:41:40 (D) | quantifying misfit with 'Default' + 2022-08-16 14:41:40 (D) | misfit for trial model (f_try) == 8.65E-04 + 2022-08-16 14:41:40 (D) | step length(s) = 0.00E+00, 2.32E+09 + 2022-08-16 14:41:40 (D) | misfit val(s) = 1.28E-03, 8.65E-04 + 2022-08-16 14:41:40 (I) | try: misfit not bracketed, increasing step length using golden ratio, alpha=3.76E+09 + 2022-08-16 14:41:40 (I) | line search model 'm_try' parameters: + 2022-08-16 14:41:40 (I) | 5800.00 <= vp <= 5800.00 + 2022-08-16 14:41:40 (I) | 3086.61 <= vs <= 3969.23 + 2022-08-16 14:41:40 (I) | trial step unsuccessful. re-attempting line search + 2022-08-16 14:41:40 (I) | LINE SEARCH STEP COUNT 02 -------------------------------------------------------------------------------- - 2022-08-15 16:22:49 (I) | evaluating objective function for source 001 - 2022-08-15 16:22:49 (D) | running forward simulation with 'Specfem2D' - 2022-08-15 16:23:01 (D) | quantifying misfit with 'Default' - 2022-08-15 16:23:01 (I) | evaluating objective function for source 002 - 2022-08-15 16:23:01 (D) | running forward simulation with 'Specfem2D' - 2022-08-15 16:23:13 (D) | quantifying misfit with 'Default' - 2022-08-15 16:23:13 (I) | evaluating objective function for source 003 - 2022-08-15 16:23:13 (D) | running forward simulation with 'Specfem2D' - 2022-08-15 16:23:25 (D) | quantifying misfit with 'Default' - 2022-08-15 16:23:25 (D) | misfit for trial model (f_try) == 1.73E-03 - 2022-08-15 16:23:25 (D) | step length(s) = 0.00E+00, 2.32E+09, 3.76E+09 - 2022-08-15 16:23:25 (D) | misfit val(s) = 1.28E-03, 8.65E-04, 1.73E-03 - 2022-08-15 16:23:25 (I) | try: bracket acceptable but step length unreasonable attempting to re-adjust step length alpha=1.59E+09 - 2022-08-15 16:23:25 (I) | line search model 'm_try' parameters: - 2022-08-15 16:23:25 (I) | 5800.00 <= vp <= 5800.00 - 2022-08-15 16:23:25 (I) | 3325.01 <= vs <= 3698.63 - 2022-08-15 16:23:25 (I) | trial step unsuccessful. re-attempting line search - 2022-08-15 16:23:25 (I) | + 2022-08-16 14:41:40 (I) | evaluating objective function for source 001 + 2022-08-16 14:41:40 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:41:44 (D) | quantifying misfit with 'Default' + 2022-08-16 14:41:44 (I) | evaluating objective function for source 002 + 2022-08-16 14:41:44 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:41:48 (D) | quantifying misfit with 'Default' + 2022-08-16 14:41:48 (I) | evaluating objective function for source 003 + 2022-08-16 14:41:48 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:41:52 (D) | quantifying misfit with 'Default' + 2022-08-16 14:41:52 (D) | misfit for trial model (f_try) == 1.73E-03 + 2022-08-16 14:41:52 (D) | step length(s) = 0.00E+00, 2.32E+09, 3.76E+09 + 2022-08-16 14:41:52 (D) | misfit val(s) = 1.28E-03, 8.65E-04, 1.73E-03 + 2022-08-16 14:41:52 (I) | try: bracket acceptable but step length unreasonable attempting to re-adjust step length alpha=1.59E+09 + 2022-08-16 14:41:52 (I) | line search model 'm_try' parameters: + 2022-08-16 14:41:52 (I) | 5800.00 <= vp <= 5800.00 + 2022-08-16 14:41:52 (I) | 3325.01 <= vs <= 3698.63 + 2022-08-16 14:41:52 (I) | trial step unsuccessful. re-attempting line search + 2022-08-16 14:41:52 (I) | LINE SEARCH STEP COUNT 03 -------------------------------------------------------------------------------- - 2022-08-15 16:23:25 (I) | evaluating objective function for source 001 - 2022-08-15 16:23:25 (D) | running forward simulation with 'Specfem2D' - 2022-08-15 16:23:37 (D) | quantifying misfit with 'Default' - 2022-08-15 16:23:37 (I) | evaluating objective function for source 002 - 2022-08-15 16:23:37 (D) | running forward simulation with 'Specfem2D' - 2022-08-15 16:23:51 (D) | quantifying misfit with 'Default' - 2022-08-15 16:23:51 (I) | evaluating objective function for source 003 - 2022-08-15 16:23:51 (D) | running forward simulation with 'Specfem2D' - 2022-08-15 16:24:03 (D) | quantifying misfit with 'Default' - 2022-08-15 16:24:04 (D) | misfit for trial model (f_try) == 2.59E-03 - 2022-08-15 16:24:04 (D) | step length(s) = 0.00E+00, 1.59E+09, 2.32E+09, 3.76E+09 - 2022-08-15 16:24:04 (D) | misfit val(s) = 1.28E-03, 2.59E-03, 8.65E-04, 1.73E-03 - 2022-08-15 16:24:04 (I) | try: bracket acceptable but step length unreasonable attempting to re-adjust step length alpha=2.82E+09 - 2022-08-15 16:24:04 (I) | line search model 'm_try' parameters: - 2022-08-15 16:24:04 (I) | 5800.00 <= vp <= 5800.00 - 2022-08-15 16:24:04 (I) | 3189.77 <= vs <= 3852.13 - 2022-08-15 16:24:04 (I) | trial step unsuccessful. re-attempting line search - 2022-08-15 16:24:04 (I) | + 2022-08-16 14:41:52 (I) | evaluating objective function for source 001 + 2022-08-16 14:41:52 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:41:56 (D) | quantifying misfit with 'Default' + 2022-08-16 14:41:56 (I) | evaluating objective function for source 002 + 2022-08-16 14:41:56 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:42:00 (D) | quantifying misfit with 'Default' + 2022-08-16 14:42:00 (I) | evaluating objective function for source 003 + 2022-08-16 14:42:00 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:42:03 (D) | quantifying misfit with 'Default' + 2022-08-16 14:42:03 (D) | misfit for trial model (f_try) == 2.59E-03 + 2022-08-16 14:42:03 (D) | step length(s) = 0.00E+00, 1.59E+09, 2.32E+09, 3.76E+09 + 2022-08-16 14:42:03 (D) | misfit val(s) = 1.28E-03, 2.59E-03, 8.65E-04, 1.73E-03 + 2022-08-16 14:42:03 (I) | try: bracket acceptable but step length unreasonable attempting to re-adjust step length alpha=2.82E+09 + 2022-08-16 14:42:03 (I) | line search model 'm_try' parameters: + 2022-08-16 14:42:03 (I) | 5800.00 <= vp <= 5800.00 + 2022-08-16 14:42:03 (I) | 3189.77 <= vs <= 3852.13 + 2022-08-16 14:42:03 (I) | trial step unsuccessful. re-attempting line search + 2022-08-16 14:42:03 (I) | LINE SEARCH STEP COUNT 04 -------------------------------------------------------------------------------- - 2022-08-15 16:24:04 (I) | evaluating objective function for source 001 - 2022-08-15 16:24:04 (D) | running forward simulation with 'Specfem2D' - 2022-08-15 16:24:15 (D) | quantifying misfit with 'Default' - 2022-08-15 16:24:15 (I) | evaluating objective function for source 002 - 2022-08-15 16:24:15 (D) | running forward simulation with 'Specfem2D' - 2022-08-15 16:24:27 (D) | quantifying misfit with 'Default' - 2022-08-15 16:24:27 (I) | evaluating objective function for source 003 - 2022-08-15 16:24:27 (D) | running forward simulation with 'Specfem2D' - 2022-08-15 16:24:39 (D) | quantifying misfit with 'Default' - 2022-08-15 16:24:39 (D) | misfit for trial model (f_try) == 3.46E-03 - 2022-08-15 16:24:39 (D) | step length(s) = 0.00E+00, 1.59E+09, 2.32E+09, 2.82E+09, 3.76E+09 - 2022-08-15 16:24:39 (D) | misfit val(s) = 1.28E-03, 2.59E-03, 8.65E-04, 3.46E-03, 1.73E-03 - 2022-08-15 16:24:39 (I) | pass: bracket acceptable and step length reasonable. returning minimum line search misfit. - 2022-08-15 16:24:39 (I) | line search model 'm_try' parameters: - 2022-08-15 16:24:39 (I) | 5800.00 <= vp <= 5800.00 - 2022-08-15 16:24:39 (I) | 3244.51 <= vs <= 3790.00 - 2022-08-15 16:24:39 (I) | trial step successful. finalizing line search - 2022-08-15 16:24:39 (I) | + 2022-08-16 14:42:03 (I) | evaluating objective function for source 001 + 2022-08-16 14:42:03 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:42:07 (D) | quantifying misfit with 'Default' + 2022-08-16 14:42:07 (I) | evaluating objective function for source 002 + 2022-08-16 14:42:07 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:42:11 (D) | quantifying misfit with 'Default' + 2022-08-16 14:42:11 (I) | evaluating objective function for source 003 + 2022-08-16 14:42:11 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:42:15 (D) | quantifying misfit with 'Default' + 2022-08-16 14:42:15 (D) | misfit for trial model (f_try) == 3.46E-03 + 2022-08-16 14:42:15 (D) | step length(s) = 0.00E+00, 1.59E+09, 2.32E+09, 2.82E+09, 3.76E+09 + 2022-08-16 14:42:15 (D) | misfit val(s) = 1.28E-03, 2.59E-03, 8.65E-04, 3.46E-03, 1.73E-03 + 2022-08-16 14:42:15 (I) | pass: bracket acceptable and step length reasonable. returning minimum line search misfit. + 2022-08-16 14:42:15 (I) | line search model 'm_try' parameters: + 2022-08-16 14:42:15 (I) | 5800.00 <= vp <= 5800.00 + 2022-08-16 14:42:15 (I) | 3244.51 <= vs <= 3790.00 + 2022-08-16 14:42:15 (I) | trial step successful. finalizing line search + 2022-08-16 14:42:15 (I) | FINALIZING LINE SEARCH -------------------------------------------------------------------------------- - 2022-08-15 16:24:39 (I) | writing optimization stats - 2022-08-15 16:24:39 (I) | renaming current (new) optimization vectors as previous model (old) - 2022-08-15 16:24:39 (I) | setting accepted trial model (try) as current model (new) - 2022-08-15 16:24:39 (I) | misfit of accepted trial model is f=8.645E-04 - 2022-08-15 16:24:39 (I) | resetting line search step count to 0 - 2022-08-15 16:24:39 (I) | - //////////////////////////////////////////////////////////////////////////////// - CLEANING WORKDIR FOR NEXT ITERATION - //////////////////////////////////////////////////////////////////////////////// - 2022-08-15 16:24:41 (I) | thrifty inversion encountering first iteration, defaulting to standard inversion workflow - 2022-08-15 16:24:42 (I) | stop workflow at `stop_after`: finalize_iteration + 2022-08-16 14:42:15 (I) | writing optimization stats + 2022-08-16 14:42:15 (I) | renaming current (new) optimization vectors as previous model (old) + 2022-08-16 14:42:15 (I) | setting accepted trial model (try) as current model (new) + 2022-08-16 14:42:15 (I) | misfit of accepted trial model is f=8.645E-04 + 2022-08-16 14:42:15 (I) | resetting line search step count to 0 + 2022-08-16 14:42:15 (I) | stop workflow at `stop_after`: perform_line_search From the log statements above, we can see that the SeisFlows line search @@ -1735,9 +1357,8 @@ of the line search made preparations for a subsequent iteration. .. parsed-literal:: - alpha.txt f_old.txt m_new.npz p_old.npz - checkpoint.npz f_try.txt m_old.npz - f_new.txt g_old.npz output_optim.txt + alpha.txt f_new.txt f_try.txt m_new.npz output_optim.txt + checkpoint.npz f_old.txt g_old.npz m_old.npz p_old.npz .. code:: ipython3 diff --git a/docs/working_directory.rst b/docs/working_directory.rst index 7e67ea05..341e7036 100644 --- a/docs/working_directory.rst +++ b/docs/working_directory.rst @@ -1,24 +1,23 @@ Working Directory Structure =========================== -SeisFlows hardcodes it’s own working directory when executing a -workflow. Below we explore the working directory set up by the -SPECFEM2D-workstation example. Working directories may change slightly -depending on the chosen workflow, but will more or less follow the -following structure. The two specfem2d directories listed below are not -part of the SeisFlows working directory. +SeisFlows sets it's own working directory when executing a workflow. Below we explore the working directory set up by the `SPECFEM2D-workstation example `__. Working directories may change slightly depending on the chosen workflow, but will more or less follow the same structure. + + **NOTE**: The two SPECFEM2D directories listed below (specfem2d/ & + specfem2d_workdir/) are not part of a standard SeisFlows working + directory. .. code:: ipython3 - %cd ~/Work/official/workshop_pyatoa_sf/ex1_specfem2d_workstation + %cd /home/bchow/Work/scratch ! ls .. parsed-literal:: - /home/bchow/Work/official/workshop_pyatoa_sf/ex1_specfem2d_workstation - logs output_sf.txt scratch stats - output parameters.yaml specfem2d_workdir + /home/bchow/Work/scratch + logs parameters.yaml sflog.txt specfem2d + output scratch sfstate.txt specfem2d_workdir -------------- @@ -26,11 +25,11 @@ part of the SeisFlows working directory. scratch/ -------- -The active working directory of SeisFlows where all of the heavy -lifting takes place. Each module in the SeisFlows package may have it’s -own sub-directory where it stores temporary work data. Additionally, we -have two eval*/ directories where objective function evaluation -(evalfunc) and gradient evaluation (evalgrad) files are stored. +The active working directory of SeisFlows where all of the heavy lifting +takes place. Each module in the SeisFlows package may have it’s own +sub-directory where it stores temporary work data. Additionally, we have +two eval*/ directories where objective function evaluation (eval_func) +and gradient evaluation (eval_grad) files are stored. .. code:: ipython3 @@ -39,7 +38,7 @@ have two eval*/ directories where objective function evaluation .. parsed-literal:: - evalfunc evalgrad optimize preprocess solver system + eval_func eval_grad optimize preprocess solver system .. warning:: @@ -62,7 +61,7 @@ contain all the necessary files to run SPECFEM binaries within). .. parsed-literal:: - 001 002 003 mainsolver + 001 002 003 mainsolver .. code:: ipython3 @@ -72,7 +71,9 @@ contain all the necessary files to run SPECFEM binaries within). .. parsed-literal:: - bin DATA kernel_paths mesher.log OUTPUT_FILES SEM solver.log traces + adj_solver.log combine_vs.log fwd_solver.log SEM + bin DATA kernel_paths traces + combine_vp.log fwd_mesher.log OUTPUT_FILES .. warning:: @@ -91,7 +92,7 @@ into observed (obs), synthetic (syn) and adjoint (adj) waveforms. .. parsed-literal:: - adj obs syn + adj obs syn .. code:: ipython3 @@ -101,7 +102,7 @@ into observed (obs), synthetic (syn) and adjoint (adj) waveforms. .. parsed-literal:: - AA.S0001.BXY.semd + AA.S0001.BXY.semd .. code:: ipython3 @@ -112,16 +113,16 @@ into observed (obs), synthetic (syn) and adjoint (adj) waveforms. .. parsed-literal:: - 251.39999999999998 -1.1814422395268879E-005 - 251.45999999999998 -1.1800275583562581E-005 - 251.51999999999998 -1.1769315129746346E-005 - 251.57999999999998 -1.1721248953632887E-005 - 251.63999999999999 -1.1655830825336088E-005 - 251.69999999999999 -1.1572872866742356E-005 - 251.75999999999999 -1.1472248505521453E-005 - 251.81999999999999 -1.1353902449899163E-005 - 251.88000000000000 -1.1217847351013855E-005 - 251.94000000000000 -1.1064166223014224E-005 + 251.39999999999998 -1.1814422395268879E-005 + 251.45999999999998 -1.1800275583562581E-005 + 251.51999999999998 -1.1769315129746346E-005 + 251.57999999999998 -1.1721248953632887E-005 + 251.63999999999999 -1.1655830825336088E-005 + 251.69999999999999 -1.1572872866742356E-005 + 251.75999999999999 -1.1472248505521453E-005 + 251.81999999999999 -1.1353902449899163E-005 + 251.88000000000000 -1.1217847351013855E-005 + 251.94000000000000 -1.1064166223014224E-005 optimize/ @@ -153,21 +154,21 @@ Optimization Variable Names are described as: .. parsed-literal:: - alpha.npy f_old.txt g_old.npy m_new.npy p_old.npy - f_new.txt f_try.txt LBFGS m_old.npy + alpha.txt f_new.txt f_try.txt m_new.npz output_optim.txt + checkpoint.npz f_old.txt g_old.npz m_old.npz p_old.npz .. code:: ipython3 import numpy as np - m_new = np.load("scratch/optimize/m_new.npy") - print(m_new) + m_new = np.load("scratch/optimize/m_new.npz") + print(m_new["vs"]) .. parsed-literal:: - [5800. 5800. 5800. ... 3499.77655379 3499.9021825 - 3499.99078301] + [[3500.0027437 3499.99441921 3499.90777902 ... 3499.77655378 + 3499.9021825 3499.99078301]] .. code:: ipython3 @@ -177,73 +178,92 @@ Optimization Variable Names are described as: .. parsed-literal:: - 2.591424e-03 - + 8.645199999999999153e-04 -evalfunc/ & evalgrad/ -~~~~~~~~~~~~~~~~~~~~~ -Scratch directories containing objective function evaluation and -gradient evaluation files. These include (1) the current **model** being -used for misfit evaluation, and (2) **residuals** which define the -misfit for each event. **evalgrad/** also contains **kernels** which -define per-event kernels which are summed and manipulated with the -postprocess module. +The ‘checkpoint.npz’ file contains information about the state of the +line search (controlled by the Optimization module). It is used to +resume failed or stopped line searches with minimal redundant use of +computational resources. .. code:: ipython3 - ! ls scratch/evalfunc - ! echo - ! ls scratch/evalgrad + line_search = np.load("scratch/optimize/checkpoint.npz") + + print(vars(line_search)["files"]) + + print("step count: ", line_search["step_count"]) + print("step lengths: ", line_search["step_lens"]) + print("misfit: ", line_search["func_vals"]) .. parsed-literal:: - model residuals - - kernels model residuals + ['restarted', 'func_vals', 'step_lens', 'gtg', 'gtp', 'step_count'] + step count: 0 + step lengths: [0.00000000e+00 2.32268310e+09 3.75818023e+09 1.59087505e+09 + 2.82031810e+09] + misfit: [0.00127902 0.00086452 0.00172904 0.00259356 0.00345808] + +eval_func/ & eval_grad/ +~~~~~~~~~~~~~~~~~~~~~~~ + +Scratch directories containing objective function evaluation and +gradient evaluation files. These include (1) the current **model** being +used for misfit evaluation, and (2) a **residual** file which defines +the misfit for each event. **eval_grad/** also contains **kernels** +which define per-event kernels which are summed and manipulated with the +postprocess module. .. code:: ipython3 - ! ls scratch/evalgrad/residuals + ! ls scratch/eval_func + ! echo + ! ls scratch/eval_grad .. parsed-literal:: - 001 002 003 + model residuals.txt + + gradient kernels misfit_kernel model residuals.txt .. code:: ipython3 - ! cat scratch/evalgrad/residuals/001 + ! cat scratch/eval_grad/residuals.txt .. parsed-literal:: - 2.413801941841247842e-02 - 2.413801941841247842e-02 - 2.413801941841247842e-02 + 2.41E-02 + 2.14E-02 + 1.55E-02 .. code:: ipython3 - ! ls scratch/evalgrad/kernels + ! ls scratch/eval_grad/kernels .. parsed-literal:: - 001 002 003 sum + 001 002 003 .. code:: ipython3 - ! ls scratch/evalgrad/kernels/sum + ! ls scratch/eval_grad/kernels/001 .. parsed-literal:: - proc000000_vp_kernel.bin proc000000_vs_kernel.bin + proc000000_bulk_beta_kernel.bin proc000000_rhop_kernel.bin + proc000000_bulk_c_kernel.bin proc000000_vp_kernel.bin + proc000000_kappa_kernel.bin proc000000_vs_kernel.bin + proc000000_mu_kernel.bin proc000000_weights_kernel.bin + proc000000_rho_kernel.bin system & preprocess @@ -253,24 +273,22 @@ These two directories are empty in our example problem, but are catch-all directories where module-specific files can be output. If you are extending SeisFlows with other base or subclasses, it is preferable to adhere to this structure where each module only interacts with it’s -own directory - -.. code:: ipython3 +own directory. - ! ls scratch/system - ! ls scratch/preprocess +When ``Pyaflowa`` is chosen as the preprocess module, it stores figures, +log files, and data (in ASDFDataSets) within its scratch directory. It +also specifies parameters for exporting these scratch files to disk for +more permanent storage. -------------- output/ ------- -The current active state of SeisFlows, containing pickle (.p) and JSON -files which describe a Python environment of a current workflow. -Additionally files to be permanently saved (e.g., models, graidents, -traces) can be located here. These are tagged in ascending order, e.g., -model_0001 refers to the updated model derived during the first -iteration. +Output files to be permanently saved (e.g., models, graidents, traces) +can be located in this directory. These are tagged in ascending order. +Because we did not run the finalization task in our SPECFEM2D problem, +the output directory only contains our initial model. .. code:: ipython3 @@ -279,31 +297,17 @@ iteration. .. parsed-literal:: - gradient_0001 seisflows_optimize.p seisflows_solver.p - kwargs seisflows_parameters.json seisflows_system.p - model_0001 seisflows_paths.json seisflows_workflow.p - model_init seisflows_postprocess.p - model_true seisflows_preprocess.p - - -.. code:: ipython3 - - ! ls output/model_0001 - - -.. parsed-literal:: - - proc000000_vp.bin proc000000_vs.bin + MODEL_INIT .. code:: ipython3 - ! ls output/gradient_0001 + ! ls output/MODEL_INIT .. parsed-literal:: - proc000000_vp_kernel.bin proc000000_vs_kernel.bin + proc000000_vp.bin proc000000_vs.bin -------------- @@ -323,106 +327,119 @@ other directory) copies are saved to this directory. .. parsed-literal:: - output_sf_001.txt parameters_001.yaml + 0001_00.log 0002_02.log 0004_01.log 0006_00.log parameters_002.yaml + 0001_01.log 0003_00.log 0004_02.log 0006_01.log parameters_003.yaml + 0001_02.log 0003_01.log 0005_00.log 0006_02.log sflog_001.txt + 0002_00.log 0003_02.log 0005_01.log 0007_00.log sflog_002.txt + 0002_01.log 0004_00.log 0005_02.log parameters_001.yaml sflog_003.txt -------------- -stats/ ------- - -Text files describing the optimization statistics of the current -workflow. This directory is only relevant if you are running an -inversion workflow. - -.. code:: ipython3 - - ! ls stats - - -.. parsed-literal:: - - factor.txt line_search.txt slope.txt theta.txt - gradient_norm_L1.txt misfit.txt step_count.txt - gradient_norm_L2.txt restarted.txt step_length.txt +sflog.txt +--------- +The main log file for SeisFlows, where all log statements written to +stdout are recorded during a workflow. Allows a user to come back to a +workflow and understand the tasks completed and any important +information collected during the workflow .. code:: ipython3 - ! cat stats/step_count.txt + ! head -50 sflog.txt .. parsed-literal:: - ITER STEP_COUNT - ==== ================== - 1 0.000000E+00 - 1 2.000000E+00 + 2022-08-16 14:32:48 (I) | + ================================================================================ + SETTING UP INVERSION WORKFLOW + ================================================================================ + 2022-08-16 14:32:55 (D) | running setup for module 'system.Workstation' + 2022-08-16 14:32:57 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/sflog_001.txt + 2022-08-16 14:32:57 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/parameters_001.yaml + 2022-08-16 14:32:57 (D) | running setup for module 'solver.Specfem2D' + 2022-08-16 14:32:57 (I) | initializing 3 solver directories + 2022-08-16 14:32:57 (D) | initializing solver directory source: 001 + 2022-08-16 14:33:04 (D) | linking source '001' as 'mainsolver' + 2022-08-16 14:33:04 (D) | initializing solver directory source: 002 + 2022-08-16 14:33:09 (D) | initializing solver directory source: 003 + 2022-08-16 14:33:16 (D) | running setup for module 'preprocess.Default' + 2022-08-16 14:33:16 (D) | running setup for module 'optimize.Gradient' + 2022-08-16 14:33:17 (I) | no optimization checkpoint found, assuming first run + 2022-08-16 14:33:17 (I) | re-loading optimization module from checkpoint + 2022-08-16 14:33:17 (I) | + //////////////////////////////////////////////////////////////////////////////// + RUNNING ITERATION 01 + //////////////////////////////////////////////////////////////////////////////// + 2022-08-16 14:33:17 (I) | + ================================================================================ + RUNNING INVERSION WORKFLOW + ================================================================================ + 2022-08-16 14:33:17 (I) | + //////////////////////////////////////////////////////////////////////////////// + EVALUATING MISFIT FOR INITIAL MODEL + //////////////////////////////////////////////////////////////////////////////// + 2022-08-16 14:33:17 (I) | checking initial model parameters + 2022-08-16 14:33:17 (I) | 5800.00 <= vp <= 5800.00 + 2022-08-16 14:33:17 (I) | 2600.00 <= rho <= 2600.00 + 2022-08-16 14:33:17 (I) | 3500.00 <= vs <= 3500.00 + 2022-08-16 14:33:17 (I) | checking true/target model parameters + 2022-08-16 14:33:17 (I) | 5900.00 <= vp <= 5900.00 + 2022-08-16 14:33:17 (I) | 2600.00 <= rho <= 2600.00 + 2022-08-16 14:33:17 (I) | 3550.00 <= vs <= 3550.00 + 2022-08-16 14:33:17 (I) | preparing observation data for source 001 + 2022-08-16 14:33:17 (I) | running forward simulation w/ target model for 001 + 2022-08-16 14:33:21 (I) | evaluating objective function for source 001 + 2022-08-16 14:33:21 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:33:25 (D) | quantifying misfit with 'Default' + 2022-08-16 14:33:25 (I) | preparing observation data for source 002 + 2022-08-16 14:33:25 (I) | running forward simulation w/ target model for 002 + 2022-08-16 14:33:29 (I) | evaluating objective function for source 002 + 2022-08-16 14:33:29 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:33:33 (D) | quantifying misfit with 'Default' + 2022-08-16 14:33:33 (I) | preparing observation data for source 003 + 2022-08-16 14:33:33 (I) | running forward simulation w/ target model for 003 + 2022-08-16 14:33:36 (I) | evaluating objective function for source 003 -------------- -output_sf.txt --------------- +sfstate.txt +----------- -The main log file for SeisFlows, where all log statements written to -stdout are recorded during a workflow. +A state file which tracks the progress of a workflow, allowing the User +to quickly resumed stopped or failed workflows without wasting +computational resources. The State file simply contains the names of +functions contained in the Workflow task list, as well as their +respective status, which can be ‘completed’, ‘failed’, or not available. .. code:: ipython3 - ! head -50 output_sf.txt + ! cat sfstate.txt .. parsed-literal:: - 2022-04-29 16:45:35 | initializing SeisFlows in sys.modules - 2022-04-29 16:45:39 | copying par/log file to: /home/bchow/Work/official/workshop_pyatoa_sf/ex1_specfem2d_workstation/logs/output_sf_001.txt - 2022-04-29 16:45:39 | copying par/log file to: /home/bchow/Work/official/workshop_pyatoa_sf/ex1_specfem2d_workstation/logs/parameters_001.yaml - 2022-04-29 16:45:39 | exporting current working environment to disk - 2022-04-29 16:45:39 | - //////////////////////////////////////////////////////////////////////////////// - WORKFLOW WILL STOP AFTER FUNC: 'finalize' - //////////////////////////////////////////////////////////////////////////////// - 2022-04-29 16:45:39 | - ================================================================================ - STARTING INVERSION WORKFLOW - ================================================================================ - 2022-04-29 16:45:39 | - //////////////////////////////////////////////////////////////////////////////// - ITERATION 1 / 1 - //////////////////////////////////////////////////////////////////////////////// - 2022-04-29 16:45:39 | - //////////////////////////////////////////////////////////////////////////////// - PERFORMING MODULE SETUP - //////////////////////////////////////////////////////////////////////////////// - 2022-04-29 16:45:39 | misfit function is: 'waveform' - 2022-04-29 16:45:40 | writing line search history file: - /home/bchow/Work/official/workshop_pyatoa_sf/ex1_specfem2d_workstation/stats/line_search.txt - 2022-04-29 16:45:40 | checking poissons ratio for: 'm_new.npy' - 2022-04-29 16:45:40 | model parameters (m_new.npy i01s00): - 2022-04-29 16:45:40 | 5800.00 <= vp <= 5800.00 - 2022-04-29 16:45:40 | 3500.00 <= vs <= 3500.00 - 2022-04-29 16:45:40 | 0.21 <= pr <= 0.21 - 2022-04-29 16:45:41 | setting up solver on system... - 2022-04-29 16:45:41 | checkpointing working environment to disk - 2022-04-29 16:45:42 | exporting current working environment to disk - 2022-04-29 16:45:43 | running task solver.setup 3 times - 2022-04-29 16:45:43 | initializing 3 solver directories - 2022-04-29 16:45:50 | source 001 symlinked as mainsolver - 2022-04-29 16:45:50 | generating 'data' with MODEL_TRUE synthetics - 2022-04-29 16:45:57 | running mesh generation for MODEL_INIT - 2022-04-29 16:46:27 | - ================================================================================ - INITIALIZING INVERSION - ================================================================================ - 2022-04-29 16:46:27 | - EVALUATE OBJECTIVE FUNCTION - -------------------------------------------------------------------------------- - 2022-04-29 16:46:27 | saving model 'm_new.npy' to: - /home/bchow/Work/official/workshop_pyatoa_sf/ex1_specfem2d_workstation/scratch/evalgrad/model - 2022-04-29 16:46:28 | evaluating objective function 3 times on system... - 2022-04-29 16:46:28 | checkpointing working environment to disk - 2022-04-29 16:46:29 | exporting current working environment to disk - 2022-04-29 16:46:30 | running task solver.eval_func 3 times - 2022-04-29 16:46:30 | running forward simulations - + # SeisFlows State File + # Tue Aug 16 14:33:17 2022 + # Acceptable states: 'completed', 'failed' + # ======================================= + evaluate_initial_misfit: completed + run_adjoint_simulations: completed + postprocess_event_kernels: completed + evaluate_gradient_from_kernels: completed + initialize_line_search: completed + perform_line_search: completed + iteration: 1 + +When submitting a workflow with an existing state file, the workflow +will check the status of each function. ‘Completed’ functions will be +skipped over. ‘Failed’ functions will be re-run. Users can delete lines +from the state file or change status’ manually to re-run tasks within +the list, taking care about the current configuration of the working +directory, which is intrinsically tied to the task list. + +For ‘Inversion’ workflows, the current ‘Iteration’ is also saved, +meaning re-submitted workflows will start at the previously checkpointed +iteration. From 94b23d1fe3307d4ea19b3ca539b3e7c0ad0ca861 Mon Sep 17 00:00:00 2001 From: bch0w Date: Tue, 16 Aug 2022 15:33:29 -0800 Subject: [PATCH 113/195] updated parameter change in example to match density forced type bool --- .../examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py | 2 +- seisflows/examples/sfexample2d.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py b/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py index fa38d25c..f5bd3b51 100644 --- a/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py +++ b/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py @@ -91,7 +91,7 @@ def setup_seisflows_working_directory(self): self.sf.par("end", 1) # only 1 iteration self.sf.par("ntask", self.ntask) # 3 sources for this example self.sf.par("materials", "elastic") # how velocity model parameterized - self.sf.par("density", "constant") # update density or keep constant + self.sf.par("density", False) # update density or keep constant self.sf.par("data_format", "ascii") # output synthetic seismograms self.sf.par("data_case", "synthetic") # synthetic-synthetic inversion self.sf.par("attenuation", False) diff --git a/seisflows/examples/sfexample2d.py b/seisflows/examples/sfexample2d.py index 010b9630..2951cc8e 100644 --- a/seisflows/examples/sfexample2d.py +++ b/seisflows/examples/sfexample2d.py @@ -292,7 +292,7 @@ def setup_seisflows_working_directory(self): self.sf.par("ntask", self.ntask) # default 3 sources for this example self.sf.par("materials", "elastic") # how velocity model parameterized - self.sf.par("density", "constant") # update density or keep constant + self.sf.par("density", False) # update density or keep constant self.sf.par("data_format", "ascii") # how to output synthetic seismograms self.sf.par("start", 1) # first iteration self.sf.par("end", self.niter) # final iteration -- we will run 2 From 6276494660cea935d67dfe87bff5f2442bb29655 Mon Sep 17 00:00:00 2001 From: bch0w Date: Wed, 17 Aug 2022 12:15:58 -0800 Subject: [PATCH 114/195] created notebook for extending seisflows --- docs/extending.rst | 45 +++++++++---- docs/notebooks/extending.ipynb | 111 +++++++++++++++++++++++++++++++++ 2 files changed, 145 insertions(+), 11 deletions(-) create mode 100644 docs/notebooks/extending.ipynb diff --git a/docs/extending.rst b/docs/extending.rst index 5e92034d..65611c1e 100644 --- a/docs/extending.rst +++ b/docs/extending.rst @@ -1,18 +1,41 @@ Extending SeisFlows -================================== +=================== .. note:: Page Under Construction + +SeisFlows works on the object oriented programming principal of inheritance. +See the `inheritance `__ page for background. This allows +Users to extend the package to work with other systems and solvers, or modify +its behavior to suit the problem at hand. -The design philosophy of SeisFlows is such that being a user of the codebase -more-than-likely requires also becoming a developer, as custom-made subclasses -are often required to tailor the functionalities of SeisFlows to a specific -problem, compute system, dataset, etc. This page is intended to provide an -outline on how to successfully implement your own base- or subclasses within -the SeisFlows framework. +Overriding SeisFlows with your own subclass +------------------------------------------- -Writing your own Base class ---------------------------- +If the existing modules of SeisFlows do not suit your needs, you may +need to override them with your own sub classes. A common example of +when you might need to do this is to override the system subclasses to +tailor SeisFlows to your specific cluster. + +In this example SeisFlows already contains a Slurm system module, and +has overriding sub-classes for Slurm-derived systems including Chinook +(University of Alaska Fairbanks), Maui (New Zealand eScience +Infrastucture) and Frontera (Texas Advanced Computing Center). Any new +Slurm systems will need write new subclasses to take advantage of the +existing structure. + +Below we provide some examples of editing existing SeisFlows modules for +modified performance. + + +Writing your own Base class from scratch +---------------------------------------- + +Less likely, but also still a possibility, is that you will have to +write your own Base class which defines foundational capabilities. One +example of this is extending SeisFlows to interact with other numerical +solvers. Currently SeisFlows is set up to work with +SPECFEM2D/3D/3D_GLOBE. To allow Seisflows to work with other solvers, +one would need to write a new Base class that defines SeisFlows’ +interaction behavior with this solver. -Writing your own subclass -------------------------- diff --git a/docs/notebooks/extending.ipynb b/docs/notebooks/extending.ipynb new file mode 100644 index 00000000..7f5ec958 --- /dev/null +++ b/docs/notebooks/extending.ipynb @@ -0,0 +1,111 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "25c5246a", + "metadata": {}, + "source": [ + "# Extending SeisFlows" + ] + }, + { + "cell_type": "raw", + "id": "e38c3935", + "metadata": {}, + "source": [ + ".. note::\n", + " Page Under Construction\n", + " \n", + "SeisFlows works on the object oriented programming principal of inheritance.\n", + "See the `inheritance `__ page for background. This allows\n", + "Users to extend the package to work with other systems and solvers, or modify\n", + "its behavior to suit the problem at hand." + ] + }, + { + "cell_type": "markdown", + "id": "5f871efa", + "metadata": {}, + "source": [ + "## Overriding SeisFlows with your own subclass\n", + "\n", + "If the existing modules of SeisFlows do not suit your needs, you may need to override them with your own sub classes. A common example of when you might need to do this is to override the system subclasses to tailor SeisFlows to your specific cluster. \n", + "\n", + "In this example SeisFlows already contains a Slurm system module, and has overriding sub-classes for Slurm-derived systems including Chinook (University of Alaska Fairbanks), Maui (New Zealand eScience\n", + "Infrastucture) and Frontera (Texas Advanced Computing Center). Any new Slurm systems will need write new subclasses to take advantage of the existing structure.\n", + "\n", + "Below we provide some examples of editing existing SeisFlows modules for modified performance. We'll first start off with a modified Slurm system which defines SeisFlows interaction with Maui, a New Zealand cluster." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9ef90e2", + "metadata": {}, + "outputs": [], + "source": [ + "from seisflows.system.slurm import Slurm\n", + "\n", + "\n", + "class Maui(Slurm):\n", + " \"\"\"\n", + " System Maui\n", + " Slurm-based interaction with New Zealand HPC, Maui\n", + " \n", + " Parameters\n", + " ----------\n", + " \n", + " Paths\n", + " -----\n", + " \n", + " ***\n", + " \"\"\"\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "23c9d7c5", + "metadata": {}, + "source": [ + "## Writing your own Base class from scratch\n", + "\n", + "Less likely, but also still a possibility, is that you will have to write your\n", + "own Base class which defines foundational capabilities. One example of this is\n", + "extending SeisFlows to interact with other numerical solvers. Currently\n", + "SeisFlows is set up to work with SPECFEM2D/3D/3D_GLOBE. To allow Seisflows\n", + "to work with other solvers, one would need to write a new Base class that\n", + "defines SeisFlows' interaction behavior with this solver." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e0e7716", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 23ae5ac95be90b74ff96f84a81a7200c424186f1 Mon Sep 17 00:00:00 2001 From: bch0w Date: Wed, 17 Aug 2022 13:16:50 -0800 Subject: [PATCH 115/195] updated slurm system design such that run and submit headers are separated out as their own class properties. This allows child classes to overwrite the headers without having to repeat any of the boilerplate for run and submit functions Updated all slurm-derived child classes (maui, chinook, frontera) to match new Seisflows class boilerplate --- seisflows/system/chinook.py | 92 +++++++++---- seisflows/system/cluster.py | 75 +++++++---- seisflows/system/frontera.py | 182 ++++++++++--------------- seisflows/system/lsf.py | 7 +- seisflows/system/maui.py | 230 ++++++++++++++------------------ seisflows/system/slurm.py | 145 +++++++++++++------- seisflows/system/workstation.py | 8 +- 7 files changed, 391 insertions(+), 348 deletions(-) diff --git a/seisflows/system/chinook.py b/seisflows/system/chinook.py index f988f0e5..653f3801 100644 --- a/seisflows/system/chinook.py +++ b/seisflows/system/chinook.py @@ -8,49 +8,83 @@ Information on Chinook can be found here: https://uaf-rcs.gitbook.io/uaf-rcs-hpc-docs/hpc """ +import os from seisflows.system.slurm import Slurm class Chinook(Slurm): """ - System interface for the University of Alaska HPC Chinook, which operates - on a SLURM system. + System Chinook + -------------- + University of Alaska Fairbanks HPC Chinook, SLURM based system + + Parameters + ---------- + :type partition: str + :param partition: Chinook has various partitions which each have their + own number of cores per compute node. Available are: analysis, t1small, + t2small, t1standard, t2standard, gpu + + Paths + ----- + + *** """ - def __init__(self): - """ - These parameters should not be set by the user. - Attributes are initialized as NoneTypes for clarity and docstrings. + __doc__ = Slurm.__doc__ + __doc__ - :type partitions: dict - :param partitions: Chinook has various partitions which each have their - own number of cores per compute node, defined here - """ - super().__init__() - self.required.par( - "PARTITION", required=False, default="t1small", par_type=int, - docstr="Name of partition on main cluster, available: " - "analysis, t1small, t2small, t1standard, t2standard, gpu") + def __init__(self, partition="t1small", **kwargs): + """Chinook init""" + super().__init__(**kwargs) - self.required.par( - "MPIEXEC", required=False, default="srun", par_type=str, - docstr="Function used to invoke parallel executables") + self.partition = partition - self.partitions = {"debug": 24, "t1small": 28, "t2small": 28, + self._partitions = {"debug": 24, "t1small": 28, "t2small": 28, "t1standard": 40, "t2standard": 40, "analysis": 28 } - def check(self, validate=True): + @property + def submit_call_header(self): """ - Checks parameters and paths - """ - super().check(validate=validate) + The submit call defines the SBATCH header which is used to submit a + workflow task list to the system. It is usually dictated by the + system's required parameters, such as account names and partitions. + Submit calls are modified and called by the `submit` function. - assert(self.par.PARTITION in self.partitions.keys()), \ - f"Chinook partition must be in {self.partitions.keys()}" + :rtype: str + :return: the system-dependent portion of a submit call + """ + _call = " ".join([ + f"sbatch", + f"--job-name={self.title}", + f"--output={self.path.output_log}", + f"--error={self.path.error_log}", + f"--ntasks=1", + f"--partition={self.partition}", + f"--time={self.walltime:d}" + ]) + return _call - assert(self.par.NODESIZE == self.partitions[self.par.PARTITION]), \ - (f"PARTITION {self.par.PARTITION} is expected to have NODESIZE=" - f"{self.partitions[self.par.PARTITION]}, not current " - f"{self.par.NODESIZE}") + @property + def run_call_header(self): + """ + The run call defines the SBATCH header which is used to run tasks during + an executing workflow. Like the submit call its arguments are dictated + by the given system. Run calls are modified and called by the `run` + function + :rtype: str + :return: the system-dependent portion of a run call + """ + _call = " ".join([ + f"sbatch", + f"{self.slurm_args or ''}", + f"--job-name={self.title}", + f"--ntasks={self.nproc:d}", + f"--tasks-per-node={self.nodesize}", + f"--time={self.tasktime:d}", + f"--output={os.path.join(self.path.log_files, '%A_%a')}", + f"--array=0-{self.ntask-1 % self.ntask_max}", + f"--parsable" + ]) + return _call \ No newline at end of file diff --git a/seisflows/system/cluster.py b/seisflows/system/cluster.py index b1a73b9c..e8aa15f6 100644 --- a/seisflows/system/cluster.py +++ b/seisflows/system/cluster.py @@ -70,8 +70,41 @@ def __init__(self, title=None, mpiexec="", ntask_max=None, walltime=10, self.tasktime = tasktime self.environs = environs or "" - def submit(self, workdir=None, parameter_file="parameters.yaml", - submit_call=None): + @property + def submit_call_header(self): + """ + The submit call defines the SBATCH header which is used to submit a + workflow task list to the system. It is usually dictated by the + system's required parameters, such as account names and partitions. + Submit calls are modified and called by the `submit` function. + + .. note:: + Generalized `cluster` returns empty string but child system + classes will need to overwrite the submit call. + + :rtype: str + :return: the system-dependent portion of a submit call + """ + return "" + + @property + def run_call_header(self): + """ + The run call defines the SBATCH header which is used to run tasks during + an executing workflow. Like the submit call its arguments are dictated + by the given system. Run calls are modified and called by the `run` + function + + .. note:: + Generalized `cluster` returns empty string but child system + classes will need to overwrite the submit call. + + :rtype: str + :return: the system-dependent portion of a run call + """ + return "" + + def submit(self, workdir=None, parameter_file="parameters.yaml"): """ Submits the main workflow job as a separate job submitted directly to the system that is running the master job @@ -81,19 +114,15 @@ def submit(self, workdir=None, parameter_file="parameters.yaml", :type parameter_file: str :param parameter_file: paramter file file name used to instantiate the SeisFlows package - :type submit_call: str - :param submit_call: child classes may require a specific submit call - if the job should be submitted to another system (e.g., on cluster - submitting jobs on compute nodes and not running directly on the - login node) """ - if submit_call is None: - # e.g., submit -w ./ -p parameters.yaml - submit_call = " ".join([ - f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'submit')}", - f"--workdir {workdir}", - f"--parameter_file {parameter_file}", - ]) + # e.g., submit -w ./ -p parameters.yaml + submit_call = " ".join([ + f"{self.submit_call_header}", + f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'submit')}", + f"--workdir {workdir}", + f"--parameter_file {parameter_file}", + ]) + logger.debug(submit_call) try: subprocess.run(submit_call, shell=True) @@ -101,7 +130,7 @@ def submit(self, workdir=None, parameter_file="parameters.yaml", logger.critical(f"SeisFlows master job has failed with: {e}") sys.exit(-1) - def run(self, funcs, single=False, run_call=None, **kwargs): + def run(self, funcs, single=False, **kwargs): """ Runs tasks multiple times in parallel by submitting NTASK new jobs to system. The list of functions and its kwargs are saved as pickles files, @@ -132,14 +161,14 @@ def run(self, funcs, single=False, run_call=None, **kwargs): f"system {self.ntask} times") # Create the run call which will simply call an external Python script - if run_call is None: - # e.g., run --funcs func.p --kwargs kwargs.p --environment ... - run_call = " ".join([ - f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'run')}", - f"--funcs {funcs_fid}", - f"--kwargs {kwargs_fid}", - f"--environment SEISFLOWS_TASKID={{task_id}},{self.environs}" - ]) + # e.g., run --funcs func.p --kwargs kwargs.p --environment ... + run_call = " ".join([ + f"{self.run_call_header}", + f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'run')}", + f"--funcs {funcs_fid}", + f"--kwargs {kwargs_fid}", + f"--environment SEISFLOWS_TASKID={{task_id}},{self.environs}" + ]) logger.debug(run_call) # Don't need to spin up concurrent.futures for a single run diff --git a/seisflows/system/frontera.py b/seisflows/system/frontera.py index 303f613c..6f403c71 100644 --- a/seisflows/system/frontera.py +++ b/seisflows/system/frontera.py @@ -2,133 +2,93 @@ """ Frontera is one of the Texas Advanced Computing Center (TACC) HPCs. https://frontera-portal.tacc.utexas.edu/ - -TODO we may need to include or create a "singularity" class or run script which -runs jobs through singularity """ import os -import numpy as np -from seisflows.tools.config import ROOT_DIR from seisflows.system.slurm import Slurm class Frontera(Slurm): """ - System interface for TACC Frontera based on SLURM workload manager + System Frontera + -------------- + Texas Advanced Computing Center HPC Frontera, SLURM based system + + Parameters + ---------- + :type partition: str + :param partition: Chinook has various partitions which each have their + own number of cores per compute node. Available are: small, normal, + large, development, flex + :type allocation: str + :param allocation: Name of allocation/project on the Frontera system. + Required if you have more than one active allocation. + + Paths + ----- + + *** """ - def __init__(self): - """ - These parameters should not be set by the user. - Attributes are initialized as NoneTypes for clarity and docstrings. - - :type partitions: dict - :param partitions: Chinook has various partitions which each have their - own number of cores per compute node, defined here - """ - super().__init__() + def __init__(self, partition="small", allocation=None, **kwargs): + """Frontera init""" + super().__init__(**kwargs) - self.required.par( - "PARTITION", required=False, default="small", par_type=str, - docstr="Name of partition on main cluster" - ) - self.required.par( - "ALLOCATION", required=False, default="", par_type=str, - docstr="Name of allocation/project on the Frontera system. " - "Required if you have more than one active allocation." - ) - self.required.par( - "MPIEXEC", required=False, default="ibrun", par_type=str, - docstr="Function used to invoke parallel executables. Defaults to" - "'ibrun' based on TACC user manual.") + self.partition = partition + self.allocation = allocation + self.mpiexec = "ibrun" # TODO find out the cores-per-node values for these partitions - # self.partitions = {"small":, "normal":, "large":, "development:" - # "flex":} - - def check(self, validate=True): - """ - Checks parameters and paths - """ - super().check(validate=validate) - - assert(self.par.PARTITION in self.partitions.keys()), \ - f"Chinook partition must be in {self.partitions.keys()}" + self.partitions = {"small": None, "normal": None, "large": None, + "development": None, "flex": None} - assert(self.par.NODESIZE == self.partitions[self.par.PARTITION]), \ - (f"PARTITION {self.par.PARTITION} is expected to have NODESIZE=" - f"{self.partitions[self.par.PARTITION]}, not current " - f"{self.par.NODESIZE}") - def submit(self, submit_call=None): + @property + def submit_call_header(self): """ - Submits workflow as a serial job on the TACC partition 'small'. - - .. note:: - The SBATCH commands can either be short or full length. TACC's - start up guide uses short length keys so that's what we do here, but - their long names can be substituted + The submit call defines the SBATCH header which is used to submit a + workflow task list to the system. It is usually dictated by the + system's required parameters, such as account names and partitions. + Submit calls are modified and called by the `submit` function. - :type submit_call: str - :param submit_call: SBATCH command line call to submit workflow.main() - to the system. If None, will generate one on the fly with - user-defined parameters + :rtype: str + :return: the system-dependent portion of a submit call """ - if submit_call is None: - submit_call = " ".join([ - "sbatch", - f"{self.par.SLURMARGS or ''}", - f"-J {self.par.TITLE}", # job name - f"-O {self.output_log}", # stdout output file - f"-E {self.error_log}", # stderr error file - f"-P {self.par.PARTITION}", # queue/partition name - f"-A {self.par.ALLOCATION}", # project/allocation name - f"-N 1", # total number of nodes requested - f"-n 1", # number of mpi tasks - f"-t {self.par.WALLTIME}", # job walltime - f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'submit')}", - f"--output {self.path.OUTPUT}" - ]) - super().submit(submit_call=submit_call) - - def run(self, classname, method, single=False, run_call=None, **kwargs): + _call = " ".join([ + f"sbatch", + f"--job-name={self.title}", # -J + f"--partition={self.partition}", # -p + f"--output={self.path.output_log}", # -o + f"--error={self.path.error_log}", + f"--nodes=1", # -N + f"--ntasks=1", # -n + f"--time={self.walltime:d}" # -t + ]) + if self.allocation is not None: + _call = f"{_call} --allocation={self.allocation}" + return _call + + @property + def run_call_header(self): """ - Runs task multiple times in embarrassingly parallel fasion on a SLURM - cluster. + The run call defines the SBATCH header which is used to run tasks during + an executing workflow. Like the submit call its arguments are dictated + by the given system. Run calls are modified and called by the `run` + function - :type classname: str - :param classname: the class to run - :type method: str - :param method: the method from the given `classname` to run - :type single: bool - :param single: run a single-process, non-parallel task, such as - smoothing the gradient, which only needs to be run by once. - This will change how the job array and the number of tasks is - defined, such that the job is submitted as a single-core job to - the system. - :type run_call: str - :param run_call: SBATCH command line run call to be submitted to the - system. If None, will generate one on the fly with user-defined - parameters + :rtype: str + :return: the system-dependent portion of a run call """ - if run_call is None: - _nodes = np.ceil(self.par.NPROC / float(self.par.NODESIZE)) - - run_call = " ".join([ - "sbatch", - f"{self.par.SLURMARGS or ''}", - f"-J {self.par.TITLE}", # job name - f"-O {self.output_log}", # stdout output file - f"-E {self.error_log}", # stderr error file - f"-P {self.par.PARTITION}", # queue/partition name - f"-A {self.par.ALLOCATION}", # project/allocation name - f"-N {_nodes}", # total number of nodes requested - f"-n {self.par.NPROC}", # number of mpi tasks - f"-t {self.par.WALLTIME}", # job walltime - f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'run')}", - f"--output {self.path.OUTPUT}" - f"--classname {classname}", - f"--funcname {method}", - f"--environment {self.par.ENVIRONS or ''}" - ]) - - super().run(classname, method, single, run_call=run_call, **kwargs) + _call = " ".join([ + f"sbatch", + f"{self.slurm_args or ''}", + f"--job-name={self.title}", + f"--partition={self.partition}", + f"--output={os.path.join(self.path.log_files, '%A_%a')}", + f"--ntasks={self.nproc:d}", + f"--nodes={self.nodes}", + f"--array=0-{self.ntask - 1 % self.ntask_max}", + f"--time={self.tasktime:d}", + f"--parsable" + ]) + if self.allocation is not None: + _call = f"{_call} --allocation={self.allocation}" + return _call \ No newline at end of file diff --git a/seisflows/system/lsf.py b/seisflows/system/lsf.py index 3a5fddf9..dd7b4f74 100644 --- a/seisflows/system/lsf.py +++ b/seisflows/system/lsf.py @@ -8,9 +8,9 @@ import os import time import subprocess - +import sys +from seisflows import ROOT_DIR from seisflows.system.cluster import Cluster -from seisflows.tools.config import ROOT_DIR class Lsf(Cluster): @@ -37,6 +37,9 @@ def __init__(self): These parameters should not be set by the user. Attributes are initialized as NoneTypes for clarity and docstrings. """ + raise NotImplementedError("This module is still a work in progress") + sys.exit(-1) + super().__init__() self.logger.warning("system.LSF is underdeveloped and " diff --git a/seisflows/system/maui.py b/seisflows/system/maui.py index 3ff3fddc..3eb844a0 100644 --- a/seisflows/system/maui.py +++ b/seisflows/system/maui.py @@ -18,41 +18,46 @@ """ import os -import numpy as np -from seisflows import logger from seisflows.system.slurm import Slurm -from seisflows.tools.config import ROOT_DIR class Maui(Slurm): """ - System interface for Maui, which operates on a SLURM system + System Maui + ----------- + New Zealand Maui-specfic modifications to base SLURM system + + Parameters + ---------- + :type account: str + :param account: Maui account to submit jobs under, will be used for the + '--account' sbatch argument + :type cpus_per_task: int + :param cpus_per_task: allow for multiple cpus per task, i.e,. + multithreaded jobs + :type cluster: str + :param cluster: cluster to submit jobs to. Available are Maui and + Mahuika + :type partition: str + :param partition: partition of the cluster to submit jobs to. + :type ancil_cluster: str + :param ancil_cluster: name of the ancilary cluster used for pre- + post-processing tasks. + :type ancil_partition: name of the partition of the ancilary cluster + :type ancil_tasktime: int + :param ancil_tasktime: Tasktime in minutes for pre and post-processing + jobs submitted to Maui ancil. + + Paths + ----- + *** """ + __doc__ = Slurm.__doc__ + __doc__ + def __init__(self, account=None, cpus_per_task=1, cluster="maui", partition="nesi_research", ancil_cluster="maui_ancil", ancil_partition="nesi_prepost", ancil_tasktime=1, **kwargs): - """ - Maui parameters - - :type account: str - :param account: Maui account to submit jobs under, will be used for the - '--account' sbatch argument - :type cpus_per_task: int - :param cpus_per_task: allow for multiple cpus per task, i.e,. - multithreaded jobs - :type cluster: str - :param cluster: cluster to submit jobs to. Available are Maui and - Mahuika - :type partition: str - :param partition: partition of the cluster to submit jobs to. - :type ancil_cluster: str - :param ancil_cluster: name of the ancilary cluster used for pre- - post-processing tasks. - :type ancil_partition: name of the partition of the ancilary cluster - :type ancil_tasktime: int - :param ancil_tasktime: Tasktime in minutes for pre and post-processing - jobs submitted to Maui ancil. - """ + """Maui init""" super().__init__(**kwargs) self.account = account @@ -64,13 +69,12 @@ def __init__(self, account=None, cpus_per_task=1, cluster="maui", self.ancil_tasktime = ancil_tasktime self._partitions = {"nesi_research": 40} - self.node_size = self._partitions[self.partition] - def check(self, validate=True): + def check(self): """ Checks parameters and paths """ - super().check(validate=validate) + super().check() assert("SLURM_MEM_PER_CPU" in (self.environs or "")), \ ("Maui runs Slurm>=21 which enforces mutually exclusivity of Slurm " @@ -79,13 +83,17 @@ def check(self, validate=True): "running SeisFlows3 on Maui, we must remove one env. variable. " "Please add 'SLURM_MEM_PER_CPU' to self.par.ENVIRONS.") - def submit(self, submit_call=None): + @property + def submit_call_header(self): """ - Submits master job workflow to maui_ancil cluster as a single-core - process + The submit call defines the SBATCH header which is used to submit a + workflow task list to the system. It is usually dictated by the + system's required parameters, such as account names and partitions. + Submit calls are modified and called by the `submit` function. .. note:: - The master job must be run on maui_ancil because Maui does + The master job must be run on `maui_ancil` because Maui does + not have the ability to run the command "sacct", nor can it not have the ability to run the command "sacct", nor can it use the Conda environment that has been set by Ancil @@ -93,104 +101,70 @@ def submit(self, submit_call=None): We do not place SLURMARGS into the sbatch command to avoid the export=None which will not propagate the conda environment - :type submit_call: str - :param submit_call: SBATCH command line call to submit workflow.main() - to the system. If None, will generate one on the fly with - user-defined parameters + :rtype: str + :return: the system-dependent portion of a submit call """ - if submit_call is None: - submit_call = " ".join([ - f"sbatch", - f"--account={self.account}", - f"--cluster={self.ancil_cluster}", - f"--partition={self.ancil_partition}", - f"--job-name={self.title}", - f"--output={self.path_output_log}", - f"--error={self.path_error_log}", - f"--ntasks=1", - f"--cpus-per-task=1", - f"--time={self.walltime:d}", - f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'submit')}", - f"--output {self.path_output}" - ]) - - super().submit(submit_call=submit_call) - - def run(self, classname, method, single=False, run_call=None, **kwargs): - """ - Runs task multiple times in embarrassingly parallel fasion on a SLURM - cluster. Executes classname.method(*args, **kwargs) `NTASK` times, - each time on `NPROC` CPU cores - - :type classname: str - :param classname: the class to run - :type method: str - :param method: the method from the given `classname` to run - :type single: bool - :param single: run a single-process, non-parallel task, such as - smoothing the gradient, which only needs to be run by once. - This will change how the job array and the number of tasks is - defined, such that the job is submitted as a single-core job to - the system. - :type run_call: str - :param run_call: SBATCH command line run call to be submitted to the - system. If None, will generate one on the fly with user-defined - parameters + _call = " ".join([ + f"sbatch", + f"--account={self.account}", + f"--cluster={self.ancil_cluster}", + f"--partition={self.ancil_partition}", + f"--job-name={self.title}", + f"--output={self.path.output_log}", + f"--error={self.path.error_log}", + f"--ntasks=1", + f"--cpus-per-task=1", + f"--time={self.walltime:d}" + ]) + return _call + + @property + def run_call_header(self): """ - if run_call is None: - # Calculate requested number of nodes based on requested proc count - _nodes = np.ceil(self.nproc / float(self.node_size)) - _nodes = _nodes.astype(int) - - run_call = " ".join([ - "sbatch", - f"{self.slurm_args or ''}", - f"--account={self.account}", - f"--job-name={self.title}", - f"--clusters={self.cluster}", - f"--partition={self.partition}", - f"--cpus-per-task={self.cpus_per_task}", - f"--nodes={_nodes:d}", - f"--ntasks={self.nproc:d}", - f"--time={self.tasktime:d}", - f"--output={os.path.join(self.path_log_files, '%A_%a')}", - f"--array=0-{self.ntask-1 % self.ntask_max}", - f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'run')}", - f"--output {self.path_output}", - f"--classname {classname}", - f"--funcname {method}", - f"--environment {self.environs or ''}" - ]) - - super().run(classname, method, single, run_call=run_call, **kwargs) - - def run_ancil(self, classname, method, **kwargs): + The run call defines the SBATCH header which is used to run tasks during + an executing workflow. Like the submit call its arguments are dictated + by the given system. Run calls are modified and called by the `run` + function + + :rtype: str + :return: the system-dependent portion of a run call """ - Runs prepost jobs on Maui ancil, the ancilary cluster which contains - the conda and Python capabilities for Maui. + _call = " ".join([ + f"sbatch", + f"{self.slurm_args or ''}", + f"--account={self.account}", + f"--job-name={self.title}", + f"--clusters={self.cluster}", + f"--partition={self.partition}", + f"--cpus-per-task={self.cpus_per_task}", + f"--nodes={self.nodes:d}", + f"--ntasks={self.nproc:d}", + f"--time={self.tasktime:d}", + f"--output={os.path.join(self.path.log_files, '%A_%a')}", + f"--array=0-{self.ntask-1 % self.ntask_max}", + f"--parsable" + ]) + return _call - :type classname: str - :param classname: the class to run - :type method: str - :param method: the method from the given `classname` to run + @property + def ancil_run_call_header(self): """ - ancil_run_call = " ".join([ - "sbatch", - f"{self.slurm_args or ''}", - f"--account={self.account}", - f"--job-name={self.title}", - f"--clusters={self.ancil_cluster}", - f"--partition={self.ancil_partition}", - f"--cpus-per-task={self.cpus_per_task}", - f"--time={self.ancil_tasktime:d}", - f"--output={os.path.join(self.path_log_files, '%A_%a')}", - f"--array=0-{self.ntask-1 % self.ntask_max}", - f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'run')}", - f"--output {self.path_output}", - f"--classname {classname}", - f"--funcname {method}", - f"--environment {self.environs or ''}" + A modified form of `run_call` which is used to run jobs on the Ancil + pre/postprocessing cluster of Maui. This is used to run Pyaflowa jobs + which require the Conda environment active on Maui Ancil. + """ + _call = " ".join([ + f"sbatch", + f"{self.slurm_args or ''}", + f"--account={self.account}", + f"--job-name={self.title}", + f"--clusters={self.ancil_cluster}", + f"--partition={self.ancil_partition}", + f"--cpus-per-task={self.cpus_per_task}", + f"--nodes={self.nodes:d}", + f"--ntasks={self.nproc:d}", + f"--time={self.ancil_tasktime:d}", + f"--output={os.path.join(self.path.log_files, '%A_%a')}", + f"--array=0-{self.ntask-1 % self.ntask_max}" ]) - logger.debug(ancil_run_call) - super().run(classname, method, single=False, run_call=ancil_run_call, - **kwargs) + return _call diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index 17f3af68..fd9b9692 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -12,16 +12,22 @@ system supers will not be up to date until access to those systems are granted. This rosetta stone, for converting from SLURM to other workload management tools will be useful: https://slurm.schedmd.com/rosetta.pdf + +TODO + Create 'slurm_singulairty', a child class for singularity-based runs which + loads and runs programs through singularity, OR add a parameter options + which will change the run and/or submit calls """ import os import sys -import math +import numpy as np import time import subprocess from seisflows import ROOT_DIR, logger from seisflows.system.cluster import Cluster from seisflows.tools import msg +from seisflows.tools.config import pickle_function_list class Slurm(Cluster): @@ -33,6 +39,9 @@ class Slurm(Cluster): Parameters ---------- + :type ntask_max: int + :param ntask_max: set the maximum number of simultaneously running array + job processes that are submitted to a cluster at one time. :type slurm_args: str :param slurm_args: Any (optional) additional SLURM arguments that will be passed to the SBATCH scripts. Should be in the form: @@ -56,47 +65,99 @@ def __init__(self, ntask_max=100, slurm_args="", **kwargs): # Must be overwritten by child class self.node_size = None + self.partition = None + self._partitions = {} - def check(self, validate=True): + def check(self): """ Checks parameters and paths """ + super().check() + assert(self.node_size is not None), ( f"Slurm system child classes require defining the `node_size` or " f"the number of cores per node inherent to the compute system") - def submit(self, submit_call=None): + assert(self.partition in self._partitions), \ + f"Cluster partition name must match {self._partitions}" + + assert("--parsable" in self.run_call_header), ( + f"System `run_call_header` requires SBATCH argument '--parsable' " + f"which is required to keep STDOUT formatted correctly when " + f"submitting jobs to the system." + ) + + @property + def nodes(self): + """Defines the number of nodes which is derived from system node size""" + _nodes = np.ceil(self.nproc / float(self.node_size)) + _nodes = _nodes.astype(int) + return _nodes + + @property + def nodesize(self): + """Defines the node size of a given cluster partition. This is a hard + set number defined by the system architecture""" + return self._partitions[self.partition] + + @property + def submit_call_header(self): + """ + The submit call defines the SBATCH header which is used to submit a + workflow task list to the system. It is usually dictated by the + system's required parameters, such as account names and partitions. + Submit calls are modified and called by the `submit` function. + + :rtype: str + :return: the system-dependent portion of a submit call + """ + _call = " ".join([ + f"sbatch", + f"{self.slurm_args or ''}", + f"--job-name={self.title}", + f"--output={self.path.output_log}", + f"--error={self.path.output_log}", + f"--ntasks-per-node={self.node_size}", + f"--nodes=1", + f"--time={self.walltime:d}" + ]) + return _call + + @property + def run_call_header(self): """ - Submits workflow as a single process master job on a SLURM system + The run call defines the SBATCH header which is used to run tasks during + an executing workflow. Like the submit call its arguments are dictated + by the given system. Run calls are modified and called by the `run` + function - :type submit_call: str - :param submit_call: subclasses (e.g., specific SLURM cluster subclasses) - can overload the sbatch command line input by setting - submit_call. If set to None, default submit_call will be set here. + :rtype: str + :return: the system-dependent portion of a run call """ - if submit_call is None: - submit_call = " ".join([ - f"sbatch", - f"{self.slurm_args or ''}", - f"--job-name={self.title}", - f"--output={self.path.output_log}", - f"--error={self.path.output_log}", - f"--ntasks-per-node={self.node_size}", - f"--nodes=1", - f"--time={self.walltime:d}", - f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'submit')}", - f"--output {self.path.output}" - ]) - - logger.debug(submit_call) - super().submit(submit_call=submit_call) - - def run(self, funcs, single=False, run_call=None, **kwargs): + _call = " ".join([ + f"sbatch", + f"{self.slurm_args or ''}", + f"--job-name={self.title}", + f"--nodes={self.nodes}", + f"--ntasks-per-node={self.node_size:d}", + f"--ntasks={self.nproc:d}", + f"--time={self.tasktime:d}", + f"--output={os.path.join(self.path.log_files, '%A_%a')}", + f"--array=0-{self.ntask-1}%{self.ntask_max}", + f"--parsable" + ]) + return _call + + + def run(self, funcs, single=False, **kwargs): """ Runs task multiple times in embarrassingly parallel fasion on a SLURM cluster. Executes classname.method(*args, **kwargs) `NTASK` times, each time on `NPROC` CPU cores + .. note:: + Completely overwrites the `Cluster.run()` command + :type funcs: list of methods :param funcs: a list of functions that should be run in order. All kwargs passed to run() will be passed into the functions. @@ -106,34 +167,22 @@ def run(self, funcs, single=False, run_call=None, **kwargs): This will change how the job array and the number of tasks is defined, such that the job is submitted as a single-core job to the system. - :type run_call: str - :param run_call: subclasses (e.g., specific SLURM cluster subclasses) - can overload the sbatch command line input by setting - run_call. If set to None, default run_call will be set here. """ - funcs_fid, kwargs_fid = self._pickle_func_list(funcs, **kwargs) + funcs_fid, kwargs_fid = pickle_function_list(funcs, + path=self.path.scratch, + **kwargs) logger.info(f"running functions {[_.__name__ for _ in funcs]} on " f"system {self.ntask} times") # Default sbatch command line input, can be overloaded by subclasses # Copy-paste this default run_call and adjust accordingly for subclass - if run_call is None: - run_call = " ".join([ - "sbatch", - f"{self.slurm_args or ''}", - f"--job-name={self.title}", - f"--nodes={math.ceil(self.nproc/float(self.node_size)):d}", - f"--ntasks-per-node={self.node_size:d}", - f"--ntasks={self.nproc:d}", - f"--time={self.tasktime:d}", - f"--output={os.path.join(self.path.log_files, '%A_%a')}", - f"--array=0-{self.ntask-1}%{self.ntask_max}", - f"--parsable", # keeps stdout cleaner - f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'run')}", - f"--funcs {funcs_fid}", - f"--kwargs {kwargs_fid}", - f"--environment {self.environs or ''}" - ]) + run_call = " ".join([ + f"{self.run_call_header}", + f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'run')}", + f"--funcs {funcs_fid}", + f"--kwargs {kwargs_fid}", + f"--environment {self.environs or ''}" + ]) logger.debug(run_call) # Single-process jobs simply need to replace a few sbatch arguments. diff --git a/seisflows/system/workstation.py b/seisflows/system/workstation.py index 1486f682..9edfc28a 100644 --- a/seisflows/system/workstation.py +++ b/seisflows/system/workstation.py @@ -127,8 +127,7 @@ def setup(self): logger.debug(f"copying par/log file to: {dst}") unix.cp(src=src, dst=dst) - def submit(self, workdir=None, parameter_file="parameters.yaml", - submit_call=None): + def submit(self, workdir=None, parameter_file="parameters.yaml"): """ Submits the main workflow job as a serial job submitted directly to the system that is running the master job @@ -138,11 +137,6 @@ def submit(self, workdir=None, parameter_file="parameters.yaml", :type parameter_file: str :param parameter_file: paramter file file name used to instantiate the SeisFlows package - :type submit_call: str - :param submit_call: child classes may require a specific submit call - if the job should be submitted to another system (e.g., on cluster - submitting jobs on compute nodes and not running directly on the - login node) """ workflow = import_seisflows(workdir=workdir or self.path.workdir, parameter_file=parameter_file) From aa4860973f9d7abc4da0789a30196f3898ceda63 Mon Sep 17 00:00:00 2001 From: bch0w Date: Wed, 17 Aug 2022 14:59:25 -0800 Subject: [PATCH 116/195] added a test_system workflow which simply attempts to use the 'run' function on a system --- seisflows/workflow/test_system.py | 77 +++++++++++++++++++++++++++++++ 1 file changed, 77 insertions(+) create mode 100644 seisflows/workflow/test_system.py diff --git a/seisflows/workflow/test_system.py b/seisflows/workflow/test_system.py new file mode 100644 index 00000000..f09d73a5 --- /dev/null +++ b/seisflows/workflow/test_system.py @@ -0,0 +1,77 @@ +#!/usr/bin/env python3 +""" +This is a SeisFlows Test workflow class which is used to test out the underlying +machinery for a given set of modules, before submitting a full workflow. Used +for debugging and development, as well as ensuring that SeisFlows is in working +order. +""" +import os +from seisflows import logger +from seisflows.tools import unix +from seisflows.tools.config import Dict, get_task_id + + +class Test: + """ + Test Workflow + ------------- + Test individual sub-modules in a 'live' testing environment + + Parameters + ---------- + + Paths + ----- + *** + """ + def __init__(self, modules=None, workdir=os.getcwd(), path_output=None, + **kwargs): + """Test workflow""" + self._modules = modules + + self.path = Dict( + workdir=workdir, + scratch=os.path.join(workdir, "scratch"), + output=path_output or os.path.join(workdir, "output") + ) + + @property + def task_list(self): + """ + A task list which includes tests for most of the modules depending on + whether they're included in the module list or not. + """ + return [] + + def setup(self): + """ + Creates required directory structure + """ + for path in [self.path.workdir, self.path.scratch, self.path.output]: + unix.mkdir(path) + + for module in self._modules: + module.setup() + + def test_system_run(self): + """ + Use the system sub-module to submit some simple functions to ensure that + we can run jobs on the system and that the job checking works + """ + if not "system" in self._modules: + logger.warning("No `system` module chosen, skipping " + "`test_system_run`") + + system = self._modules["system"] + system.run(funcs=[self.test_function_print_hello_world], single=False) + system.run(funcs=[self.test_function_wait_ten_seconds], single=True) + + def test_function_print_hello_world(self): + """Simple print function to be called by system run""" + print(f"hello world from task id: {get_task_id()}") + + def test_function_wait_ten_seconds(self): + """Simple wait function to be called by system run, used to test + job status check""" + for i in range(11): + print(f"hello world from task id: {get_task_id()} after {i} sec.") From 013bfe4483aa6bf867c6313859b5d63a35feb05b Mon Sep 17 00:00:00 2001 From: bch0w Date: Wed, 17 Aug 2022 23:09:00 +0000 Subject: [PATCH 117/195] bugfix configure was writing docstrings for Null modules which is unncessary, these are now skipped over --- seisflows/seisflows.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index a5707c18..91770f49 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -489,6 +489,8 @@ def split_module_docstring(mod, idx): f = open(self._args.parameter_file, "a") # Write all module parameters and corresponding docstrings for module in modules: + if not module: + continue docstring = split_module_docstring(module, 0) f.write(f"# {'=' * 77}\n#{docstring}\n# {'=' * 77}\n") # Write the parameters, make sure to not have the same one twice @@ -508,6 +510,8 @@ def split_module_docstring(mod, idx): f.write("#\t Paths\n") f.write("#\t -----\n") for module in modules: + if not module: + return docstring = split_module_docstring(module, -1) f.write(f"#{docstring}\n") f.write(f"# {'=' * 77}\n") @@ -515,6 +519,8 @@ def split_module_docstring(mod, idx): # Write values for publically accessible path structure written = [] for module in modules: + if not module: + continue for key, val in module.path.items(): # '_key' means hidden path so don't include in par file if key in written or key.startswith("_"): From 9e9a170689eb4e8a93f14ba486f63ce9b6ca1c9d Mon Sep 17 00:00:00 2001 From: bch0w Date: Thu, 18 Aug 2022 00:52:14 +0000 Subject: [PATCH 118/195] tempfix logger printing twice as stdout and log statements were both going to the log file, temp turned off stdout bugfix check job status slurm was returning empty strings when running sacct because it was running to quick after job submission, added sleep timer maui now force adds environment variable to internal parameters bugfix seisflows clean was throwing an error for deleting paths that did not exist for test workflow --- seisflows/seisflows.py | 12 ++++++------ seisflows/system/chinook.py | 4 ++-- seisflows/system/frontera.py | 4 ++-- seisflows/system/maui.py | 16 ++++++++++++++-- seisflows/system/slurm.py | 25 ++++++++++++++----------- seisflows/system/workstation.py | 2 +- seisflows/tools/config.py | 8 ++++---- seisflows/workflow/test_system.py | 23 ++++++++++++++++++----- 8 files changed, 61 insertions(+), 33 deletions(-) diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 91770f49..1c78cbfe 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -511,7 +511,7 @@ def split_module_docstring(mod, idx): f.write("#\t -----\n") for module in modules: if not module: - return + continue docstring = split_module_docstring(module, -1) f.write(f"#{docstring}\n") f.write(f"# {'=' * 77}\n") @@ -650,11 +650,11 @@ def clean(self, force=False, **kwargs): if check == "y": pars = load_yaml(self._args.parameter_file) - unix.rm(pars.path_scratch) - unix.rm(pars.path_output) - unix.rm(pars.path_log_files) - unix.rm(pars.path_state_file) - unix.rm(pars.path_output_log) + for name in ["scratch", "output", "log_files", "state_file", + "output_log"]: + path = f"path_{name}" + if path in pars: + unix.rm(path) def restart(self, force=False, **kwargs): """ diff --git a/seisflows/system/chinook.py b/seisflows/system/chinook.py index 653f3801..4e17c7b9 100644 --- a/seisflows/system/chinook.py +++ b/seisflows/system/chinook.py @@ -58,7 +58,7 @@ def submit_call_header(self): f"sbatch", f"--job-name={self.title}", f"--output={self.path.output_log}", - f"--error={self.path.error_log}", + f"--error={self.path.output_log}", f"--ntasks=1", f"--partition={self.partition}", f"--time={self.walltime:d}" @@ -87,4 +87,4 @@ def run_call_header(self): f"--array=0-{self.ntask-1 % self.ntask_max}", f"--parsable" ]) - return _call \ No newline at end of file + return _call diff --git a/seisflows/system/frontera.py b/seisflows/system/frontera.py index 6f403c71..604fad1d 100644 --- a/seisflows/system/frontera.py +++ b/seisflows/system/frontera.py @@ -57,7 +57,7 @@ def submit_call_header(self): f"--job-name={self.title}", # -J f"--partition={self.partition}", # -p f"--output={self.path.output_log}", # -o - f"--error={self.path.error_log}", + f"--error={self.path.output_log}", f"--nodes=1", # -N f"--ntasks=1", # -n f"--time={self.walltime:d}" # -t @@ -91,4 +91,4 @@ def run_call_header(self): ]) if self.allocation is not None: _call = f"{_call} --allocation={self.allocation}" - return _call \ No newline at end of file + return _call diff --git a/seisflows/system/maui.py b/seisflows/system/maui.py index 3eb844a0..fd83d473 100644 --- a/seisflows/system/maui.py +++ b/seisflows/system/maui.py @@ -67,8 +67,13 @@ def __init__(self, account=None, cpus_per_task=1, cluster="maui", self.ancil_cluster = ancil_cluster self.ancil_partition = ancil_partition self.ancil_tasktime = ancil_tasktime + if self.environs and "SLURM_MEM_PER_CPU" not in self.environs: + self.environs = f"{self.environs},SLURM_MEM_PER_CPU" + else: + self.environs = "SLURM_MEM_PER_CPU" self._partitions = {"nesi_research": 40} + self._available_clusters = ["maui", "mahuika"] def check(self): """ @@ -80,9 +85,16 @@ def check(self): ("Maui runs Slurm>=21 which enforces mutually exclusivity of Slurm " "memory environment variables SLURM_MEM_PER_CPU and " "SLURM_MEM_PER_NODE. Due to the cross-cluster nature of " - "running SeisFlows3 on Maui, we must remove one env. variable. " + "running SeisFlows on Maui, we must remove one env. variable. " "Please add 'SLURM_MEM_PER_CPU' to self.par.ENVIRONS.") + assert(self.cluster in self._available_clusters), ( + f"System 'Maui' parameter cluster must be in " + f"{self._available_clusters}" + ) + + assert(self.account), f"System 'Maui' requires parameter 'account'" + @property def submit_call_header(self): """ @@ -111,7 +123,7 @@ def submit_call_header(self): f"--partition={self.ancil_partition}", f"--job-name={self.title}", f"--output={self.path.output_log}", - f"--error={self.path.error_log}", + f"--error={self.path.output_log}", f"--ntasks=1", f"--cpus-per-task=1", f"--time={self.walltime:d}" diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index fd9b9692..fb1fe6b7 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -34,8 +34,8 @@ class Slurm(Cluster): """ System Slurm ------------------ - Runs tasks in serial on a local machine.Interface for submitting jobs to Simple Linux Utility for - Resource Management (SLURM) system. + Runs tasks in serial on a local machine. Interface for submitting jobs to + Simple Linux Utility for Resource Management (SLURM) system. Parameters ---------- @@ -64,7 +64,6 @@ def __init__(self, ntask_max=100, slurm_args="", **kwargs): self.slurm_args = slurm_args # Must be overwritten by child class - self.node_size = None self.partition = None self._partitions = {} @@ -75,8 +74,8 @@ def check(self): super().check() assert(self.node_size is not None), ( - f"Slurm system child classes require defining the `node_size` or " - f"the number of cores per node inherent to the compute system") + f"Slurm system child classes require defining the node_size or " + f"the number of cores per node inherent to the compute system.") assert(self.partition in self._partitions), \ f"Cluster partition name must match {self._partitions}" @@ -95,7 +94,7 @@ def nodes(self): return _nodes @property - def nodesize(self): + def node_size(self): """Defines the node size of a given cluster partition. This is a hard set number defined by the system architecture""" return self._partitions[self.partition] @@ -171,8 +170,12 @@ def run(self, funcs, single=False, **kwargs): funcs_fid, kwargs_fid = pickle_function_list(funcs, path=self.path.scratch, **kwargs) - logger.info(f"running functions {[_.__name__ for _ in funcs]} on " - f"system {self.ntask} times") + if single: + logger.info(f"running functions {[_.__name__ for _ in funcs]} on " + f"system 1 time") + else: + logger.info(f"running functions {[_.__name__ for _ in funcs]} on " + f"system {self.ntask} times") # Default sbatch command line input, can be overloaded by subclasses # Copy-paste this default run_call and adjust accordingly for subclass @@ -183,7 +186,6 @@ def run(self, funcs, single=False, **kwargs): f"--kwargs {kwargs_fid}", f"--environment {self.environs or ''}" ]) - logger.debug(run_call) # Single-process jobs simply need to replace a few sbatch arguments. # Do it AFTER `run_call` has been defined so that subclasses submitting @@ -193,6 +195,8 @@ def run(self, funcs, single=False, **kwargs): "process job") run_call = _modify_run_call_single_proc(run_call) + logger.debug(run_call) + # Stdout will be job number (e.g., 1234). Federated clusters will return # job # and cluster name (e.g., 1234;Cluster1). We only want job # job_id = subprocess.run(run_call, stdout=subprocess.PIPE, @@ -229,6 +233,7 @@ def check_job_status(job_id): bad_states = ["TIMEOUT", "FAILED", "NODE_FAIL", "OUT_OF_MEMORY", "CANCELLED"] while True: + time.sleep(5) # give job time to process and also prevent over-query job_ids, states = query_job_states(job_id) if [state == "COMPLETED" for state in states]: return 1 # Pass @@ -238,8 +243,6 @@ def check_job_status(job_id): if state in bad_states: logger.debug(f"{job_id}: {state}") return -1 # Fail - else: - time.sleep(5) # Don't query 'sacct' command too often def query_job_states(job_id): diff --git a/seisflows/system/workstation.py b/seisflows/system/workstation.py index 9edfc28a..531316a0 100644 --- a/seisflows/system/workstation.py +++ b/seisflows/system/workstation.py @@ -48,7 +48,7 @@ class Workstation: """ def __init__(self, ntask=1, nproc=1, log_level="DEBUG", verbose=False, workdir=os.getcwd(), path_output=None, path_system=None, - path_par_file=None, path_output_log=None, path_log_files=None, + path_par_file=None, path_output_log=None, path_log_files=None, **kwargs): """ Workstation System Class Parameters diff --git a/seisflows/tools/config.py b/seisflows/tools/config.py index 36a91012..6a345733 100755 --- a/seisflows/tools/config.py +++ b/seisflows/tools/config.py @@ -239,9 +239,9 @@ def config_logger(level="DEBUG", filename=None, filemode="a", verbose=True): formatter = logging.Formatter(fmt_str, datefmt="%Y-%m-%d %H:%M:%S") # Stream handler to print log statements to stdout - st_handler = logging.StreamHandler(sys.stdout) - st_handler.setFormatter(formatter) - logger.addHandler(st_handler) + # st_handler = logging.StreamHandler(sys.stdout) + # st_handler.setFormatter(formatter) + # logger.addHandler(st_handler) # File handler to print log statements to text file `filename` if filename is not None: @@ -400,4 +400,4 @@ def number_fid(fid, i=0): ext = os.path.splitext(fid_only)[-1] # e.g., .txt new_ext = f"_{i:0>3}{ext}" # e.g., _000.txt new_fid = fid_only.replace(ext, new_ext) - return new_fid \ No newline at end of file + return new_fid diff --git a/seisflows/workflow/test_system.py b/seisflows/workflow/test_system.py index f09d73a5..d7d6273a 100644 --- a/seisflows/workflow/test_system.py +++ b/seisflows/workflow/test_system.py @@ -11,9 +11,9 @@ from seisflows.tools.config import Dict, get_task_id -class Test: +class TestSystem: """ - Test Workflow + TestSystem Workflow ------------- Test individual sub-modules in a 'live' testing environment @@ -43,21 +43,34 @@ def task_list(self): """ return [] + def check(self): + """ + Run check functions for all underlying modules + """ + logger.info("running check for test workflow") + for name, module in self._modules.items(): + if module: + module.check() + def setup(self): """ Creates required directory structure """ + logger.info("running setup for test workflow") for path in [self.path.workdir, self.path.scratch, self.path.output]: unix.mkdir(path) - for module in self._modules: - module.setup() + for name, module in self._modules.items(): + if module: + module.setup() - def test_system_run(self): + def run(self): """ Use the system sub-module to submit some simple functions to ensure that we can run jobs on the system and that the job checking works """ + logger.info("running test workflow") + if not "system" in self._modules: logger.warning("No `system` module chosen, skipping " "`test_system_run`") From 238a7045efffd2b68b4b585232abca93c46c89d6 Mon Sep 17 00:00:00 2001 From: bch0w Date: Thu, 18 Aug 2022 01:00:42 +0000 Subject: [PATCH 119/195] bugfix slurm system not actually replacing single proc calls --- seisflows/system/slurm.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index fb1fe6b7..214873e5 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -291,9 +291,9 @@ def _modify_run_call_single_proc(run_call): """ for part in run_call.split(" "): if "--array" in part: - run_call.replace(part, "--array=0-0") + run_call = run_call.replace(part, "--array=0-0") elif "--ntasks" in part: - run_call.replace(part, "--ntasks=1") + run_call = run_call.replace(part, "--ntasks=1") # Append taskid to environment variable, deal with the case where # self.par.ENVIRONS is an empty string From 1a463c2d77f296dd1c9f90af237f150b9544a6f0 Mon Sep 17 00:00:00 2001 From: bch0w Date: Thu, 18 Aug 2022 18:49:23 +0000 Subject: [PATCH 120/195] improving test_flow workflow for testing modules in a live environment improvement for slurm systems, setting time as a datettime str rather than an integer, which allows for fractions of a minute walltime and tasktime --- seisflows/system/chinook.py | 4 +- seisflows/system/cluster.py | 9 +- seisflows/system/frontera.py | 4 +- seisflows/system/maui.py | 4 +- seisflows/system/slurm.py | 15 ++- seisflows/workflow/forward.py | 4 +- seisflows/workflow/test_flow.py | 175 ++++++++++++++++++++++++++++++ seisflows/workflow/test_system.py | 90 --------------- 8 files changed, 200 insertions(+), 105 deletions(-) create mode 100644 seisflows/workflow/test_flow.py delete mode 100644 seisflows/workflow/test_system.py diff --git a/seisflows/system/chinook.py b/seisflows/system/chinook.py index 4e17c7b9..ee6d7696 100644 --- a/seisflows/system/chinook.py +++ b/seisflows/system/chinook.py @@ -61,7 +61,7 @@ def submit_call_header(self): f"--error={self.path.output_log}", f"--ntasks=1", f"--partition={self.partition}", - f"--time={self.walltime:d}" + f"--time={self._walltime}" ]) return _call @@ -82,7 +82,7 @@ def run_call_header(self): f"--job-name={self.title}", f"--ntasks={self.nproc:d}", f"--tasks-per-node={self.nodesize}", - f"--time={self.tasktime:d}", + f"--time={self._tasktime}", f"--output={os.path.join(self.path.log_files, '%A_%a')}", f"--array=0-{self.ntask-1 % self.ntask_max}", f"--parsable" diff --git a/seisflows/system/cluster.py b/seisflows/system/cluster.py index e8aa15f6..9e3bd81c 100644 --- a/seisflows/system/cluster.py +++ b/seisflows/system/cluster.py @@ -36,14 +36,15 @@ class Cluster(Workstation): For example 'mpirun', 'mpiexec', 'srun', 'ibrun' :type ntask_max: int :param ntask_max: limit the number of concurrent tasks in a given array job - :type walltime: int + :type walltime: float :param walltime: maximum job time in minutes for the master SeisFlows - job submitted to cluster - :type tasktime: int + job submitted to cluster. Fractions of minutes acceptable. + :type tasktime: float :param tasktime: maximum job time in minutes for each job spawned by the SeisFlows master job during a workflow. These include, e.g., running the forward solver, adjoint solver, smoother, kernel combiner. - All spawned tasks receive the same task time. + All spawned tasks receive the same task time. Fractions of minutes + acceptable. :type environs: str :param environs: Optional environment variables to be provided in the following format VAR1=var1,VAR2=var2... Will be set using diff --git a/seisflows/system/frontera.py b/seisflows/system/frontera.py index 604fad1d..f816cddc 100644 --- a/seisflows/system/frontera.py +++ b/seisflows/system/frontera.py @@ -60,7 +60,7 @@ def submit_call_header(self): f"--error={self.path.output_log}", f"--nodes=1", # -N f"--ntasks=1", # -n - f"--time={self.walltime:d}" # -t + f"--time={self._walltime}" # -t ]) if self.allocation is not None: _call = f"{_call} --allocation={self.allocation}" @@ -86,7 +86,7 @@ def run_call_header(self): f"--ntasks={self.nproc:d}", f"--nodes={self.nodes}", f"--array=0-{self.ntask - 1 % self.ntask_max}", - f"--time={self.tasktime:d}", + f"--time={self._tasktime}", f"--parsable" ]) if self.allocation is not None: diff --git a/seisflows/system/maui.py b/seisflows/system/maui.py index fd83d473..45da8e92 100644 --- a/seisflows/system/maui.py +++ b/seisflows/system/maui.py @@ -126,7 +126,7 @@ def submit_call_header(self): f"--error={self.path.output_log}", f"--ntasks=1", f"--cpus-per-task=1", - f"--time={self.walltime:d}" + f"--time={self._walltime}" ]) return _call @@ -151,7 +151,7 @@ def run_call_header(self): f"--cpus-per-task={self.cpus_per_task}", f"--nodes={self.nodes:d}", f"--ntasks={self.nproc:d}", - f"--time={self.tasktime:d}", + f"--time={self._tasktime}", f"--output={os.path.join(self.path.log_files, '%A_%a')}", f"--array=0-{self.ntask-1 % self.ntask_max}", f"--parsable" diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index 214873e5..e6c61fd9 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -13,6 +13,11 @@ granted. This rosetta stone, for converting from SLURM to other workload management tools will be useful: https://slurm.schedmd.com/rosetta.pdf +.. note:: + SLURM systems expect walltime/tasktime in format: "minutes", + "minutes:seconds", "hours:minutes:seconds". SeisFlows uses the latter + and converts task and walltimes from input of minutes to a time string. + TODO Create 'slurm_singulairty', a child class for singularity-based runs which loads and runs programs through singularity, OR add a parameter options @@ -67,6 +72,10 @@ def __init__(self, ntask_max=100, slurm_args="", **kwargs): self.partition = None self._partitions = {} + # Convert walltime and tasktime to datetime str 'H:MM:SS' + self._tasktime = str(time.timedelta(minutes=self.tasktime)) + self._walltime = str(time.timedelta(minutes=self.walltime)) + def check(self): """ Checks parameters and paths @@ -118,7 +127,7 @@ def submit_call_header(self): f"--error={self.path.output_log}", f"--ntasks-per-node={self.node_size}", f"--nodes=1", - f"--time={self.walltime:d}" + f"--time={self._walltime}" ]) return _call @@ -140,7 +149,7 @@ def run_call_header(self): f"--nodes={self.nodes}", f"--ntasks-per-node={self.node_size:d}", f"--ntasks={self.nproc:d}", - f"--time={self.tasktime:d}", + f"--time={self._tasktime}", f"--output={os.path.join(self.path.log_files, '%A_%a')}", f"--array=0-{self.ntask-1}%{self.ntask_max}", f"--parsable" @@ -214,7 +223,7 @@ def run(self, funcs, single=False, **kwargs): ) sys.exit(-1) else: - logger.info(f"tasks finished successfully") + logger.info(f"task {job_id} finished successfully") def check_job_status(job_id): diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index bab0a073..9a9b9def 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -52,8 +52,8 @@ class Forward: Paths ----- :type workdir: str - :param workdir: working directory in which to look for data and store - results. Defaults to current working directory + :param workdir: working directory in which to perform a SeisFlows workflow. + SeisFlows internal directory structure will be created here. Default cwd :type path_output: str :param path_output: path to directory used for permanent storage on disk. Results and exported scratch files are saved here. diff --git a/seisflows/workflow/test_flow.py b/seisflows/workflow/test_flow.py new file mode 100644 index 00000000..a03bd7ff --- /dev/null +++ b/seisflows/workflow/test_flow.py @@ -0,0 +1,175 @@ +#!/usr/bin/env python3 +""" +This is a SeisFlows Test workflow class which is used to test out the underlying +machinery for a given set of modules, before submitting a full workflow. Used +for debugging and development, as well as ensuring that SeisFlows is in working +order before committing a large number of compute resources. +""" +import os +import time +from glob import glob +from seisflows import logger +from seisflows.tools import unix, msg +from seisflows.tools.config import Dict, get_task_id + + +class TestFlow: + """ + TestFlow Workflow + ------------- + Test individual sub-modules in a 'live' testing environment in order to + ensure SeisFlows works appropriately given an established system and solver. + + .. note:: + You do not need to set System parameters `ntask`, `nproc`, `tasktime`, + `walltime`. These will be overwritten by the setup task. + + Parameters + ---------- + + Paths + ----- + :type workdir: str + :param workdir: working directory in which to perform a SeisFlows workflow. + SeisFlows internal directory structure will be created here. Default cwd + :type path_output: str + :param path_output: path to directory used for permanent storage on disk. + Results and expored scratch files are saved here. + *** + """ + def __init__(self, modules=None, workdir=os.getcwd(), path_output=None, + **kwargs): + """Test workflow""" + self._modules = modules + + self.path = Dict( + workdir=workdir, + scratch=os.path.join(workdir, "scratch"), + output=path_output or os.path.join(workdir, "output") + ) + + @property + def task_list(self): + """ + A task list which includes tests for most of the modules depending on + whether they're included in the module list or not. + """ + _task_list = [] + if not self.system: + logger.warning("No `system` module chosen, skipping system tests") + else: + _task_list.append(self.test_system_print_hello_world) + _task_list.append(self.test_system_wait_ten_seconds_then_fail) + + return _task_list + + def check(self): + """ + Run check functions for all underlying modules + """ + logger.info("running check for test workflow") + for name, module in self._modules.items(): + if module: + module.check() + + def setup(self): + """ + Creates required directory structure + """ + logger.info("running setup for test workflow") + for path in [self.path.workdir, self.path.scratch, self.path.output]: + unix.mkdir(path) + + for name, module in self._modules.items(): + if module: + module.setup() + + # Assign modules to internal attributes, some may be Null + self.system = self._modules["system"] + self.solver = self._modules["solver"] + self.preprocess = self._modules["preprocess"] + self.optimize = self._modules["optimize"] + + # Force some internal module variables to keep testing lightweight + logger.info("overwriting internal System parameters from given values") + self.system.ntask = 3 + self.system.nproc = 1 + self.system.tasktime = .25 # 15 seconds + self.system.walltime = 2.5 # 2.5 minutes + + def run(self): + """ + Run through the task list which should consist of various test functions + which are meant to ensure SeisFlows works in a live working environment + without committing a large number of resources + """ + logger.info(msg.mjr("RUNNING TEST WORKFLOW")) + + for func in self.task_list: + func() + + def test_system_print_hello_world(self): + """ + Use the system to run a simple print function which names the currently + running task id to check that task id printing works. Check that the + output log messages show the correct task id and log statement + """ + logger.info("running system test for job array submission") + + def _test_function(): + print(f"hello world from task id: {get_task_id()}") + + # Clear the log files dir. as this is how we will check results + unix.rm(self.system.path.log_files) + unix.mkdir(self.system.path.log_files) + + # Run an array job + self.system.run(funcs=[_test_function], single=False) + + # Check the results of the run + log_files = glob(os.path.join(self.system.path.log_files, "*_*")) + assert(len(log_files) == self.system.ntask), \ + f"number of log files does not match expected number of tasks" + + for i, fid in enumerate(sorted(log_files)): + with open(fid, "r") as f: + assert(f"hello world from task id: {i}" in f.read()), \ + f"log file '{fid}' does not show correct log message" + + logger.info("job array submission system test finished successfully") + + + def test_system_wait_ten_seconds_then_fail(self): + """ + Simple wait function to be called by system run, used to test + job status check function which monitors job queue. Throw in a job + failure at the end of the function to check that system exit works + """ + logger.info("running system test for job queue monitoring and " + "job failure catching") + + def _test_function(): + for i in range(1, 11): + time.sleep(1) + print(f"Task ID {get_task_id()} waited {i} sec.") + + raise Exception("intentional exception for job failure test") + + # Clear the log files dir. as this is how we will check results + unix.rm(self.system.path.log_files) + unix.mkdir(self.system.path.log_files) + + # Catch the system exit exception that is thrown when job fails + try: + self.system.run(funcs=[_test_function], single=True) + logger.critical("job was expected to fail but did not") + sys.exit(-1) + except SystemExit: + pass + + # Check the log file for job failure + log_files = glob(os.path.join(self.system.path.log_files, "*_*")) + assert(len(log_files) == 1), f"only one log file expected" + + logger.info("job queue and fail system test finished successfully") + diff --git a/seisflows/workflow/test_system.py b/seisflows/workflow/test_system.py deleted file mode 100644 index d7d6273a..00000000 --- a/seisflows/workflow/test_system.py +++ /dev/null @@ -1,90 +0,0 @@ -#!/usr/bin/env python3 -""" -This is a SeisFlows Test workflow class which is used to test out the underlying -machinery for a given set of modules, before submitting a full workflow. Used -for debugging and development, as well as ensuring that SeisFlows is in working -order. -""" -import os -from seisflows import logger -from seisflows.tools import unix -from seisflows.tools.config import Dict, get_task_id - - -class TestSystem: - """ - TestSystem Workflow - ------------- - Test individual sub-modules in a 'live' testing environment - - Parameters - ---------- - - Paths - ----- - *** - """ - def __init__(self, modules=None, workdir=os.getcwd(), path_output=None, - **kwargs): - """Test workflow""" - self._modules = modules - - self.path = Dict( - workdir=workdir, - scratch=os.path.join(workdir, "scratch"), - output=path_output or os.path.join(workdir, "output") - ) - - @property - def task_list(self): - """ - A task list which includes tests for most of the modules depending on - whether they're included in the module list or not. - """ - return [] - - def check(self): - """ - Run check functions for all underlying modules - """ - logger.info("running check for test workflow") - for name, module in self._modules.items(): - if module: - module.check() - - def setup(self): - """ - Creates required directory structure - """ - logger.info("running setup for test workflow") - for path in [self.path.workdir, self.path.scratch, self.path.output]: - unix.mkdir(path) - - for name, module in self._modules.items(): - if module: - module.setup() - - def run(self): - """ - Use the system sub-module to submit some simple functions to ensure that - we can run jobs on the system and that the job checking works - """ - logger.info("running test workflow") - - if not "system" in self._modules: - logger.warning("No `system` module chosen, skipping " - "`test_system_run`") - - system = self._modules["system"] - system.run(funcs=[self.test_function_print_hello_world], single=False) - system.run(funcs=[self.test_function_wait_ten_seconds], single=True) - - def test_function_print_hello_world(self): - """Simple print function to be called by system run""" - print(f"hello world from task id: {get_task_id()}") - - def test_function_wait_ten_seconds(self): - """Simple wait function to be called by system run, used to test - job status check""" - for i in range(11): - print(f"hello world from task id: {get_task_id()} after {i} sec.") From c0f23a04df9a955d0c3b1605b2eb05e59ca07f33 Mon Sep 17 00:00:00 2001 From: bch0w Date: Thu, 18 Aug 2022 19:04:03 +0000 Subject: [PATCH 121/195] bugfix slurm system not exiting when jobs failed due to incorrect check logic --- seisflows/system/slurm.py | 9 +++++---- seisflows/workflow/test_flow.py | 8 ++++++-- 2 files changed, 11 insertions(+), 6 deletions(-) diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index e6c61fd9..3c5a829f 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -29,6 +29,7 @@ import time import subprocess +from datetime import timedelta from seisflows import ROOT_DIR, logger from seisflows.system.cluster import Cluster from seisflows.tools import msg @@ -73,8 +74,8 @@ def __init__(self, ntask_max=100, slurm_args="", **kwargs): self._partitions = {} # Convert walltime and tasktime to datetime str 'H:MM:SS' - self._tasktime = str(time.timedelta(minutes=self.tasktime)) - self._walltime = str(time.timedelta(minutes=self.walltime)) + self._tasktime = str(timedelta(minutes=self.tasktime)) + self._walltime = str(timedelta(minutes=self.walltime)) def check(self): """ @@ -242,9 +243,9 @@ def check_job_status(job_id): bad_states = ["TIMEOUT", "FAILED", "NODE_FAIL", "OUT_OF_MEMORY", "CANCELLED"] while True: - time.sleep(5) # give job time to process and also prevent over-query + time.sleep(2) # give job time to process and also prevent over-query job_ids, states = query_job_states(job_id) - if [state == "COMPLETED" for state in states]: + if all([state == "COMPLETED" for state in states]): return 1 # Pass elif any([check in states for check in bad_states]): # Any bad states? logger.info("atleast 1 system job returned a failing exit code") diff --git a/seisflows/workflow/test_flow.py b/seisflows/workflow/test_flow.py index a03bd7ff..b7482534 100644 --- a/seisflows/workflow/test_flow.py +++ b/seisflows/workflow/test_flow.py @@ -7,6 +7,7 @@ """ import os import time +import sys from glob import glob from seisflows import logger from seisflows.tools import unix, msg @@ -108,6 +109,8 @@ def run(self): for func in self.task_list: func() + logger.info(msg.mjr("FINISHED TEST WORKFLOW")) + def test_system_print_hello_world(self): """ Use the system to run a simple print function which names the currently @@ -126,6 +129,8 @@ def _test_function(): # Run an array job self.system.run(funcs=[_test_function], single=False) + time.sleep(5) # give the system a second to catch up + # Check the results of the run log_files = glob(os.path.join(self.system.path.log_files, "*_*")) assert(len(log_files) == self.system.ntask), \ @@ -153,7 +158,7 @@ def _test_function(): time.sleep(1) print(f"Task ID {get_task_id()} waited {i} sec.") - raise Exception("intentional exception for job failure test") + sys.exit(-1) # intentional job failure # Clear the log files dir. as this is how we will check results unix.rm(self.system.path.log_files) @@ -163,7 +168,6 @@ def _test_function(): try: self.system.run(funcs=[_test_function], single=True) logger.critical("job was expected to fail but did not") - sys.exit(-1) except SystemExit: pass From 975aecd8eaca63c0742203edae068a67a757621c Mon Sep 17 00:00:00 2001 From: bch0w Date: Thu, 18 Aug 2022 19:43:08 +0000 Subject: [PATCH 122/195] fixed up 'seisflows swap' command to replace a module in an existing parameter file --- seisflows/seisflows.py | 51 ++++++++++++++++----------------- seisflows/solver/specfem.py | 2 +- seisflows/system/slurm.py | 21 ++++++++++---- seisflows/workflow/test_flow.py | 1 + 4 files changed, 42 insertions(+), 33 deletions(-) diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 1c78cbfe..60788047 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -565,33 +565,32 @@ def swap(self, module, classname, **kwargs): # Load in old parameter file and then move it to a hidden file ogpars = load_yaml(self._args.parameter_file) - ogpaths = Dict(ogpars.pop("PATHS")) unix.mv(self._args.parameter_file, f"_{self._args.parameter_file}") - - # Create a new parameter file with updated module - unix.cp(PAR_FILE, self._args.workdir) - for name in NAMES: - setpar(key=name, val=ogpars[name.upper()], - file=self._args.parameter_file, delim=":") - - # Overwrite with new parameters - setpar(key=module, val=classname, file=self._args.parameter_file, - delim=":") - self.configure() - for key, val in ogpars.items(): - try: - setpar(key=key, val=val, file=self._args.parameter_file, - delim=":") - except KeyError: - continue - for key, val in ogpaths.items(): - try: - setpar(key=key, val=val, file=self._args.parameter_file, - delim=":") - except KeyError: - continue - - unix.rm(f"_{self._args.parameter_file}") + try: + # Create a new parameter file with updated module + unix.cp(PAR_FILE, self._args.workdir) + for name in NAMES: + setpar(key=name, val=ogpars[name], + file=self._args.parameter_file, delim=":") + + # Overwrite with new parameters + setpar(key=module, val=classname, file=self._args.parameter_file, + delim=":") + self.configure() + + ogpars.pop(module) # don't edit the parameter were changing + for key, val in ogpars.items(): + try: + setpar(key=key, val=val, file=self._args.parameter_file, + delim=":") + except KeyError: + continue + # Replace parameter file if any errors happen + except Exception as e: + unix.mv(f"_{self._args.parameter_file}", self._args.parameter_file) + raise(e) + finally: + unix.rm(f"_{self._args.parameter_file}") def check(self, **kwargs): """ diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index b2fb0f9c..0d391cdd 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -438,8 +438,8 @@ def forward_simulation(self, executables=None, save_traces=False, """ if executables is None: executables = ["bin/xmeshfem2D", "bin/xspecfem2D"] - unix.cd(self.cwd) + unix.cd(self.cwd) setpar(key="SIMULATION_TYPE", val="1", file="DATA/Par_file") setpar(key="SAVE_FORWARD", val=".true.", file="DATA/Par_file") diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index 3c5a829f..dbae9582 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -45,9 +45,6 @@ class Slurm(Cluster): Parameters ---------- - :type ntask_max: int - :param ntask_max: set the maximum number of simultaneously running array - job processes that are submitted to a cluster at one time. :type slurm_args: str :param slurm_args: Any (optional) additional SLURM arguments that will be passed to the SBATCH scripts. Should be in the form: @@ -60,7 +57,13 @@ class Slurm(Cluster): __doc__ = Cluster.__doc__ + __doc__ def __init__(self, ntask_max=100, slurm_args="", **kwargs): - """Slurm-specific setup parameters""" + """ + Slurm-specific setup parameters + + :type ntask_max: int + :param ntask_max: set the maximum number of simultaneously running array + job processes that are submitted to a cluster at one time. + """ super().__init__(**kwargs) # Overwrite the existing 'mpiexec' @@ -218,7 +221,7 @@ def run(self, funcs, single=False, **kwargs): if status == -1: # Failed job logger.critical( msg.cli(f"Stopping workflow. Please check logs for details.", - items=[f"TASKS: {[_.__name_ for _ in funcs]}", + items=[f"TASKS: {[_.__name__ for _ in funcs]}", f"SBATCH: {run_call}"], header="slurm run error", border="=") ) @@ -233,6 +236,12 @@ def check_job_status(job_id): If the job goes into a bad state like 'FAILED', log the failing job's id and their states. If all jobs complete nominally, return + .. note:: + The time.sleep() is critical before querying job status because the + system will likely take a second to intitiate jobs so if we + `query_job_states` before this has happenend, it will return empty + lists and cause the function to error out + :type job_id: str :param job_id: main job id to query, returned from the subprocess.run that ran the jobs @@ -243,7 +252,7 @@ def check_job_status(job_id): bad_states = ["TIMEOUT", "FAILED", "NODE_FAIL", "OUT_OF_MEMORY", "CANCELLED"] while True: - time.sleep(2) # give job time to process and also prevent over-query + time.sleep(5) # give job time to process and also prevent over-query job_ids, states = query_job_states(job_id) if all([state == "COMPLETED" for state in states]): return 1 # Pass diff --git a/seisflows/workflow/test_flow.py b/seisflows/workflow/test_flow.py index b7482534..744a10c2 100644 --- a/seisflows/workflow/test_flow.py +++ b/seisflows/workflow/test_flow.py @@ -169,6 +169,7 @@ def _test_function(): self.system.run(funcs=[_test_function], single=True) logger.critical("job was expected to fail but did not") except SystemExit: + logger.info("job failure catch was successful") pass # Check the log file for job failure From db1d4d97987f6e3c9efdd2283b0a41f7cba2bcdb Mon Sep 17 00:00:00 2001 From: bch0w Date: Thu, 18 Aug 2022 20:01:09 +0000 Subject: [PATCH 123/195] updating template parameter file docstring --- seisflows/examples/parameters.yaml | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/seisflows/examples/parameters.yaml b/seisflows/examples/parameters.yaml index d637bf70..d0eed426 100644 --- a/seisflows/examples/parameters.yaml +++ b/seisflows/examples/parameters.yaml @@ -8,10 +8,12 @@ # SeisFlows' behavior at runtime. Each module requires its own sub-parameters. # # .. rubric:: -# - To determine available options for modules listed below, run: +# - Determine available options for modules by running: # > seisflows print modules -# - To auto-fill with docstrings and default values (recommended), run: +# - Auto-fill with docstrings and default values (recommended) by running: # > seisflows configure +# - Swap out module parameters for a configured parameter file by running: +# > seisflows swap {module} {name} (e.g., seisflows swap solver specfem3d) # - To set values as NoneType, use: null # - To set values as infinity, use: inf # From 3eeb5642b4913958681903ca82ecb0ac1e9244d7 Mon Sep 17 00:00:00 2001 From: bch0w Date: Thu, 18 Aug 2022 20:58:13 +0000 Subject: [PATCH 124/195] updated some docstrings to make parameter file more readable --- seisflows/examples/sfexample2d.py | 3 ++- seisflows/optimize/gradient.py | 3 ++- seisflows/solver/specfem.py | 1 + seisflows/system/slurm.py | 4 ++-- seisflows/system/workstation.py | 3 ++- seisflows/tools/config.py | 2 +- seisflows/workflow/forward.py | 8 +++++--- seisflows/workflow/inversion.py | 3 ++- 8 files changed, 17 insertions(+), 10 deletions(-) diff --git a/seisflows/examples/sfexample2d.py b/seisflows/examples/sfexample2d.py index 2951cc8e..4ad89955 100644 --- a/seisflows/examples/sfexample2d.py +++ b/seisflows/examples/sfexample2d.py @@ -358,7 +358,8 @@ def main(self): f"This is a [SPECFEM2D] [WORKSTATION] example, which will " f"run an inversion to assess misfit between two homogeneous halfspace " f"models with slightly different velocities. [3 events, 1 station, 2 " - f"iterations]. The tasks involved include: ", + f"iterations]. The inversion is expected to fail after the 5th line " + f"search step count of the 2nd iteration. The tasks involved include: ", items=["1. (optional) Download, configure, compile SPECFEM2D", "2. Set up a SPECFEM2D working directory", "3. Generate starting model from Tape2007 example", diff --git a/seisflows/optimize/gradient.py b/seisflows/optimize/gradient.py index 64497521..72678143 100644 --- a/seisflows/optimize/gradient.py +++ b/seisflows/optimize/gradient.py @@ -41,7 +41,8 @@ class Gradient: """ Gradient Optimization --------------------- - Gradient/steepest descent optimization algorithm. + Defines foundational structure for Optimization module. Applies a + gradient/steepest descent optimization algorithm. Parameters ---------- diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 0d391cdd..9f562bdb 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -32,6 +32,7 @@ class Specfem: """ Solver SPECFEM -------------- + Defines foundational structure for Specfem-based solver module. Generalized SPECFEM interface to manipulate SPECFEM2D/3D/3D_GLOBE w/ Python Parameters diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index dbae9582..1fb9aa3a 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -40,8 +40,8 @@ class Slurm(Cluster): """ System Slurm ------------------ - Runs tasks in serial on a local machine. Interface for submitting jobs to - Simple Linux Utility for Resource Management (SLURM) system. + Interface for submitting and monitoring jobs on HPC systems running the + Simple Linux Utility for Resource Management (SLURM) workload manager. Parameters ---------- diff --git a/seisflows/system/workstation.py b/seisflows/system/workstation.py index 531316a0..c0af8830 100644 --- a/seisflows/system/workstation.py +++ b/seisflows/system/workstation.py @@ -16,7 +16,8 @@ class Workstation: """ Workstation System ------------------ - Runs tasks in serial on a local machine. + Defines foundational structure for System module. When used standalone, + runs tasks in serial on a local machine. Parameters ---------- diff --git a/seisflows/tools/config.py b/seisflows/tools/config.py index 6a345733..a269a408 100755 --- a/seisflows/tools/config.py +++ b/seisflows/tools/config.py @@ -154,7 +154,7 @@ def set_task_id(task_id): def import_seisflows(workdir=os.getcwd(), parameter_file="parameters.yaml"): """ Standard SeisFlows workflow setup block which runs a number of setup - tasks including: loading a user-defiend parameter file, configuring the + tasks including: loading a user-defined parameter file, configuring the package-wide logger based on user-input path to log file and desired verbosity, and instantiating all modules in a generic fashion based on user choice. Returns the 'workflow' module, which contains all other submodules diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index 9a9b9def..e0f94790 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -18,8 +18,9 @@ class Forward: """ Forward Workflow ---------------- - Run forward solver in parallel and (optionally) calculate - data-synthetic misfit and adjoint sources. + Defines foundational structure for Workflow module. When used standalone + is in charge of running forward solver in parallel and (optionally) + calculating data-synthetic misfit and adjoint sources. Parameters ---------- @@ -47,7 +48,8 @@ class Forward: :type export_residuals: bool :param export_residuals: export all residuals (data-synthetic misfit) that are generated by the external solver to `path_output`. If False, - residuals stored in scratch may be discarded at any time in the workflow + residuals stored in scratch may be discarded at any time in the + workflow Paths ----- diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index 069023fc..edc156cf 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -297,6 +297,7 @@ def initialize_line_search(self): direction (p_new) to recover the trial model (m_try). This model is then exposed on disk to the solver. """ + logger.info(msg.mnr("RUNNING LINE SEARCH")) logger.info(f"initializing " f"'{self.optimize.line_search_method}'ing " f"line search") @@ -410,7 +411,7 @@ def finalize_iteration(self): carried out. Contains some logic to consider whether or not to continue with a thrifty inversion. """ - logger.info(msg.mnr("CLEANING WORKDIR FOR NEXT ITERATION")) + logger.info(msg.sub("CLEANING WORKDIR FOR NEXT ITERATION")) # Export scratch files to output if requested if self.export_model: From e6a3e81871f25909f5ae799ba183a0a168f1be29 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Fri, 19 Aug 2022 15:34:44 -0500 Subject: [PATCH 125/195] slurm query job status now has an internal recursive check function incase a job is not initiated immediately. gives a ~1m buffer for the system to catch up before error'ing out --- seisflows/system/slurm.py | 22 +++++++++++++++++++++- 1 file changed, 21 insertions(+), 1 deletion(-) diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index 1fb9aa3a..f72421b1 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -264,7 +264,7 @@ def check_job_status(job_id): return -1 # Fail -def query_job_states(job_id): +def query_job_states(job_id, _recheck=0): """ Queries completion status of an array job by running the SLURM cmd `sacct` Available job states are listed here: https://slurm.schedmd.com/sacct.html @@ -285,11 +285,31 @@ def query_job_states(job_id): :type job_id: str :param job_id: main job id to query, returned from the subprocess.run that ran the jobs + :type rechecks: int + :param rechecks: Used for recursive calling of the function. It can take + time for jobs to be initiated on a system, which may result in the + stdout of the 'sacct' command to be empty. In this case we wait and call + the function again. Rechecks are used to prevent endless loops by + putting a stop criteria """ job_ids, job_states = [], [] cmd = f"sacct -nLX -o jobid,state -j {job_id}" stdout = subprocess.run(cmd, stdout=subprocess.PIPE, text=True, shell=True).stdout + + # Recursively re-check job state incase the job has not been instantiated + # in which cause 'stdout' is an empty string + if not stdout: + _recheck += 1 + if _recheck > 10: + logger.critical(f"cannot access job information through '{cmd}', " + f"waited 50s with no return, please check job " + f"scheduler and log messages") + sys.exit(-1) + time.sleep(5) + query_job_states(job_id, _recheck) + + # Return the job numbers and respective states for the given job ID for job_line in str(stdout).strip().split("\n"): job_id, job_state = job_line.split() job_ids.append(job_id) From 5bfe9164ae5944f8de29bd72c6b7745bb216d643 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Fri, 19 Aug 2022 15:35:14 -0500 Subject: [PATCH 126/195] bugfix seisflows 'swap' sets NoneType as null, before they were input as strings --- seisflows/seisflows.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 60788047..37445f5d 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -580,6 +580,8 @@ def swap(self, module, classname, **kwargs): ogpars.pop(module) # don't edit the parameter were changing for key, val in ogpars.items(): + if val is None: + val = "null" try: setpar(key=key, val=val, file=self._args.parameter_file, delim=":") From d6cf1b30c4e2de38e8407ad43e4983bcca93f4f8 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Fri, 19 Aug 2022 16:07:33 -0500 Subject: [PATCH 127/195] slurm system function to evaluate job id from the subprocess.run stdout message. This was hardcoded before but Frontera did not match the expected stdout format. Frontera subclass can now get a job number from its more complicated sbatch stdout message --- seisflows/system/frontera.py | 45 ++++++++++++++++++++++++-- seisflows/system/slurm.py | 61 +++++++++++++++++++++++++++++------- 2 files changed, 92 insertions(+), 14 deletions(-) diff --git a/seisflows/system/frontera.py b/seisflows/system/frontera.py index f816cddc..87649c1f 100644 --- a/seisflows/system/frontera.py +++ b/seisflows/system/frontera.py @@ -28,7 +28,7 @@ class Frontera(Slurm): *** """ - def __init__(self, partition="small", allocation=None, **kwargs): + def __init__(self, partition="development", allocation=None, **kwargs): """Frontera init""" super().__init__(**kwargs) @@ -37,8 +37,8 @@ def __init__(self, partition="small", allocation=None, **kwargs): self.mpiexec = "ibrun" # TODO find out the cores-per-node values for these partitions - self.partitions = {"small": None, "normal": None, "large": None, - "development": None, "flex": None} + self._partitions = {"small": 28, "normal": 28, "large": 28, + "development": 28, "flex": 28} @property @@ -92,3 +92,42 @@ def run_call_header(self): if self.allocation is not None: _call = f"{_call} --allocation={self.allocation}" return _call + + def _stdout_to_job_id(stdout) + """ + The stdout message after an SBATCH job is submitted. On Frontera, the + standard message is preceded by a log message which looks like: + + ``` + ----------------------------------------------------------------- + Welcome to the Frontera Supercomputer + ----------------------------------------------------------------- + + No reservation for this job + --> Verifying valid submit host (login3)...OK + --> Verifying valid jobname...OK + --> Verifying valid ssh keys...OK + --> Verifying access to desired queue (development)...OK + --> Checking available allocation (EAR21042)...OK + --> Verifying that quota for filesystem ... is at 3.87% allocated...OK + --> Verifying that quota for filesystem ... is at 0.91% allocated...OK + 4738284 + + ``` + :type stdout: str + :param stdout: standard SBATCH response after submitting a job with the + '--parsable' flag + :rtype: str + :return: a matching job ID. We convert str->int->str to ensure that + the job id is an integer value (which it must be) + """ + job_id = stdout.split("OK")[-1].strip().split(";")[0] + try: + int(job_id) + except ValueError: + logger.critical(f"parsed job id '{job_id}' does not evaluate as an " + f"integer, please check that function " + f"`system._stdout_to_job_id()` is set correctly") + sys.exit(-1) + + return job_id diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index f72421b1..3c841c51 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -160,12 +160,45 @@ def run_call_header(self): ]) return _call + def _stdout_to_job_id(stdout) + """ + The stdout message after an SBATCH job is submitted, from which we get + the job number, differs between systems, allow this to vary + + .. note:: Examples + 1) standard example: Submitted batch job 4738244 + 2) (1) with '--parsable' flag: 4738244 + 3) federated cluster: Submitted batch job 4738244; Maui + 4) (3) with '--parsable' flag: 4738244; Maui + + This function deals with cases (2) and (4). Other systems that have more + complicated stdout messages will need to overwrite this function + + :type stdout: str + :param stdout: standard SBATCH response after submitting a job with the + '--parsable' flag + :rtype: str + :return: a matching job ID. We convert str->int->str to ensure that + the job id is an integer value (which it must be) + :raises SystemExit: if the job id does not evaluate as an integer + """ + job_id = str(stdout).split(";")[0] + try: + int(job_id) + except ValueError: + logger.critical(f"parsed job id '{job_id}' does not evaluate as an " + f"integer, please check that function " + f"`system._stdout_to_job_id()` is set correctly") + sys.exit(-1) + + return job_id + def run(self, funcs, single=False, **kwargs): """ Runs task multiple times in embarrassingly parallel fasion on a SLURM - cluster. Executes classname.method(*args, **kwargs) `NTASK` times, - each time on `NPROC` CPU cores + cluster. Executes the list of functions (`funcs`) NTASK times with each + task occupying NPROC cores. .. note:: Completely overwrites the `Cluster.run()` command @@ -210,14 +243,20 @@ def run(self, funcs, single=False, **kwargs): logger.debug(run_call) - # Stdout will be job number (e.g., 1234). Federated clusters will return - # job # and cluster name (e.g., 1234;Cluster1). We only want job # - job_id = subprocess.run(run_call, stdout=subprocess.PIPE, + # Grab the job id (used to monitor job status) from the stdout message + stdout = subprocess.run(run_call, stdout=subprocess.PIPE, text=True, shell=True).stdout - job_id = str(job_id).split(";")[0] + job_id = self._stdout_to_job_id(stdout) # Monitor the job queue until all jobs have completed, or any one fails - status = check_job_status(job_id) + try: + status = check_job_status(job_id) + except FileNotFoundError: + logger.critical(f"cannot access job information through 'sacct', " + f"waited 50s with no return, please check job " + f"scheduler and log messages") + sys.exit(-1) + if status == -1: # Failed job logger.critical( msg.cli(f"Stopping workflow. Please check logs for details.", @@ -249,6 +288,8 @@ def check_job_status(job_id): :return: status of all running jobs. 1 for pass (all jobs COMPLETED). -1 for fail (one or more jobs returned failing status) """ + logger.debug(f"checking job status for submitted job: {job_id}") + bad_states = ["TIMEOUT", "FAILED", "NODE_FAIL", "OUT_OF_MEMORY", "CANCELLED"] while True: @@ -291,6 +332,7 @@ def query_job_states(job_id, _recheck=0): stdout of the 'sacct' command to be empty. In this case we wait and call the function again. Rechecks are used to prevent endless loops by putting a stop criteria + :raises FileNotFoundError: if 'sacct' does not return any output for ~1 min. """ job_ids, job_states = [], [] cmd = f"sacct -nLX -o jobid,state -j {job_id}" @@ -302,10 +344,7 @@ def query_job_states(job_id, _recheck=0): if not stdout: _recheck += 1 if _recheck > 10: - logger.critical(f"cannot access job information through '{cmd}', " - f"waited 50s with no return, please check job " - f"scheduler and log messages") - sys.exit(-1) + raise FileNotFoundError time.sleep(5) query_job_states(job_id, _recheck) From 622c7b44d3d0f693cafe692cbbda83f4b4451fb8 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Fri, 19 Aug 2022 18:16:20 -0500 Subject: [PATCH 128/195] bugfix seisflows clean wasn't actually deleting files feature frontera now ssh's into login node (from compute node) before submitting sbatch script to work around compute nodes not being allowed to submit jobs --- seisflows/seisflows.py | 2 +- seisflows/system/frontera.py | 25 ++++++++++++++++++++++--- seisflows/system/slurm.py | 3 ++- 3 files changed, 25 insertions(+), 5 deletions(-) diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 37445f5d..906d0e90 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -655,7 +655,7 @@ def clean(self, force=False, **kwargs): "output_log"]: path = f"path_{name}" if path in pars: - unix.rm(path) + unix.rm(pars[path]) def restart(self, force=False, **kwargs): """ diff --git a/seisflows/system/frontera.py b/seisflows/system/frontera.py index 87649c1f..26d6ceb8 100644 --- a/seisflows/system/frontera.py +++ b/seisflows/system/frontera.py @@ -2,8 +2,17 @@ """ Frontera is one of the Texas Advanced Computing Center (TACC) HPCs. https://frontera-portal.tacc.utexas.edu/ + +.. note:: + One caveat of the TACC Systems is that you cannot submit 'sbatch' from + compute nodes, which is how SeisFlows operates. To work around this, + the run call SSHs from the compute node to the login node to submit the + sbatch script. This requires knowing the User name, and that SSH keys + are available. Thanks to Ian Wang for the suggestion. """ import os +import sys +from seisflows import logger from seisflows.system.slurm import Slurm @@ -15,6 +24,10 @@ class Frontera(Slurm): Parameters ---------- + :type user: str + :param user: User's username on TACC systems. Can be determined by 'whoami' + or will be gathered from the 'USER' environment variable. Used for + internal ssh'ing from compute nodes to login nodes. :type partition: str :param partition: Chinook has various partitions which each have their own number of cores per compute node. Available are: small, normal, @@ -28,15 +41,18 @@ class Frontera(Slurm): *** """ - def __init__(self, partition="development", allocation=None, **kwargs): + def __init__(self, user=None, partition="development", allocation=None, + **kwargs): """Frontera init""" super().__init__(**kwargs) + self.user = user or os.environ["USER"] # alt. getpass.getuser() self.partition = partition self.allocation = allocation self.mpiexec = "ibrun" - # TODO find out the cores-per-node values for these partitions + # See note in file docstring for why we need this SSH call + self._ssh_call = f"ssh {self.user}@frontera.tacc.utexas.edu" self._partitions = {"small": 28, "normal": 28, "large": 28, "development": 28, "flex": 28} @@ -53,6 +69,7 @@ def submit_call_header(self): :return: the system-dependent portion of a submit call """ _call = " ".join([ + f"{self._ssh_call}", f"sbatch", f"--job-name={self.title}", # -J f"--partition={self.partition}", # -p @@ -78,6 +95,7 @@ def run_call_header(self): :return: the system-dependent portion of a run call """ _call = " ".join([ + f"{self._ssh_call}", f"sbatch", f"{self.slurm_args or ''}", f"--job-name={self.title}", @@ -93,7 +111,8 @@ def run_call_header(self): _call = f"{_call} --allocation={self.allocation}" return _call - def _stdout_to_job_id(stdout) + @staticmethod + def _stdout_to_job_id(stdout): """ The stdout message after an SBATCH job is submitted. On Frontera, the standard message is preceded by a log message which looks like: diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index 3c841c51..08d01342 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -160,7 +160,8 @@ def run_call_header(self): ]) return _call - def _stdout_to_job_id(stdout) + @staticmethod + def _stdout_to_job_id(stdout): """ The stdout message after an SBATCH job is submitted, from which we get the job number, differs between systems, allow this to vary From 058ff4cdf9dd305fade23251e36868c2be0921f6 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Fri, 19 Aug 2022 19:00:34 -0500 Subject: [PATCH 129/195] frontera added more checks for partition and forced partition choices because some of frontera's partitions only allow 1 job per user, which does not work with seisflows --- seisflows/system/frontera.py | 44 +++++++++++++++++++++++++++++++----- seisflows/system/slurm.py | 2 +- 2 files changed, 39 insertions(+), 7 deletions(-) diff --git a/seisflows/system/frontera.py b/seisflows/system/frontera.py index 26d6ceb8..8ad1f015 100644 --- a/seisflows/system/frontera.py +++ b/seisflows/system/frontera.py @@ -3,12 +3,17 @@ Frontera is one of the Texas Advanced Computing Center (TACC) HPCs. https://frontera-portal.tacc.utexas.edu/ -.. note:: - One caveat of the TACC Systems is that you cannot submit 'sbatch' from +.. note:: Caveat 1 + On TACC Systems is that you cannot submit 'sbatch' from compute nodes, which is how SeisFlows operates. To work around this, the run call SSHs from the compute node to the login node to submit the sbatch script. This requires knowing the User name, and that SSH keys are available. Thanks to Ian Wang for the suggestion. + +.. note:: Caveat 2 + TACC does not allow the '--array' option, which SeisFlows used to submit + multiple jobs in a single SBATCH command. To work around this, the Frontera + module submits jobs one by one. """ import os import sys @@ -53,9 +58,37 @@ def __init__(self, user=None, partition="development", allocation=None, # See note in file docstring for why we need this SSH call self._ssh_call = f"ssh {self.user}@frontera.tacc.utexas.edu" - self._partitions = {"small": 28, "normal": 28, "large": 28, - "development": 28, "flex": 28} + # Internally used check parameters. Because 'development' and 'large' + # partitions do not allow >1 job per user, we cannot use them + self._acceptable_partitions = ["small", "normal", "flex"] + self._partitions = {"small": 28, "normal": 28, "large": 28, "flex": 28, + "development": 28} + self._max_jobs = {"small": 20, "large": 1, "normal": 100, "flex": 15, + "development": 1} + + # Hard set `ntask_max` based on TACCS 'QOSMaxJobsPerUserLimit' + self.ntask_max = self._max_jobs[self.partition] + + def check(self): + """ + Checks parameters and paths + """ + super().check() + + assert(self.partition in self._acceptable_partitions), \ + f"Frontera `partition` must be in {self._acceptable_partitions}" + + assert(self._max_jobs[self.partition] > 1), ( + f"Frontera partition '{self.partition}' does not allow more than 1 " + f"simultaneously running job, meaning SeisFlows will not work. " + f"please choose a different partition" + ) + + if self.tasktime > 60 and self.partition == "flex": + logger.warning("Frontera's 'Flex' partition may cancel jobs that " + "exceed 60 minute wall time. Consider choosing a " + "different partition if this may be a problem") @property def submit_call_header(self): @@ -69,7 +102,6 @@ def submit_call_header(self): :return: the system-dependent portion of a submit call """ _call = " ".join([ - f"{self._ssh_call}", f"sbatch", f"--job-name={self.title}", # -J f"--partition={self.partition}", # -p @@ -103,7 +135,7 @@ def run_call_header(self): f"--output={os.path.join(self.path.log_files, '%A_%a')}", f"--ntasks={self.nproc:d}", f"--nodes={self.nodes}", - f"--array=0-{self.ntask - 1 % self.ntask_max}", + # f"--array=0-{self.ntask - 1 % self.ntask_max}", f"--time={self._tasktime}", f"--parsable" ]) diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index 08d01342..4c11a1ac 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -289,7 +289,7 @@ def check_job_status(job_id): :return: status of all running jobs. 1 for pass (all jobs COMPLETED). -1 for fail (one or more jobs returned failing status) """ - logger.debug(f"checking job status for submitted job: {job_id}") + logger.debug(f"monitoring job status for submitted job: {job_id}") bad_states = ["TIMEOUT", "FAILED", "NODE_FAIL", "OUT_OF_MEMORY", "CANCELLED"] From 27b1f035c00d0fc33231a8d5a183cff82cba56f1 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Fri, 19 Aug 2022 19:30:04 -0500 Subject: [PATCH 130/195] testflow slightly working on frontera with ssh option, need to find work around for frontera now allowing array job submission --- seisflows/system/frontera.py | 14 +++++++++++--- seisflows/system/slurm.py | 3 ++- seisflows/workflow/test_flow.py | 2 +- 3 files changed, 14 insertions(+), 5 deletions(-) diff --git a/seisflows/system/frontera.py b/seisflows/system/frontera.py index 8ad1f015..e4298355 100644 --- a/seisflows/system/frontera.py +++ b/seisflows/system/frontera.py @@ -33,6 +33,12 @@ class Frontera(Slurm): :param user: User's username on TACC systems. Can be determined by 'whoami' or will be gathered from the 'USER' environment variable. Used for internal ssh'ing from compute nodes to login nodes. + :type conda_env: str + :param conda_env: name of the Conda environment in which you are running + SeisFlows. Defaults to environment variable 'CONDA_DEFAULT_ENV'. Used + to activate the conda environment AFTER ssh'ing from compute to login + node, to ensure that the newly submitted job has access to the SeisFlows + environment :type partition: str :param partition: Chinook has various partitions which each have their own number of cores per compute node. Available are: small, normal, @@ -46,18 +52,20 @@ class Frontera(Slurm): *** """ - def __init__(self, user=None, partition="development", allocation=None, - **kwargs): + def __init__(self, user=None, conda_env=None, partition="development", + allocation=None, **kwargs): """Frontera init""" super().__init__(**kwargs) self.user = user or os.environ["USER"] # alt. getpass.getuser() + self.conda_env = conda_env or os.environ["CONDA_DEFAULT_ENV"] self.partition = partition self.allocation = allocation self.mpiexec = "ibrun" # See note in file docstring for why we need this SSH call - self._ssh_call = f"ssh {self.user}@frontera.tacc.utexas.edu" + self._ssh_call = (f"ssh {self.user}@frontera.tacc.utexas.edu " + f"'conda activate {self.conda_env}; ") # Internally used check parameters. Because 'development' and 'large' # partitions do not allow >1 job per user, we cannot use them diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index 4c11a1ac..d64bcca6 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -231,7 +231,8 @@ def run(self, funcs, single=False, **kwargs): f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'run')}", f"--funcs {funcs_fid}", f"--kwargs {kwargs_fid}", - f"--environment {self.environs or ''}" + f"--environment {self.environs or ''}'" + # f"--environment {self.environs or ''}" ]) # Single-process jobs simply need to replace a few sbatch arguments. diff --git a/seisflows/workflow/test_flow.py b/seisflows/workflow/test_flow.py index 744a10c2..0da33c51 100644 --- a/seisflows/workflow/test_flow.py +++ b/seisflows/workflow/test_flow.py @@ -93,7 +93,7 @@ def setup(self): # Force some internal module variables to keep testing lightweight logger.info("overwriting internal System parameters from given values") - self.system.ntask = 3 + self.system.ntask = 1 self.system.nproc = 1 self.system.tasktime = .25 # 15 seconds self.system.walltime = 2.5 # 2.5 minutes From 07d198133bed311576124333bde1b0880595b43d Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Fri, 19 Aug 2022 20:33:00 -0500 Subject: [PATCH 131/195] Frontera module now defines its own run() function which side-steps the fact that Frontera does not allow the '--array' option. Instead we submit jobs one by one, each job must ssh into the login node, activate the conda environment, and sbatch a job. Also had to restructure the job monitoring system since we are no longer looking at a single array job, but a list of unique job ids. --- seisflows/system/frontera.py | 117 +++++++++++++++++++++++++++----- seisflows/system/slurm.py | 109 +++++++++++++++++++---------- seisflows/workflow/test_flow.py | 2 +- 3 files changed, 174 insertions(+), 54 deletions(-) diff --git a/seisflows/system/frontera.py b/seisflows/system/frontera.py index e4298355..2e9682e1 100644 --- a/seisflows/system/frontera.py +++ b/seisflows/system/frontera.py @@ -3,22 +3,29 @@ Frontera is one of the Texas Advanced Computing Center (TACC) HPCs. https://frontera-portal.tacc.utexas.edu/ -.. note:: Caveat 1 - On TACC Systems is that you cannot submit 'sbatch' from - compute nodes, which is how SeisFlows operates. To work around this, - the run call SSHs from the compute node to the login node to submit the - sbatch script. This requires knowing the User name, and that SSH keys - are available. Thanks to Ian Wang for the suggestion. - -.. note:: Caveat 2 - TACC does not allow the '--array' option, which SeisFlows used to submit +.. note:: Frontera Caveat 1 + On TACC Systems you cannot submit 'sbatch' from compute nodes. Work around: + SSHs from compute node to login node, activate conda environemtn, submit + sbatch script. This requires knowing the User name, conda environment name, + and ensuring SSH keys are available. Thanks to Ian Wang for the suggestion + to SSH around the problem. + + Essentially we are running, from the compute node: + $ ssh user@hostname 'conda activate env; sbatch --arg=val run_function.sh' + +.. note:: Frontera Caveat 2 + TACC does not allow the '--array' option, which SeisFlows uses to submit multiple jobs in a single SBATCH command. To work around this, the Frontera module submits jobs one by one. """ import os import sys -from seisflows import logger -from seisflows.system.slurm import Slurm +import subprocess +from seisflows import ROOT_DIR, logger +from seisflows.tools import msg +from seisflows.tools.config import pickle_function_list +from seisflows.system.slurm import (Slurm, query_job_states, BAD_STATES, + check_job_status_list) class Frontera(Slurm): @@ -63,9 +70,9 @@ def __init__(self, user=None, conda_env=None, partition="development", self.allocation = allocation self.mpiexec = "ibrun" - # See note in file docstring for why we need this SSH call - self._ssh_call = (f"ssh {self.user}@frontera.tacc.utexas.edu " - f"'conda activate {self.conda_env}; ") + # See 'Frontera Caveat 1' note for why we need these calls + self._ssh_call = f"ssh {self.user}@frontera.tacc.utexas.edu" + self._conda_activate = f"conda activate {self.conda_env}" # Internally used check parameters. Because 'development' and 'large' # partitions do not allow >1 job per user, we cannot use them @@ -135,15 +142,13 @@ def run_call_header(self): :return: the system-dependent portion of a run call """ _call = " ".join([ - f"{self._ssh_call}", f"sbatch", f"{self.slurm_args or ''}", f"--job-name={self.title}", f"--partition={self.partition}", - f"--output={os.path.join(self.path.log_files, '%A_%a')}", + f"--output={os.path.join(self.path.log_files, '%A')}", f"--ntasks={self.nproc:d}", f"--nodes={self.nodes}", - # f"--array=0-{self.ntask - 1 % self.ntask_max}", f"--time={self._tasktime}", f"--parsable" ]) @@ -190,3 +195,81 @@ def _stdout_to_job_id(stdout): sys.exit(-1) return job_id + + def run(self, funcs, single=False, **kwargs): + """ + Runs task multiple times in embarrassingly parallel fasion on Frontera. + Executes the list of functions (`funcs`) NTASK times with each + task occupying NPROC cores. + + .. note:: + Completely overwrites the `Slurm.run()` command + + TODO + * can we ssh once or do we have to do it for each process? + * the ssh command prints the ssh prompt to the log file, how do + we supress that? + + :type funcs: list of methods + :param funcs: a list of functions that should be run in order. All + kwargs passed to run() will be passed into the functions. + :type single: bool + :param single: run a single-process, non-parallel task, such as + smoothing the gradient, which only needs to be run by once. + This will change how the job array and the number of tasks is + defined, such that the job is submitted as a single-core job to + the system. + """ + funcs_fid, kwargs_fid = pickle_function_list(funcs, + path=self.path.scratch, + **kwargs) + if single: + logger.info(f"running functions {[_.__name__ for _ in funcs]} on " + f"system 1 time") + _ntask = 1 + else: + logger.info(f"running functions {[_.__name__ for _ in funcs]} on " + f"system {self.ntask} times") + _ntask = self.ntask + + # Run call is slightly different for Frontera, which needs ssh and + # cannot run array jobs + job_ids = [] + for taskid in range(_ntask): + run_call = " ".join([ + f"{self.run_call_header}", + f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'run')}", + f"--funcs {funcs_fid}", + f"--kwargs {kwargs_fid}", + f"--environment {self.environs or ''},SEISFLOWS_TASKID={taskid}" + ]) + # Need to wrap run call in quotes so it can go through ssh + run_call = f"{self._ssh_call} '{self._conda_activate}; {run_call}'" + if taskid == 0: + logger.debug(run_call) + + stdout = subprocess.run(run_call, stdout=subprocess.PIPE, + text=True, shell=True).stdout + job_ids.append(self._stdout_to_job_id(stdout)) + + # Monitor the job queue until all jobs have completed, or any one fails + try: + status = check_job_status_list(job_ids) + except FileNotFoundError: + logger.critical(f"cannot access job information through 'sacct', " + f"waited 50s with no return, please check job " + f"scheduler and log messages") + sys.exit(-1) + + if status == -1: # Failed job + logger.critical( + msg.cli(f"Stopping workflow. Please check logs for details.", + items=[f"TASKS: {[_.__name__ for _ in funcs]}", + f"SBATCH: {run_call}"], + header="slurm run error", border="=") + ) + sys.exit(-1) + else: + logger.info(f"{self.ntask} tasks finished successfully") + + diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index d64bcca6..ddb1a883 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -36,6 +36,10 @@ from seisflows.tools.config import pickle_function_list +# Define bad states defined by SLURM which signifiy failed jobs +BAD_STATES = ["TIMEOUT", "FAILED", "NODE_FAIL", "OUT_OF_MEMORY", "CANCELLED"] + + class Slurm(Cluster): """ System Slurm @@ -194,6 +198,32 @@ def _stdout_to_job_id(stdout): return job_id + @staticmethod + def _modify_run_call_single_proc(run_call): + """ + Modifies a SLURM SBATCH command to use only 1 processor as a single run + by replacing the --array and --ntasks options + + :type run_call: str + :param run_call: The SBATCH command to modify + :rtype: str + :return: a modified SBATCH command that should only run on 1 processor + """ + for part in run_call.split(" "): + if "--array" in part: + run_call = run_call.replace(part, "--array=0-0") + elif "--ntasks" in part: + run_call = run_call.replace(part, "--ntasks=1") + + # Append taskid to environment variable, deal with the case where + # self.par.ENVIRONS is an empty string + task_id_str = "SEISFLOWS_TASKID=0" + if not run_call.strip().endswith("--environment"): + task_id_str = f",{task_id_str}" # appending to the list of vars + + run_call += task_id_str + + return run_call def run(self, funcs, single=False, **kwargs): """ @@ -231,8 +261,7 @@ def run(self, funcs, single=False, **kwargs): f"{os.path.join(ROOT_DIR, 'system', 'runscripts', 'run')}", f"--funcs {funcs_fid}", f"--kwargs {kwargs_fid}", - f"--environment {self.environs or ''}'" - # f"--environment {self.environs or ''}" + f"--environment {self.environs or ''}" ]) # Single-process jobs simply need to replace a few sbatch arguments. @@ -241,7 +270,7 @@ def run(self, funcs, single=False, **kwargs): if single: logger.info("replacing parts of sbatch run call for single " "process job") - run_call = _modify_run_call_single_proc(run_call) + run_call = self._modify_run_call_single_proc(run_call) logger.debug(run_call) @@ -252,7 +281,7 @@ def run(self, funcs, single=False, **kwargs): # Monitor the job queue until all jobs have completed, or any one fails try: - status = check_job_status(job_id) + status = check_job_status_array(job_id) except FileNotFoundError: logger.critical(f"cannot access job information through 'sacct', " f"waited 50s with no return, please check job " @@ -271,7 +300,7 @@ def run(self, funcs, single=False, **kwargs): logger.info(f"task {job_id} finished successfully") -def check_job_status(job_id): +def check_job_status_array(job_id): """ Repeatedly check the status of a currently running job using 'sacct'. If the job goes into a bad state like 'FAILED', log the failing @@ -289,23 +318,56 @@ def check_job_status(job_id): :rtype: int :return: status of all running jobs. 1 for pass (all jobs COMPLETED). -1 for fail (one or more jobs returned failing status) + :raises FileNotFoundError: if 'sacct' does not return any output for ~1 min. """ - logger.debug(f"monitoring job status for submitted job: {job_id}") - - bad_states = ["TIMEOUT", "FAILED", "NODE_FAIL", - "OUT_OF_MEMORY", "CANCELLED"] + logger.info(f"monitoring job status for submitted job: {job_id}") while True: time.sleep(5) # give job time to process and also prevent over-query job_ids, states = query_job_states(job_id) if all([state == "COMPLETED" for state in states]): return 1 # Pass - elif any([check in states for check in bad_states]): # Any bad states? + elif any([check in states for check in BAD_STATES]): # Any bad states? logger.info("atleast 1 system job returned a failing exit code") for job_id, state in zip(job_ids, states): - if state in bad_states: + if state in BAD_STATES: logger.debug(f"{job_id}: {state}") return -1 # Fail +def check_job_status_list(job_ids): + """ + Check the status of a list of currently running jobs. This is used for + systems that cannot submit array jobs (e.g., Frontera) where we instead + submit jobs one by one and have to check the status of all those jobs + together. + + :type job_ids: list of str + :param job_id: job ID's to query with SACCT. Will be considered one group + of jobs, who all need to finish successfully otherwise the entire group + is considered failed + :rtype: int + :return: status of all running jobs. 1 for pass (all jobs COMPLETED). -1 for + fail (one or more jobs returned failing status) + :raises FileNotFoundError: if 'sacct' does not return any output for ~1 min. + """ + logger.info(f"monitoring job status for {len(job_ids)} submitted jobs") + logger.debug(job_ids) + + while True: + time.sleep(5) + job_id_list, states = [], [] + for job_id in job_ids: + _job_ids, _states = query_job_states(job_id) + job_id_list += _job_ids + states += _states + if all([state == "COMPLETED" for state in states]): + return 1 # Pass + elif any([check in states for check in BAD_STATES]): # Any bad states? + logger.info("atleast 1 system job returned a failing exit code") + for job_id, state in zip(job_ids, states): + if state in BAD_STATES: + logger.debug(f"{job_id}: {state}") + return -1 # Fail + def query_job_states(job_id, _recheck=0): """ @@ -359,29 +421,4 @@ def query_job_states(job_id, _recheck=0): return job_ids, job_states -def _modify_run_call_single_proc(run_call): - """ - Modifies a SLURM SBATCH command to use only 1 processor as a single run - by replacing the --array and --ntasks options - - :type run_call: str - :param run_call: The SBATCH command to modify - :rtype: str - :return: a modified SBATCH command that should only run on 1 processor - """ - for part in run_call.split(" "): - if "--array" in part: - run_call = run_call.replace(part, "--array=0-0") - elif "--ntasks" in part: - run_call = run_call.replace(part, "--ntasks=1") - - # Append taskid to environment variable, deal with the case where - # self.par.ENVIRONS is an empty string - task_id_str = "SEISFLOWS_TASKID=0" - if not run_call.strip().endswith("--environment"): - task_id_str = f",{task_id_str}" # appending to the list of vars - - run_call += task_id_str - - return run_call diff --git a/seisflows/workflow/test_flow.py b/seisflows/workflow/test_flow.py index 0da33c51..744a10c2 100644 --- a/seisflows/workflow/test_flow.py +++ b/seisflows/workflow/test_flow.py @@ -93,7 +93,7 @@ def setup(self): # Force some internal module variables to keep testing lightweight logger.info("overwriting internal System parameters from given values") - self.system.ntask = 1 + self.system.ntask = 3 self.system.nproc = 1 self.system.tasktime = .25 # 15 seconds self.system.walltime = 2.5 # 2.5 minutes From ccf0bc02f87a9730c212e34b9fcb9913b0b87ba8 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Mon, 22 Aug 2022 16:00:56 -0800 Subject: [PATCH 132/195] re-instantes logging to stream (stdout) but turns off stream logging on clusters (#128) --- .../system/runscripts/submit_workflow.py | 3 ++- seisflows/tools/config.py | 19 ++++++++++++------- 2 files changed, 14 insertions(+), 8 deletions(-) diff --git a/seisflows/system/runscripts/submit_workflow.py b/seisflows/system/runscripts/submit_workflow.py index 3bb85c47..ecb8210d 100644 --- a/seisflows/system/runscripts/submit_workflow.py +++ b/seisflows/system/runscripts/submit_workflow.py @@ -40,7 +40,8 @@ def parse_args(): """ args = parse_args() workflow = import_seisflows(workdir=args.workdir, - parameter_file=args.parameter_file) + parameter_file=args.parameter_file, + stream_handler=False) workflow.check() workflow.setup() workflow.run() diff --git a/seisflows/tools/config.py b/seisflows/tools/config.py index a269a408..c7a7c0a6 100755 --- a/seisflows/tools/config.py +++ b/seisflows/tools/config.py @@ -151,7 +151,8 @@ def set_task_id(task_id): os.environ["SEISFLOWS_TASKID"] = str(task_id) -def import_seisflows(workdir=os.getcwd(), parameter_file="parameters.yaml"): +def import_seisflows(workdir=os.getcwd(), parameter_file="parameters.yaml", + **kwargs): """ Standard SeisFlows workflow setup block which runs a number of setup tasks including: loading a user-defined parameter file, configuring the @@ -176,7 +177,7 @@ def import_seisflows(workdir=os.getcwd(), parameter_file="parameters.yaml"): parameters = load_yaml(os.path.join(workdir, parameter_file)) config_logger(level=parameters.log_level, filename=parameters.path_output_log, - verbose=parameters.verbose) + verbose=parameters.verbose, **kwargs) # Instantiate SeisFlows modules dynamically based on choices and parameters # provided in the input parameter file @@ -194,7 +195,8 @@ def import_seisflows(workdir=os.getcwd(), parameter_file="parameters.yaml"): return workflow -def config_logger(level="DEBUG", filename=None, filemode="a", verbose=True): +def config_logger(level="DEBUG", filename=None, filemode="a", verbose=True, + stream_handler=True): """ Explicitely configure the logging module with some parameters defined by the user in the System module. Instantiates a stream logger to write @@ -238,10 +240,13 @@ def config_logger(level="DEBUG", filename=None, filemode="a", verbose=True): logger.setLevel(level) formatter = logging.Formatter(fmt_str, datefmt="%Y-%m-%d %H:%M:%S") - # Stream handler to print log statements to stdout - # st_handler = logging.StreamHandler(sys.stdout) - # st_handler.setFormatter(formatter) - # logger.addHandler(st_handler) + # Stream handler to print log statements to stdout. Sometimes we don't want + # this, e.g., on an HPC system having both stream and file will print + # double log messages to your log file + if stream_handler: + st_handler = logging.StreamHandler(sys.stdout) + st_handler.setFormatter(formatter) + logger.addHandler(st_handler) # File handler to print log statements to text file `filename` if filename is not None: From f6845ba778ed7d98ee10009874913315a85831c5 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Mon, 22 Aug 2022 18:32:35 -0800 Subject: [PATCH 133/195] updating 2D examples so that the 'specfem2d' directory can be input from the command line rather than via an input() statement. (#129) --- ...pecfem2d_workstation_inversion_w_pyatoa.py | 14 +++-- seisflows/examples/sfexample2d.py | 21 ++++---- seisflows/seisflows.py | 52 +++++++++---------- 3 files changed, 46 insertions(+), 41 deletions(-) diff --git a/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py b/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py index f5bd3b51..04f728ed 100644 --- a/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py +++ b/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py @@ -26,7 +26,7 @@ class SFPyatoaEx2D(SFExample2D): advantage of the default SPECFEM2D stuff, onyl changes the generation of MODEL TRUE, the number of stations, and the setup of the parameter file. """ - def __init__(self, ntask=2, niter=2, nsta=5): + def __init__(self, ntask=2, niter=2, nsta=5, specfem2d_repo=None): """ Overload init and attempt to import Pyatoa before running example, overload the default number of tasks to 2, and add a new init parameter @@ -40,8 +40,13 @@ def __init__(self, ntask=2, niter=2, nsta=5): :type nsta: int :param nsta: number of stations to include in inversion, between 1 and 131 + :type specfem2d_repo: str + :param specfem2d_repo: path to the SPECFEM2D directory which should + contain binary executables. If not given, SPECFEM2D will be + downloaded configured and compiled automatically. """ - super().__init__(ntask=ntask, niter=niter) + super().__init__(ntask=ntask, niter=niter, + specfem2d_repo=specfem2d_repo) self.nsta = nsta # -1 because it represents index but we need to talk in terms of count assert(1 <= self.nsta <= 131), \ @@ -154,5 +159,6 @@ def finalize_specfem2d_par_file(self): # use argparser here because we're being called by SeisFlows CLI tool which # is occupying argparser if len(sys.argv) > 1: - sfex2d = SFPyatoaEx2D() - sfex2d.main() + _, _, specfem2d_repo = sys.argv + sfex2d = SFPyatoaEx2D(specfem2d_repo=specfem2d_repo) + sfex2d.main() \ No newline at end of file diff --git a/seisflows/examples/sfexample2d.py b/seisflows/examples/sfexample2d.py index 4ad89955..c37a7d29 100644 --- a/seisflows/examples/sfexample2d.py +++ b/seisflows/examples/sfexample2d.py @@ -44,7 +44,7 @@ class SFExample2D: A class for running SeisFlows examples. Simplifies calls structure so that multiple example runs can benefit from the code written here """ - def __init__(self, ntask=3, niter=2): + def __init__(self, ntask=3, niter=2, specfem2d_repo=None): """ Set path structure which is used to navigate around SPECFEM repositories and the example working directory @@ -54,14 +54,11 @@ def __init__(self, ntask=3, niter=2): defaults to 3 :type niter: int :param niter: number of iterations to run. defaults to 2 + :type specfem2d_repo: str + :param specfem2d_repo: path to the SPECFEM2D directory which should + contain binary executables. If not given, SPECFEM2D will be + downloaded configured and compiled automatically. """ - specfem2d_repo = input( - msg.cli("If you have already downloaded SPECMFE2D, please input " - "the full path to the repo. If left blank, this example " - "will pull the latest version from GitHub and attempt " - "to configure and make the binaries:\n> ") - ) - self.cwd = os.getcwd() self.sem2d_paths, self.workdir_paths = self.define_dir_structures( cwd=self.cwd, specfem2d_repo=specfem2d_repo @@ -365,14 +362,16 @@ def main(self): "3. Generate starting model from Tape2007 example", "4. Generate target model w/ perturbed starting model", "5. Set up a SeisFlows working directory", - f"6. Run an inversion workflow"], + "6. Run an inversion workflow"], header="seisflows example 1", border="=") ) # Dynamically traverse sys.argv to get user-input command line. Cannot # use argparser here because we're being called by SeisFlows CLI tool which - # is occupying argparser + # is occupying argparser. Call looks something like: + # $ python /path/to/example.py run path/to/specfem2d if len(sys.argv) > 1: - sfex2d = SFExample2D() + _, _, specfem2d_repo = sys.argv + sfex2d = SFExample2D(specfem2d_repo=specfem2d_repo) sfex2d.main() diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 906d0e90..48288ecf 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -286,6 +286,15 @@ def _format_action(self, action): help="Run your choice of example problem") examples.add_argument("choice", type=str, nargs="?", default=None, help="Name of the specific example problem to run") + examples.add_argument("-r", "--specfem2d_repo", type=str, nargs="?", + default=None, + help= "path to the SPECFEM2D directory which should " + "contain binary executables. If not given, " + "assumes directory is called 'specfem2d/' in " + "the current working directory. If that dir " + "is not found, SPECFEM2D will be downloaded, " + "configured and compiled automatically in the " + "current working directory.") # ========================================================================= # Defines all arguments/functions that expect a sub-argument subparser_dict = {"check": check, "par": par, "inspect": inspect, @@ -851,7 +860,7 @@ def par(self, parameter, value=None, skip_print=False, **kwargs): if not skip_print: print(msg.cli(f"{key}: {cur_val} -> {value}")) - def examples(self, run=None, choice=None, **kwargs): + def examples(self, run=None, choice=None, specfem2d_repo=None, **kwargs): """ List or run a SeisFlows example problem @@ -872,7 +881,12 @@ def examples(self, run=None, choice=None, **kwargs): :type choice: str :param choice: The choice of example, must match the given tag or file name that is assigned to it + :type specfem2d_repo: str + :param specfem2d_repo: path to the SPECFEM2D directory which should + contain binary executables. If not given, SPECFEM2D will be + downloaded configured and compiled automatically. """ + # Gather all the available examples in the repository examples_dir = os.path.join(ROOT_DIR, "examples") examples_list = [] example_names = sorted(glob(os.path.join(examples_dir, "ex*.py"))) @@ -890,7 +904,7 @@ def examples(self, run=None, choice=None, **kwargs): # Case 2: seisflows examples run 1 OR seisflows examples run ex1_... elif run in ["run", "setup"]: arg1 = choice - arg2 = f" {run}" # space so that we do $ python ex.py run + arg2 = f"{run}" # space so that we do $ python ex.py run if arg1: # Allow for matching against index (int) and name (str) try: @@ -902,8 +916,16 @@ def examples(self, run=None, choice=None, **kwargs): j, exname, fid = ex_tup if arg1 in [j, exname]: print(f"{run.capitalize()} example: {exname}") - subprocess.run(f"python {fid}{arg2}", shell=True, - check=False) + # Set default value for SPECFEM2D repository and make + # sure paths are fully expanded to avoid any pathing error + if specfem2d_repo is None: + specfem2d_repo = os.path.join(os.getcwd(), "specfem2d") + specfem2d_repo = os.path.expanduser( + os.path.abspath(specfem2d_repo) + ) + # $ python /path/to/example.py run path/to/specfem2d + subprocess.run(f"python {fid} {arg2} {specfem2d_repo}", + shell=True, check=False) return # Default behavior is to just print this help dialogue @@ -920,28 +942,6 @@ def examples(self, run=None, choice=None, **kwargs): )) print(msg.cli(items=items)) - # def check(self, choice=None, **kwargs): - # """ - # Check parameters, state or values of an active SeisFlows environment. - # Type 'seisflows check --help' for a detailed help message. - # - # :type choice: str - # :param choice: underlying sub-function to choose - # """ - # acceptable_args = {"model": self._check_model_parameters, - # "iter": self._check_current_iteration, - # "src": self._check_source_names, - # "isrc": self._check_source_index} - # - # # Ensure that help message is thrown for empty commands - # if choice not in acceptable_args.keys(): - # self._subparser.print_help() - # sys.exit(0) - # - # self._register_parameters() - # self._load_modules() - # acceptable_args[choice](*self._args.args, **kwargs) - def print(self, choice=None, **kwargs): """ Print information relating to an active SeisFlows environment. From f510a1a73e563602e7cc797f51a92271025b0d94 Mon Sep 17 00:00:00 2001 From: bch0w Date: Mon, 22 Aug 2022 18:39:25 -0800 Subject: [PATCH 134/195] bugfix seisflows examples print statement was trying to evaluate the example problem --- .../examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py | 2 +- seisflows/examples/sfexample2d.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py b/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py index 04f728ed..ea852341 100644 --- a/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py +++ b/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py @@ -158,7 +158,7 @@ def finalize_specfem2d_par_file(self): # Dynamically traverse sys.argv to get user-input command line. Cannot # use argparser here because we're being called by SeisFlows CLI tool which # is occupying argparser - if len(sys.argv) > 1: + if len(sys.argv) > 2: _, _, specfem2d_repo = sys.argv sfex2d = SFPyatoaEx2D(specfem2d_repo=specfem2d_repo) sfex2d.main() \ No newline at end of file diff --git a/seisflows/examples/sfexample2d.py b/seisflows/examples/sfexample2d.py index c37a7d29..2d22829d 100644 --- a/seisflows/examples/sfexample2d.py +++ b/seisflows/examples/sfexample2d.py @@ -371,7 +371,7 @@ def main(self): # use argparser here because we're being called by SeisFlows CLI tool which # is occupying argparser. Call looks something like: # $ python /path/to/example.py run path/to/specfem2d - if len(sys.argv) > 1: + if len(sys.argv) > 2: _, _, specfem2d_repo = sys.argv sfex2d = SFExample2D(specfem2d_repo=specfem2d_repo) sfex2d.main() From 48817dd53ada1014ac8b9002528bc0676e8a3a9f Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Tue, 23 Aug 2022 14:57:24 -0800 Subject: [PATCH 135/195] editing install requirements and pinning deps that werent previously pinned (#130) --- environment.yml | 8 -------- requirements.txt | 5 ++++- setup.py | 11 ++++++----- 3 files changed, 10 insertions(+), 14 deletions(-) delete mode 100644 environment.yml diff --git a/environment.yml b/environment.yml deleted file mode 100644 index 423f2e00..00000000 --- a/environment.yml +++ /dev/null @@ -1,8 +0,0 @@ -name: seisflows -channels: - - conda-forge -dependencies: - - python=3.10 - - obspy>=1.2.2 - - pyyaml>=5.3.1 - - IPython>=7.31.1 diff --git a/requirements.txt b/requirements.txt index 9299cf23..21116deb 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,6 @@ -obspy>=1.2.2 +obspy>=1.3.0 pyyaml>=5.3.1 +scipy>=1.8.0,<1.9.0 # scipy/obspy hanning window rename error IPython>=7.31.1 +dill>=0.3.5.1 +pytest>=7.1.2 diff --git a/setup.py b/setup.py index cee8ca75..9bbb3f84 100644 --- a/setup.py +++ b/setup.py @@ -1,18 +1,19 @@ from setuptools import setup, find_packages setup(name="seisflows", - version="1.0.0", - description="SeisFlows3: A seismic inversion package", - url="https://github.com/seisflows/seisflows", + version="2.0.0", + description="SeisFlows: A seismic inversion package", + url="https://github.com/adjtomo/seisflows", author="Seisflows Development Team", packages=find_packages(), entry_points={ "console_scripts": ["seisflows=seisflows.seisflows:main",]}, license="GPL", install_requires=[ - "obspy>=1.2.2", + "obspy>=1.3.0", "pyyaml>=5.3.1", - "IPython>=7.31.1" + "IPython>=7.31.1", + "dill>=0.3.5.1", ], zip_save=False ) From bd262ae4e41d0bd4cb6d3abb530fe771626e2857 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Tue, 23 Aug 2022 15:05:21 -0800 Subject: [PATCH 136/195] remove template parameter file (#131) * seisflows setup now writes template parameter file from a string within the package rather than copying a file from the source directory * removing old test data * fixing tests after removing example parameter file --- seisflows/examples/parameters.yaml | 32 - seisflows/examples/sfexample2d.py | 2 +- seisflows/seisflows.py | 25 +- .../old_test_solver/001/DATA/CMTSOLUTION_001 | 13 - .../old_test_solver/001/DATA/CMTSOLUTION_002 | 13 - .../old_test_solver/001/DATA/CMTSOLUTION_003 | 13 - .../old_test_solver/001/DATA/CMTSOLUTION_004 | 13 - .../old_test_solver/001/DATA/CMTSOLUTION_005 | 13 - .../old_test_solver/001/DATA/CMTSOLUTION_006 | 13 - .../hold/old_test_solver/001/DATA/Par_file | 1 - .../hold/old_test_solver/001/DATA/STATIONS | 2 - .../hold/old_test_solver/001/bin/xcombine_sem | 3 - .../hold/old_test_solver/001/bin/xmeshfem2D | 2 - .../hold/old_test_solver/001/bin/xsmooth_sem | 3 - .../hold/old_test_solver/001/bin/xspecfem2D | 3 - .../001/traces/obs/AA.S0001.BXY.semd | 5000 ----------------- .../001/traces/obs/AA.S0002.BXY.semd | 5000 ----------------- .../001/traces/syn/AA.S0001.BXY.semd | 5000 ----------------- .../001/traces/syn/AA.S0002.BXY.semd | 5000 ----------------- .../old_test_solver/002/DATA/CMTSOLUTION_001 | 13 - .../old_test_solver/002/DATA/CMTSOLUTION_002 | 13 - .../old_test_solver/002/DATA/CMTSOLUTION_003 | 13 - .../old_test_solver/002/DATA/CMTSOLUTION_004 | 13 - .../old_test_solver/002/DATA/CMTSOLUTION_005 | 13 - .../old_test_solver/002/DATA/CMTSOLUTION_006 | 13 - .../hold/old_test_solver/002/DATA/Par_file | 1 - .../hold/old_test_solver/002/DATA/STATIONS | 2 - .../hold/old_test_solver/002/bin/xcombine_sem | 3 - .../hold/old_test_solver/002/bin/xmeshfem2D | 2 - .../hold/old_test_solver/002/bin/xsmooth_sem | 3 - .../hold/old_test_solver/002/bin/xspecfem2D | 3 - .../002/traces/obs/AA.S0001.BXY.semd | 5000 ----------------- .../002/traces/obs/AA.S0002.BXY.semd | 5000 ----------------- .../002/traces/syn/AA.S0001.BXY.semd | 5000 ----------------- .../002/traces/syn/AA.S0002.BXY.semd | 5000 ----------------- .../old_test_solver/sources/CMTSOLUTION_001 | 1 - .../old_test_solver/sources/CMTSOLUTION_002 | 1 - .../hold/old_test_solver/sources/SOURCE_001 | 1 - .../hold/old_test_solver/sources/SOURCE_002 | 1 - .../hold/scripts/make_parameter_files.sh | 26 - .../scripts/make_test_directory_structure.py | 29 - .../hold/specfem/DATA/CMTSOLUTION_c46e1d99 | 13 - .../hold/specfem/DATA/FORCESOLUTION_c46e1d99 | 10 - .../specfem/DATA/Par_file_SPECFEM2D_cf893667 | 439 -- .../specfem/DATA/Par_file_SPECFEM3D_c46e1d99 | 379 -- .../hold/specfem/DATA/SOURCE_cf893667 | 57 - .../specfem/OUTPUT_FILES/AA.S0001.BXY.semd | 5000 ----------------- .../specfem/OUTPUT_FILES/Uy_file_single_d.su | Bin 20240 -> 0 bytes .../test_data/hold/test_conf_parameters.yaml | 301 - .../hold/test_filled_parameters.yaml | 301 - .../test_data/hold/test_setup_parameters.yaml | 32 - seisflows/tests/test_data/parameters.yaml | 1 - seisflows/tests/test_seisflows.py | 43 +- seisflows/tools/msg.py | 37 + 54 files changed, 67 insertions(+), 46848 deletions(-) delete mode 100644 seisflows/examples/parameters.yaml delete mode 100755 seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_001 delete mode 100755 seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_002 delete mode 100755 seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_003 delete mode 100755 seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_004 delete mode 100755 seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_005 delete mode 100755 seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_006 delete mode 100644 seisflows/tests/test_data/hold/old_test_solver/001/DATA/Par_file delete mode 100644 seisflows/tests/test_data/hold/old_test_solver/001/DATA/STATIONS 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////////////////////////////////////////////////////////////////////////////// -# -# SeisFlows YAML Parameter File -# -# ////////////////////////////////////////////////////////////////////////////// -# -# Modules correspond to the structure of the source code, and determine -# SeisFlows' behavior at runtime. Each module requires its own sub-parameters. -# -# .. rubric:: -# - Determine available options for modules by running: -# > seisflows print modules -# - Auto-fill with docstrings and default values (recommended) by running: -# > seisflows configure -# - Swap out module parameters for a configured parameter file by running: -# > seisflows swap {module} {name} (e.g., seisflows swap solver specfem3d) -# - To set values as NoneType, use: null -# - To set values as infinity, use: inf -# -# MODULES -# /////// -# workflow (str): The types and order of functions for running SeisFlows -# system (str): Computer architecture of the system being used -# solver (str): External numerical solver to use for waveform simulations -# preprocess (str): Preprocessing schema for waveform data -# optimize (str): Optimization algorithm for the inverse problem -# ============================================================================== -workflow: forward -system: workstation -solver: specfem2d -preprocess: default -optimize: gradient diff --git a/seisflows/examples/sfexample2d.py b/seisflows/examples/sfexample2d.py index 2d22829d..ece6a636 100644 --- a/seisflows/examples/sfexample2d.py +++ b/seisflows/examples/sfexample2d.py @@ -127,7 +127,7 @@ def download_specfem2d(self): Last successfully tested 4/28/22 """ if not os.path.exists(self.sem2d_paths.repo): - cmd = ("git clone --recursive --branch devel " + cmd = ("git clone --recursive --branch devel --depth=1" "https://github.com/geodynamics/specfem2d.git") print(f"Downloading SPECFEM2D with command: {cmd}") diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 48288ecf..60e4d7c1 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -25,8 +25,7 @@ from seisflows import logger, ROOT_DIR, NAMES from seisflows.tools import unix, msg -from seisflows.tools.config import (Dict, load_yaml, custom_import, - import_seisflows) +from seisflows.tools.config import load_yaml, custom_import, import_seisflows from seisflows.tools.specfem import (getpar, setpar, getpar_vel_model, setpar_vel_model) @@ -413,14 +412,11 @@ def setup(self, force=False, **kwargs): .. note:: Future working directory setup functions can be placed here - :type symlink: bool - :param symlink: flag to turn on source code symlinking :type force: bool :param force: flag to force parameter file overwriting """ - par_file = os.path.join(ROOT_DIR, "examples", "parameters.yaml") - - if os.path.exists(self._args.parameter_file): + par_file = os.path.join(self._args.workdir, self._args.parameter_file) + if os.path.exists(par_file): if force: check = "y" else: @@ -429,13 +425,12 @@ def setup(self, force=False, **kwargs): f"({self._args.parameter_file}) found. Do you " f"wish to overwrite with a blank file? (y/[n])" )) - if check == "y": - unix.rm(self._args.parameter_file) - else: + if check != "y": sys.exit(0) - unix.cp(par_file, self._args.workdir) - print(msg.cli(f"creating parameter file: {self._args.parameter_file}")) + with open(par_file, "w") as f: + f.write(msg.base_parameter_file) + print(msg.cli(f"created parameter file: {self._args.parameter_file}")) def configure(self, absolute_paths=False, **kwargs): """ @@ -469,6 +464,8 @@ def configure(self, absolute_paths=False, **kwargs): else if False, use path names relative to the working directory. Defaults to False, uses relative paths. """ + print("configuring SeisFlows parameter file") + def split_module_docstring(mod, idx): """ Since our docstrings are concatenated, we need to break them @@ -565,8 +562,6 @@ def swap(self, module, classname, **kwargs): .. rubric:: $ seisflows swap system slurm """ - PAR_FILE = os.path.join(ROOT_DIR, "examples", "parameters.yaml") - if module not in NAMES: print(msg.cli(text=f"{module} does not match {NAMES}", header="error")) @@ -577,7 +572,7 @@ def swap(self, module, classname, **kwargs): unix.mv(self._args.parameter_file, f"_{self._args.parameter_file}") try: # Create a new parameter file with updated module - unix.cp(PAR_FILE, self._args.workdir) + self.setup(force=True) for name in NAMES: setpar(key=name, val=ogpars[name], file=self._args.parameter_file, delim=":") diff --git a/seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_001 b/seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_001 deleted file mode 100755 index cacf1f81..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_001 +++ /dev/null @@ -1,13 +0,0 @@ -XXXX 2012 03 30 18 47 38.66 -40.9316 175.4123 17.6 0.0 0.0 NORTH ISLAND, NEW ZEALAND -event name: 001 -time shift: 0.0000 -half duration: 0.0000 -latitude: -40.9316 -longitude: 175.4123 -depth: 17.5977 -Mrr: -1.451500E+22 -Mtt: 2.777100E+22 -Mpp: -1.325600E+22 -Mrt: 1.085300E+22 -Mrp: -2.075000E+21 -Mtp: -1.439600E+22 diff --git a/seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_002 b/seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_002 deleted file mode 100755 index 53709cf7..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_002 +++ /dev/null @@ -1,13 +0,0 @@ -XXXX 2012 03 30 18 47 38.66 -40.9316 175.4123 17.6 0.0 0.0 NORTH ISLAND, NEW ZEALAND -event name: 002 -time shift: 0.0000 -half duration: 0.0000 -latitude: -40.9316 -longitude: 175.4123 -depth: 17.5977 -Mrr: -1.451500E+22 -Mtt: 2.777100E+22 -Mpp: -1.325600E+22 -Mrt: 1.085300E+22 -Mrp: -2.075000E+21 -Mtp: -1.439600E+22 diff --git a/seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_003 b/seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_003 deleted file mode 100755 index 7d46df94..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_003 +++ /dev/null @@ -1,13 +0,0 @@ -XXXX 2012 03 30 18 47 38.66 -40.9316 175.4123 17.6 0.0 0.0 NORTH ISLAND, NEW ZEALAND -event name: 003 -time shift: 0.0000 -half duration: 0.0000 -latitude: -40.9316 -longitude: 175.4123 -depth: 17.5977 -Mrr: -1.451500E+22 -Mtt: 2.777100E+22 -Mpp: -1.325600E+22 -Mrt: 1.085300E+22 -Mrp: -2.075000E+21 -Mtp: -1.439600E+22 diff --git a/seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_004 b/seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_004 deleted file mode 100755 index 51d51497..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_004 +++ /dev/null @@ -1,13 +0,0 @@ -XXXX 2012 03 30 18 47 38.66 -40.9316 175.4123 17.6 0.0 0.0 NORTH ISLAND, NEW ZEALAND -event name: 004 -time shift: 0.0000 -half duration: 0.0000 -latitude: -40.9316 -longitude: 175.4123 -depth: 17.5977 -Mrr: -1.451500E+22 -Mtt: 2.777100E+22 -Mpp: -1.325600E+22 -Mrt: 1.085300E+22 -Mrp: -2.075000E+21 -Mtp: -1.439600E+22 diff --git a/seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_005 b/seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_005 deleted file mode 100755 index 50b4aeb0..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_005 +++ /dev/null @@ -1,13 +0,0 @@ -XXXX 2012 03 30 18 47 38.66 -40.9316 175.4123 17.6 0.0 0.0 NORTH ISLAND, NEW ZEALAND -event name: 005 -time shift: 0.0000 -half duration: 0.0000 -latitude: -40.9316 -longitude: 175.4123 -depth: 17.5977 -Mrr: -1.451500E+22 -Mtt: 2.777100E+22 -Mpp: -1.325600E+22 -Mrt: 1.085300E+22 -Mrp: -2.075000E+21 -Mtp: -1.439600E+22 diff --git a/seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_006 b/seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_006 deleted file mode 100755 index 8d034437..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/001/DATA/CMTSOLUTION_006 +++ /dev/null @@ -1,13 +0,0 @@ -XXXX 2012 03 30 18 47 38.66 -40.9316 175.4123 17.6 0.0 0.0 NORTH ISLAND, NEW ZEALAND -event name: 006 -time shift: 0.0000 -half duration: 0.0000 -latitude: -40.9316 -longitude: 175.4123 -depth: 17.5977 -Mrr: -1.451500E+22 -Mtt: 2.777100E+22 -Mpp: -1.325600E+22 -Mrt: 1.085300E+22 -Mrp: -2.075000E+21 -Mtp: -1.439600E+22 diff --git a/seisflows/tests/test_data/hold/old_test_solver/001/DATA/Par_file b/seisflows/tests/test_data/hold/old_test_solver/001/DATA/Par_file deleted file mode 100644 index 8b137891..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/001/DATA/Par_file +++ /dev/null @@ -1 +0,0 @@ - diff --git a/seisflows/tests/test_data/hold/old_test_solver/001/DATA/STATIONS b/seisflows/tests/test_data/hold/old_test_solver/001/DATA/STATIONS deleted file mode 100644 index d2383d2e..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/001/DATA/STATIONS +++ /dev/null @@ -1,2 +0,0 @@ - S0001 AA -99.999 -66.666 0.0 0.0 - S0002 AA -88.888 -55.555 0.0 0.0 diff --git a/seisflows/tests/test_data/hold/old_test_solver/001/bin/xcombine_sem b/seisflows/tests/test_data/hold/old_test_solver/001/bin/xcombine_sem deleted file mode 100755 index 969a4d93..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/001/bin/xcombine_sem +++ /dev/null @@ -1,3 +0,0 @@ -#!/bin/bash -e -echo "xcombine_sem" - diff --git a/seisflows/tests/test_data/hold/old_test_solver/001/bin/xmeshfem2D b/seisflows/tests/test_data/hold/old_test_solver/001/bin/xmeshfem2D deleted file mode 100755 index 149ca704..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/001/bin/xmeshfem2D +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/bash -e -echo "xmeshfem2D" diff --git a/seisflows/tests/test_data/hold/old_test_solver/001/bin/xsmooth_sem b/seisflows/tests/test_data/hold/old_test_solver/001/bin/xsmooth_sem deleted file mode 100755 index 376b4aa5..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/001/bin/xsmooth_sem +++ /dev/null @@ -1,3 +0,0 @@ -#!/bin/bash -e -echo "xsmooth_sem" - 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251.81999999999999 3.1065398409177109E-007 - 251.88000000000000 5.8084079838092886E-008 - 251.94000000000000 -2.0056016975898630E-007 diff --git a/seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_001 b/seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_001 deleted file mode 100755 index cacf1f81..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_001 +++ /dev/null @@ -1,13 +0,0 @@ -XXXX 2012 03 30 18 47 38.66 -40.9316 175.4123 17.6 0.0 0.0 NORTH ISLAND, NEW ZEALAND -event name: 001 -time shift: 0.0000 -half duration: 0.0000 -latitude: -40.9316 -longitude: 175.4123 -depth: 17.5977 -Mrr: -1.451500E+22 -Mtt: 2.777100E+22 -Mpp: -1.325600E+22 -Mrt: 1.085300E+22 -Mrp: -2.075000E+21 -Mtp: -1.439600E+22 diff --git a/seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_002 b/seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_002 deleted file mode 100755 index 53709cf7..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_002 +++ /dev/null @@ -1,13 +0,0 @@ -XXXX 2012 03 30 18 47 38.66 -40.9316 175.4123 17.6 0.0 0.0 NORTH ISLAND, NEW ZEALAND -event name: 002 -time shift: 0.0000 -half duration: 0.0000 -latitude: -40.9316 -longitude: 175.4123 -depth: 17.5977 -Mrr: -1.451500E+22 -Mtt: 2.777100E+22 -Mpp: -1.325600E+22 -Mrt: 1.085300E+22 -Mrp: -2.075000E+21 -Mtp: -1.439600E+22 diff --git a/seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_003 b/seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_003 deleted file mode 100755 index 7d46df94..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_003 +++ /dev/null @@ -1,13 +0,0 @@ -XXXX 2012 03 30 18 47 38.66 -40.9316 175.4123 17.6 0.0 0.0 NORTH ISLAND, NEW ZEALAND -event name: 003 -time shift: 0.0000 -half duration: 0.0000 -latitude: -40.9316 -longitude: 175.4123 -depth: 17.5977 -Mrr: -1.451500E+22 -Mtt: 2.777100E+22 -Mpp: -1.325600E+22 -Mrt: 1.085300E+22 -Mrp: -2.075000E+21 -Mtp: -1.439600E+22 diff --git a/seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_004 b/seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_004 deleted file mode 100755 index 51d51497..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_004 +++ /dev/null @@ -1,13 +0,0 @@ -XXXX 2012 03 30 18 47 38.66 -40.9316 175.4123 17.6 0.0 0.0 NORTH ISLAND, NEW ZEALAND -event name: 004 -time shift: 0.0000 -half duration: 0.0000 -latitude: -40.9316 -longitude: 175.4123 -depth: 17.5977 -Mrr: -1.451500E+22 -Mtt: 2.777100E+22 -Mpp: -1.325600E+22 -Mrt: 1.085300E+22 -Mrp: -2.075000E+21 -Mtp: -1.439600E+22 diff --git a/seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_005 b/seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_005 deleted file mode 100755 index 50b4aeb0..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_005 +++ /dev/null @@ -1,13 +0,0 @@ -XXXX 2012 03 30 18 47 38.66 -40.9316 175.4123 17.6 0.0 0.0 NORTH ISLAND, NEW ZEALAND -event name: 005 -time shift: 0.0000 -half duration: 0.0000 -latitude: -40.9316 -longitude: 175.4123 -depth: 17.5977 -Mrr: -1.451500E+22 -Mtt: 2.777100E+22 -Mpp: -1.325600E+22 -Mrt: 1.085300E+22 -Mrp: -2.075000E+21 -Mtp: -1.439600E+22 diff --git a/seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_006 b/seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_006 deleted file mode 100755 index 8d034437..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/002/DATA/CMTSOLUTION_006 +++ /dev/null @@ -1,13 +0,0 @@ -XXXX 2012 03 30 18 47 38.66 -40.9316 175.4123 17.6 0.0 0.0 NORTH ISLAND, NEW ZEALAND -event name: 006 -time shift: 0.0000 -half duration: 0.0000 -latitude: -40.9316 -longitude: 175.4123 -depth: 17.5977 -Mrr: -1.451500E+22 -Mtt: 2.777100E+22 -Mpp: -1.325600E+22 -Mrt: 1.085300E+22 -Mrp: -2.075000E+21 -Mtp: -1.439600E+22 diff --git a/seisflows/tests/test_data/hold/old_test_solver/002/DATA/Par_file b/seisflows/tests/test_data/hold/old_test_solver/002/DATA/Par_file deleted file mode 100644 index 8b137891..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/002/DATA/Par_file +++ /dev/null @@ -1 +0,0 @@ - diff --git a/seisflows/tests/test_data/hold/old_test_solver/002/DATA/STATIONS b/seisflows/tests/test_data/hold/old_test_solver/002/DATA/STATIONS deleted file mode 100644 index d2383d2e..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/002/DATA/STATIONS +++ /dev/null @@ -1,2 +0,0 @@ - S0001 AA -99.999 -66.666 0.0 0.0 - S0002 AA -88.888 -55.555 0.0 0.0 diff --git a/seisflows/tests/test_data/hold/old_test_solver/002/bin/xcombine_sem b/seisflows/tests/test_data/hold/old_test_solver/002/bin/xcombine_sem deleted file mode 100755 index 969a4d93..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/002/bin/xcombine_sem +++ /dev/null @@ -1,3 +0,0 @@ -#!/bin/bash -e -echo "xcombine_sem" - diff --git a/seisflows/tests/test_data/hold/old_test_solver/002/bin/xmeshfem2D b/seisflows/tests/test_data/hold/old_test_solver/002/bin/xmeshfem2D deleted file mode 100755 index 149ca704..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/002/bin/xmeshfem2D +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/bash -e -echo "xmeshfem2D" diff --git a/seisflows/tests/test_data/hold/old_test_solver/002/bin/xsmooth_sem b/seisflows/tests/test_data/hold/old_test_solver/002/bin/xsmooth_sem deleted file mode 100755 index 376b4aa5..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/002/bin/xsmooth_sem +++ /dev/null @@ -1,3 +0,0 @@ -#!/bin/bash -e -echo "xsmooth_sem" - 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251.81999999999999 3.1065398409177109E-007 - 251.88000000000000 5.8084079838092886E-008 - 251.94000000000000 -2.0056016975898630E-007 diff --git a/seisflows/tests/test_data/hold/old_test_solver/sources/CMTSOLUTION_001 b/seisflows/tests/test_data/hold/old_test_solver/sources/CMTSOLUTION_001 deleted file mode 100644 index 8b137891..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/sources/CMTSOLUTION_001 +++ /dev/null @@ -1 +0,0 @@ - diff --git a/seisflows/tests/test_data/hold/old_test_solver/sources/CMTSOLUTION_002 b/seisflows/tests/test_data/hold/old_test_solver/sources/CMTSOLUTION_002 deleted file mode 100644 index 8b137891..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/sources/CMTSOLUTION_002 +++ /dev/null @@ -1 +0,0 @@ - diff --git a/seisflows/tests/test_data/hold/old_test_solver/sources/SOURCE_001 b/seisflows/tests/test_data/hold/old_test_solver/sources/SOURCE_001 deleted file mode 100644 index 8b137891..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/sources/SOURCE_001 +++ /dev/null @@ -1 +0,0 @@ - diff --git a/seisflows/tests/test_data/hold/old_test_solver/sources/SOURCE_002 b/seisflows/tests/test_data/hold/old_test_solver/sources/SOURCE_002 deleted file mode 100644 index 8b137891..00000000 --- a/seisflows/tests/test_data/hold/old_test_solver/sources/SOURCE_002 +++ /dev/null @@ -1 +0,0 @@ - diff --git a/seisflows/tests/test_data/hold/scripts/make_parameter_files.sh b/seisflows/tests/test_data/hold/scripts/make_parameter_files.sh deleted file mode 100644 index d712e063..00000000 --- a/seisflows/tests/test_data/hold/scripts/make_parameter_files.sh +++ /dev/null @@ -1,26 +0,0 @@ -# Run a few SeisFlows commands to generate test data. Useful if the parameter -# file ever changes, at which point we will need to generate new files. -cd .. -rm *yaml - -seisflows setup -cp parameters.yaml test_setup_parameters.yaml - -seisflows configure -cp parameters.yaml test_conf_parameters.yaml - -seisflows par -p materials elastic -seisflows par -p density constant -seisflows par -p format ascii -seisflows par -p begin 1 -seisflows par -p end 1 -seisflows par -p case data -seisflows par -p attenuation False -seisflows par -p specfem_bin ./bin -seisflows par -p specfem_data ./DATA -seisflows par -p model_init ./MODEL_INIT -seisflows par -p model_true ./MODEL_TRUE - -mv parameters.yaml test_filled_parameters.yaml - - diff --git a/seisflows/tests/test_data/hold/scripts/make_test_directory_structure.py b/seisflows/tests/test_data/hold/scripts/make_test_directory_structure.py deleted file mode 100644 index 2527d830..00000000 --- a/seisflows/tests/test_data/hold/scripts/make_test_directory_structure.py +++ /dev/null @@ -1,29 +0,0 @@ -""" -Create a test directory structure with actual data to test individual modules -""" -import os -from seisflows.tools import unix -from seisflows.tools.config import CFGPATHS, ROOT_DIR - - -testdir = os.path.join(ROOT_DIR, "tests", "test_data") -workdir = os.path.join(testdir, "workdir") -for key, path in CFGPATHS.__dict__.items(): - full_path = os.path.join(workdir, path) - if os.path.exists(full_path): - unix.rm(full_path) - # Identify files by the separator between filename and extension - if "." in path: - open(full_path, "w") - else: - unix.mkdir(os.path.join(workdir, path)) - -# Make the scratch solver directory -scratchdir = os.path.join(workdir, CFGPATHS.SCRATCHDIR) -solverdir = os.path.join(scratchdir, "solver", "001") -tracesdir = os.path.join(solverdir, "traces") -for dir_ in [scratchdir, solverdir, tracesdir]: - unix.mkdir(dir_) - -for dir_ in ["obs", "syn", "adj"]: - unix.mkdir(os.path.join(tracesdir, dir_)) diff --git a/seisflows/tests/test_data/hold/specfem/DATA/CMTSOLUTION_c46e1d99 b/seisflows/tests/test_data/hold/specfem/DATA/CMTSOLUTION_c46e1d99 deleted file mode 100644 index 3d78a2e2..00000000 --- a/seisflows/tests/test_data/hold/specfem/DATA/CMTSOLUTION_c46e1d99 +++ /dev/null @@ -1,13 +0,0 @@ -PDE 1999 01 01 00 00 00.00 67000 67000 -25000 4.2 4.2 homog_test -event name: homog_test -time shift: 0.0000 -half duration: 5.0 -latorUTM: 67000.0 -longorUTM: 67000.0 -depth: 30.0 -Mrr: -7.600000e+27 -Mtt: 7.700000e+27 -Mpp: -2.000000e+26 -Mrt: -2.500000e+28 -Mrp: 4.000000e+26 -Mtp: -2.500000e+27 diff --git a/seisflows/tests/test_data/hold/specfem/DATA/FORCESOLUTION_c46e1d99 b/seisflows/tests/test_data/hold/specfem/DATA/FORCESOLUTION_c46e1d99 deleted file mode 100644 index 17c19713..00000000 --- a/seisflows/tests/test_data/hold/specfem/DATA/FORCESOLUTION_c46e1d99 +++ /dev/null @@ -1,10 +0,0 @@ -FORCE 001 -time shift: 0.0000 -f0: 5.0 -latorUTM: 67000.0 -longorUTM: 67000.0 -depth: 30.0 -factor force source: 1.d10 -component dir vect source E: 1.d0 -component dir vect source N: -2.d0 -component dir vect source Z_UP: -1.d0 diff --git a/seisflows/tests/test_data/hold/specfem/DATA/Par_file_SPECFEM2D_cf893667 b/seisflows/tests/test_data/hold/specfem/DATA/Par_file_SPECFEM2D_cf893667 deleted file mode 100644 index 0746c242..00000000 --- a/seisflows/tests/test_data/hold/specfem/DATA/Par_file_SPECFEM2D_cf893667 +++ /dev/null @@ -1,439 +0,0 @@ -#----------------------------------------------------------- -# -# Simulation input parameters -# -#----------------------------------------------------------- - -# title of job -title = Test of SPECFEM2D with curved interfaces - -# forward or adjoint simulation -# 1 = forward, 2 = adjoint, 3 = both simultaneously -# note: 2 is purposely UNUSED (for compatibility with the numbering of our 3D codes) -SIMULATION_TYPE = 1 -# 0 = regular wave propagation simulation, 1/2/3 = noise simulation -NOISE_TOMOGRAPHY = 0 -# save the last frame, needed for adjoint simulation -SAVE_FORWARD = .false. - -# parameters concerning partitioning -NPROC = 1 # number of processes - -# time step parameters -# total number of time steps -NSTEP = 1600 - -# duration of a time step (see section "How to choose the time step" of the manual for how to do this) -DT = 1.1d-3 - -# time stepping -# 1 = Newmark (2nd order), 2 = LDDRK4-6 (4th-order 6-stage low storage Runge-Kutta), 3 = classical RK4 4th-order 4-stage Runge-Kutta -time_stepping_scheme = 1 - -# set the type of calculation (P-SV or SH/membrane waves) -P_SV = .true. - -# axisymmetric (2.5D) or Cartesian planar (2D) simulation -AXISYM = .false. - -#----------------------------------------------------------- -# -# Mesh -# -#----------------------------------------------------------- - -# Partitioning algorithm for decompose_mesh -PARTITIONING_TYPE = 3 # SCOTCH = 3, ascending order (very bad idea) = 1 - -# number of control nodes per element (4 or 9) -NGNOD = 9 - -# creates/reads a binary database that allows to skip all time consuming setup steps in initialization -# 0 = does not read/create database -# 1 = creates database -# 2 = reads database -setup_with_binary_database = 0 - -# available models -# default - define model using nbmodels below -# ascii - read model from ascii database file -# binary - read model from binary databse file -# binary_voigt - read Voigt model from binary database file -# external - define model using define_external_model subroutine -# gll - read GLL model from binary database file -# legacy - read model from model_velocity.dat_input -MODEL = default - -# Output the model with the requested type, does not save if turn to default or .false. -# (available output formats: ascii,binary,gll,legacy) -SAVE_MODEL = default - - -#----------------------------------------------------------- -# -# Attenuation -# -#----------------------------------------------------------- - -# attenuation parameters -ATTENUATION_VISCOELASTIC = .false. # turn attenuation (viscoelasticity) on or off for non-poroelastic solid parts of the model -ATTENUATION_VISCOACOUSTIC = .false. # turn attenuation (viscoacousticity) on or off for non-poroelastic fluid parts of the model - -# for viscoelastic or viscoacoustic attenuation -N_SLS = 3 # number of standard linear solids for attenuation (3 is usually the minimum) -ATTENUATION_f0_REFERENCE = 5.196 # in case of attenuation, reference frequency in Hz at which the velocity values in the velocity model are given (unused otherwise); relevant only if source is a Dirac or a Heaviside, otherwise it is automatically set to f0 the dominant frequency of the source in the DATA/SOURCE file -READ_VELOCITIES_AT_f0 = .false. # read seismic velocities at ATTENUATION_f0_REFERENCE instead of at infinite frequency (see user manual for more information) -USE_SOLVOPT = .false. # use more precise but much more expensive way of determining the Q factor relaxation times, as in https://doi.org/10.1093/gji/ggw024 - -# for poroelastic attenuation -ATTENUATION_PORO_FLUID_PART = .false. # turn viscous attenuation on or off for the fluid part of poroelastic parts of the model -Q0_poroelastic = 1 # quality factor for viscous attenuation (ignore it if you are not using a poroelastic material) -freq0_poroelastic = 10 # frequency for viscous attenuation (ignore it if you are not using a poroelastic material) - -# to undo attenuation and/or PMLs for sensitivity kernel calculations or forward runs with SAVE_FORWARD -# use the flag below. It performs undoing of attenuation and/or of PMLs in an exact way for sensitivity kernel calculations -# but requires disk space for temporary storage, and uses a significant amount of memory used as buffers for temporary storage. -# When that option is on the second parameter indicates how often the code dumps restart files to disk (if in doubt, use something between 100 and 1000). -UNDO_ATTENUATION_AND_OR_PML = .false. -NT_DUMP_ATTENUATION = 500 - -# Instead of reconstructing the forward wavefield, this option reads it from the disk using asynchronous I/O. -# Outperforms conventional mode using a value of NTSTEP_BETWEEN_COMPUTE_KERNELS high enough. -NO_BACKWARD_RECONSTRUCTION = .false. - -#----------------------------------------------------------- -# -# Sources -# -#----------------------------------------------------------- - -# source parameters -NSOURCES = 1 # number of sources (source information is then read from the DATA/SOURCE file) -force_normal_to_surface = .false. # angleforce normal to surface (external mesh and curve file needed) - -# use an existing initial wave field as source or start from zero (medium initially at rest) -initialfield = .false. -add_Bielak_conditions_bottom = .false. # add Bielak conditions or not if initial plane wave -add_Bielak_conditions_right = .false. -add_Bielak_conditions_top = .false. -add_Bielak_conditions_left = .false. - -# acoustic forcing -ACOUSTIC_FORCING = .false. # acoustic forcing of an acoustic medium with a rigid interface - -# noise simulations - type of noise source time function: -# 0=external (S_squared), 1=Ricker(second derivative), 2=Ricker(first derivative), 3=Gaussian, 4=Figure 2a of Tromp et al. 2010 -# (default value 4 is chosen to reproduce the time function from Fig 2a of "Tromp et al., 2010, Noise Cross-Correlation Sensitivity Kernels") -noise_source_time_function_type = 4 - -# moving sources -# Set write_moving_sources_database to .true. if the generation of moving source databases takes -# a long time. Then the simulation is done in two steps: first you run the code and it writes the databases to file -# (in DATA folder by default). Then you rerun the code and it will read the databases in there directly possibly -# saving a lot of time. -# This is only useful for GPU version (for now) -write_moving_sources_database = .false. - -#----------------------------------------------------------- -# -# Receivers -# -#----------------------------------------------------------- - -# receiver set parameters for recording stations (i.e. recording points) -# seismotype : record 1=displ 2=veloc 3=accel 4=pressure 5=curl of displ 6=the fluid potential -seismotype = 1 # several values can be chosen. For example : 1,2,4 - -# interval in time steps for writing of seismograms -# every how many time steps we save the seismograms -# (costly, do not use a very small value; if you use a very large value that is larger than the total number -# of time steps of the run, the seismograms will automatically be saved once at the end of the run anyway) -NTSTEP_BETWEEN_OUTPUT_SEISMOS = 10000 - -# set to n to reduce the sampling rate of output seismograms by a factor of n -# defaults to 1, which means no down-sampling -NTSTEP_BETWEEN_OUTPUT_SAMPLE = 1 - -# so far, this option can only be used if all the receivers are in acoustic elements -USE_TRICK_FOR_BETTER_PRESSURE = .false. - -# use this t0 as earliest starting time rather than the automatically calculated one -USER_T0 = 0.0d0 - -# seismogram formats -save_ASCII_seismograms = .true. # save seismograms in ASCII format or not -save_binary_seismograms_single = .true. # save seismograms in single precision binary format or not (can be used jointly with ASCII above to save both) -save_binary_seismograms_double = .false. # save seismograms in double precision binary format or not (can be used jointly with both flags above to save all) -SU_FORMAT = .false. # output single precision binary seismograms in Seismic Unix format (adjoint traces will be read in the same format) - -# use an existing STATION file found in ./DATA or create a new one from the receiver positions below in this Par_file -use_existing_STATIONS = .false. - -# number of receiver sets (i.e. number of receiver lines to create below) -nreceiversets = 2 - -# orientation -anglerec = 0.d0 # angle to rotate components at receivers -rec_normal_to_surface = .false. # base anglerec normal to surface (external mesh and curve file needed) - -# first receiver set (repeat these 6 lines and adjust nreceiversets accordingly) -nrec = 11 # number of receivers -xdeb = 300. # first receiver x in meters -zdeb = 2200. # first receiver z in meters -xfin = 3700. # last receiver x in meters (ignored if only one receiver) -zfin = 2200. # last receiver z in meters (ignored if only one receiver) -record_at_surface_same_vertical = .true. # receivers inside the medium or at the surface (z values are ignored if this is set to true, they are replaced with the topography height) - -# second receiver set -nrec = 11 # number of receivers -xdeb = 2500. # first receiver x in meters -zdeb = 2500. # first receiver z in meters -xfin = 2500. # last receiver x in meters (ignored if only one receiver) -zfin = 0. # last receiver z in meters (ignored if only one receiver) -record_at_surface_same_vertical = .false. # receivers inside the medium or at the surface (z values are ignored if this is set to true, they are replaced with the topography height) - - -#----------------------------------------------------------- -# -# adjoint kernel outputs -# -#----------------------------------------------------------- - -# save sensitivity kernels in ASCII format (much bigger files, but compatible with current GMT scripts) or in binary format -save_ASCII_kernels = .true. - -# since the accuracy of kernel integration may not need to respect the CFL, this option permits to save computing time, and memory with UNDO_ATTENUATION_AND_OR_PML mode -NTSTEP_BETWEEN_COMPUTE_KERNELS = 1 - -# outputs approximate Hessian for preconditioning -APPROXIMATE_HESS_KL = .false. - -#----------------------------------------------------------- -# -# Boundary conditions -# -#----------------------------------------------------------- - -# Perfectly Matched Layer (PML) boundaries -# absorbing boundary active or not -PML_BOUNDARY_CONDITIONS = .true. -NELEM_PML_THICKNESS = 3 -ROTATE_PML_ACTIVATE = .false. -ROTATE_PML_ANGLE = 30. -# change the four parameters below only if you know what you are doing; they change the damping profiles inside the PMLs -K_MIN_PML = 1.0d0 # from Gedney page 8.11 -K_MAX_PML = 1.0d0 -damping_change_factor_acoustic = 0.5d0 -damping_change_factor_elastic = 1.0d0 -# set the parameter below to .false. unless you know what you are doing; this implements automatic adjustment of the PML parameters for elongated models. -# The goal is to improve the absorbing efficiency of PML for waves with large incidence angles, but this can lead to artefacts. -# In particular, this option is efficient only when the number of sources NSOURCES is equal to one. -PML_PARAMETER_ADJUSTMENT = .false. - -# Stacey ABC -STACEY_ABSORBING_CONDITIONS = .false. - -# periodic boundaries -ADD_PERIODIC_CONDITIONS = .false. -PERIODIC_HORIZ_DIST = 4000.d0 - -#----------------------------------------------------------- -# -# Velocity and density models -# -#----------------------------------------------------------- - -# number of model materials -nbmodels = 4 -# available material types (see user manual for more information) -# acoustic: model_number 1 rho Vp 0 0 0 QKappa 9999 0 0 0 0 0 0 (for QKappa use 9999 to ignore it) -# elastic: model_number 1 rho Vp Vs 0 0 QKappa Qmu 0 0 0 0 0 0 (for QKappa and Qmu use 9999 to ignore them) -# anisotropic: model_number 2 rho c11 c13 c15 c33 c35 c55 c12 c23 c25 0 QKappa Qmu -# anisotropic in AXISYM: model_number 2 rho c11 c13 c15 c33 c35 c55 c12 c23 c25 c22 QKappa Qmu -# poroelastic: model_number 3 rhos rhof phi c kxx kxz kzz Ks Kf Kfr etaf mufr Qmu -# tomo: model_number -1 0 0 A 0 0 0 0 0 0 0 0 0 0 -# -# note: When viscoelasticity or viscoacousticity is turned on, -# the Vp and Vs values that are read here are the UNRELAXED ones i.e. the values at infinite frequency -# unless the READ_VELOCITIES_AT_f0 parameter above is set to true, in which case they are the values at frequency f0. -# -# Please also note that Qmu is always equal to Qs, but Qkappa is in general not equal to Qp. -# To convert one to the other see doc/Qkappa_Qmu_versus_Qp_Qs_relationship_in_2D_plane_strain.pdf and -# utils/attenuation/conversion_from_Qkappa_Qmu_to_Qp_Qs_from_Dahlen_Tromp_959_960.f90. -1 1 2700.d0 3000.d0 1732.051d0 0 0 9999 9999 0 0 0 0 0 0 -2 1 2500.d0 2700.d0 0 0 0 9999 9999 0 0 0 0 0 0 -3 1 2200.d0 2500.d0 1443.375d0 0 0 9999 9999 0 0 0 0 0 0 -4 1 2200.d0 2200.d0 1343.375d0 0 0 9999 9999 0 0 0 0 0 0 - -# external tomography file -TOMOGRAPHY_FILE = ./DATA/tomo_file.xyz - -# use an external mesh created by an external meshing tool or use the internal mesher -read_external_mesh = .false. - -#----------------------------------------------------------- -# -# PARAMETERS FOR EXTERNAL MESHING -# -#----------------------------------------------------------- - -# data concerning mesh, when generated using third-party app (more info in README) -# (see also absorbing_conditions above) -mesh_file = ./DATA/mesh_file # file containing the mesh -nodes_coords_file = ./DATA/nodes_coords_file # file containing the nodes coordinates -materials_file = ./DATA/materials_file # file containing the material number for each element -free_surface_file = ./DATA/free_surface_file # file containing the free surface -axial_elements_file = ./DATA/axial_elements_file # file containing the axial elements if AXISYM is true -absorbing_surface_file = ./DATA/absorbing_surface_file # file containing the absorbing surface -acoustic_forcing_surface_file = ./DATA/MSH/Surf_acforcing_Bottom_enforcing_mesh # file containing the acoustic forcing surface -absorbing_cpml_file = ./DATA/absorbing_cpml_file # file containing the CPML element numbers -tangential_detection_curve_file = ./DATA/courbe_eros_nodes # file containing the curve delimiting the velocity model - -#----------------------------------------------------------- -# -# PARAMETERS FOR INTERNAL MESHING -# -#----------------------------------------------------------- - -# file containing interfaces for internal mesh -interfacesfile = ../EXAMPLES/simple_topography_and_also_a_simple_fluid_layer/DATA/interfaces_simple_topo_curved.dat - -# geometry of the model (origin lower-left corner = 0,0) and mesh description -xmin = 0.d0 # abscissa of left side of the model -xmax = 4000.d0 # abscissa of right side of the model -nx = 80 # number of elements along X - -# absorbing boundary parameters (see absorbing_conditions above) -absorbbottom = .true. -absorbright = .true. -absorbtop = .false. -absorbleft = .true. - -# define the different regions of the model in the (nx,nz) spectral-element mesh -nbregions = 5 # then set below the different regions and model number for each region -# format of each line: nxmin nxmax nzmin nzmax material_number -1 80 1 20 1 -1 59 21 40 2 -71 80 21 40 2 -1 80 41 60 3 -60 70 21 40 4 - -#----------------------------------------------------------- -# -# Display parameters -# -#----------------------------------------------------------- - -# interval at which we output time step info and max of norm of displacement -# (every how many time steps we display information about the simulation. costly, do not use a very small value) -NTSTEP_BETWEEN_OUTPUT_INFO = 100 - -# meshing output -output_grid_Gnuplot = .false. # generate a GNUPLOT file containing the grid, and a script to plot it -output_grid_ASCII = .false. # dump the grid in an ASCII text file consisting of a set of X,Y,Z points or not - -# to plot total energy curves, for instance to monitor how CPML absorbing layers behave; -# should be turned OFF in most cases because a bit expensive -OUTPUT_ENERGY = .false. - -# every how many time steps we compute energy (which is a bit expensive to compute) -NTSTEP_BETWEEN_OUTPUT_ENERGY = 10 - -# Compute the field int_0^t v^2 dt for a set of GLL points and write it to file. Use -# the script utils/visualisation/plotIntegratedEnergyFile.py to watch. It is refreshed at the same time than the seismograms -COMPUTE_INTEGRATED_ENERGY_FIELD = .false. - -#----------------------------------------------------------- -# -# Movies/images/snaphots visualizations -# -#----------------------------------------------------------- - -# every how many time steps we draw JPEG or PostScript pictures of the simulation -# and/or we dump results of the simulation as ASCII or binary files (costly, do not use a very small value) -NTSTEP_BETWEEN_OUTPUT_IMAGES = 100 - -# minimum amplitude kept in % for the JPEG and PostScript snapshots; amplitudes below that are muted -cutsnaps = 1. - -#### for JPEG color images #### -output_color_image = .true. # output JPEG color image of the results every NTSTEP_BETWEEN_OUTPUT_IMAGES time steps or not -imagetype_JPEG = 2 # display 1=displ_Ux 2=displ_Uz 3=displ_norm 4=veloc_Vx 5=veloc_Vz 6=veloc_norm 7=accel_Ax 8=accel_Az 9=accel_norm 10=pressure -factor_subsample_image = 1.0d0 # (double precision) factor to subsample or oversample (if set to e.g. 0.5) color images output by the code (useful for very large models, or to get nicer looking denser pictures) -USE_CONSTANT_MAX_AMPLITUDE = .false. # by default the code normalizes each image independently to its maximum; use this option to use the global maximum below instead -CONSTANT_MAX_AMPLITUDE_TO_USE = 1.17d4 # constant maximum amplitude to use for all color images if the above USE_CONSTANT_MAX_AMPLITUDE option is true -POWER_DISPLAY_COLOR = 0.30d0 # non linear display to enhance small amplitudes in JPEG color images -DRAW_SOURCES_AND_RECEIVERS = .true. # display sources as orange crosses and receivers as green squares in JPEG images or not -DRAW_WATER_IN_BLUE = .true. # display acoustic layers as constant blue in JPEG images, because they likely correspond to water in the case of ocean acoustics or in the case of offshore oil industry experiments (if off, display them as greyscale, as for elastic or poroelastic elements, for instance for acoustic-only oil industry models of solid media) -USE_SNAPSHOT_NUMBER_IN_FILENAME = .false. # use snapshot number in the file name of JPEG color snapshots instead of the time step (for instance to create movies in an easier way later) - -#### for PostScript snapshots #### -output_postscript_snapshot = .true. # output Postscript snapshot of the results every NTSTEP_BETWEEN_OUTPUT_IMAGES time steps or not -imagetype_postscript = 1 # display 1=displ vector 2=veloc vector 3=accel vector; small arrows are displayed for the vectors -meshvect = .true. # display mesh on PostScript plots or not -modelvect = .false. # display velocity model on PostScript plots or not -boundvect = .true. # display boundary conditions on PostScript plots or not -interpol = .true. # interpolation of the PostScript display on a regular grid inside each spectral element, or use the non-evenly spaced GLL points -pointsdisp = 6 # number of points in each direction for interpolation of PostScript snapshots (set to 1 for lower-left corner only) -subsamp_postscript = 1 # subsampling of background velocity model in PostScript snapshots -sizemax_arrows = 1.d0 # maximum size of arrows on PostScript plots in centimeters -US_LETTER = .false. # use US letter or European A4 paper for PostScript plots - -#### for wavefield dumps #### -output_wavefield_dumps = .false. # output wave field to a text file (creates very big files) -imagetype_wavefield_dumps = 1 # display 1=displ vector 2=veloc vector 3=accel vector 4=pressure -use_binary_for_wavefield_dumps = .false. # use ASCII or single-precision binary format for the wave field dumps - -#----------------------------------------------------------- - -# Ability to run several calculations (several earthquakes) -# in an embarrassingly-parallel fashion from within the same run; -# this can be useful when using a very large supercomputer to compute -# many earthquakes in a catalog, in which case it can be better from -# a batch job submission point of view to start fewer and much larger jobs, -# each of them computing several earthquakes in parallel. -# To turn that option on, set parameter NUMBER_OF_SIMULTANEOUS_RUNS to a value greater than 1. -# To implement that, we create NUMBER_OF_SIMULTANEOUS_RUNS MPI sub-communicators, -# each of them being labeled "my_local_mpi_comm_world", and we use them -# in all the routines in "src/shared/parallel.f90", except in MPI_ABORT() because in that case -# we need to kill the entire run. -# When that option is on, of course the number of processor cores used to start -# the code in the batch system must be a multiple of NUMBER_OF_SIMULTANEOUS_RUNS, -# all the individual runs must use the same number of processor cores, -# which as usual is NPROC in the Par_file, -# and thus the total number of processor cores to request from the batch system -# should be NUMBER_OF_SIMULTANEOUS_RUNS * NPROC. -# All the runs to perform must be placed in directories called run0001, run0002, run0003 and so on -# (with exactly four digits). -# -# Imagine you have 10 independent calculations to do, each of them on 100 cores; you have three options: -# -# 1/ submit 10 jobs to the batch system -# -# 2/ submit a single job on 1000 cores to the batch, and in that script create a sub-array of jobs to start 10 jobs, -# each running on 100 cores (see e.g. http://www.schedmd.com/slurmdocs/job_array.html ) -# -# 3/ submit a single job on 1000 cores to the batch, start SPECFEM2D on 1000 cores, create 10 sub-communicators, -# cd into one of 10 subdirectories (called e.g. run0001, run0002,... run0010) depending on the sub-communicator -# your MPI rank belongs to, and run normally on 100 cores using that sub-communicator. -# -# The option below implements 3/. -# -NUMBER_OF_SIMULTANEOUS_RUNS = 1 - -# if we perform simultaneous runs in parallel, if only the source and receivers vary between these runs -# but not the mesh nor the model (velocity and density) then we can also read the mesh and model files -# from a single run in the beginning and broadcast them to all the others; for a large number of simultaneous -# runs for instance when solving inverse problems iteratively this can DRASTICALLY reduce I/Os to disk in the solver -# (by a factor equal to NUMBER_OF_SIMULTANEOUS_RUNS), and reducing I/Os is crucial in the case of huge runs. -# Thus, always set this option to .true. if the mesh and the model are the same for all simultaneous runs. -# In that case there is no need to duplicate the mesh and model file database (the content of the DATABASES_MPI -# directories) in each of the run0001, run0002,... directories, it is sufficient to have one in run0001 -# and the code will broadcast it to the others) -BROADCAST_SAME_MESH_AND_MODEL = .true. - -#----------------------------------------------------------- - -# set to true to use GPUs -GPU_MODE = .false. - diff --git a/seisflows/tests/test_data/hold/specfem/DATA/Par_file_SPECFEM3D_c46e1d99 b/seisflows/tests/test_data/hold/specfem/DATA/Par_file_SPECFEM3D_c46e1d99 deleted file mode 100644 index 11f54a77..00000000 --- a/seisflows/tests/test_data/hold/specfem/DATA/Par_file_SPECFEM3D_c46e1d99 +++ /dev/null @@ -1,379 +0,0 @@ -#----------------------------------------------------------- -# -# Simulation input parameters -# -#----------------------------------------------------------- - -# forward or adjoint simulation -# 1 = forward, 2 = adjoint, 3 = both simultaneously -SIMULATION_TYPE = 1 -# 0 = earthquake simulation, 1/2/3 = three steps in noise simulation -NOISE_TOMOGRAPHY = 0 -SAVE_FORWARD = .false. - -# solve a full FWI inverse problem from a single calling program with no I/Os, storing everything in memory, -# or run a classical forward or adjoint problem only and save the seismograms and/or sensitivity kernels to disk (with costlier I/Os) -INVERSE_FWI_FULL_PROBLEM = .false. - -# UTM projection parameters -# Use a negative zone number for the Southern hemisphere: -# The Northern hemisphere corresponds to zones +1 to +60, -# The Southern hemisphere corresponds to zones -1 to -60. -UTM_PROJECTION_ZONE = 11 -SUPPRESS_UTM_PROJECTION = .true. - -# number of MPI processors -NPROC = 4 - -# time step parameters -NSTEP = 5000 -DT = 0.05 - -# set to true to use local-time stepping (LTS) -LTS_MODE = .false. - -# Partitioning algorithm for decompose_mesh -# choose partitioner: 1==SCOTCH (default), 2==METIS, 3==PATOH, 4==ROWS_PART -PARTITIONING_TYPE = 1 - -#----------------------------------------------------------- -# -# LDDRK time scheme -# -#----------------------------------------------------------- -USE_LDDRK = .false. -INCREASE_CFL_FOR_LDDRK = .false. -RATIO_BY_WHICH_TO_INCREASE_IT = 1.4 - -#----------------------------------------------------------- -# -# Mesh -# -#----------------------------------------------------------- - -# Number of nodes for 2D and 3D shape functions for hexahedra. -# We use either 8-node mesh elements (bricks) or 27-node elements. -# If you use our internal mesher, the only option is 8-node bricks (27-node elements are not supported). -NGNOD = 8 - -# models: -# available options are: -# default (model parameters described by mesh properties) -# 1D models available are: -# 1d_prem,1d_socal,1d_cascadia -# 3D models available are: -# aniso,external,gll,salton_trough,tomo,SEP,coupled,... -MODEL = default - -# path for external tomographic models files -TOMOGRAPHY_PATH = DATA/tomo_files/ -# if you are using a SEP model (oil-industry format) -SEP_MODEL_DIRECTORY = DATA/my_SEP_model/ - -#----------------------------------------------------------- - -# parameters describing the model -APPROXIMATE_OCEAN_LOAD = .false. -TOPOGRAPHY = .false. -ATTENUATION = .false. -ANISOTROPY = .false. -GRAVITY = .false. - -# in case of attenuation, reference frequency in Hz at which the velocity values in the velocity model are given (unused otherwise) -ATTENUATION_f0_REFERENCE = 18.d0 - -# attenuation period range over which we try to mimic a constant Q factor -MIN_ATTENUATION_PERIOD = 999999998.d0 -MAX_ATTENUATION_PERIOD = 999999999.d0 -# ignore this range and ask the code to compute it automatically instead based on the estimated resolution of the mesh (use this unless you know what you are doing) -COMPUTE_FREQ_BAND_AUTOMATIC = .true. - -# Olsen's constant for Q_mu = constant * V_s attenuation rule -USE_OLSEN_ATTENUATION = .false. -OLSEN_ATTENUATION_RATIO = 0.05 - -#----------------------------------------------------------- -# -# Absorbing boundary conditions -# -#----------------------------------------------------------- - -# C-PML boundary conditions for a regional simulation -# (if set to .false., and STACEY_ABSORBING_CONDITIONS is also set to .false., you get a free surface instead -# in the case of elastic or viscoelastic mesh elements, and a rigid surface in the case of acoustic (fluid) elements -PML_CONDITIONS = .false. - -# C-PML top surface -PML_INSTEAD_OF_FREE_SURFACE = .false. - -# C-PML dominant frequency -f0_FOR_PML = 0.05555 - -# parameters used to rotate C-PML boundary conditions by a given angle (not completed yet) -# ROTATE_PML_ACTIVATE = .false. -# ROTATE_PML_ANGLE = 0. - -# absorbing boundary conditions for a regional simulation -# (if set to .false., and PML_CONDITIONS is also set to .false., you get a free surface instead -# in the case of elastic or viscoelastic mesh elements, and a rigid surface in the case of acoustic (fluid) elements -STACEY_ABSORBING_CONDITIONS = .true. - -# absorbing top surface (defined in mesh as 'free_surface_file') -STACEY_INSTEAD_OF_FREE_SURFACE = .false. - -# When STACEY_ABSORBING_CONDITIONS is set to .true. : -# absorbing conditions are defined in xmin, xmax, ymin, ymax and zmin -# this option BOTTOM_FREE_SURFACE can be set to .true. to -# make zmin free surface instead of absorbing condition -BOTTOM_FREE_SURFACE = .false. - -#----------------------------------------------------------- -# -# undoing attenuation and/or PMLs for sensitivity kernel calculations -# -#----------------------------------------------------------- - -# to undo attenuation and/or PMLs for sensitivity kernel calculations or forward runs with SAVE_FORWARD -# use the flag below. It performs undoing of attenuation and/or of PMLs in an exact way for sensitivity kernel calculations -# but requires disk space for temporary storage, and uses a significant amount of memory used as buffers for temporary storage. -# When that option is on the second parameter indicates how often the code dumps restart files to disk (if in doubt, use something between 100 and 1000). -UNDO_ATTENUATION_AND_OR_PML = .false. -NT_DUMP_ATTENUATION = 500 - -#----------------------------------------------------------- -# -# Visualization -# -#----------------------------------------------------------- - -# save AVS or OpenDX movies -# MOVIE_TYPE = 1 to show the top surface -# MOVIE_TYPE = 2 to show all the external faces of the mesh -CREATE_SHAKEMAP = .false. -MOVIE_SURFACE = .false. -MOVIE_TYPE = 1 -MOVIE_VOLUME = .false. -SAVE_DISPLACEMENT = .false. -USE_HIGHRES_FOR_MOVIES = .false. -NTSTEP_BETWEEN_FRAMES = 200 -HDUR_MOVIE = 0.0 - -# save AVS or OpenDX mesh files to check the mesh -SAVE_MESH_FILES = .true. - -# path to store the local database file on each node -LOCAL_PATH = OUTPUT_FILES/DATABASES_MPI - -# interval at which we output time step info and max of norm of displacement -NTSTEP_BETWEEN_OUTPUT_INFO = 500 - -#----------------------------------------------------------- -# -# Sources -# -#----------------------------------------------------------- - -# sources and receivers Z coordinates given directly (i.e. as their true position) instead of as their depth -USE_SOURCES_RECEIVERS_Z = .false. - -# use a (tilted) FORCESOLUTION force point source (or several) instead of a CMTSOLUTION moment-tensor source. -# This can be useful e.g. for oil industry foothills simulations or asteroid simulations -# in which the source is a vertical force, normal force, tilted force, impact etc. -# If this flag is turned on, the FORCESOLUTION file must be edited by giving: -# - the corresponding time-shift parameter, -# - the half duration parameter of the source, -# - the coordinates of the source, -# - the magnitude of the force source, -# - the components of a (non necessarily unitary) direction vector for the force source in the E/N/Z_UP basis. -# The direction vector is made unitary internally in the code and thus only its direction matters here; -# its norm is ignored and the norm of the force used is the factor force source times the source time function. -USE_FORCE_POINT_SOURCE = .false. - -# set to true to use a Ricker source time function instead of the source time functions set by default -# to represent a (tilted) FORCESOLUTION force point source or a CMTSOLUTION moment-tensor source. -USE_RICKER_TIME_FUNCTION = .false. - -# Use an external source time function. -# if .true. you must add a file with your source time function and the file name -# path relative to lauching directory at the end of CMTSOLUTION or FORCESOURCE file -# (with multiple sources, one file per source is required). -# This file must have a single column containing the amplitude of the source at that time step, -# and on its first line it must contain the time step used, which must be equal to DT as defined at the beginning of this Par_file (the code will check that). -# Be sure when this option is .false. to remove the name of stf file in CMTSOLUTION or FORCESOURCE -USE_EXTERNAL_SOURCE_FILE = .false. - -# print source time function -PRINT_SOURCE_TIME_FUNCTION = .false. - -# source encoding -# (for acoustic simulations only for now) determines source encoding factor +/-1 depending on sign of moment tensor -# (see e.g. Krebs et al., 2009. Fast full-wavefield seismic inversion using encoded sources, Geophysics, 74 (6), WCC177-WCC188.) -USE_SOURCE_ENCODING = .false. - -#----------------------------------------------------------- -# -# Seismograms -# -#----------------------------------------------------------- - -# interval in time steps for writing of seismograms -NTSTEP_BETWEEN_OUTPUT_SEISMOS = 10000 - -# set to n to reduce the sampling rate of output seismograms by a factor of n -# defaults to 1, which means no down-sampling -NTSTEP_BETWEEN_OUTPUT_SAMPLE = 1 - -# decide if we save displacement, velocity, acceleration and/or pressure in forward runs (they can be set to true simultaneously) -# currently pressure seismograms are implemented in acoustic (i.e. fluid) elements only -SAVE_SEISMOGRAMS_DISPLACEMENT = .true. -SAVE_SEISMOGRAMS_VELOCITY = .false. -SAVE_SEISMOGRAMS_ACCELERATION = .false. -SAVE_SEISMOGRAMS_PRESSURE = .false. # currently implemented in acoustic (i.e. fluid) elements only - -# save seismograms also when running the adjoint runs for an inverse problem -# (usually they are unused and not very meaningful, leave this off in almost all cases) -SAVE_SEISMOGRAMS_IN_ADJOINT_RUN = .true. - -# save seismograms in binary or ASCII format (binary is smaller but may not be portable between machines) -USE_BINARY_FOR_SEISMOGRAMS = .false. - -# output seismograms in Seismic Unix format (binary with 240-byte-headers) -SU_FORMAT = .false. - -# output seismograms in ASDF (requires asdf-library) -ASDF_FORMAT = .false. - -# decide if main process writes all the seismograms or if all processes do it in parallel -WRITE_SEISMOGRAMS_BY_MAIN = .false. - -# save all seismograms in one large combined file instead of one file per seismogram -# to avoid overloading shared non-local file systems such as LUSTRE or GPFS for instance -SAVE_ALL_SEISMOS_IN_ONE_FILE = .false. - -# use a trick to increase accuracy of pressure seismograms in fluid (acoustic) elements: -# use the second derivative of the source for the source time function instead of the source itself, -# and then record -potential_acoustic() as pressure seismograms instead of -potential_dot_dot_acoustic(); -# this is mathematically equivalent, but numerically significantly more accurate because in the explicit -# Newmark time scheme acceleration is accurate at zeroth order while displacement is accurate at second order, -# thus in fluid elements potential_dot_dot_acoustic() is accurate at zeroth order while potential_acoustic() -# is accurate at second order and thus contains significantly less numerical noise. -USE_TRICK_FOR_BETTER_PRESSURE = .false. - -#----------------------------------------------------------- -# -# Energy calculation -# -#----------------------------------------------------------- - -# to plot energy curves, for instance to monitor how CPML absorbing layers behave; -# should be turned OFF in most cases because a bit expensive -OUTPUT_ENERGY = .false. -# every how many time steps we compute energy (which is a bit expensive to compute) -NTSTEP_BETWEEN_OUTPUT_ENERGY = 10 - -#----------------------------------------------------------- -# -# Adjoint kernel outputs -# -#----------------------------------------------------------- - -# interval in time steps for reading adjoint traces -# 0 = read the whole adjoint sources at start time -NTSTEP_BETWEEN_READ_ADJSRC = 0 - -# read adjoint sources using ASDF (requires asdf-library) -READ_ADJSRC_ASDF = .false. - -# this parameter must be set to .true. to compute anisotropic kernels -# in crust and mantle (related to the 21 Cij in geographical coordinates) -# default is .false. to compute isotropic kernels (related to alpha and beta) -ANISOTROPIC_KL = .false. - -# compute transverse isotropic kernels (alpha_v,alpha_h,beta_v,beta_h,eta,rho) -# rather than fully anisotropic kernels in case ANISOTROPIC_KL is set to .true. -SAVE_TRANSVERSE_KL = .false. - -# this parameter must be set to .true. to compute anisotropic kernels for -# cost function using velocity observable rather than displacement -ANISOTROPIC_VELOCITY_KL = .false. - -# outputs approximate Hessian for preconditioning -APPROXIMATE_HESS_KL = .false. - -# save Moho mesh and compute Moho boundary kernels -SAVE_MOHO_MESH = .false. - -#----------------------------------------------------------- -# -# Coupling with an injection technique (DSM, AxiSEM, or FK) -# -#----------------------------------------------------------- -COUPLE_WITH_INJECTION_TECHNIQUE = .false. -INJECTION_TECHNIQUE_TYPE = 3 # 1 = DSM, 2 = AxiSEM, 3 = FK -MESH_A_CHUNK_OF_THE_EARTH = .false. -TRACTION_PATH = DATA/AxiSEM_tractions/3/ -FKMODEL_FILE = FKmodel -RECIPROCITY_AND_KH_INTEGRAL = .false. # does not work yet - -#----------------------------------------------------------- - -# Dimitri Komatitsch, July 2014, CNRS Marseille, France: -# added the ability to run several calculations (several earthquakes) -# in an embarrassingly-parallel fashion from within the same run; -# this can be useful when using a very large supercomputer to compute -# many earthquakes in a catalog, in which case it can be better from -# a batch job submission point of view to start fewer and much larger jobs, -# each of them computing several earthquakes in parallel. -# To turn that option on, set parameter NUMBER_OF_SIMULTANEOUS_RUNS to a value greater than 1. -# To implement that, we create NUMBER_OF_SIMULTANEOUS_RUNS MPI sub-communicators, -# each of them being labeled "my_local_mpi_comm_world", and we use them -# in all the routines in "src/shared/parallel.f90", except in MPI_ABORT() because in that case -# we need to kill the entire run. -# When that option is on, of course the number of processor cores used to start -# the code in the batch system must be a multiple of NUMBER_OF_SIMULTANEOUS_RUNS, -# all the individual runs must use the same number of processor cores, -# which as usual is NPROC in the Par_file, -# and thus the total number of processor cores to request from the batch system -# should be NUMBER_OF_SIMULTANEOUS_RUNS * NPROC. -# All the runs to perform must be placed in directories called run0001, run0002, run0003 and so on -# (with exactly four digits). -# -# Imagine you have 10 independent calculations to do, each of them on 100 cores; you have three options: -# -# 1/ submit 10 jobs to the batch system -# -# 2/ submit a single job on 1000 cores to the batch, and in that script create a sub-array of jobs to start 10 jobs, -# each running on 100 cores (see e.g. http://www.schedmd.com/slurmdocs/job_array.html ) -# -# 3/ submit a single job on 1000 cores to the batch, start SPECFEM3D on 1000 cores, create 10 sub-communicators, -# cd into one of 10 subdirectories (called e.g. run0001, run0002,... run0010) depending on the sub-communicator -# your MPI rank belongs to, and run normally on 100 cores using that sub-communicator. -# -# The option below implements 3/. -# -NUMBER_OF_SIMULTANEOUS_RUNS = 1 - -# if we perform simultaneous runs in parallel, if only the source and receivers vary between these runs -# but not the mesh nor the model (velocity and density) then we can also read the mesh and model files -# from a single run in the beginning and broadcast them to all the others; for a large number of simultaneous -# runs for instance when solving inverse problems iteratively this can DRASTICALLY reduce I/Os to disk in the solver -# (by a factor equal to NUMBER_OF_SIMULTANEOUS_RUNS), and reducing I/Os is crucial in the case of huge runs. -# Thus, always set this option to .true. if the mesh and the model are the same for all simultaneous runs. -# In that case there is no need to duplicate the mesh and model file database (the content of the DATABASES_MPI -# directories) in each of the run0001, run0002,... directories, it is sufficient to have one in run0001 -# and the code will broadcast it to the others) -BROADCAST_SAME_MESH_AND_MODEL = .true. - -#----------------------------------------------------------- - -# set to true to use GPUs -GPU_MODE = .false. - -# ADIOS Options for I/Os -ADIOS_ENABLED = .false. -ADIOS_FOR_DATABASES = .false. -ADIOS_FOR_MESH = .false. -ADIOS_FOR_FORWARD_ARRAYS = .false. -ADIOS_FOR_KERNELS = .false. -ADIOS_FOR_UNDO_ATTENUATION = .false. - diff --git a/seisflows/tests/test_data/hold/specfem/DATA/SOURCE_cf893667 b/seisflows/tests/test_data/hold/specfem/DATA/SOURCE_cf893667 deleted file mode 100644 index 3170a66b..00000000 --- a/seisflows/tests/test_data/hold/specfem/DATA/SOURCE_cf893667 +++ /dev/null @@ -1,57 +0,0 @@ -## Source 1 -source_surf = .false. # source inside the medium, or source automatically moved exactly at the surface by the solver -xs = 2500. # source location x in meters -zs = 2500. # source location z in meters (zs is ignored if source_surf is set to true, it is replaced with the topography height) -## Source type parameters: -# 1 = elastic force or acoustic pressure -# 2 = moment tensor -# or Initial field type (when initialfield set in Par_file): -# For a plane wave including converted and reflected waves at the free surface: -# 1 = P wave, -# 2 = S wave, -# 3 = Rayleigh wave -# For a plane wave without converted nor reflected waves at the free surface, i.e. with the incident wave only: -# 4 = P wave, -# 5 = S wave -# For initial mode displacement: -# 6 = mode (2,3) of a rectangular membrane -source_type = 1 -# Source time function: -# In the case of a source located in an acoustic medium, -# to get pressure for a Ricker in the seismograms, here we need to select a Gaussian for the potential Chi -# used as a source, rather than a Ricker, because pressure = - Chi_dot_dot. -# This is true both when USE_TRICK_FOR_BETTER_PRESSURE is set to .true. or to .false. -# Options: -# 1 = second derivative of a Gaussian (a.k.a. Ricker), -# 2 = first derivative of a Gaussian, -# 3 = Gaussian, -# 4 = Dirac, -# 5 = Heaviside (4 and 5 will produce noisy recordings because of frequencies above the mesh resolution limit), -# 6 = ocean acoustics type I, -# 7 = ocean acoustics type II, -# 8 = external source time function = 8 (source read from file), -# 9 = burst, -# 10 = Sinus source time function, -# 11 = Marmousi Ormsby wavelet -time_function_type = 1 -# If time_function_type == 8, enter below the custom source file to read (two columns file with time and amplitude) : -# (For the moment dt must be equal to the dt of the simulation. File name cannot exceed 150 characters) -# IMPORTANT: do NOT put quote signs around the file name, just put the file name itself otherwise the run will stop -name_of_source_file = YYYYYYYY # Only for option 8 : file containing the source wavelet -burst_band_width = 0. # Only for option 9 : band width of the burst -f0 = 10.0 # dominant source frequency (Hz) if not Dirac or Heaviside -tshift = 0.0 # time shift when multi sources (if one source, must be zero) -## Force source -# angle of the source (for a force only); for a plane wave, this is the incidence angle; for moment tensor sources this is unused -anglesource = 0. -## Moment tensor -# The components of a moment tensor source must be given in N.m, not in dyne.cm as in the DATA/CMTSOLUTION source file of the 3D version of the code. -Mxx = 1. # Mxx component (for a moment tensor source only) -Mzz = 1. # Mzz component (for a moment tensor source only) -Mxz = 0. # Mxz component (for a moment tensor source only) -## Amplification 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Each module requires its own sub-parameters. -# -# .. rubric:: -# - To determine available options for modules listed below, run: -# > seisflows print modules -# - To auto-fill with docstrings and default values (recommended), run: -# > seisflows configure -# - To set values as NoneType, use: null -# - To set values as infinity, use: inf -# -# MODULES -# /////// -# WORKFLOW (str): The method for running SeisFlows; equivalent to main() -# SOLVER (str): External numerical solver to use for waveform simulations -# SYSTEM (str): Computer architecture of the system being used -# OPTIMIZE (str): Optimization algorithm for the inverse problem -# PREPROCESS (str): Preprocessing schema for waveform data -# POSTPROCESS (str): Postprocessing schema for kernels and gradients -# ============================================================================== -WORKFLOW: inversion -SOLVER: specfem2d -SYSTEM: workstation -OPTIMIZE: gradient -PREPROCESS: default -POSTPROCESS: default - -# ============================================================================= -# SYSTEM -# ////// -# TITLE (str): -# The name used to submit jobs to the system, defaults to the name of the -# working directory -# MPIEXEC (str): -# Function used to invoke executables on the system. For example 'srun' on -# SLURM systems, or './' on a workstation. If left blank, will guess based -# on the system. -# NTASK (int): -# Number of separate, individual tasks. Also equal to the number of desired -# sources in workflow -# NPROC (int): -# Number of processor to use for each simulation -# LOG_LEVEL (str): -# Verbosity output of SF logger. Available from least to most verbosity: -# 'CRITICAL', 'WARNING', 'INFO', 'DEBUG'; defaults to 'DEBUG' -# VERBOSE (bool): -# Level of verbosity provided to the output log. If True, log statements -# will declare what module/class/function they are being called from. -# Useful for debugging but also very noisy. -# ============================================================================= -TITLE: test_data -MPIEXEC: -NTASK: 1 -NPROC: 1 -LOG_LEVEL: DEBUG -VERBOSE: False - -# ============================================================================= -# PREPROCESS -# ////////// -# MISFIT (str): -# Misfit function for waveform comparisons, for available see -# seisflows.plugins.misfit -# BACKPROJECT (str): -# Backprojection function for migration, for available see -# seisflows.plugins.adjoint -# NORMALIZE (list): -# Data normalization parameters used to normalize the amplitudes of -# waveforms. Choose from two sets: ENORML1: normalize per event by L1 of -# traces; OR ENORML2: normalize per event by L2 of traces; AND TNORML1: -# normalize per trace by L1 of itself; OR TNORML2: normalize per trace by -# L2 of itself -# FILTER (str): -# Data filtering type, available options are:BANDPASS (req. MIN/MAX -# PERIOD/FREQ);LOWPASS (req. MAX_FREQ or MIN_PERIOD); HIGHPASS (req. -# MIN_FREQ or MAX_PERIOD) -# MIN_PERIOD (float): -# Minimum filter period applied to time series.See also MIN_FREQ, MAX_FREQ, -# if User defines FREQ parameters, they will overwrite PERIOD parameters. -# MAX_PERIOD (float): -# Maximum filter period applied to time series.See also MIN_FREQ, MAX_FREQ, -# if User defines FREQ parameters, they will overwrite PERIOD parameters. -# MIN_FREQ (float): -# Maximum filter frequency applied to time series.See also MIN_PERIOD, -# MAX_PERIOD, if User defines FREQ parameters, they will overwrite PERIOD -# parameters. -# MAX_FREQ (float): -# Maximum filter frequency applied to time series,See also MIN_PERIOD, -# MAX_PERIOD, if User defines FREQ parameters, they will overwrite PERIOD -# parameters. -# MUTE (list): -# Data mute parameters used to zero out early / late arrivals or offsets. -# Choose any number of: EARLY: mute early arrivals; LATE: mute late -# arrivals; SHORT: mute short source-receiver distances; LONG: mute long -# source-receiver distances -# ============================================================================= -MISFIT: waveform -BACKPROJECT: null -NORMALIZE: [] -FILTER: null -MIN_PERIOD: -MAX_PERIOD: -MIN_FREQ: -MAX_FREQ: -MUTE: [] - -# ============================================================================= -# SOLVER -# ////// -# MATERIALS (str): -# Material parameters used to define model. Available: ['ELASTIC': Vp, Vs, -# 'ACOUSTIC': Vp, 'ISOTROPIC', 'ANISOTROPIC'] -# DENSITY (str): -# How to treat density during inversion. Available: ['CONSTANT': Do not -# update density, 'VARIABLE': Update density] -# ATTENUATION (bool): -# If True, turn on attenuation during forward simulations, otherwise set -# attenuation off. Attenuation is always off for adjoint simulations. -# FORMAT (float): -# Format of synthetic waveforms used during workflow, available options: -# ['ASCII', 'SU'] -# COMPONENTS (str): -# Components used to generate data, formatted as a single string, e.g. ZNE -# or NZ or E -# SOLVERIO (str): -# The format external solver files. Available: ['fortran_binary'] -# SOURCE_PREFIX (str): -# Prefix of SOURCE files in path SPECFEM_DATA. By default, 'SOURCE' for -# SPECFEM2D -# ============================================================================= -MATERIALS: ELASTIC -DENSITY: CONSTANT -ATTENUATION: False -FORMAT: ASCII -COMPONENTS: ZNE -SOLVERIO: fortran_binary -SOURCE_PREFIX: SOURCE - -# ============================================================================= -# POSTPROCESS -# /////////// -# SMOOTH_H (float): -# Gaussian half-width for horizontal smoothing in units of meters. If 0., -# no smoothing applied -# SMOOTH_V (float): -# Gaussian half-width for vertical smoothing in units of meters -# TASKTIME_SMOOTH (int): -# Large radii smoothing may take longer than normal tasks. Allocate -# additional smoothing task time as a multiple of TASKTIME -# ============================================================================= -SMOOTH_H: 0.0 -SMOOTH_V: 0.0 -TASKTIME_SMOOTH: 1 - -# ============================================================================= -# OPTIMIZE -# //////// -# LINESEARCH (str): -# Algorithm to use for line search, see seisflows.plugins.line_search for -# available choices -# PRECOND (str): -# Algorithm to use for preconditioning gradients, see -# seisflows.plugins.preconds for available choices -# STEPCOUNTMAX (int): -# Max number of trial steps in line search before a change in line search -# behavior -# STEPLENINIT (float): -# Initial line search step length, as a fraction of current model -# parameters -# STEPLENMAX (float): -# Max allowable step length, as a fraction of current model parameters -# ============================================================================= -LINESEARCH: Bracket -PRECOND: -STEPCOUNTMAX: 10 -STEPLENINIT: 0.05 -STEPLENMAX: 0.5 - -# ============================================================================= -# WORKFLOW -# //////// -# SAVETRACES (bool): -# Save waveform traces to disk after they have been generated by the -# external solver -# SAVERESIDUALS (bool): -# Save data-synthetic residuals each time they are caluclated -# CASE (str): -# How to address 'data' in your workflow, available options: 1) 'data': -# Real data inversion. Observed waveforms must be provided by the user in -# PATH.DATA/{SOURCE_NAME}. OR if PAR.PREPROCESS=='pyatoa' data should be -# discoverable via IRIS webservices based on event ID and station codes2) -# 'synthetic': A synthetic-synthetic workflow. 'Data' will be generated as -# synthetics using PATH.MODEL_TRUE. -# SAVEGRADIENT (bool): -# Save gradient files each time the gradient is evaluated -# SAVEKERNELS (bool): -# Save event kernel files each time they are evaluated -# SAVEAS (str): -# Format to save models, gradients, kernels. Available: ['binary': save -# files in native SPECFEM .bin format, 'vector': save files as NumPy .npy -# files, 'both': save as both binary and vectors] -# BEGIN (int): -# First iteration of an inversion workflow, 1 <= BEGIN <= inf -# END (int): -# Last iteration of the inverison workflow,BEGIN <= END <= inf -# RESUME_FROM (str): -# Name of flow task to resume workflow from. Useful for restarting failed -# workflows or re-trying sections of workflows with new parameters. To -# determine available options for your given workflow: > seisflows print -# flow -# STOP_AFTER (str): -# Name of flow task to stop workflow after. Useful for stopping mid- -# workflow to look at results before proceeding (e.g., to look at waveform -# misfits before evaluating the gradient). To determine available options -# for your given workflow: > seisflows print flow -# SAVEMODEL (bool): -# Save updated model files after each iteration -# ============================================================================= -SAVETRACES: False -SAVERESIDUALS: False -CASE: data -SAVEGRADIENT: True -SAVEKERNELS: False -SAVEAS: binary -BEGIN: 1 -END: 1 -RESUME_FROM: -STOP_AFTER: -SAVEMODEL: True - -# ============================================================================= -# PATHS -# ///// -# SCRATCH: -# scratch path to hold temporary data during workflow -# OUTPUT: -# directory to save workflow outputs to disk -# SYSTEM: -# scratch path to hold any system related data -# LOGFILE: -# the main output log file where all processes will track their status -# PREPROCESS: -# scratch path to store any preprocessing outputs -# SOLVER: -# scratch path to hold solver working directories -# DATA: -# path to observed waveform data available to workflow -# SPECFEM_BIN: -# path to the SPECFEM binary executables -# SPECFEM_DATA: -# path to the SPECFEM DATA/ directory containing the 'Par_file', 'STATIONS' -# file and 'CMTSOLUTION' files -# MASK: -# Directory to mask files for gradient masking -# OPTIMIZE: -# scratch path to store data related to nonlinear optimization library. -# Data stored here include model, gradient, and search direction vectors -# (numpy arrays), andadditional arrays related to specific optimization -# algorithms -# MODEL_INIT: -# Path location of the initial model to be used to generate the the first -# evaluation of synthetic seismograms. -# GRAD: -# scratch path to store any models or kernels related to gradient -# evaluations. Sub-directories will be generated inside PATH.GRAD to save -# various stages of gradient manipulation -# MODEL_TRUE: -# Target model to be used for PAR.CASE == 'synthetic'. The TRUE model will -# be used to evaluate forward simulations ONCE at the beginning of the -# workflow, to generate 'data'. -# FUNC: -# scratch path to store data related to misfit function evaluations that -# take place during the line search. Data stored here include residuals -# from data-synthetic misfit, and a given 'try' model being used to -# generate synthetics. -# HESS: -# scratch path to store data related to Hessian evaluations -# ============================================================================= -PATHS: - SCRATCH: scratch - OUTPUT: output - SYSTEM: scratch/system - LOGFILE: sfoutput.txt - PREPROCESS: scratch/preprocess - SOLVER: scratch/solver - DATA: - SPECFEM_BIN: specfem/bin - SPECFEM_DATA: specfem/DATA - MASK: - OPTIMIZE: scratch/optimize - MODEL_INIT: specfem/MODEL_INIT - GRAD: scratch/evalgrad - MODEL_TRUE: specfem/MODEL_TRUE - FUNC: scratch/evalfunc - HESS: scratch/evalhess diff --git a/seisflows/tests/test_data/hold/test_filled_parameters.yaml b/seisflows/tests/test_data/hold/test_filled_parameters.yaml deleted file mode 100644 index a25f1ebe..00000000 --- a/seisflows/tests/test_data/hold/test_filled_parameters.yaml +++ /dev/null @@ -1,301 +0,0 @@ -# ////////////////////////////////////////////////////////////////////////////// -# -# SeisFlows YAML Parameter File -# -# ////////////////////////////////////////////////////////////////////////////// -# -# Modules correspond to the structure of the source code, and determine -# SeisFlows' behavior at runtime. Each module requires its own sub-parameters. -# -# .. rubric:: -# - To determine available options for modules listed below, run: -# > seisflows print modules -# - To auto-fill with docstrings and default values (recommended), run: -# > seisflows configure -# - To set values as NoneType, use: null -# - To set values as infinity, use: inf -# -# MODULES -# /////// -# WORKFLOW (str): The method for running SeisFlows; equivalent to main() -# SOLVER (str): External numerical solver to use for waveform simulations -# SYSTEM (str): Computer architecture of the system being used -# OPTIMIZE (str): Optimization algorithm for the inverse problem -# PREPROCESS (str): Preprocessing schema for waveform data -# POSTPROCESS (str): Postprocessing schema for kernels and gradients -# ============================================================================== -WORKFLOW: inversion -SOLVER: specfem2d -SYSTEM: workstation -OPTIMIZE: gradient -PREPROCESS: default -POSTPROCESS: default - -# ============================================================================= -# SYSTEM -# ////// -# TITLE (str): -# The name used to submit jobs to the system, defaults to the name of the -# working directory -# MPIEXEC (str): -# Function used to invoke executables on the system. For example 'srun' on -# SLURM systems, or './' on a workstation. If left blank, will guess based -# on the system. -# NTASK (int): -# Number of separate, individual tasks. Also equal to the number of desired -# sources in workflow -# NPROC (int): -# Number of processor to use for each simulation -# LOG_LEVEL (str): -# Verbosity output of SF logger. Available from least to most verbosity: -# 'CRITICAL', 'WARNING', 'INFO', 'DEBUG'; defaults to 'DEBUG' -# VERBOSE (bool): -# Level of verbosity provided to the output log. If True, log statements -# will declare what module/class/function they are being called from. -# Useful for debugging but also very noisy. -# ============================================================================= -TITLE: test_data -MPIEXEC: -NTASK: 1 -NPROC: 1 -LOG_LEVEL: DEBUG -VERBOSE: False - -# ============================================================================= -# PREPROCESS -# ////////// -# MISFIT (str): -# Misfit function for waveform comparisons, for available see -# seisflows.plugins.misfit -# BACKPROJECT (str): -# Backprojection function for migration, for available see -# seisflows.plugins.adjoint -# NORMALIZE (list): -# Data normalization parameters used to normalize the amplitudes of -# waveforms. Choose from two sets: ENORML1: normalize per event by L1 of -# traces; OR ENORML2: normalize per event by L2 of traces; AND TNORML1: -# normalize per trace by L1 of itself; OR TNORML2: normalize per trace by -# L2 of itself -# FILTER (str): -# Data filtering type, available options are:BANDPASS (req. MIN/MAX -# PERIOD/FREQ);LOWPASS (req. MAX_FREQ or MIN_PERIOD); HIGHPASS (req. -# MIN_FREQ or MAX_PERIOD) -# MIN_PERIOD (float): -# Minimum filter period applied to time series.See also MIN_FREQ, MAX_FREQ, -# if User defines FREQ parameters, they will overwrite PERIOD parameters. -# MAX_PERIOD (float): -# Maximum filter period applied to time series.See also MIN_FREQ, MAX_FREQ, -# if User defines FREQ parameters, they will overwrite PERIOD parameters. -# MIN_FREQ (float): -# Maximum filter frequency applied to time series.See also MIN_PERIOD, -# MAX_PERIOD, if User defines FREQ parameters, they will overwrite PERIOD -# parameters. -# MAX_FREQ (float): -# Maximum filter frequency applied to time series,See also MIN_PERIOD, -# MAX_PERIOD, if User defines FREQ parameters, they will overwrite PERIOD -# parameters. -# MUTE (list): -# Data mute parameters used to zero out early / late arrivals or offsets. -# Choose any number of: EARLY: mute early arrivals; LATE: mute late -# arrivals; SHORT: mute short source-receiver distances; LONG: mute long -# source-receiver distances -# ============================================================================= -MISFIT: waveform -BACKPROJECT: null -NORMALIZE: [] -FILTER: null -MIN_PERIOD: -MAX_PERIOD: -MIN_FREQ: -MAX_FREQ: -MUTE: [] - -# ============================================================================= -# SOLVER -# ////// -# MATERIALS (str): -# Material parameters used to define model. Available: ['ELASTIC': Vp, Vs, -# 'ACOUSTIC': Vp, 'ISOTROPIC', 'ANISOTROPIC'] -# DENSITY (str): -# How to treat density during inversion. Available: ['CONSTANT': Do not -# update density, 'VARIABLE': Update density] -# ATTENUATION (bool): -# If True, turn on attenuation during forward simulations, otherwise set -# attenuation off. Attenuation is always off for adjoint simulations. -# FORMAT (float): -# Format of synthetic waveforms used during workflow, available options: -# ['ASCII', 'SU'] -# COMPONENTS (str): -# Components used to generate data, formatted as a single string, e.g. ZNE -# or NZ or E -# SOLVERIO (str): -# The format external solver files. Available: ['fortran_binary'] -# SOURCE_PREFIX (str): -# Prefix of SOURCE files in path SPECFEM_DATA. By default, 'SOURCE' for -# SPECFEM2D -# ============================================================================= -MATERIALS: elastic -DENSITY: constant -ATTENUATION: False -FORMAT: ascii -COMPONENTS: ZNE -SOLVERIO: fortran_binary -SOURCE_PREFIX: SOURCE - -# ============================================================================= -# POSTPROCESS -# /////////// -# SMOOTH_H (float): -# Gaussian half-width for horizontal smoothing in units of meters. If 0., -# no smoothing applied -# SMOOTH_V (float): -# Gaussian half-width for vertical smoothing in units of meters -# TASKTIME_SMOOTH (int): -# Large radii smoothing may take longer than normal tasks. Allocate -# additional smoothing task time as a multiple of TASKTIME -# ============================================================================= -SMOOTH_H: 0.0 -SMOOTH_V: 0.0 -TASKTIME_SMOOTH: 1 - -# ============================================================================= -# OPTIMIZE -# //////// -# LINESEARCH (str): -# Algorithm to use for line search, see seisflows.plugins.line_search for -# available choices -# PRECOND (str): -# Algorithm to use for preconditioning gradients, see -# seisflows.plugins.preconds for available choices -# STEPCOUNTMAX (int): -# Max number of trial steps in line search before a change in line search -# behavior -# STEPLENINIT (float): -# Initial line search step length, as a fraction of current model -# parameters -# STEPLENMAX (float): -# Max allowable step length, as a fraction of current model parameters -# ============================================================================= -LINESEARCH: Bracket -PRECOND: -STEPCOUNTMAX: 10 -STEPLENINIT: 0.05 -STEPLENMAX: 0.5 - -# ============================================================================= -# WORKFLOW -# //////// -# SAVETRACES (bool): -# Save waveform traces to disk after they have been generated by the -# external solver -# SAVERESIDUALS (bool): -# Save data-synthetic residuals each time they are caluclated -# CASE (str): -# How to address 'data' in your workflow, available options: 1) 'data': -# Real data inversion. Observed waveforms must be provided by the user in -# PATH.DATA/{SOURCE_NAME}. OR if PAR.PREPROCESS=='pyatoa' data should be -# discoverable via IRIS webservices based on event ID and station codes2) -# 'synthetic': A synthetic-synthetic workflow. 'Data' will be generated as -# synthetics using PATH.MODEL_TRUE. -# SAVEGRADIENT (bool): -# Save gradient files each time the gradient is evaluated -# SAVEKERNELS (bool): -# Save event kernel files each time they are evaluated -# SAVEAS (str): -# Format to save models, gradients, kernels. Available: ['binary': save -# files in native SPECFEM .bin format, 'vector': save files as NumPy .npy -# files, 'both': save as both binary and vectors] -# BEGIN (int): -# First iteration of an inversion workflow, 1 <= BEGIN <= inf -# END (int): -# Last iteration of the inverison workflow,BEGIN <= END <= inf -# RESUME_FROM (str): -# Name of flow task to resume workflow from. Useful for restarting failed -# workflows or re-trying sections of workflows with new parameters. To -# determine available options for your given workflow: > seisflows print -# flow -# STOP_AFTER (str): -# Name of flow task to stop workflow after. Useful for stopping mid- -# workflow to look at results before proceeding (e.g., to look at waveform -# misfits before evaluating the gradient). To determine available options -# for your given workflow: > seisflows print flow -# SAVEMODEL (bool): -# Save updated model files after each iteration -# ============================================================================= -SAVETRACES: False -SAVERESIDUALS: False -CASE: data -SAVEGRADIENT: True -SAVEKERNELS: False -SAVEAS: binary -BEGIN: 1 -END: 1 -RESUME_FROM: -STOP_AFTER: -SAVEMODEL: True - -# ============================================================================= -# PATHS -# ///// -# SCRATCH: -# scratch path to hold temporary data during workflow -# OUTPUT: -# directory to save workflow outputs to disk -# SYSTEM: -# scratch path to hold any system related data -# LOGFILE: -# the main output log file where all processes will track their status -# PREPROCESS: -# scratch path to store any preprocessing outputs -# SOLVER: -# scratch path to hold solver working directories -# DATA: -# path to observed waveform data available to workflow -# SPECFEM_BIN: -# path to the SPECFEM binary executables -# SPECFEM_DATA: -# path to the SPECFEM DATA/ directory containing the 'Par_file', 'STATIONS' -# file and 'CMTSOLUTION' files -# MASK: -# Directory to mask files for gradient masking -# OPTIMIZE: -# scratch path to store data related to nonlinear optimization library. -# Data stored here include model, gradient, and search direction vectors -# (numpy arrays), andadditional arrays related to specific optimization -# algorithms -# MODEL_INIT: -# Path location of the initial model to be used to generate the the first -# evaluation of synthetic seismograms. -# GRAD: -# scratch path to store any models or kernels related to gradient -# evaluations. Sub-directories will be generated inside PATH.GRAD to save -# various stages of gradient manipulation -# MODEL_TRUE: -# Target model to be used for PAR.CASE == 'synthetic'. The TRUE model will -# be used to evaluate forward simulations ONCE at the beginning of the -# workflow, to generate 'data'. -# FUNC: -# scratch path to store data related to misfit function evaluations that -# take place during the line search. Data stored here include residuals -# from data-synthetic misfit, and a given 'try' model being used to -# generate synthetics. -# HESS: -# scratch path to store data related to Hessian evaluations -# ============================================================================= -PATHS: - SCRATCH: scratch - OUTPUT: output - SYSTEM: scratch/system - LOGFILE: sfoutput.txt - PREPROCESS: scratch/preprocess - SOLVER: scratch/solver - DATA: - SPECFEM_BIN: ./bin - SPECFEM_DATA: ./DATA - MASK: - OPTIMIZE: scratch/optimize - MODEL_INIT: ./MODEL_INIT - GRAD: scratch/evalgrad - MODEL_TRUE: ./MODEL_TRUE - FUNC: scratch/evalfunc - HESS: scratch/evalhess diff --git a/seisflows/tests/test_data/hold/test_setup_parameters.yaml b/seisflows/tests/test_data/hold/test_setup_parameters.yaml deleted file mode 100644 index f32a3cb2..00000000 --- a/seisflows/tests/test_data/hold/test_setup_parameters.yaml +++ /dev/null @@ -1,32 +0,0 @@ -# ////////////////////////////////////////////////////////////////////////////// -# -# SeisFlows YAML Parameter File -# -# ////////////////////////////////////////////////////////////////////////////// -# -# Modules correspond to the structure of the source code, and determine -# SeisFlows' behavior at runtime. Each module requires its own sub-parameters. -# -# .. rubric:: -# - To determine available options for modules listed below, run: -# > seisflows print modules -# - To auto-fill with docstrings and default values (recommended), run: -# > seisflows configure -# - To set values as NoneType, use: null -# - To set values as infinity, use: inf -# -# MODULES -# /////// -# WORKFLOW (str): The method for running SeisFlows; equivalent to main() -# SOLVER (str): External numerical solver to use for waveform simulations -# SYSTEM (str): Computer architecture of the system being used -# OPTIMIZE (str): Optimization algorithm for the inverse problem -# PREPROCESS (str): Preprocessing schema for waveform data -# POSTPROCESS (str): Postprocessing schema for kernels and gradients -# ============================================================================== -WORKFLOW: inversion -SOLVER: specfem2d -SYSTEM: workstation -OPTIMIZE: gradient -PREPROCESS: default -POSTPROCESS: default diff --git a/seisflows/tests/test_data/parameters.yaml b/seisflows/tests/test_data/parameters.yaml deleted file mode 120000 index bc0c1173..00000000 --- a/seisflows/tests/test_data/parameters.yaml +++ /dev/null @@ -1 +0,0 @@ -../../examples/parameters.yaml \ No newline at end of file diff --git a/seisflows/tests/test_seisflows.py b/seisflows/tests/test_seisflows.py index ecb1b83a..49d862f1 100644 --- a/seisflows/tests/test_seisflows.py +++ b/seisflows/tests/test_seisflows.py @@ -11,32 +11,37 @@ import subprocess from unittest.mock import patch -from seisflows.tools.config import Dict from seisflows import ROOT_DIR +from seisflows.tools import msg from seisflows.seisflows import SeisFlows -from seisflows.tools.config import load_yaml +from seisflows.tools.config import load_yaml, Dict TEST_DIR = os.path.join(ROOT_DIR, "tests") @pytest.fixture -def par_file(): +def par_file(tmpdir): """ - Return the test parameter file as a dictionary object - :rtype: seisflows.config.Dict - :return: dictionary of parameters + Create a template parameter file """ - return os.path.join(TEST_DIR, "test_data", "parameters.yaml") + fid = os.path.join(tmpdir, "parameters.yaml") + with open(fid, "w") as f: + f.write(msg.base_parameter_file) + return fid @pytest.fixture -def par_file_dict(par_file): +def par_file_dict(): """ - Return the test parameter file as a dictionary object + Return default parameter file parameters as a dictionary object + :rtype: seisflows.config.Dict :return: dictionary of parameters """ - return Dict(load_yaml(par_file)) + par_file = Dict(workflow="forward", system="workstation", + solver="specfem2d", preprocess="default", + optimize="gradient") + return par_file def test_call_seisflows(par_file, par_file_dict): @@ -129,18 +134,13 @@ def test_cmd_configure(tmpdir, par_file): """ os.chdir(tmpdir) - # Copy in the setup par file so we can configure it - src = par_file - dst = os.path.join(tmpdir, "parameters.yaml") - shutil.copy(src, dst) - # run seisflows configure with patch.object(sys, "argv", ["seisflows"]): sf = SeisFlows(workdir=tmpdir, parameter_file="parameters.yaml") sf.configure() # Check some random values that were not in the template file - parameters = load_yaml(dst) + parameters = load_yaml(par_file) assert("path_model_init" in parameters.keys()) assert("smooth_h" in parameters.keys()) assert("ntask" in parameters.keys()) @@ -152,11 +152,6 @@ def test_cmd_par(tmpdir, par_file): :param tmpdir: :return: """ - # Copy given parameter file with a weird name - src = par_file - dst = os.path.join(tmpdir, "parameters.yaml") - shutil.copy(src, dst) - parameter = "workflow" expected_val = "forward" new_val = "migration" @@ -164,7 +159,7 @@ def test_cmd_par(tmpdir, par_file): # testing the get option: seisflows par `parameter` with patch.object(sys, "argv", ["seisflows"]): # Run this with subprocess so we can capture the print statement - cmd_line_arg = ["seisflows", "-p", dst, "par", parameter] + cmd_line_arg = ["seisflows", "-p", par_file, "par", parameter] out = subprocess.run(cmd_line_arg, capture_output=True, universal_newlines=True) @@ -176,12 +171,12 @@ def test_cmd_par(tmpdir, par_file): # testing the set option: seisflows par `parameter` `value` with patch.object(sys, "argv", ["seisflows"]): # Run this with subprocess so we can capture the print statement - cmd_line_arg = ["seisflows", "-p", dst, "par", parameter, new_val] + cmd_line_arg = ["seisflows", "-p", par_file, "par", parameter, new_val] out1 = subprocess.run(cmd_line_arg, capture_output=True, universal_newlines=True) # Run this with subprocess so we can capture the print statement - cmd_line_arg = ["seisflows", "-p", dst, "par", parameter] + cmd_line_arg = ["seisflows", "-p", par_file, "par", parameter] out2 = subprocess.run(cmd_line_arg, capture_output=True, universal_newlines=True) diff --git a/seisflows/tools/msg.py b/seisflows/tools/msg.py index f5ccb4aa..c1a647a5 100644 --- a/seisflows/tools/msg.py +++ b/seisflows/tools/msg.py @@ -155,6 +155,43 @@ def cli(text="", items=None, wraplen=80, header=None, border=None, hchar="/"): return output_str +# Template parameter file used for 'seisflows setup' to initiate a workflow +base_parameter_file = """ +# ////////////////////////////////////////////////////////////////////////////// +# +# SeisFlows YAML Parameter File +# +# ////////////////////////////////////////////////////////////////////////////// +# +# Modules correspond to the structure of the source code, and determine +# SeisFlows' behavior at runtime. Each module requires its own sub-parameters. +# +# .. rubric:: +# - Determine available options for modules by running: +# > seisflows print modules +# - Auto-fill with docstrings and default values (recommended) by running: +# > seisflows configure +# - Swap out module parameters for a configured parameter file by running: +# > seisflows swap {module} {name} (e.g., seisflows swap solver specfem3d) +# - To set values as NoneType, use: null +# - To set values as infinity, use: inf +# +# MODULES +# /////// +# workflow (str): The types and order of functions for running SeisFlows +# system (str): Computer architecture of the system being used +# solver (str): External numerical solver to use for waveform simulations +# preprocess (str): Preprocessing schema for waveform data +# optimize (str): Optimization algorithm for the inverse problem +# ============================================================================== +workflow: forward +system: workstation +solver: specfem2d +preprocess: default +optimize: gradient +""" + + # SeisFlows 'Globe' logo in ASCII. Used for CLI print statements ascii_logo = """ From 9c763c5827fd25dc9f69bd5acefe327d2bbffc1b Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Tue, 23 Aug 2022 17:14:58 -0800 Subject: [PATCH 137/195] Update documentation (#132) * added more explanation of examples to specfem2d example notebook started in on a cluster-specific notebook * added a 'running examples on cluster' docs page which should help users transition from the 2D example to running ona cluster * updated docs page changelog * added a 'containers' docs page to explain how to use SeisFlows with docker/ singularity' * finished rough outline of container page which shows running with Docker and Singularity * removed '$' from container docs page to match formatting on rest of docs * updated specfem2d example documentation to match the new way examples are called, which removed the input statement and allows users to feed in the location of a specfem2d directory through the command line * adding some more container information, re-arranging docs pages * added additional tool descriptions to CLI docs page * bugfix 2d example problem, update changelog --- docs/changelog.rst | 14 +- docs/command_line_tool.rst | 114 +++- docs/containers.rst | 161 +++++ docs/example_on_cluster.rst | 419 +++++++++++++ docs/extending.rst | 4 +- docs/index.rst | 80 ++- docs/notebooks/command_line_tool.ipynb | 143 ++++- docs/notebooks/example_on_cluster.ipynb | 564 ++++++++++++++++++ docs/notebooks/sflog.txt | 0 docs/notebooks/specfem2d_example.ipynb | 163 ++++- docs/notebooks/working_directory.ipynb | 6 +- docs/specfem2d_example.rst | 140 ++++- docs/working_directory.rst | 4 +- ...pecfem2d_workstation_inversion_w_pyatoa.py | 4 +- seisflows/examples/sfexample2d.py | 4 +- seisflows/system/slurm.py | 2 +- seisflows/workflow/test_flow.py | 2 +- 17 files changed, 1755 insertions(+), 69 deletions(-) create mode 100644 docs/containers.rst create mode 100644 docs/example_on_cluster.rst create mode 100644 docs/notebooks/example_on_cluster.ipynb delete mode 100644 docs/notebooks/sflog.txt diff --git a/docs/changelog.rst b/docs/changelog.rst index cef7e222..0bb80957 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -1,8 +1,8 @@ Change Log =============== -The following list documents changes made from ``SeisFlows Legacy`` -codebase (Last updated Aug. 12, 2022). +The following list documents changes made since v2.0.0 +(Last updated Aug. 18, 2022). Major ------ @@ -37,6 +37,7 @@ Major Moderate -------- +* Support added for SLURM-based TACC Frontera system * All modules now have their own setup() and check() functions which help establish a workflow before anything needs to be submitted to the system. * System run functions no longer require global pickling into sys modules, but @@ -90,6 +91,12 @@ Moderate getting and setting values in the SPECFEM Par_file * Separated system mid-tier classes into "Cluster" and "Workstation" to differentiate working on HPC systems +* Added a TestFlow workflow which 'live' tests System abilities directly on + login/compute nodes of a cluster +* Reworked some of the internal system.Cluster architecture to take advantage + of inheritance - Systems now specify headers for their run and submit calls. + e.g., slurm-based systems specify their own SBATCH commands but all use the + same call structure after the SBATCH header Minor @@ -129,5 +136,6 @@ Minor * Enforced package-wide constants at the top of seisflows.config * Added __init__() functions to most of the modules to define any instance-dependent variables, which previously were not explained. - +* New Docs page to describe how to transition from a 2D workstation example + to a 2D cluster example. diff --git a/docs/command_line_tool.rst b/docs/command_line_tool.rst index 2ec34f47..b22c5a34 100644 --- a/docs/command_line_tool.rst +++ b/docs/command_line_tool.rst @@ -58,8 +58,8 @@ help dialogue, you can type ``seisflows`` or ``seisflows -h`` 'seisflows [command] -h' for more detailed descriptions of each command. -Setting up a parameter file -~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Setting up +~~~~~~~~~~ seisflows setup ^^^^^^^^^^^^^^^ @@ -309,8 +309,8 @@ SeisFlows workflow. the package will raise AssertionErrors for incorrectly or improperly set parameters. -Filling out the parameter file -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Editing the parameter file +~~~~~~~~~~~~~~~~~~~~~~~~~~ seisflows par ^^^^^^^^^^^^^ @@ -413,6 +413,40 @@ having to wait on queue times or waste computational resources. Here we can see that a given path has not been set correctly in the parameter file. +seisflows swap +^^^^^^^^^^^^^^ + +The ``seisflows swap`` command allows you to swap out a set of module +parameters without affecting other parts of the parameter file. Some +cases for when this might be useful include switching from a +‘workstation’ system to a ‘cluster’ system, or swapping solvers from +‘specfem2d’ to ‘specfem3d’. + +.. code:: ipython3 + + ! seisflows swap -h + + +.. parsed-literal:: + + usage: seisflows swap [-h] [module] [classname] + + During workflow development, it may be necessary to swap between different + sub-modules (e.g., system.workstation -> system.cluster). However this would + typically involving re-generating and re-filling a parameter file. The 'swap' + function makes it easier to swap parameters between modules. + + positional arguments: + module Module name to swap + classname Classname to swap to + + optional arguments: + -h, --help show this help message and exit + + +Running workflows +~~~~~~~~~~~~~~~~~ + seisflows submit ^^^^^^^^^^^^^^^^ @@ -426,3 +460,75 @@ chosen ``system``. For Users running on laptops and workstations, tasks in the ``workflow`` task list. On clusters, ``submit`` will launch a master job on a compute node, which will itself step through tasks in the task list, ensuring that no processing is run on login nodes. + +.. code:: ipython3 + + ! seisflows submit -h + + +.. parsed-literal:: + + usage: seisflows submit [-h] [-s STOP_AFTER] + + The main SeisFlows execution command. Submit a SeisFlows workflow to the + chosen system, equal to executing seisflows.workflow.main(). This function + will create and fill the working directory with required paths, perform path + and parameter error checking, and establish the active working environment + before executing the workflow. + + optional arguments: + -h, --help show this help message and exit + -s STOP_AFTER, --stop_after STOP_AFTER + Optional override of the 'STOP_AFTER' parameter + + +seisflows clean +^^^^^^^^^^^^^^^ + +The ``clean`` function is used to clear an existing working directory. +It deletes all SeisFlows created files and directories using paths in +the parameter file, but does not delete the parameter file itself. Use +the ``-f/--force`` flag to skip over the ‘are you sure?’ check +statement. + +.. code:: ipython3 + + ! seisflows clean -h + + +.. parsed-literal:: + + usage: seisflows clean [-h] [-f] + + Delete all SeisFlows related files in the working directory, except for the + parameter file. + + optional arguments: + -h, --help show this help message and exit + -f, --force Skip the warning check that precedes the clean function + + +seisflows restart +^^^^^^^^^^^^^^^^^ + +The ``restart`` function is a convenience function which wraps ``clean`` +and ``submit``. It is used to restart workflows using the same parameter +file. It also takes the ``-f/--force`` flag that the clean function +defines. + +.. code:: ipython3 + + ! seisflows restart -h + + +.. parsed-literal:: + + usage: seisflows restart [-h] [-f] + + Akin to running seisflows clean; seisflows submit. Restarts the workflow by + removing the current state and submitting a fresh workflow. + + optional arguments: + -h, --help show this help message and exit + -f, --force Skip the clean warning check statement + diff --git a/docs/containers.rst b/docs/containers.rst new file mode 100644 index 00000000..d725d46e --- /dev/null +++ b/docs/containers.rst @@ -0,0 +1,161 @@ +SeisFlows in Containers +======================= + +As part of the `NSF-funded SCOPED project +`__ SeisFlows has +been containerized using `Docker `__, which removes the +oftentimes troubling task of installing and configuring software. These +containers can be used to run SeisFlows on workstations (using Docker) or +on high performance computers using +`Singularity/Apptainer `__. + +.. note:: + The SeisFlows Docker image can be found here: + https://github.com/SeisSCOPED/pyatoa + +.. note:: + The `Pyatoa` container is shipped with the latest versions of + `SeisFlows `__, + `Pyatoa `__, + `PySEP `__. + +The remainder of this documentation page assumes you are familiar with Docker +and containers. To learn more about Docker and containers you can visit: +https://www.docker.com/resources/what-container/ + +To install Docker on your workstation, visit: +https://docs.docker.com/get-docker/ + + +Workstation example with Docker +------------------------------- + +Here we will step through how running the +`SeisFlows-SPECFEM2D `__ example using the available +Docker image. + +To get the latest version of the `Pyatoa` Docker image, run: + +.. code-block:: bash + + docker pull ghcr.io/seisscoped/pyatoa:nightly + +Their are two methods for running the SeisFlows example using this Docker image, +either through a `JupyterHub interface `__, or through +the command line. The former provides a graphical user interface that mimics +a virtual desktop for easier navigation, while the latter provides more +flexibility for scripting and using SeisFlows as a research tool. + +From JupyterHub +^^^^^^^^^^^^^^^ + +We can run SeisFlows through JupyterHub by running through a port: + +.. code-block:: bash + + docker run -p 8888:8888 ghcr.io/seisscoped/pyatoa:nightly + +To open the running JupyterHub instance, open the URL that is pasted to stdout +in your favorite web browser. This will likely look something like +http://127.0.0.1:8888/lab?token=xxx where xxx is your unique token + +Once you have opened the JupyterHub instance, you should see a graphical +user interface. The Pyatoa, PySEP and SeisFlows repositories will be downloaded +and navigable in the file system on the left hand side of the window. + +From inside the JupyterHub instance, click the 'Terminal' icon to open up a +terminal window, and run the following example command. Note, it's best to run +the example in a new directory to avoid muddling up the home directory. + +.. code-block:: bash + + mkdir sf_sem2d_example + cd sf_sem2d_example + seisflows examples run 2 + +This example will download, configure and compile SPECFEM2D into your +JupyterHub instance, and then run a SeisFlows-Pyatoa-SPECFEM2D inversion +problem. + + +From the command line +^^^^^^^^^^^^^^^^^^^^^ + +.. warning:: + Command line implementation does not currently work. + +To run the SeisFlows SPECFEM2D example from the command line, we simply need +to point Docker to the image we just downloaded, and call the SeisFlows command +line tool. To run the help message: + +.. code-block:: bash + + docker run ghcr.io/seisscoped/pyatoa:nightly seisflows -h + +Running the example should be as easy as: + +.. code-block:: bash + + docker run ghcr.io/seisscoped/pyatoa:nightly seisflows examples run 2 + +In the above example, SeisFlows automatically identifies the SPECFEM2D +installation within the Docker image and uses this as the external numerical +solver. All results will be output to your current working directory. + + +HPC example with Apptainer/Singularity +-------------------------------------- + +.. note:: + Section Under Construction + +Apptainer/Singularity is a container system for high performance computers (HPC) +that allows Users to run container images on HPCs. You might want to use +Apptainer if you cannot download software using Conda on your HPC, or you simply +do not want to go through the trouble of downloading software on your system. + +Relevant Links: + +* Singularity on Chinook: + https://uaf-rcs.gitbook.io/uaf-rcs-hpc-docs/third-party-software/singularity +* Singularity on TACC: + https://containers-at-tacc.readthedocs.io/en/latest/singularity/ +01.singularity_basics.html + + +.. note:: + This section was written working on TACC's Frontera, a SLURM based HPC. + Instructions may differ depending on your Systems setup and workload + manager. Because Singularity cannot be run on the Login nodes, the following + code blocks are run in the `idev `__ interactive + environment. + +To download the required image on your system: + +.. code-block:: bash + + module load tacc-singularity # on TACC Frontera + # module load singularity # on UAF Chinook + singularity pull seisflows.sif docker://ghcr.io/seisscoped/pyatoa:nightly + +To run the SeisFlows help message + +.. code-block:: bash + + singularity run seisflows.sif seisflows -h + +To set your system to use Singularity, you just need to append '-singularity' to +an existing system subclass in the SeisFlows parameter file. For example, since +we are running on Frontera, we set our system to 'frontera-singularity'. + +.. code-block:: bash + + seisflows setup # create the 'parameters.yaml' file + seisflows par system frontera-singularity # set the system + # ... set any other main modules here + seisflows configure # fill out the parameter file + # ... edit your parameters here and then run SeisFlows + singularity run ghcr.io/seisscoped/pyatoa:nightly seisflows submit + + diff --git a/docs/example_on_cluster.rst b/docs/example_on_cluster.rst new file mode 100644 index 00000000..482b8ca0 --- /dev/null +++ b/docs/example_on_cluster.rst @@ -0,0 +1,419 @@ +Running Examples on a Cluster +============================= + +Here we detail how a User might transition from developing a 2D example +problem on their workstation, to performing large-scale inversion on a +cluster. In this notebook we show an example running on the New Zealand +eScience Infrastructure HPC, named Maui, but is meant to provide a +generalizable approach for running SeisFlows on clusters. + +Example Setup +------------- + +We first set up our working directory using the example setup shown in the `SPECFEM2D example page `__. This ensures that we have our initial and final models, and a properly set parameter file that can be used for our inversion. + +.. code:: ipython3 + + # This is an empty working directory + %cd /home/bchow/Work/scratch + + +.. parsed-literal:: + + /home/bchow/Work/scratch + + +.. code:: bash + + ! ln -s /home/bchow/REPOSITORIES/specfem2d . # place SPECFEM2D repository in the working directory + ! seisflows examples setup 2 # run example setup but do not `submit` workflow + +.. code:: ipython3 + + ! ls + + +.. parsed-literal:: + + parameters.yaml specfem2d specfem2d_workdir + + +Module ‘swap’ +------------- + +As we saw in the example tutorial, the ``System`` module for this +example problem is set as ‘Workstation’, which is meant to run the +workflow in serial directly on the system that submits it. For clusters +this means we would run our entire inversion on the login node. + +.. code:: ipython3 + + ! seisflows par system + + +.. parsed-literal:: + + system: workstation + + +To ‘swap’ out the ``System`` module for a cluster-specific class, we can +use the ``seisflows swap`` command, which replaces one module for +another without affecting the other modules. This is very helpful if you +have a completed parameter file and do not want to copy-paste all the +edited parameter just to change out a module. The rubric for running +``seisflows swap`` can be found in the help message: + +.. code:: ipython3 + + ! seisflows swap -h + + +.. parsed-literal:: + + usage: seisflows swap [-h] [module] [classname] + + During workflow development, it may be necessary to swap between different + sub-modules (e.g., system.workstation -> system.cluster). However this would + typically involving re-generating and re-filling a parameter file. The 'swap' + function makes it easier to swap parameters between modules. + + positional arguments: + module Module name to swap + classname Classname to swap to + + optional arguments: + -h, --help show this help message and exit + + +You can check available names by running ``seisflows print modules``. +Here we want to swap out our ``System`` module from ‘Workstation’ to +‘Maui’, which defines how SeisFlows interacts with the SLURM-based +system, Maui. + +.. code:: ipython3 + + ! seisflows print modules + + +.. parsed-literal:: + + SEISFLOWS MODULES + ///////////////// + '-': module, '*': class + + - workflow + * forward + * inversion + * migration + * test_flow + - system + * chinook + * cluster + * frontera + * lsf + * maui + * slurm + * workstation + - solver + * specfem + * specfem2d + * specfem3d + * specfem3d_globe + - preprocess + * default + * pyaflowa + - optimize + * LBFGS + * NLCG + * gradient + + +.. code:: ipython3 + + ! seisflows swap system maui + + +.. parsed-literal:: + + L-BFGS optimization requires 'backtrack'ing line search. Overwriting 'bracket' + + +We can see now that the parameter file has swapped out the ‘Workstation’ +System module for the ‘Maui’ System module, which contains its own set +of parameters that must be filled out by the User. + +.. code:: ipython3 + + ! head -235 parameters.yaml | tail -n 110 + + +.. parsed-literal:: + + # ============================================================================= + # + # Workstation System + # ------------------ + # Defines foundational structure for System module. When used standalone, + # runs tasks in serial on a local machine. + # + # Parameters + # ---------- + # :type ntask: int + # :param ntask: number of individual tasks/events to run during workflow. + # Must be <= the number of source files in `path_specfem_data` + # :type nproc: int + # :param nproc: number of processors to use for each simulation + # :type log_level: str + # :param log_level: logger level to pass to logging module. + # Available: 'debug', 'info', 'warning', 'critical' + # :type verbose: bool + # :param verbose: if True, formats the log messages to include the file + # name, line number and message type. Useful for debugging but + # also very verbose. + # + # + # Cluster System + # ------------------ + # Generic or common HPC/cluster interfacing commands + # + # Parameters + # ---------- + # :type title: str + # :param title: The name used to submit jobs to the system, defaults + # to the name of the current working directory + # :type mpiexec: str + # :param mpiexec: Function used to invoke executables on the system. + # For example 'mpirun', 'mpiexec', 'srun', 'ibrun' + # :type ntask_max: int + # :param ntask_max: limit the number of concurrent tasks in a given array job + # :type walltime: float + # :param walltime: maximum job time in minutes for the master SeisFlows + # job submitted to cluster. Fractions of minutes acceptable. + # :type tasktime: float + # :param tasktime: maximum job time in minutes for each job spawned by + # the SeisFlows master job during a workflow. These include, e.g., + # running the forward solver, adjoint solver, smoother, kernel combiner. + # All spawned tasks receive the same task time. Fractions of minutes + # acceptable. + # :type environs: str + # :param environs: Optional environment variables to be provided in the + # following format VAR1=var1,VAR2=var2... Will be set using + # os.environs + # + # + # System Slurm + # ------------------ + # Interface for submitting and monitoring jobs on HPC systems running the + # Simple Linux Utility for Resource Management (SLURM) workload manager. + # + # Parameters + # ---------- + # :type slurm_args: str + # :param slurm_args: Any (optional) additional SLURM arguments that will + # be passed to the SBATCH scripts. Should be in the form: + # '--key1=value1 --key2=value2" + # + # + # System Maui + # ----------- + # New Zealand Maui-specfic modifications to base SLURM system + # + # Parameters + # ---------- + # :type account: str + # :param account: Maui account to submit jobs under, will be used for the + # '--account' sbatch argument + # :type cpus_per_task: int + # :param cpus_per_task: allow for multiple cpus per task, i.e,. + # multithreaded jobs + # :type cluster: str + # :param cluster: cluster to submit jobs to. Available are Maui and + # Mahuika + # :type partition: str + # :param partition: partition of the cluster to submit jobs to. + # :type ancil_cluster: str + # :param ancil_cluster: name of the ancilary cluster used for pre- + # post-processing tasks. + # :type ancil_partition: name of the partition of the ancilary cluster + # :type ancil_tasktime: int + # :param ancil_tasktime: Tasktime in minutes for pre and post-processing + # jobs submitted to Maui ancil. + # + # + # ============================================================================= + ntask: 1 + nproc: 1 + log_level: DEBUG + verbose: False + title: scratch + mpiexec: None + ntask_max: 100 + walltime: 10 + tasktime: 1 + environs: SLURM_MEM_PER_CPU + slurm_args: None + partition: nesi_research + account: None + cluster: maui + cpus_per_task: 1 + ancil_cluster: maui_ancil + ancil_partition: nesi_prepost + ancil_tasktime: 1 + + +’Check’ing parameter validity +----------------------------- + +Most of the default values should be okay for our purposes, but it’s up +the User to read the docstrings and determine if any of the default +values should be changed. If we run ``seisflows check`` we can check if +any of our parameters are incorrectly set. + +.. code:: ipython3 + + ! seisflows check + + +.. parsed-literal:: + + + ================================================================================ + PARAMETER ERRROR + //////////////// + System 'Maui' requires parameter 'account' + ================================================================================ + + +The ``Maui`` System check function has told us that it requires that the +parameter ``account`` be set. Note that these requirements will change +between different clusters, which dictate different SLURM parameters +when submitting jobs. We can specify the account parameter using the +``seisflows par`` command. + +.. code:: ipython3 + + ! seisflows par account gns03247 + + +.. parsed-literal:: + + account: null -> gns03247 + + +.. code:: ipython3 + + ! seisflows check + +The ``seisflows check`` function has passed and we have succesfully +swapped out our System module with the ``Maui`` child class. Under the +hood, this class should take care of all the required interactions +between SeisFlows and the compute node. Now all that is left to do is to +run ``seisflows submit``, which should submit the master job to the +system and run our inversion on compute nodes. + +TestFlow: Live testing SeisFlows on System +------------------------------------------ + +While developing, debugging or testing SeisFlows on System, it is not +ideal to submit simulation-based workflows, as these eat large amounts +of computational resources and may introduce problems of there own. + +Here we introduce ‘TestFlow’, a SeisFlows workflow that runs simple test +functions on a cluster. This allows Users to check if SeisFlows can +appropriately interact with the HPC system with tasks like submitting +jobs, monitoring the job queue and catching failing jobs. + +Below we show how to set up TestFlow for our test bed HPC, Maui. First +we generate a template parameter file and set the modules appropriately. + +.. code:: ipython3 + + # This is an empty working directory + %rm -r /home/bchow/Work/scratch + %mkdir /home/bchow/Work/scratch + %cd /home/bchow/Work/scratch + + +.. parsed-literal:: + + shell-init: error retrieving current directory: getcwd: cannot access parent directories: No such file or directory + /home/bchow/Work/scratch + + +.. code:: ipython3 + + # Generate a template parameter file + ! seisflows setup -f + + +.. parsed-literal:: + + creating parameter file: parameters.yaml + + +.. code:: ipython3 + + # Set the modules appropriately + ! seisflows par workflow test_flow + ! seisflows par system maui # we want to test SeisFlows on Maui + ! seisflows par solver null # currently test_flow does not test solver + ! seisflows par preprocess null # currently test_flow does not test preprocess + ! seisflows par optimize null # currently test_flow does not test optimize + + +.. parsed-literal:: + + workflow: forward -> test_flow + system: workstation -> maui + solver: specfem2d -> null + preprocess: default -> null + optimize: gradient -> null + + +.. code:: ipython3 + + # Dynamically fill out the parameter file + ! seisflows configure + +.. code:: ipython3 + + ! head -48 parameters.yaml | tail -n 16 + + +.. parsed-literal:: + + # ============================================================================= + # + # TestFlow Workflow + # ------------- + # Test individual sub-modules in a 'live' testing environment in order to + # ensure SeisFlows works appropriately given an established system and solver. + # + # .. note:: + # You do not need to set System parameters `ntask`, `nproc`, `tasktime`, + # `walltime`. These will be overwritten by the setup task. + # + # Parameters + # ---------- + # + # + # ============================================================================= + + +As we can see above, the ``TestFlow`` workflow does not require any +input parameters, and will additionally automatically set some key +``System`` parameters to ensure that these tests are lightweight to +avoid long queue times. Under the hood the ``TestFlow`` workflow will: + +1) Submit an array job to the system to test job submission capabilities +2) Submit a single job to the system which is intended to fail, this + tests job queue monitoring as well as failed job catching. + +Developers who are implementing new ``System`` classes (e.g., for new +clusters), can use TestFlow as foundation for their development and +debugging sessions. To run the ``TestFlow`` workflow you just need to +run ``seisflows submit`` + +.. code:: ipython3 + + ! seisflows submit diff --git a/docs/extending.rst b/docs/extending.rst index 65611c1e..48bccc2a 100644 --- a/docs/extending.rst +++ b/docs/extending.rst @@ -1,5 +1,5 @@ -Extending SeisFlows -=================== +Extending the Codebase +====================== .. note:: Page Under Construction diff --git a/docs/index.rst b/docs/index.rst index 1d956e32..36bdd286 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,8 +1,3 @@ -.. SeisFlows documentation master file, created by - sphinx-quickstart on Mon Oct 26 16:35:00 2020. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - .. image:: images/sf_globe_banner_alpha.png :align: center @@ -23,7 +18,7 @@ optimization problems. .. note:: Major backwards-incompatible changes from the legacy codebase include: - - > complete shift to Python3.7 source code, abandoning Python2 support + - > complete shift to Python>=3.7 source code, abandoning Python2 support - > reworked internal architecture to be more 'Pythonic' - > richer source code emphasizing readability and standards - > a new command line tool for improved package control @@ -45,40 +40,57 @@ optimization problems. Installation ================= -Successful applications of SeisFlows will typically require direct editing of -source code. For this reason, SeisFlows should be installed directly via the -package library using Pip. The ``-e`` flag ensures that SeisFlows is -installed in development mode, allowing source code changes to be immediately -acccessible to Python. +``SeisFlows`` is installed using a combination of Pip and +`Conda `__. In the +future we aim to unify this into a single install command. + +*Preamble: many packages will require the conda-forge channel.* + +.. code:: bash + + conda config --add channels conda-forge + +To install ``SeisFlows`` and it's dependencies, we recommend installing within +a Conda environment to not affect your root environment. The `devel` branch +houses the most up-to-date codebase. + +.. code:: bash + + conda create -n seisflows python=3.10 + conda activate seisflows + git clone --branch devel https://github.com/adjtomo/seisflows.git + cd seisflows + conda install --file requirements.txt + pip install -e . + -The `devel` branch houses the most up-to-date codebase. We recommend installing -SeisFlows within a virtual environment (e.g., Conda) to not affect your root -environment. +SeisFlows requires the waveform measurement capabilities of +`Pyatoa `__, which currently must be +installed manually to the same Conda environment. .. code:: bash - $ conda create -n seisflows python=3.10 - $ conda activate seisflows - $ git clone --branch devel https://github.com/adjtomo/seisflows.git - $ cd seisflows - $ conda install --file requirements.txt - $ pip install -e . + cd .. + git clone --branch devel https://github.com/adjtomo/pyatoa.git + cd pyatoa + conda install --file requirements.txt + pip install -e . --------------------------------- -Requirements -============= +.. note:: + Successful applications of SeisFlows will typically require editing the + source code. For this reason, SeisFlows is installed using the Pip ``-e`` + which enables development mode, where source code changes are immediately + acccessible to Python. + -In most production-scale workflows, SeisFlows must be run on a cluster, or -high performance computing system. However, serially run example problems -making use of 2D solvers like SPECFEM2D are available. +.. note:: + In most production-scale workflows, SeisFlows must be run on high + performance computing systems running external numerical solvers like + SPECFEM3D. The User will need to install and compile these separately. + However, example problems in SeisFlows make use of, and can install, + SPECFEM2D. -SeisFlows + Pyatoa --------------------- -To include the waveform measurement capabilities of Pyatoa, you must install -separately. See the `Pyatoa documentation -`__ for the most up to date -install instructions. .. toctree:: @@ -109,8 +121,10 @@ install instructions. .. toctree:: :maxdepth: 1 :hidden: - :caption: How To + :caption: How To's + example_on_cluster + containers extending .. toctree:: diff --git a/docs/notebooks/command_line_tool.ipynb b/docs/notebooks/command_line_tool.ipynb index 4123c3a8..9f2f4503 100644 --- a/docs/notebooks/command_line_tool.ipynb +++ b/docs/notebooks/command_line_tool.ipynb @@ -68,7 +68,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Setting up a parameter file\n", + "### Setting up \n", "#### seisflows setup\n", "\n", "The first step of any SeisFlows workflow is to setting up a parameter file. The `seisflows setup` command copies in a template parameter file." @@ -372,7 +372,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Filling out the parameter file\n", + "### Editing the parameter file\n", "#### seisflows par\n", "\n", "You can always open your favorite text editor to make changes to the parameter file, however the `seisflows par` command makes things easier by allowing you to view and edit values from the command line. This makes it convenient to change parameters quickly and allows you to script your parameter file setup for improved reproducibility. " @@ -510,17 +510,152 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "#### seisflows swap\n", + "\n", + "The `seisflows swap` command allows you to swap out a set of module parameters without affecting other parts of the parameter file. Some cases for when this might be useful include switching from a 'workstation' system to a 'cluster' system, or swapping solvers from 'specfem2d' to 'specfem3d'." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "usage: seisflows swap [-h] [module] [classname]\r\n", + "\r\n", + "During workflow development, it may be necessary to swap between different\r\n", + "sub-modules (e.g., system.workstation -> system.cluster). However this would\r\n", + "typically involving re-generating and re-filling a parameter file. The 'swap'\r\n", + "function makes it easier to swap parameters between modules.\r\n", + "\r\n", + "positional arguments:\r\n", + " module Module name to swap\r\n", + " classname Classname to swap to\r\n", + "\r\n", + "optional arguments:\r\n", + " -h, --help show this help message and exit\r\n" + ] + } + ], + "source": [ + "! seisflows swap -h" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Running workflows\n", "#### seisflows submit\n", "\n", "To run SeisFlows, we use the `submit` call. This will submit the `workflow` to the `system` and continue until a User-defined stop criteria is met. \n", "\n", "Under the hood, the `submit` function will differ depending on the chosen `system`. For Users running on laptops and workstations, `submit` will simply launch a Python process and step through the tasks in the `workflow` task list. On clusters, `submit` will launch a master job on a compute node, which will itself step through tasks in the task list, ensuring that no processing is run on login nodes. " ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "usage: seisflows submit [-h] [-s STOP_AFTER]\r\n", + "\r\n", + "The main SeisFlows execution command. Submit a SeisFlows workflow to the\r\n", + "chosen system, equal to executing seisflows.workflow.main(). This function\r\n", + "will create and fill the working directory with required paths, perform path\r\n", + "and parameter error checking, and establish the active working environment\r\n", + "before executing the workflow.\r\n", + "\r\n", + "optional arguments:\r\n", + " -h, --help show this help message and exit\r\n", + " -s STOP_AFTER, --stop_after STOP_AFTER\r\n", + " Optional override of the 'STOP_AFTER' parameter\r\n" + ] + } + ], + "source": [ + "! seisflows submit -h" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### seisflows clean\n", + "\n", + "The `clean` function is used to clear an existing working directory. It deletes all SeisFlows created files and directories using paths in the parameter file, but does not delete the parameter file itself. Use the ``-f/--force`` flag to skip over the 'are you sure?' check statement." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "usage: seisflows clean [-h] [-f]\r\n", + "\r\n", + "Delete all SeisFlows related files in the working directory, except for the\r\n", + "parameter file.\r\n", + "\r\n", + "optional arguments:\r\n", + " -h, --help show this help message and exit\r\n", + " -f, --force Skip the warning check that precedes the clean function\r\n" + ] + } + ], + "source": [ + "! seisflows clean -h " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### seisflows restart \n", + "\n", + "The `restart` function is a convenience function which wraps `clean` and `submit`. It is used to restart workflows using the same parameter file. It also takes the ``-f/--force`` flag that the clean function defines." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "usage: seisflows restart [-h] [-f]\r\n", + "\r\n", + "Akin to running seisflows clean; seisflows submit. Restarts the workflow by\r\n", + "removing the current state and submitting a fresh workflow.\r\n", + "\r\n", + "optional arguments:\r\n", + " -h, --help show this help message and exit\r\n", + " -f, --force Skip the clean warning check statement\r\n" + ] + } + ], + "source": [ + "! seisflows restart -h" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -534,7 +669,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.12" + "version": "3.7.6" } }, "nbformat": 4, diff --git a/docs/notebooks/example_on_cluster.ipynb b/docs/notebooks/example_on_cluster.ipynb new file mode 100644 index 00000000..a36fffd7 --- /dev/null +++ b/docs/notebooks/example_on_cluster.ipynb @@ -0,0 +1,564 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Running Examples on a Cluster\n", + "\n", + "Here we detail how a User might transition from developing a 2D example problem on their workstation, to performing large-scale inversion on a cluster. In this notebook we show an example running on the New Zealand eScience Infrastructure HPC, named Maui, but is meant to provide a generalizable approach for running SeisFlows on clusters. \n", + "\n", + "## Example Setup" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "We first set up our working directory using the example setup shown in the `SPECFEM2D example page `__. This ensures that we have our initial and final models, and a properly set parameter file that can be used for our inversion." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/bchow/Work/scratch\n" + ] + } + ], + "source": [ + "# This is an empty working directory\n", + "%cd /home/bchow/Work/scratch " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! ln -s /home/bchow/REPOSITORIES/specfem2d . # place SPECFEM2D repository in the working directory\n", + "! seisflows examples setup 2 # run example setup but do not `submit` workflow" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "parameters.yaml specfem2d specfem2d_workdir\r\n" + ] + } + ], + "source": [ + "! ls " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Module 'swap'\n", + "\n", + "As we saw in the example tutorial, the `System` module for this example problem is set as 'Workstation', which is meant to run the workflow in serial directly on the system that submits it. For clusters this means we would run our entire inversion on the login node. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "system: workstation\r\n" + ] + } + ], + "source": [ + "! seisflows par system" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To 'swap' out the `System` module for a cluster-specific class, we can use the `seisflows swap` command, which replaces one module for another without affecting the other modules. This is very helpful if you have a completed parameter file and do not want to copy-paste all the edited parameter just to change out a module. The rubric for running `seisflows swap` can be found in the help message: " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "usage: seisflows swap [-h] [module] [classname]\r\n", + "\r\n", + "During workflow development, it may be necessary to swap between different\r\n", + "sub-modules (e.g., system.workstation -> system.cluster). However this would\r\n", + "typically involving re-generating and re-filling a parameter file. The 'swap'\r\n", + "function makes it easier to swap parameters between modules.\r\n", + "\r\n", + "positional arguments:\r\n", + " module Module name to swap\r\n", + " classname Classname to swap to\r\n", + "\r\n", + "optional arguments:\r\n", + " -h, --help show this help message and exit\r\n" + ] + } + ], + "source": [ + "! seisflows swap -h" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can check available names by running `seisflows print modules`. Here we want to swap out our `System` module from 'Workstation' to 'Maui', which defines how SeisFlows interacts with the SLURM-based system, Maui." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " SEISFLOWS MODULES \r\n", + " ///////////////// \r\n", + "'-': module, '*': class\r\n", + "\r\n", + "- workflow\r\n", + " * forward\r\n", + " * inversion\r\n", + " * migration\r\n", + " * test_flow\r\n", + "- system\r\n", + " * chinook\r\n", + " * cluster\r\n", + " * frontera\r\n", + " * lsf\r\n", + " * maui\r\n", + " * slurm\r\n", + " * workstation\r\n", + "- solver\r\n", + " * specfem\r\n", + " * specfem2d\r\n", + " * specfem3d\r\n", + " * specfem3d_globe\r\n", + "- preprocess\r\n", + " * default\r\n", + " * pyaflowa\r\n", + "- optimize\r\n", + " * LBFGS\r\n", + " * NLCG\r\n", + " * gradient\r\n" + ] + } + ], + "source": [ + "! seisflows print modules" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "L-BFGS optimization requires 'backtrack'ing line search. Overwriting 'bracket'\r\n" + ] + } + ], + "source": [ + "! seisflows swap system maui" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see now that the parameter file has swapped out the 'Workstation' System module for the 'Maui' System module, which contains its own set of parameters that must be filled out by the User." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# =============================================================================\r\n", + "#\r\n", + "# Workstation System\r\n", + "# ------------------\r\n", + "# Defines foundational structure for System module. When used standalone, \r\n", + "# runs tasks in serial on a local machine.\r\n", + "#\r\n", + "# Parameters\r\n", + "# ----------\r\n", + "# :type ntask: int\r\n", + "# :param ntask: number of individual tasks/events to run during workflow.\r\n", + "# Must be <= the number of source files in `path_specfem_data`\r\n", + "# :type nproc: int\r\n", + "# :param nproc: number of processors to use for each simulation\r\n", + "# :type log_level: str\r\n", + "# :param log_level: logger level to pass to logging module.\r\n", + "# Available: 'debug', 'info', 'warning', 'critical'\r\n", + "# :type verbose: bool\r\n", + "# :param verbose: if True, formats the log messages to include the file\r\n", + "# name, line number and message type. Useful for debugging but\r\n", + "# also very verbose.\r\n", + "#\r\n", + "# \r\n", + "# Cluster System\r\n", + "# ------------------\r\n", + "# Generic or common HPC/cluster interfacing commands\r\n", + "#\r\n", + "# Parameters\r\n", + "# ----------\r\n", + "# :type title: str\r\n", + "# :param title: The name used to submit jobs to the system, defaults\r\n", + "# to the name of the current working directory\r\n", + "# :type mpiexec: str\r\n", + "# :param mpiexec: Function used to invoke executables on the system.\r\n", + "# For example 'mpirun', 'mpiexec', 'srun', 'ibrun'\r\n", + "# :type ntask_max: int\r\n", + "# :param ntask_max: limit the number of concurrent tasks in a given array job\r\n", + "# :type walltime: float\r\n", + "# :param walltime: maximum job time in minutes for the master SeisFlows\r\n", + "# job submitted to cluster. Fractions of minutes acceptable.\r\n", + "# :type tasktime: float\r\n", + "# :param tasktime: maximum job time in minutes for each job spawned by\r\n", + "# the SeisFlows master job during a workflow. These include, e.g.,\r\n", + "# running the forward solver, adjoint solver, smoother, kernel combiner.\r\n", + "# All spawned tasks receive the same task time. Fractions of minutes\r\n", + "# acceptable.\r\n", + "# :type environs: str\r\n", + "# :param environs: Optional environment variables to be provided in the\r\n", + "# following format VAR1=var1,VAR2=var2... Will be set using\r\n", + "# os.environs\r\n", + "#\r\n", + "# \r\n", + "# System Slurm\r\n", + "# ------------------\r\n", + "# Interface for submitting and monitoring jobs on HPC systems running the \r\n", + "# Simple Linux Utility for Resource Management (SLURM) workload manager.\r\n", + "#\r\n", + "# Parameters\r\n", + "# ----------\r\n", + "# :type slurm_args: str\r\n", + "# :param slurm_args: Any (optional) additional SLURM arguments that will\r\n", + "# be passed to the SBATCH scripts. Should be in the form:\r\n", + "# '--key1=value1 --key2=value2\"\r\n", + "#\r\n", + "# \r\n", + "# System Maui\r\n", + "# -----------\r\n", + "# New Zealand Maui-specfic modifications to base SLURM system\r\n", + "#\r\n", + "# Parameters\r\n", + "# ----------\r\n", + "# :type account: str\r\n", + "# :param account: Maui account to submit jobs under, will be used for the\r\n", + "# '--account' sbatch argument\r\n", + "# :type cpus_per_task: int\r\n", + "# :param cpus_per_task: allow for multiple cpus per task, i.e,.\r\n", + "# multithreaded jobs\r\n", + "# :type cluster: str\r\n", + "# :param cluster: cluster to submit jobs to. Available are Maui and\r\n", + "# Mahuika\r\n", + "# :type partition: str\r\n", + "# :param partition: partition of the cluster to submit jobs to.\r\n", + "# :type ancil_cluster: str\r\n", + "# :param ancil_cluster: name of the ancilary cluster used for pre-\r\n", + "# post-processing tasks.\r\n", + "# :type ancil_partition: name of the partition of the ancilary cluster\r\n", + "# :type ancil_tasktime: int\r\n", + "# :param ancil_tasktime: Tasktime in minutes for pre and post-processing\r\n", + "# jobs submitted to Maui ancil.\r\n", + "#\r\n", + "# \r\n", + "# =============================================================================\r\n", + "ntask: 1\r\n", + "nproc: 1\r\n", + "log_level: DEBUG\r\n", + "verbose: False\r\n", + "title: scratch\r\n", + "mpiexec: None\r\n", + "ntask_max: 100\r\n", + "walltime: 10\r\n", + "tasktime: 1\r\n", + "environs: SLURM_MEM_PER_CPU\r\n", + "slurm_args: None\r\n", + "partition: nesi_research\r\n", + "account: None\r\n", + "cluster: maui\r\n", + "cpus_per_task: 1\r\n", + "ancil_cluster: maui_ancil\r\n", + "ancil_partition: nesi_prepost\r\n", + "ancil_tasktime: 1\r\n" + ] + } + ], + "source": [ + "! head -235 parameters.yaml | tail -n 110 " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 'Check'ing parameter validity\n", + "\n", + "Most of the default values should be okay for our purposes, but it's up the User to read the docstrings and determine if any of the default values should be changed. If we run `seisflows check` we can check if any of our parameters are incorrectly set." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r\n", + "================================================================================\r\n", + " PARAMETER ERRROR \r\n", + " //////////////// \r\n", + "System 'Maui' requires parameter 'account'\r\n", + "================================================================================\r\n" + ] + } + ], + "source": [ + "! seisflows check" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `Maui` System check function has told us that it requires that the parameter `account` be set. Note that these requirements will change between different clusters, which dictate different SLURM parameters when submitting jobs. We can specify the account parameter using the `seisflows par` command." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "account: null -> gns03247\r\n" + ] + } + ], + "source": [ + "! seisflows par account gns03247" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "! seisflows check" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `seisflows check` function has passed and we have succesfully swapped out our System module with the `Maui` child class. Under the hood, this class should take care of all the required interactions between SeisFlows and the compute node. Now all that is left to do is to run `seisflows submit`, which should submit the master job to the system and run our inversion on compute nodes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TestFlow: Live testing SeisFlows on System\n", + "\n", + "While developing, debugging or testing SeisFlows on System, it is not ideal to submit simulation-based workflows, as these eat large amounts of computational resources and may introduce problems of there own. \n", + "\n", + "Here we introduce 'TestFlow', a SeisFlows workflow that runs simple test functions on a cluster. This allows Users to check if SeisFlows can appropriately interact with the HPC system with tasks like submitting jobs, monitoring the job queue and catching failing jobs. \n", + "\n", + "Below we show how to set up TestFlow for our test bed HPC, Maui. First we generate a template parameter file and set the modules appropriately." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shell-init: error retrieving current directory: getcwd: cannot access parent directories: No such file or directory\n", + "/home/bchow/Work/scratch\n" + ] + } + ], + "source": [ + "# This is an empty working directory\n", + "%rm -r /home/bchow/Work/scratch \n", + "%mkdir /home/bchow/Work/scratch \n", + "%cd /home/bchow/Work/scratch " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating parameter file: parameters.yaml\r\n" + ] + } + ], + "source": [ + "# Generate a template parameter file\n", + "! seisflows setup -f" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "workflow: forward -> test_flow\n", + "system: workstation -> maui\n", + "solver: specfem2d -> null\n", + "preprocess: default -> null\n", + "optimize: gradient -> null\n" + ] + } + ], + "source": [ + "# Set the modules appropriately\n", + "! seisflows par workflow test_flow\n", + "! seisflows par system maui # we want to test SeisFlows on Maui\n", + "! seisflows par solver null # currently test_flow does not test solver\n", + "! seisflows par preprocess null # currently test_flow does not test preprocess\n", + "! seisflows par optimize null # currently test_flow does not test optimize" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Dynamically fill out the parameter file\n", + "! seisflows configure" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# =============================================================================\r\n", + "#\r\n", + "# TestFlow Workflow\r\n", + "# -------------\r\n", + "# Test individual sub-modules in a 'live' testing environment in order to\r\n", + "# ensure SeisFlows works appropriately given an established system and solver.\r\n", + "#\r\n", + "# .. note::\r\n", + "# You do not need to set System parameters `ntask`, `nproc`, `tasktime`,\r\n", + "# `walltime`. These will be overwritten by the setup task.\r\n", + "#\r\n", + "# Parameters\r\n", + "# ----------\r\n", + "#\r\n", + "# \r\n", + "# =============================================================================\r\n" + ] + } + ], + "source": [ + "! head -48 parameters.yaml | tail -n 16" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see above, the `TestFlow` workflow does not require any input parameters, and will additionally automatically set some key `System` parameters to ensure that these tests are lightweight to avoid long queue times. Under the hood the `TestFlow` workflow will:\n", + "\n", + "1) Submit an array job to the system to test job submission capabilities \n", + "2) Submit a single job to the system which is intended to fail, this tests job queue monitoring as well as failed job catching.\n", + "\n", + "Developers who are implementing new `System` classes (e.g., for new clusters), can use TestFlow as foundation for their development and debugging sessions. To run the `TestFlow` workflow you just need to run `seisflows submit`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! seisflows submit" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/notebooks/sflog.txt b/docs/notebooks/sflog.txt deleted file mode 100644 index e69de29b..00000000 diff --git a/docs/notebooks/specfem2d_example.ipynb b/docs/notebooks/specfem2d_example.ipynb index 5f5457ca..3a85c76b 100644 --- a/docs/notebooks/specfem2d_example.ipynb +++ b/docs/notebooks/specfem2d_example.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Specfem2D workstation example\n", + "# Specfem2D Workstation Example\n", "\n", "To demonstrate the inversion capabilities of SeisFlows, we will run a __Specfem2D synthetic-synthetic example__ on a __local machine__ (tested on a Linux workstation running CentOS 7, and an Apple Laptop running macOS 10.14.6). Many of the setup steps here may be unique to our OS and workstation, but hopefully they may serve as templates for new Users wanting to explore SeisFlows. \n", "\n", @@ -27,7 +27,162 @@ "metadata": {}, "source": [ ".. warning:: \n", - " This example attempts to automatically download and compile SPECFEM2D. This step may fail if you are software required by SPECFEM2D, there are issues with the SPECFEM2D repository itself, or the configuration and compiling steps fail. If you run any issues, it is recommended that you manually install and compile SPECFEM2D, and directly provide its path to this example problem when prompted." + " If you do not have a compiled version of SPECFEM2D, then this example will attempt to automatically download and compile SPECFEM2D. This step may fail if you do not have software required by SPECFEM2D, if there are issues with the SPECFEM2D repository itself, or if the configuration and compiling steps fail. If you run any issues, it is recommended that you manually install and compile SPECFEM2D, and directly provide its path to this example problem using the -r or --specfem2d_repo flags (shown below)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example \\#1\n", + "Example \\#1 runs a 2 iteration inversion using SPECFEM2D, the default preprocessing module and a gradient descent optimization algorithm." + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + ".. note::\n", + " Example number 1 is meant to FAIL during the line search of Iteration #2, after exceeding the maximum allowable line search step count. This is meant to illustrate line search behavior and allow the User to explore a working directory mid-workflow." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 example: ex1_specfem2d_workstation_inversion\n", + "\n", + " @@@@@@@@@@ \n", + " .@@@@. .%&( %@. \n", + " @@@@ @@@@ &@@@@@@ ,%@ \n", + " @@@@ @@@, /@@ @ \n", + " @@@ @@@@ @@@ @ \n", + " @@@@ @@@@ @@@ @ @ \n", + " @@@ @@@@ ,@@@ @ @ \n", + " @@@@ @@@@ @@@@ @@ @ @\n", + " @@@@ @@@@@ @@@@@ @@@ @@ @\n", + " @@@@ @@@@@ @@@@@@@@@@@@@@ @@ @\n", + " @@@@ @@@@@@ @@@& @@@ @ \n", + " @@@@@ @@@@@@@@ %@@@@# @@ \n", + " @@@@# @@@@@@@@@@@@@@@@@ @@ \n", + " &@@@@@ @@@@( @@& \n", + " @@@@@@@ /@@@@ \n", + " @@@@@@@@@@@@@@@@@\n", + " @@@@@@@@@@ \n", + "\n", + "\n", + "================================================================================\n", + " SEISFLOWS EXAMPLE 1 \n", + " /////////////////// \n", + "This is a [SPECFEM2D] [WORKSTATION] example, which will run an inversion to\n", + "assess misfit between two homogeneous halfspace models with slightly different\n", + "velocities. [3 events, 1 station, 2 iterations]. The inversion is expected to\n", + "fail after the 5th line search step count of the 2nd iteration. The tasks\n", + "involved include:\n", + "\n", + "1. (optional) Download, configure, compile SPECFEM2D\n", + "2. Set up a SPECFEM2D working directory\n", + "3. Generate starting model from Tape2007 example\n", + "4. Generate target model w/ perturbed starting model\n", + "5. Set up a SeisFlows working directory\n", + "6. Run an inversion workflow\n", + "================================================================================\n" + ] + } + ], + "source": [ + "! seisflows examples 1 # print example help dialogue" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can either setup and run the example in separate tasks using the `examples setup` and `submit` commands. or directly run the example after setup using the `examples run` command. Use the `-r` or `--specfem2d_repo` flag to point SeisFlows at an existing SPECFEM2D/ repository (with compiled binaries). If not given, SeisFlows will automatically download, configure and compile SPECFEM2D in your current working directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! seisflows examples setup 1 -r path/to/specfem2d\n", + "! seisflows submit\n", + "# The above commands are the same as the below\n", + "! seisflows examples run 1 --specfem2d_repo path/to/specfem2d" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example \\#2\n", + "Example \\#2 runs a 1 iteration inversion using SPECFEM2D, the Pyaflowa preprocessing module and an L-BFGS optimization algorithm. It successfully completes the line search and is meant to illustrate the output of the Pyaflowa preprocessing module." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 example: ex2_specfem2d_workstation_inversion_w_pyatoa\n", + "\n", + " @@@@@@@@@@ \n", + " .@@@@. .%&( %@. \n", + " @@@@ @@@@ &@@@@@@ ,%@ \n", + " @@@@ @@@, /@@ @ \n", + " @@@ @@@@ @@@ @ \n", + " @@@@ @@@@ @@@ @ @ \n", + " @@@ @@@@ ,@@@ @ @ \n", + " @@@@ @@@@ @@@@ @@ @ @\n", + " @@@@ @@@@@ @@@@@ @@@ @@ @\n", + " @@@@ @@@@@ @@@@@@@@@@@@@@ @@ @\n", + " @@@@ @@@@@@ @@@& @@@ @ \n", + " @@@@@ @@@@@@@@ %@@@@# @@ \n", + " @@@@# @@@@@@@@@@@@@@@@@ @@ \n", + " &@@@@@ @@@@( @@& \n", + " @@@@@@@ /@@@@ \n", + " @@@@@@@@@@@@@@@@@\n", + " @@@@@@@@@@ \n", + "\n", + "\n", + "================================================================================\n", + " SEISFLOWS EXAMPLE 2 \n", + " /////////////////// \n", + "This is a [SPECFEM2D] [WORKSTATION] example, which will run an inversion to\n", + "assess misfit between a homogeneous halfspace and checkerboard model using\n", + "Pyatoa for misfit quantification [2 events, 5 stations, 1 iterations]. The tasks\n", + "involved include:\n", + "\n", + "1. (optional) Download, configure, compile SPECFEM2D\n", + "2. Set up a SPECFEM2D working directory\n", + "3. Generate starting model from Tape2007 example\n", + "4. Generate target model w/ perturbed starting model\n", + "5. Set up a SeisFlows working directory\n", + "6. Run an inversion workflow. The line search is expected to attempt 4 evaluations (i01s04)\n", + "================================================================================\n" + ] + } + ], + "source": [ + "! seisflows examples 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can run the example with the same command as shown for Example 1:" ] }, { @@ -36,7 +191,7 @@ "metadata": {}, "outputs": [], "source": [ - "! seisflows examples run 1" + "! seisflows examples run 2 -r path/to/specfem2d" ] }, { @@ -46,7 +201,7 @@ "--------------\n", "## Option 2: Manual run\n", "\n", - "The notebook below details a walkthrough of the automated run shown above. This is meant for those who want to understand what is going on under the hood. You are welcome to follow along on your workstation. The following Table of Contents outlines the steps we will take in this tutorial:\n", + "The notebook below details a walkthrough of Example \\#1 shown above. This is meant for those who want to understand what is going on under the hood. You are welcome to follow along on your workstation. The following Table of Contents outlines the steps we will take in this tutorial:\n", "\n" ] }, diff --git a/docs/notebooks/working_directory.ipynb b/docs/notebooks/working_directory.ipynb index ec727e6a..521d84e8 100644 --- a/docs/notebooks/working_directory.ipynb +++ b/docs/notebooks/working_directory.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Working Directory Structure" + "# Working Directory" ] }, { @@ -616,7 +616,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -630,7 +630,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.12" + "version": "3.7.6" } }, "nbformat": 4, diff --git a/docs/specfem2d_example.rst b/docs/specfem2d_example.rst index 81bc7477..25c9ae46 100644 --- a/docs/specfem2d_example.rst +++ b/docs/specfem2d_example.rst @@ -1,4 +1,4 @@ -Specfem2D workstation example +Specfem2D Workstation Example ============================= To demonstrate the inversion capabilities of SeisFlows, we will run a @@ -26,22 +26,146 @@ and compile SPECFEM2D, (2) setup a SPECFEM2D working directory to generate initial and target models, and (3) Run a SeisFlows inversion. .. warning:: - This example attempts to automatically download and compile SPECFEM2D. This step may fail if you are software required by SPECFEM2D, there are issues with the SPECFEM2D repository itself, or the configuration and compiling steps fail. If you run any issues, it is recommended that you manually install and compile SPECFEM2D, and directly provide its path to this example problem when prompted. + If you do not have a compiled version of SPECFEM2D, then this example will attempt to automatically download and compile SPECFEM2D. This step may fail if you do not have software required by SPECFEM2D, if there are issues with the SPECFEM2D repository itself, or if the configuration and compiling steps fail. If you run any issues, it is recommended that you manually install and compile SPECFEM2D, and directly provide its path to this example problem using the -r or --specfem2d_repo flags (shown below). + +Example #1 +~~~~~~~~~~ + +Example #1 runs a 2 iteration inversion using SPECFEM2D, the default +preprocessing module and a gradient descent optimization algorithm. + +.. note:: + Example number 1 is meant to FAIL during the line search of Iteration #2, after exceeding the maximum allowable line search step count. This is meant to illustrate line search behavior and allow the User to explore a working directory mid-workflow. + +.. code:: ipython3 + + ! seisflows examples 1 # print example help dialogue + + +.. parsed-literal:: + + 1 example: ex1_specfem2d_workstation_inversion + + @@@@@@@@@@ + .@@@@. .%&( %@. + @@@@ @@@@ &@@@@@@ ,%@ + @@@@ @@@, /@@ @ + @@@ @@@@ @@@ @ + @@@@ @@@@ @@@ @ @ + @@@ @@@@ ,@@@ @ @ + @@@@ @@@@ @@@@ @@ @ @ + @@@@ @@@@@ @@@@@ @@@ @@ @ + @@@@ @@@@@ @@@@@@@@@@@@@@ @@ @ + @@@@ @@@@@@ @@@& @@@ @ + @@@@@ @@@@@@@@ %@@@@# @@ + @@@@# @@@@@@@@@@@@@@@@@ @@ + &@@@@@ @@@@( @@& + @@@@@@@ /@@@@ + @@@@@@@@@@@@@@@@@ + @@@@@@@@@@ + + + ================================================================================ + SEISFLOWS EXAMPLE 1 + /////////////////// + This is a [SPECFEM2D] [WORKSTATION] example, which will run an inversion to + assess misfit between two homogeneous halfspace models with slightly different + velocities. [3 events, 1 station, 2 iterations]. The inversion is expected to + fail after the 5th line search step count of the 2nd iteration. The tasks + involved include: + + 1. (optional) Download, configure, compile SPECFEM2D + 2. Set up a SPECFEM2D working directory + 3. Generate starting model from Tape2007 example + 4. Generate target model w/ perturbed starting model + 5. Set up a SeisFlows working directory + 6. Run an inversion workflow + ================================================================================ + + +You can either setup and run the example in separate tasks using the +``examples setup`` and ``submit`` commands. or directly run the example +after setup using the ``examples run`` command. Use the ``-r`` or +``--specfem2d_repo`` flag to point SeisFlows at an existing SPECFEM2D/ +repository (with compiled binaries). If not given, SeisFlows will +automatically download, configure and compile SPECFEM2D in your current +working directory. + +.. code:: ipython3 + + ! seisflows examples setup 1 -r path/to/specfem2d + ! seisflows submit + # The above commands are the same as the below + ! seisflows examples run 1 --specfem2d_repo path/to/specfem2d + +Example #2 +~~~~~~~~~~ + +Example #2 runs a 1 iteration inversion using SPECFEM2D, the Pyaflowa +preprocessing module and an L-BFGS optimization algorithm. It +successfully completes the line search and is meant to illustrate the +output of the Pyaflowa preprocessing module. + +.. code:: ipython3 + + ! seisflows examples 2 + + +.. parsed-literal:: + + 2 example: ex2_specfem2d_workstation_inversion_w_pyatoa + + @@@@@@@@@@ + .@@@@. .%&( %@. + @@@@ @@@@ &@@@@@@ ,%@ + @@@@ @@@, /@@ @ + @@@ @@@@ @@@ @ + @@@@ @@@@ @@@ @ @ + @@@ @@@@ ,@@@ @ @ + @@@@ @@@@ @@@@ @@ @ @ + @@@@ @@@@@ @@@@@ @@@ @@ @ + @@@@ @@@@@ @@@@@@@@@@@@@@ @@ @ + @@@@ @@@@@@ @@@& @@@ @ + @@@@@ @@@@@@@@ %@@@@# @@ + @@@@# @@@@@@@@@@@@@@@@@ @@ + &@@@@@ @@@@( @@& + @@@@@@@ /@@@@ + @@@@@@@@@@@@@@@@@ + @@@@@@@@@@ + + + ================================================================================ + SEISFLOWS EXAMPLE 2 + /////////////////// + This is a [SPECFEM2D] [WORKSTATION] example, which will run an inversion to + assess misfit between a homogeneous halfspace and checkerboard model using + Pyatoa for misfit quantification [2 events, 5 stations, 1 iterations]. The tasks + involved include: + + 1. (optional) Download, configure, compile SPECFEM2D + 2. Set up a SPECFEM2D working directory + 3. Generate starting model from Tape2007 example + 4. Generate target model w/ perturbed starting model + 5. Set up a SeisFlows working directory + 6. Run an inversion workflow. The line search is expected to attempt 4 evaluations (i01s04) + ================================================================================ + + +You can run the example with the same command as shown for Example 1: .. code:: ipython3 - ! seisflows examples run 1 + ! seisflows examples run 2 -r path/to/specfem2d -------------- Option 2: Manual run -------------------- -The notebook below details a walkthrough of the automated run shown -above. This is meant for those who want to understand what is going on -under the hood. You are welcome to follow along on your workstation. The -following Table of Contents outlines the steps we will take in this -tutorial: +The notebook below details a walkthrough of Example #1 shown above. This +is meant for those who want to understand what is going on under the +hood. You are welcome to follow along on your workstation. The following +Table of Contents outlines the steps we will take in this tutorial: .. warning:: Navigation links will not work outside of Jupyter. Please use the navigation bar to the left. diff --git a/docs/working_directory.rst b/docs/working_directory.rst index 341e7036..a370b6c5 100644 --- a/docs/working_directory.rst +++ b/docs/working_directory.rst @@ -1,5 +1,5 @@ -Working Directory Structure -=========================== +Working Directory +================= SeisFlows sets it's own working directory when executing a workflow. Below we explore the working directory set up by the `SPECFEM2D-workstation example `__. Working directories may change slightly depending on the chosen workflow, but will more or less follow the same structure. diff --git a/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py b/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py index ea852341..fcae1abf 100644 --- a/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py +++ b/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py @@ -150,7 +150,7 @@ def finalize_specfem2d_par_file(self): "4. Generate target model w/ perturbed starting model", "5. Set up a SeisFlows working directory", f"6. Run an inversion workflow. The line search is expected to " - f"attempt 4 evaluations (i01s04)"], + f"attempt 2 evaluations (i01s02)"], header="seisflows example 2", border="=") ) @@ -161,4 +161,4 @@ def finalize_specfem2d_par_file(self): if len(sys.argv) > 2: _, _, specfem2d_repo = sys.argv sfex2d = SFPyatoaEx2D(specfem2d_repo=specfem2d_repo) - sfex2d.main() \ No newline at end of file + sfex2d.main() diff --git a/seisflows/examples/sfexample2d.py b/seisflows/examples/sfexample2d.py index ece6a636..13fc1985 100644 --- a/seisflows/examples/sfexample2d.py +++ b/seisflows/examples/sfexample2d.py @@ -91,7 +91,7 @@ def define_dir_structures(cwd, specfem2d_repo, ex="Tape2007"): :type ex: str :type ex: The name of the example problem inside SPECFEM2D/EXAMPLES """ - if not specfem2d_repo: + if not os.path.exists(specfem2d_repo): print(f"No existing SPECFEM2D repo given, default to: " f"{cwd}/specfem2d") specfem2d_repo = os.path.join(cwd, "specfem2d") @@ -127,7 +127,7 @@ def download_specfem2d(self): Last successfully tested 4/28/22 """ if not os.path.exists(self.sem2d_paths.repo): - cmd = ("git clone --recursive --branch devel --depth=1" + cmd = ("git clone --recursive --branch devel --depth=1 " "https://github.com/geodynamics/specfem2d.git") print(f"Downloading SPECFEM2D with command: {cmd}") diff --git a/seisflows/system/slurm.py b/seisflows/system/slurm.py index ddb1a883..a7d182e1 100644 --- a/seisflows/system/slurm.py +++ b/seisflows/system/slurm.py @@ -43,7 +43,7 @@ class Slurm(Cluster): """ System Slurm - ------------------ + ------------ Interface for submitting and monitoring jobs on HPC systems running the Simple Linux Utility for Resource Management (SLURM) workload manager. diff --git a/seisflows/workflow/test_flow.py b/seisflows/workflow/test_flow.py index 744a10c2..4cea0c73 100644 --- a/seisflows/workflow/test_flow.py +++ b/seisflows/workflow/test_flow.py @@ -17,7 +17,7 @@ class TestFlow: """ TestFlow Workflow - ------------- + ----------------- Test individual sub-modules in a 'live' testing environment in order to ensure SeisFlows works appropriately given an established system and solver. From 32a5d47878b494c7f31be4d1ba177a1c926d0d48 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Wed, 24 Aug 2022 10:24:10 -0800 Subject: [PATCH 138/195] unpinning lowest scipy version due to Conda UnsatisfiedError package dependency conflicts during requirements installation --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 21116deb..586b5ccc 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ obspy>=1.3.0 pyyaml>=5.3.1 -scipy>=1.8.0,<1.9.0 # scipy/obspy hanning window rename error +scipy<1.9.0 # scipy/obspy hanning window rename error @ 1.9.0 IPython>=7.31.1 dill>=0.3.5.1 pytest>=7.1.2 From a418d41b8d0d041d265645dccd9fd5d50f38bfc0 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Wed, 24 Aug 2022 16:48:38 -0800 Subject: [PATCH 139/195] extended the seisflows containers docs page with working examples for running seisflows with docker, including images of the jupyterhub interface. (#133) --- docs/containers.rst | 161 +++++++++++++++++-------- docs/extending.rst | 79 ++++++++++++ docs/images/container_1_jupyterhub.png | Bin 0 -> 144148 bytes docs/images/container_2_example.png | Bin 0 -> 293353 bytes 4 files changed, 188 insertions(+), 52 deletions(-) create mode 100644 docs/images/container_1_jupyterhub.png create mode 100644 docs/images/container_2_example.png diff --git a/docs/containers.rst b/docs/containers.rst index d725d46e..350ab5bc 100644 --- a/docs/containers.rst +++ b/docs/containers.rst @@ -16,91 +16,145 @@ on high performance computers using .. note:: The `Pyatoa` container is shipped with the latest versions of `SeisFlows `__, - `Pyatoa `__, + `Pyatoa `__, and `PySEP `__. The remainder of this documentation page assumes you are familiar with Docker -and containers. To learn more about Docker and containers you can visit: +and containers. + +To learn more about Docker and containers, see: https://www.docker.com/resources/what-container/ -To install Docker on your workstation, visit: +For instructions to install Docker on your workstation, visit: https://docs.docker.com/get-docker/ Workstation example with Docker ------------------------------- -Here we will step through how running the -`SeisFlows-SPECFEM2D `__ example using the available -Docker image. - -To get the latest version of the `Pyatoa` Docker image, run: +Here we will step through how to run the +`SeisFlows-SPECFEM2D `__ example using Docker. +First we need to get the latest version of the `Pyatoa` Docker image: .. code-block:: bash - docker pull ghcr.io/seisscoped/pyatoa:nightly + docker pull ghcr.io/seisscoped/pyatoa:latest + +.. note:: + These docs were run using Docker image ID ``c57883926aae`` (last accessed + Aug. 24, 2022). If you notice that the docs page is out of date with respect + to the latest Docker image, please raise a + `SeisFlows GitHub issue `__. Their are two methods for running the SeisFlows example using this Docker image, -either through a `JupyterHub interface `__, or through -the command line. The former provides a graphical user interface that mimics -a virtual desktop for easier navigation, while the latter provides more -flexibility for scripting and using SeisFlows as a research tool. +either 1) through a `JupyterHub interface `__, or +2) via the command line. The former provides a graphical user interface that +mimics a virtual desktop for easier navigation, while the latter provides a +push-button approach. From JupyterHub ^^^^^^^^^^^^^^^ -We can run SeisFlows through JupyterHub by running through a port: +We can run SeisFlows through JupyterHub by opening the container through a port: .. code-block:: bash - docker run -p 8888:8888 ghcr.io/seisscoped/pyatoa:nightly + docker run -p 8888:8888 ghcr.io/seisscoped/pyatoa:latest -To open the running JupyterHub instance, open the URL that is pasted to stdout -in your favorite web browser. This will likely look something like -http://127.0.0.1:8888/lab?token=xxx where xxx is your unique token +To access the JupyterHub instance, open the URL that was output to the display +in your favorite web browser. The URL will likely look something like +http://127.0.0.1:8888/lab?token=??? where '???' is a unique token -Once you have opened the JupyterHub instance, you should see a graphical -user interface. The Pyatoa, PySEP and SeisFlows repositories will be downloaded -and navigable in the file system on the left hand side of the window. +Within the JupyterHub instance, you will be greeted with a graphical +user interface (see below). The Pyatoa, PySEP and SeisFlows repositories are +navigable in the file system on the left-hand side of the window. -From inside the JupyterHub instance, click the 'Terminal' icon to open up a -terminal window, and run the following example command. Note, it's best to run -the example in a new directory to avoid muddling up the home directory. +.. image:: images/container_1_jupyterhub.png + :align: center +| +From inside the JupyterHub instance, click the `Terminal` icon (highlighted red +above) to open up a terminal window. Using the terminal we will run our example +in an empty directory to avoid muddling up the home directory. .. code-block:: bash + # From inside the JupyterHub terminal mkdir sf_sem2d_example cd sf_sem2d_example seisflows examples run 2 -This example will download, configure and compile SPECFEM2D into your +.. image:: images/container_2_example.png + :align: center +| +This example will download, configure and compile SPECFEM2D within your JupyterHub instance, and then run a SeisFlows-Pyatoa-SPECFEM2D inversion -problem. +problem. See `the SPECFEM2D example docs page `__ +for a more thorough explanation of what's going on under the hood. + +.. warning:: + Once you close the JupyterHub instance, all progress you've made since + opening it will be lost. If you would like to save your progress, you can + use the ``--mount`` command to mount your local filesystem inside the + container. + +Aside: Mounting a local filesystem +""""""""""""""""""""""""""""""""""" + +By default, JupyterHub does not provide explicit access to your local +filesystem. This is not ideal as we would usually like to save/view results. So +we often provide the container access to the local filesystem using the +``--mount`` flag. + +For example, if you already have SPECFEM2D downloaded and compiled on your local +filesystem, you can mount it to the container to avoid having to redo this +action. Or, as in the following code snippet, we bind our local filesystem's +working directory (WORKDIR) into the containers filesystem as +*/home/scoped/work* to save results. + +See the `Docker bind mounts `__ +documentation for more information. + +.. code-block:: bash + + WORKDIR=/Users/Chow/Work/scratch + docker run -p 8888:8888 \ + --mount type=bind,source=${WORKDIR},target=/home/scoped/work \ + ghcr.io/seisscoped/pyatoa:latest From the command line ^^^^^^^^^^^^^^^^^^^^^ -.. warning:: - Command line implementation does not currently work. - -To run the SeisFlows SPECFEM2D example from the command line, we simply need -to point Docker to the image we just downloaded, and call the SeisFlows command -line tool. To run the help message: +Running the container from the command line is much simpler. To print the +SeisFlows help message, we simply have to run the following: .. code-block:: bash - docker run ghcr.io/seisscoped/pyatoa:nightly seisflows -h + docker run ghcr.io/seisscoped/pyatoa:latest seisflows -h -Running the example should be as easy as: +The following code snippet will run a SeisFlows-Pyatoa-Specfem2D example. +The extra fluff in the command allows the container to save files to your +computer while it runs the example. .. code-block:: bash - docker run ghcr.io/seisscoped/pyatoa:nightly seisflows examples run 2 + WORKDIR=/Users/Chow/Work/scratch # choose your own directory here + cd ${WORKDIR} + docker run \ + --workdir $(pwd) \ + --mount type=bind,source=$(pwd),target=$(pwd), + ghcr.io/seisscoped/pyatoa:nightly \ + seisflows examples run 2 -In the above example, SeisFlows automatically identifies the SPECFEM2D -installation within the Docker image and uses this as the external numerical -solver. All results will be output to your current working directory. +In the above example, we set the working directory (-w/--workdir) to the +current working directory (on the local filesystem). We also mount the current +working directory inside the container (--mount), meaning the container has +access to our local filesystem for reading/writing. We then use the Docker +image to run a SeisFlows-Pyatoa-Specfem2D example. Outputs of the example will +be written into the working directory (WORKDIR). + +See `the SPECFEM2D example docs page `__ +for a more thorough explanation of what's going on under the hood. HPC example with Apptainer/Singularity @@ -118,20 +172,21 @@ Relevant Links: * Singularity on Chinook: https://uaf-rcs.gitbook.io/uaf-rcs-hpc-docs/third-party-software/singularity -* Singularity on TACC: - https://containers-at-tacc.readthedocs.io/en/latest/singularity/ -01.singularity_basics.html - +* Singularity at TACC: + https://containers-at-tacc.readthedocs.io/en/latest/singularity/01.singularity_basics.html +* Singularity on Maui: + https://support.nesi.org.nz/hc/en-gb/articles/360001107916-Singularity .. note:: - This section was written working on TACC's Frontera, a SLURM based HPC. + This section was written while working on TACC's Frontera, a SLURM system. Instructions may differ depending on your Systems setup and workload - manager. Because Singularity cannot be run on the Login nodes, the following - code blocks are run in the `idev `__ interactive - environment. + manager. Because Singularity cannot be run on the login nodes at TACC, the + following code blocks are run in the `idev `__ + interactive environment. -To download the required image on your system: +To download the required image on your system, we first need to load the +singularity module, and then use a familiar ``pull`` command. .. code-block:: bash @@ -139,15 +194,17 @@ To download the required image on your system: # module load singularity # on UAF Chinook singularity pull seisflows.sif docker://ghcr.io/seisscoped/pyatoa:nightly -To run the SeisFlows help message +We have now downloaded our image as a `.sif` file. To use the image to run the +SeisFlows help message: .. code-block:: bash singularity run seisflows.sif seisflows -h -To set your system to use Singularity, you just need to append '-singularity' to -an existing system subclass in the SeisFlows parameter file. For example, since -we are running on Frontera, we set our system to 'frontera-singularity'. +To get SeisFlows to use your system's Singularity (if supported), you just need +to append '-singularity' to an existing system subclass in the SeisFlows +parameter file. For example, since we are running on Frontera, we set our +system to 'frontera-singularity'. .. code-block:: bash diff --git a/docs/extending.rst b/docs/extending.rst index 48bccc2a..286a2bfe 100644 --- a/docs/extending.rst +++ b/docs/extending.rst @@ -39,3 +39,82 @@ SPECFEM2D/3D/3D_GLOBE. To allow Seisflows to work with other solvers, one would need to write a new Base class that defines SeisFlows’ interaction behavior with this solver. + +Setting up 'Cluster' System sub-classes +----------------------------------------- + +In this section, we outline how various Cluster-derived System sub-classes +are created. Although the tasks performed here will not translate one-to-one +for other systems, we hope they serve as a roadmap for setting up SeisFlows on +other HPC systems. + +Because many HPC systems have their own unique caveats, it is standard practice +to trial and error our way into a working sub-class. + +.. note:: + It is assumed that SeisFlows has been installed with the + ``pip install -e .`` command (develop mode). This ensures that any changes + to the codebase are immediately propagated into the SeisFlows executable. + +University of Alaska Fairbanks 'Chinook' +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Chinook is University of Alaska Fairbank's high performance computer. At the +time of writing (Aug. 24, 2022), it is running CentOS 6.10 and the SLURM +workload manager. + +Because Chinook is a SLURM-based system, we can derive our 'Chinook' class +based on the SLURM system class. In seisflows/system we create a new +Python file called 'chinook.py', which has a base template of: + +.. code:: python3 + + from seisflows.system.slurm import Slurm + + class Chinook(Slurm): + """ + Chinook System - UAF SLURM-based HPC + + Parameters + ========== + + Paths + ===== + + *** + """ + __doc__ == Slurm.__doc__ + __doc__ + + def __init__(self): + """Chinook intitiation""" + super().__init__() + + +The above code snippet is boilerplate code for any new SeisFlows class. We can +see that `Chinook` inherits base functionalities from `Slurm`, it defines some +boilerplate class docstring and appends it's docstring to the `Slurm` class +(required by ``seisflows configure``), and inherits the SLURM startup +procedure defined in __init__. + +.. note:: + Because of an outdated operating system, we cannot run the + SeisFlows directly from Chinook's Conda installation. We therefore have to + containerize the codebase and run SeisFlows through 'Singularity/Apptainer'. + +We can now set up a TestFlow workflow to see how the Chinook implementation +differs from the bog-standard SLURM impementation. + +.. code:: bash + + mkdir ${CENTER1}/scratch # CENTER1 is the Chinook work filesystem + cd ${CENTER1}/scratch + seisflows setup # create the parameters.yaml file + # Set the core SeisFlows modules + seisflows par workflow test_flow + seisflows par system chinook + seisflows par solver null + seisflows par preprocess null + seisflows par optimize null + + seisflows configure # set up the remainder of the parameter file + diff --git a/docs/images/container_1_jupyterhub.png b/docs/images/container_1_jupyterhub.png new file mode 100644 index 0000000000000000000000000000000000000000..5e9236a1bfc19b297824d67361990934846df3c4 GIT binary patch literal 144148 zcmZs@2O!n^|37|cYu$>Fv{2c}C=^=i5S5u-S(({;-R5mb9ib44?2)}kxd_=So9tco 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zTE-e!kMhb>I4xnVU;bM4oa5~FgN4VndB~qXNBWB^)Q91(eSwPwB z(yYDz``7+YT==*Dr*w+cW~txw=oS9NehyEam9SF^IgqRtR?QJY0l%=uq&}A<&(6{P z?-iQ=yn4Wfgh5bMm2#%m-t=W_6i=Sq#?Y@-R)`W7>D_5>`(pl|F2(oJN;#1{$n7d{ zG}RuntqOM$N@VcLbX&<*RS+UR=xI?{Wo|I^y{NM$#oE=f|7$lqFq;RtdnQ0-scWg>zB{F zlvn%GShP0MQivn_w}P)Br5&6;x`}1v)canbSR-?-9sl+Ts*gtP@_5=k8~V+?l(;qs zF2vE0dfn&z;_fL1Dr?>uGH!2gan$yV*w(!nk*8A7+Ta{bkEh} z>vd0+kyY zGret{N)^8?9$jC5-ygX|d`uL3+{f2rO4;k$(n~nMK%n&Y9+;+EUGpuKY$fz_uzQSg zI?mSRg(ymdT0oNaW{c-b<0}-#uiDw)7stb4WGA9^#3MP4+&v>L-53RT=% ztN9w9x;Pd(fnxXhW`flcd2|t<(xPD!<5dn##;}c=l8s*`r!2+SJyv2eHZ&WO%tJ!D zpA|JMe03cq8|A=rq)BcpQjARCl@~*j2o=3}LV_emTIrjOrP15+MLH&4?Ps01_^5saL`J0?X$2-K@kE4nb-bIemkq1|2@AOM9t(8l1IC&+& zPZ5&`r)HNQH$+Z_4a^>mlw4ljeDAkq?MTK<$qV@nVXNzJr#Yr{8b>c~oEcoC^V*nc zpa|!%2#Fh!uq-5Qy&5)vzOebPi|?j6`n|+z-awXk#Ls)Z<;{ARrO9|U-<2wb@Y^%` md|6gAdptyisG9?7Hz Date: Thu, 25 Aug 2022 15:39:19 -0800 Subject: [PATCH 140/195] added a completion log message for finished example problems --- seisflows/examples/sfexample2d.py | 1 + 1 file changed, 1 insertion(+) diff --git a/seisflows/examples/sfexample2d.py b/seisflows/examples/sfexample2d.py index 13fc1985..b5f00e36 100644 --- a/seisflows/examples/sfexample2d.py +++ b/seisflows/examples/sfexample2d.py @@ -347,6 +347,7 @@ def main(self): if self.run_example: print(msg.cli("RUNNING SEISFLOWS INVERSION WORKFLOW", border="=")) self.run_sf_example() + print(msg.cli("EXAMPLE COMPLETED SUCCESFULLY")) if __name__ == "__main__": From f80bef5a5934c269bfd84287cf3f3375a15d368a Mon Sep 17 00:00:00 2001 From: bch0w Date: Thu, 25 Aug 2022 17:38:55 -0800 Subject: [PATCH 141/195] splitting 2D example docs into the automated runs which will now include some analysis of results, and the walkthrough of the 2D example --- docs/2D_example_walkthrough.rst | 1349 +++++++++++++++ docs/containers.rst | 48 +- docs/index.rst | 8 +- docs/notebooks/2D_example_walkthrough.ipynb | 1616 ++++++++++++++++++ docs/notebooks/specfem2d_example.ipynb | 1647 +------------------ docs/specfem2d_example.rst | 1361 +-------------- seisflows/seisflows.py | 2 + 7 files changed, 3046 insertions(+), 2985 deletions(-) create mode 100644 docs/2D_example_walkthrough.rst create mode 100644 docs/notebooks/2D_example_walkthrough.ipynb diff --git a/docs/2D_example_walkthrough.rst b/docs/2D_example_walkthrough.rst new file mode 100644 index 00000000..bc79170e --- /dev/null +++ b/docs/2D_example_walkthrough.rst @@ -0,0 +1,1349 @@ +2D Example Walkthrough +====================== + +The notebook below details a walkthrough of Example \#1 shown in the `SPECFEM2D example \#1 `__. This is meant for those who want to understand what is going on under the hood. You are welcome to follow along on your workstation. The following Table of Contents outlines the steps we will take in this tutorial: + +.. warning:: + Navigation links will not work outside of Jupyter. Please use the navigation bar to the left. + +1. `Setup SPECFEM2D <#1.-Setup-SPECFEM2D>`__ + + a. `Download and compile + codebase <#1a.-Download-and-compile-codebase*>`__ + b. `Create a separate SPECFEM2D working + directory <#1b.-Create-a-separate-SPECFEM2D-working-directory>`__ + c. `Generate initial and target + models <#1c.-Generate-initial-and-target-models>`__ + +2. `Initialize SeisFlows (SF) <#2.-Initialize-SeisFlows-(SF)>`__ + + a. `SeisFlows working directory and parameter + file <#2a.-SF-working-directory-and-parameter-file>`__ + +3. `Run SeisFlows <#2.-Run-SeisFlows>`__ + + a. `Forward simulations <#3a.-Forward-simulations>`__ + b. `Exploring the SeisFlows directory + structure <#3b.-Exploring-the-SF-directory-structure>`__ + c. `Adjoint simulations <#3c.-Adjoint-simulations>`__ + d. `Line search and model + update <#3d.-Line-search-and-model-update>`__ + +4. `Conclusions <#4.-Conclusions>`__ + +1. Setup SPECFEM2D +~~~~~~~~~~~~~~~~~~ + +1a. Download and compile codebase (optional) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + + **NOTE**: If you have already downloaded and compiled SPECFEM2D, you + can skip most of this subsection (1a). However you will need to edit + the first two paths in the following cell (WORKDIR and + SPECFEM2D_ORIGINAL), and execute the path structure defined in the + cell. + +First we’ll download and compile SPECFEM2D to generate the binaries +necessary to run our simulations. We will then populate a new SPECFEM2D +working directory that will be used by SeisFlows. We’ll use to Python OS +module to do our filesystem processes just to keep everything in Python, +but this can easily be accomplished in bash. + +.. code:: ipython3 + + import os + import glob + import shutil + import numpy as np + +.. code:: ipython3 + + # vvv USER MUST EDIT THE FOLLOWING PATHS vvv + WORKDIR = "/home/bchow/Work/scratch" + SPECFEM2D = "/home/bchow/REPOSITORIES/specfem2d" + # where WORKDIR: points to your own working directory + # and SPECFEM2D: points to an existing specfem2D repository if available (if not set as '') + # ^^^ USER MUST EDIT THE FOLLOWING PATHS ^^^ + # ====================================================================================================== + + # Distribute the necessary file structure of the SPECFEM2D repository that we will downloaded/reference + SPECFEM2D_ORIGINAL = os.path.join(WORKDIR, "specfem2d") + SPECFEM2D_BIN_ORIGINAL = os.path.join(SPECFEM2D_ORIGINAL, "bin") + SPECFEM2D_DATA_ORIGINAL = os.path.join(SPECFEM2D_ORIGINAL, "DATA") + TAPE_2007_EXAMPLE = os.path.join(SPECFEM2D_ORIGINAL, "EXAMPLES", "Tape2007") + + # The SPECFEM2D working directory that we will create separate from the downloaded repo + SPECFEM2D_WORKDIR = os.path.join(WORKDIR, "specfem2d_workdir") + SPECFEM2D_BIN = os.path.join(SPECFEM2D_WORKDIR, "bin") + SPECFEM2D_DATA = os.path.join(SPECFEM2D_WORKDIR, "DATA") + SPECFEM2D_OUTPUT = os.path.join(SPECFEM2D_WORKDIR, "OUTPUT_FILES") + + # Pre-defined locations of velocity models we will generate using the solver + SPECFEM2D_MODEL_INIT = os.path.join(SPECFEM2D_WORKDIR, "OUTPUT_FILES_INIT") + SPECFEM2D_MODEL_TRUE = os.path.join(SPECFEM2D_WORKDIR, "OUTPUT_FILES_TRUE") + +.. code:: ipython3 + + # Download SPECFEM2D from GitHub, devel branch for latest codebase OR symlink from existing repo + if not os.path.exists(WORKDIR): + os.makedirs(WORKDIR) + os.chdir(WORKDIR) + + if os.path.exists("specfem2d"): + print("SPECFEM2D repository already found, you may skip this subsection") + pass + elif os.path.exists(SPECFEM2D): + print("Existing SPECMFE2D respository found, symlinking to working directory") + os.symlink(SPECFEM2D, "./specfem2d") + else: + print("Cloning respository from GitHub") + ! git clone --recursive --branch devel https://github.com/geodynamics/specfem2d.git + + +.. parsed-literal:: + + Existing SPECMFE2D respository found, symlinking to working directory + + +.. code:: ipython3 + + # Compile SPECFEM2D to generate the Makefile + os.chdir(SPECFEM2D_ORIGINAL) + if not os.path.exists("./config.log"): + os.system("./configure") + +.. code:: ipython3 + + # Run make to generate SPECFEM2D binaries + if not os.path.exists("bin"): + os.system("make all") + +.. code:: ipython3 + + # Check out the binary files that have been created + os.chdir(SPECFEM2D_ORIGINAL) + ! pwd + ! ls bin/ + + +.. parsed-literal:: + + /home/bchow/REPOSITORIES/specfem2d + xadj_seismogram xconvolve_source_timefunction xspecfem2D + xcheck_quality_external_mesh xmeshfem2D xsum_kernels + xcombine_sem xsmooth_sem + + +1b. Create a separate SPECFEM2D working directory +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Next we’ll create a new SPECFEM2D working directory, separate from the +original repository. The intent here is to isolate the original +SPECFEM2D repository from our working state, to protect it from things +like accidental file deletions or manipulations. This is not a mandatory +step for using SeisFlows, but it helps keep file structure clean in the +long run, and is the SeisFlows3 dev team’s preferred method of using +SPECFEM. + +.. note:: + All SPECFEM2D/3D/3D_GLOBE need to run successfully are the bin/, DATA/, and OUTPUT_FILES/ directories. Everything else in the repository is not mandatory for running binaries. + +In this tutorial we will be using the `Tape2007 example +problem `__ +to define our **DATA/** directory (last tested 8/15/22, bdba4389). + +.. code:: ipython3 + + # Incase we've run this docs page before, delete the working directory before remaking + if os.path.exists(SPECFEM2D_WORKDIR): + shutil.rmtree(SPECFEM2D_WORKDIR) + + os.mkdir(SPECFEM2D_WORKDIR) + os.chdir(SPECFEM2D_WORKDIR) + + # Copy the binary files incase we update the source code. These can also be symlinked. + shutil.copytree(SPECFEM2D_BIN_ORIGINAL, "bin") + + # Copy the DATA/ directory because we will be making edits here frequently and it's useful to + # retain the original files for reference. We will be running one of the example problems: Tape2007 + shutil.copytree(os.path.join(TAPE_2007_EXAMPLE, "DATA"), "DATA") + + ! pwd + ! ls + + +.. parsed-literal:: + + /home/bchow/Work/scratch/specfem2d_workdir + bin DATA + + +.. code:: ipython3 + + # Run the Tape2007 example to make sure SPECFEM2D is working as expected + os.chdir(TAPE_2007_EXAMPLE) + ! ./run_this_example.sh > output_log.txt + + assert(os.path.exists("OUTPUT_FILES/forward_image000004800.jpg")), \ + (f"Example did not run, the remainder of this docs page will likely not work." + f"Please check the following directory: {TAPE_2007_EXAMPLE}") + + ! tail output_log.txt + + +.. parsed-literal:: + + ------------------------------------------------------------------------------- + ------------------------------------------------------------------------------- + D a t e : 16 - 08 - 2022 T i m e : 14:26:37 + ------------------------------------------------------------------------------- + ------------------------------------------------------------------------------- + + see results in directory: OUTPUT_FILES/ + + done + Tue Aug 16 02:26:37 PM AKDT 2022 + + +-------------- + +Now we need to manually set up our SPECFEM2D working directory. As +mentioned in the previous cell, the only required elements of this +working directory are the following (these files will form the basis for +how SeisFlows3 operates within the SPECFEM2D framework): + +1. **bin/** directory containing SPECFEM2D binaries +2. **DATA/** directory containing SOURCE and STATION files, as well as a + SPECFEM2D Par_file +3. \__OUTPUT_FILES/proc??????_*.bin_\_ files which define the starting + (and target) models + +.. note:: + This file structure is the same for all versions of SPECFEM (2D/3D/3D_GLOBE) + +.. code:: ipython3 + + # First we will set the correct SOURCE and STATION files. + # This is the same task as shown in ./run_this_example.sh + os.chdir(SPECFEM2D_DATA) + + # Symlink source 001 as our main source + if os.path.exists("SOURCE"): + os.remove("SOURCE") + os.symlink("SOURCE_001", "SOURCE") + + # Copy the correct Par_file so that edits do not affect the original file + if os.path.exists("Par_file"): + os.remove("Par_file") + shutil.copy("Par_file_Tape2007_onerec", "Par_file") + + ! ls + + +.. parsed-literal:: + + interfaces_Tape2007.dat SOURCE_003 SOURCE_012 SOURCE_021 + model_velocity.dat_checker SOURCE_004 SOURCE_013 SOURCE_022 + Par_file SOURCE_005 SOURCE_014 SOURCE_023 + Par_file_Tape2007_132rec_checker SOURCE_006 SOURCE_015 SOURCE_024 + Par_file_Tape2007_onerec SOURCE_007 SOURCE_016 SOURCE_025 + proc000000_model_velocity.dat_input SOURCE_008 SOURCE_017 STATIONS + SOURCE SOURCE_009 SOURCE_018 STATIONS_checker + SOURCE_001 SOURCE_010 SOURCE_019 + SOURCE_002 SOURCE_011 SOURCE_020 + + +1c. Generate initial and target models +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Since we’re doing a synthetic-synthetic inversion, we need to manually +set up the velocity models with which we generate our synthetic +waveforms. The naming conventions for these models are: + +1. **MODEL_INIT:** The initial or starting model. Used to generate the + actual synthetic seismograms. This is considered M00. +2. **MODEL_TRUE:** The target or true model. Used to generate ‘data’ + (also synthetic). This is the reference model that our inversion is + trying to resolve. + +The starting model is defined as a homogeneous halfspace uin the +Tape2007 example problem. We will need to run both ``xmeshfem2D`` and +``xspecfem2D`` to generate the required velocity model database files. +We will generate our target model by slightly perturbing the parameters +of the initial model. + +.. note:: + We can use the SeisFlows3 command line option `seisflows sempar` to directly edit the SPECFEM2D Par_file in the command line. This will work for the SPECFEM3D Par_file as well. + +.. code:: ipython3 + + os.chdir(SPECFEM2D_DATA) + + # Ensure that SPECFEM2D outputs the velocity model in the expected binary format + ! seisflows sempar setup_with_binary_database 1 # allow creation of .bin files + ! seisflows sempar save_model binary # output model in .bin database format + ! seisflows sempar save_ascii_kernels .false. # output kernels in .bin format, not ASCII + + +.. parsed-literal:: + + setup_with_binary_database: 0 -> 1 + SAVE_MODEL: default -> binary + save_ASCII_kernels: .true. -> .false. + + +.. code:: ipython3 + + # SPECFEM requires that we create the OUTPUT_FILES directory before running + os.chdir(SPECFEM2D_WORKDIR) + + if os.path.exists(SPECFEM2D_OUTPUT): + shutil.rmtree(SPECFEM2D_OUTPUT) + + os.mkdir(SPECFEM2D_OUTPUT) + + ! ls + + +.. parsed-literal:: + + bin DATA OUTPUT_FILES + + +.. code:: ipython3 + + # GENERATE MODEL_INIT + os.chdir(SPECFEM2D_WORKDIR) + + # Run the mesher and solver to generate our initial model + ! ./bin/xmeshfem2D > OUTPUT_FILES/mesher_log.txt + ! ./bin/xspecfem2D > OUTPUT_FILES/solver_log.txt + + # Move the model files (*.bin) into the OUTPUT_FILES directory, where SeisFlows3 expects them + ! mv DATA/*bin OUTPUT_FILES + + # Make sure we don't overwrite this initial model when creating our target model in the next step + ! mv OUTPUT_FILES OUTPUT_FILES_INIT + + ! head OUTPUT_FILES_INIT/solver_log.txt + ! tail OUTPUT_FILES_INIT/solver_log.txt + + +.. parsed-literal:: + + + ********************************************** + **** Specfem 2-D Solver - serial version **** + ********************************************** + + Running Git version of the code corresponding to commit cf89366717d9435985ba852ef1d41a10cee97884 + dating From Date: Mon Nov 29 23:20:51 2021 -0800 + + + NDIM = 2 + ------------------------------------------------------------------------------- + Program SPECFEM2D: + ------------------------------------------------------------------------------- + ------------------------------------------------------------------------------- + Tape-Liu-Tromp (GJI 2007) + ------------------------------------------------------------------------------- + ------------------------------------------------------------------------------- + D a t e : 16 - 08 - 2022 T i m e : 14:26:52 + ------------------------------------------------------------------------------- + ------------------------------------------------------------------------------- + + +-------------- + +Now we want to perturb the initial model to create our target model +(**MODEL_TRUE**). The seisflows command line subargument +``seisflows sempar velocity_model`` will let us view and edit the +velocity model. You can also do this manually by editing the Par_file +directly. + +.. code:: ipython3 + + # GENERATE MODEL_TRUE + os.chdir(SPECFEM2D_DATA) + + # Edit the Par_file by increasing velocities by ~10% + ! seisflows sempar velocity_model '1 1 2600.d0 5900.d0 3550.0d0 0 0 10.d0 10.d0 0 0 0 0 0 0' + + +.. parsed-literal:: + + VELOCITY_MODEL: + + 1 1 2600.d0 5800.d0 3500.0d0 0 0 10.d0 10.d0 0 0 0 0 0 0 + -> + 1 1 2600.d0 5900.d0 3550.0d0 0 0 10.d0 10.d0 0 0 0 0 0 0 + + +.. code:: ipython3 + + # Re-run the mesher and solver to generate our target velocity model + os.chdir(SPECFEM2D_WORKDIR) + + # Make sure the ./OUTPUT_FILES directory exists since we moved the old one + if os.path.exists(SPECFEM2D_OUTPUT): + shutil.rmtree(SPECFEM2D_OUTPUT) + os.mkdir(SPECFEM2D_OUTPUT) + + # Run the binaries to generate MODEL_TRUE + ! ./bin/xmeshfem2D > OUTPUT_FILES/mesher_log.txt + ! ./bin/xspecfem2D > OUTPUT_FILES/solver_log.txt + + # Move all the relevant files into OUTPUT_FILES + ! mv ./DATA/*bin OUTPUT_FILES + ! mv OUTPUT_FILES OUTPUT_FILES_TRUE + + ! head OUTPUT_FILES_INIT/solver_log.txt + ! tail OUTPUT_FILES_INIT/solver_log.txt + + +.. parsed-literal:: + + + ********************************************** + **** Specfem 2-D Solver - serial version **** + ********************************************** + + Running Git version of the code corresponding to commit cf89366717d9435985ba852ef1d41a10cee97884 + dating From Date: Mon Nov 29 23:20:51 2021 -0800 + + + NDIM = 2 + ------------------------------------------------------------------------------- + Program SPECFEM2D: + ------------------------------------------------------------------------------- + ------------------------------------------------------------------------------- + Tape-Liu-Tromp (GJI 2007) + ------------------------------------------------------------------------------- + ------------------------------------------------------------------------------- + D a t e : 16 - 08 - 2022 T i m e : 14:26:52 + ------------------------------------------------------------------------------- + ------------------------------------------------------------------------------- + + +.. code:: ipython3 + + # Great, we have all the necessary SPECFEM files to run our SeisFlows inversion! + ! ls + + +.. parsed-literal:: + + bin DATA OUTPUT_FILES_INIT OUTPUT_FILES_TRUE + + +2. Initialize SeisFlows (SF) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +In this Section we will look at a SeisFlows working directory, parameter +file, and working state. + +2a. SeisFlows working directory and parameter file +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +As with SPECFEM, SeisFlows requires a parameter file +(**parameters.yaml**) that controls how an automated workflow will +proceed. Because SeisFlows is modular, there are a large number of +potential parameters which may be present in a SeisFlows parameter file, +as each sub-module may have its own set of unique parameters. + +In contrast to SPECFEM’s method of listing all available parameters and +leaving it up the User to determine which ones are relevant to them, +SeisFlows dynamically builds its parameter file based on User inputs. In +this subsection we will use the built-in SeisFlows command line tools to +generate and populate the parameter file. + +.. note:: + See the `parameter file documentation page `__ for a more in depth exploration of this central SeisFlows file. + +In the previous section we saw the ``sempar`` command in action. We can +use the ``-h`` or help flag to list all available SiesFlows3 command +line commands. + +.. code:: ipython3 + + ! seisflows -h + + +.. parsed-literal:: + + usage: seisflows [-h] [-w [WORKDIR]] [-p [PARAMETER_FILE]] + {setup,configure,swap,init,submit,resume,restart,clean,par,sempar,check,print,reset,debug,examples} + ... + + ================================================================================ + + SeisFlows: Waveform Inversion Package + + ================================================================================ + + optional arguments: + -h, --help show this help message and exit + -w [WORKDIR], --workdir [WORKDIR] + The SeisFlows working directory, default: cwd + -p [PARAMETER_FILE], --parameter_file [PARAMETER_FILE] + Parameters file, default: 'parameters.yaml' + + command: + Available SeisFlows arguments and their intended usages + + setup Setup working directory from scratch + configure Fill parameter file with defaults + swap Swap module parameters in an existing parameter file + init Initiate working environment + submit Submit initial workflow to system + resume Re-submit previous workflow to system + restart Remove current environment and submit new workflow + clean Remove files relating to an active working environment + par View and edit SeisFlows parameter file + sempar View and edit SPECFEM parameter file + check Check state of an active environment + print Print information related to an active environment + reset Reset modules within an active state + debug Start interactive debug environment + examples Look at and run pre-configured example problems + + 'seisflows [command] -h' for more detailed descriptions of each command. + + +.. code:: ipython3 + + # The command 'setup' creates the 'parameters.yaml' file that controls all of SeisFlows + # the '-f' flag removes any exist 'parameters.yaml' file that might be in the directory + os.chdir(WORKDIR) + ! seisflows setup -f + ! ls + + +.. parsed-literal:: + + creating parameter file: parameters.yaml + parameters.yaml sflog.txt specfem2d specfem2d_workdir + + +.. code:: ipython3 + + # Let's have a look at this file, which has not yet been populated + ! cat parameters.yaml + + +.. parsed-literal:: + + # ////////////////////////////////////////////////////////////////////////////// + # + # SeisFlows YAML Parameter File + # + # ////////////////////////////////////////////////////////////////////////////// + # + # Modules correspond to the structure of the source code, and determine + # SeisFlows' behavior at runtime. Each module requires its own sub-parameters. + # + # .. rubric:: + # - To determine available options for modules listed below, run: + # > seisflows print modules + # - To auto-fill with docstrings and default values (recommended), run: + # > seisflows configure + # - To set values as NoneType, use: null + # - To set values as infinity, use: inf + # + # MODULES + # /////// + # workflow (str): The types and order of functions for running SeisFlows + # system (str): Computer architecture of the system being used + # solver (str): External numerical solver to use for waveform simulations + # preprocess (str): Preprocessing schema for waveform data + # optimize (str): Optimization algorithm for the inverse problem + # ============================================================================== + workflow: forward + system: workstation + solver: specfem2d + preprocess: default + optimize: gradient + + +.. code:: ipython3 + + # We can use the `seisflows print modules` command to list out the available options + ! seisflows print modules + + +.. parsed-literal:: + + SEISFLOWS MODULES + ///////////////// + '-': module, '*': class + + - workflow + * forward + * inversion + * migration + - system + * chinook + * cluster + * frontera + * lsf + * maui + * slurm + * workstation + - solver + * specfem + * specfem2d + * specfem3d + * specfem3d_globe + - preprocess + * default + * pyaflowa + - optimize + * LBFGS + * NLCG + * gradient + + +.. code:: ipython3 + + # For this example, we can use most of the default modules, however we need to + # change the SOLVER module to let SeisFlows know we're using SPECFEM2D (as opposed to 3D) + ! seisflows par workflow inversion + ! cat parameters.yaml + + +.. parsed-literal:: + + workflow: forward -> inversion + # ////////////////////////////////////////////////////////////////////////////// + # + # SeisFlows YAML Parameter File + # + # ////////////////////////////////////////////////////////////////////////////// + # + # Modules correspond to the structure of the source code, and determine + # SeisFlows' behavior at runtime. Each module requires its own sub-parameters. + # + # .. rubric:: + # - To determine available options for modules listed below, run: + # > seisflows print modules + # - To auto-fill with docstrings and default values (recommended), run: + # > seisflows configure + # - To set values as NoneType, use: null + # - To set values as infinity, use: inf + # + # MODULES + # /////// + # workflow (str): The types and order of functions for running SeisFlows + # system (str): Computer architecture of the system being used + # solver (str): External numerical solver to use for waveform simulations + # preprocess (str): Preprocessing schema for waveform data + # optimize (str): Optimization algorithm for the inverse problem + # ============================================================================== + workflow: inversion + system: workstation + solver: specfem2d + preprocess: default + optimize: gradient + + +-------------- + +The ``seisflows configure`` command populates the parameter file based +on the chosen modules. SeisFlows will attempt to fill in all parameters +with reasonable default values. Docstrings above each module show +descriptions and available options for each of these parameters. + +In the follownig cell we will use the ``seisflows par`` command to edit +the parameters.yaml file directly, replacing some default parameters +with our own values. Comments next to each evaluation describe the +choice for each. + +.. code:: ipython3 + + ! seisflows configure + ! head --lines=50 parameters.yaml + + +.. parsed-literal:: + + # ////////////////////////////////////////////////////////////////////////////// + # + # SeisFlows YAML Parameter File + # + # ////////////////////////////////////////////////////////////////////////////// + # + # Modules correspond to the structure of the source code, and determine + # SeisFlows' behavior at runtime. Each module requires its own sub-parameters. + # + # .. rubric:: + # - To determine available options for modules listed below, run: + # > seisflows print modules + # - To auto-fill with docstrings and default values (recommended), run: + # > seisflows configure + # - To set values as NoneType, use: null + # - To set values as infinity, use: inf + # + # MODULES + # /////// + # workflow (str): The types and order of functions for running SeisFlows + # system (str): Computer architecture of the system being used + # solver (str): External numerical solver to use for waveform simulations + # preprocess (str): Preprocessing schema for waveform data + # optimize (str): Optimization algorithm for the inverse problem + # ============================================================================== + workflow: inversion + system: workstation + solver: specfem2d + preprocess: default + optimize: gradient + # ============================================================================= + # + # Forward Workflow + # ---------------- + # Run forward solver in parallel and (optionally) calculate + # data-synthetic misfit and adjoint sources. + # + # Parameters + # ---------- + # :type modules: list of module + # :param modules: instantiated SeisFlows modules which should have been + # generated by the function `seisflows.config.import_seisflows` with a + # parameter file generated by seisflows.configure + # :type data_case: str + # :param data_case: How to address 'data' in the workflow, available options: + # 'data': real data will be provided by the user in + # `path_data/{source_name}` in the same format that the solver will + # produce synthetics (controlled by `solver.format`) OR + # synthetic': 'data' will be generated as synthetic seismograms using + # a target model provided in `path_model_true`. If None, workflow will + + +.. code:: ipython3 + + # EDIT THE SEISFLOWS PARAMETER FILE + ! seisflows par ntask 3 # set the number of sources/events to use + ! seisflows par materials elastic # update Vp and Vs during inversion + ! seisflows par end 2 # final iteration -- we will only run 1 + ! seisflows par data_case synthetic # synthetic-synthetic means we need both INIT and TRUE models + ! seisflows par components Y # this default example creates Y-component seismograms + ! seisflows par step_count_max 5 # limit the number of steps in the line search + + # Use Python syntax here to access path constants + os.system(f"seisflows par path_specfem_bin {SPECFEM2D_BIN}") # set path to SPECFEM2D binaries + os.system(f"seisflows par path_specfem_data {SPECFEM2D_DATA}") # set path to SEPCFEM2D DATA/ + os.system(f"seisflows par path_model_init {SPECFEM2D_MODEL_INIT}") # set path to INIT model + os.system(f"seisflows par path_model_true {SPECFEM2D_MODEL_TRUE}") # set path to TRUE model + + +.. parsed-literal:: + + ntask: 1 -> 3 + materials: acoustic -> elastic + end: 1 -> 2 + data_case: data -> synthetic + components: ZNE -> Y + step_count_max: 10 -> 5 + path_specfem_bin: null -> /home/bchow/Work/scratch/specfem2d_workdir/bin + path_specfem_data: null -> /home/bchow/Work/scratch/specfem2d_workdir/DATA + path_model_init: null -> + /home/bchow/Work/scratch/specfem2d_workdir/OUTPUT_FILES_INIT + path_model_true: null -> + /home/bchow/Work/scratch/specfem2d_workdir/OUTPUT_FILES_TRUE + + + + +.. parsed-literal:: + + 0 + + + +-------------- + +One last thing, we will need to edit the SPECFEM2D Par_file parameter +``MODEL`` such that ``xmeshfem2d`` reads our pre-built velocity models +(*.bin files) rather than the meshing parameters defined in the +Par_file. + +.. code:: ipython3 + + os.chdir(SPECFEM2D_DATA) + ! seisflows sempar model gll + + +.. parsed-literal:: + + MODEL: default -> gll + + +3. Run SeisFlows +~~~~~~~~~~~~~~~~ + +In this Section we will run SeisFlows to generate synthetic seismograms, +kernels, a gradient, and an updated velocity model. + +3a. Forward simulations +^^^^^^^^^^^^^^^^^^^^^^^ + +SeisFlows is an automated workflow tool, such that once we run +``seisflows submit`` we should not need to intervene in the workflow. +However the package does allow the User flexibility in how they want the +workflow to behave. + +For example, we can run our workflow in stages by taking advantage of +the ``stop_after`` parameter. As its name suggests, ``stop_after`` +allows us to stop a workflow prematurely so that we may stop and look at +results, or debug a failing workflow. + +The ``seisflows print flow`` command tells us what functions we can use +for the ``stop_after`` parameter. + +.. code:: ipython3 + + os.chdir(WORKDIR) + ! seisflows print tasks + + +.. parsed-literal:: + + SEISFLOWS WORKFLOW TASK LIST + //////////////////////////// + Task list for + + 1: evaluate_initial_misfit + 2: run_adjoint_simulations + 3: postprocess_event_kernels + 4: evaluate_gradient_from_kernels + 5: initialize_line_search + 6: perform_line_search + 7: finalize_iteration + + +-------------- + +In the Inversion workflow, the tasks listed are described as follows: + +1. **evaluate_initial_misfit:** + + a. Prepare data for inversion by either copying data from disk or + generating ‘synthetic data’ with MODEL_TRUE + b. Call numerical solver to run forward simulations using MODEL_INIT, + generating synthetics + c. Evaluate the objective function by performing waveform comparisons + d. Prepare ``run_adjoint_simulations`` step by generating adjoint + sources and auxiliary files + +2. **run_adjoint_simulations:** Call numerical solver to run adjoint + simulation, generating kernels +3. **postprocess_event_kernels:** Combine all event kernels into a + misfit kernel. +4. **evaluate_gradient_from_kernels:** Smooth and mask the misfit kernel + to create the gradient +5. **initialize_line_search:** Call on the optimization library to scale + the gradient by a step length to compute the search direction. + Prepare file structure for line search. +6. **perform_line_search:** Perform a line search by algorithmically + scaling the gradient and evaluating the misfit function (forward + simulations and misfit quantification) until misfit is acceptably + reduced. +7. **finalize_iteration:** Run any finalization steps such as saving + traces, kernels, gradients and models to disk, setting up SeisFlows3 + for any subsequent iterations. Clean the scratch/ directory in + preparation for subsequent iterations + +Let’s set the ``stop_after`` argument to **evaluate_initial_misfit**, +this will halt the workflow after the intialization step. + +.. code:: ipython3 + + ! seisflows par stop_after evaluate_initial_misfit + + +.. parsed-literal:: + + stop_after: null -> evaluate_initial_misfit + + +-------------- + +Now let’s run SeisFlows. There are two ways to do this: ``submit`` and +``restart`` + +1. ``seisflows submit`` is used to run new workflows and resume stopped + or failed workflows. +2. The ``restart`` command is simply a convenience function that runs + ``clean`` (to remove an active working state) and ``submit`` (to + submit a fresh workflow). + +Since this is our first run, we’ll use ``seisflows submit``. + +.. code:: ipython3 + + ! seisflows submit + + +.. parsed-literal:: + + 2022-08-16 14:32:48 (I) | + ================================================================================ + SETTING UP INVERSION WORKFLOW + ================================================================================ + 2022-08-16 14:32:55 (D) | running setup for module 'system.Workstation' + 2022-08-16 14:32:57 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/sflog_001.txt + 2022-08-16 14:32:57 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/parameters_001.yaml + 2022-08-16 14:32:57 (D) | running setup for module 'solver.Specfem2D' + 2022-08-16 14:32:57 (I) | initializing 3 solver directories + 2022-08-16 14:32:57 (D) | initializing solver directory source: 001 + 2022-08-16 14:33:04 (D) | linking source '001' as 'mainsolver' + 2022-08-16 14:33:04 (D) | initializing solver directory source: 002 + 2022-08-16 14:33:09 (D) | initializing solver directory source: 003 + 2022-08-16 14:33:16 (D) | running setup for module 'preprocess.Default' + 2022-08-16 14:33:16 (D) | running setup for module 'optimize.Gradient' + 2022-08-16 14:33:17 (I) | no optimization checkpoint found, assuming first run + 2022-08-16 14:33:17 (I) | re-loading optimization module from checkpoint + 2022-08-16 14:33:17 (I) | + //////////////////////////////////////////////////////////////////////////////// + RUNNING ITERATION 01 + //////////////////////////////////////////////////////////////////////////////// + 2022-08-16 14:33:17 (I) | + ================================================================================ + RUNNING INVERSION WORKFLOW + ================================================================================ + 2022-08-16 14:33:17 (I) | + //////////////////////////////////////////////////////////////////////////////// + EVALUATING MISFIT FOR INITIAL MODEL + //////////////////////////////////////////////////////////////////////////////// + 2022-08-16 14:33:17 (I) | checking initial model parameters + 2022-08-16 14:33:17 (I) | 5800.00 <= vp <= 5800.00 + 2022-08-16 14:33:17 (I) | 2600.00 <= rho <= 2600.00 + 2022-08-16 14:33:17 (I) | 3500.00 <= vs <= 3500.00 + 2022-08-16 14:33:17 (I) | checking true/target model parameters + 2022-08-16 14:33:17 (I) | 5900.00 <= vp <= 5900.00 + 2022-08-16 14:33:17 (I) | 2600.00 <= rho <= 2600.00 + 2022-08-16 14:33:17 (I) | 3550.00 <= vs <= 3550.00 + 2022-08-16 14:33:17 (I) | preparing observation data for source 001 + 2022-08-16 14:33:17 (I) | running forward simulation w/ target model for 001 + 2022-08-16 14:33:21 (I) | evaluating objective function for source 001 + 2022-08-16 14:33:21 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:33:25 (D) | quantifying misfit with 'Default' + 2022-08-16 14:33:25 (I) | preparing observation data for source 002 + 2022-08-16 14:33:25 (I) | running forward simulation w/ target model for 002 + 2022-08-16 14:33:29 (I) | evaluating objective function for source 002 + 2022-08-16 14:33:29 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:33:33 (D) | quantifying misfit with 'Default' + 2022-08-16 14:33:33 (I) | preparing observation data for source 003 + 2022-08-16 14:33:33 (I) | running forward simulation w/ target model for 003 + 2022-08-16 14:33:36 (I) | evaluating objective function for source 003 + 2022-08-16 14:33:36 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:33:40 (D) | quantifying misfit with 'Default' + 2022-08-16 14:33:40 (I) | stop workflow at `stop_after`: evaluate_initial_misfit + + +.. note:: + For a detailed exploration of a SeisFlows working directory, see the `working directory `__ documentation page where we explain each of the files and directories that have been generated during this workflow. Below we just look at two files which are required for our adjoint simulation, the adjoint sources (.adj) and STATIONS_ADJOINT file + +.. code:: ipython3 + + # The adjoint source is created in the same format as the synthetics (two-column ASCII) + ! head scratch/solver/001/traces/adj/AA.S0001.BXY.adj + + +.. parsed-literal:: + + -48.0000000 0.0000000 + -47.9400000 0.0000000 + -47.8800000 0.0000000 + -47.8200000 0.0000000 + -47.7600000 0.0000000 + -47.7000000 0.0000000 + -47.6400000 0.0000000 + -47.5800000 0.0000000 + -47.5200000 0.0000000 + -47.4600000 0.0000000 + + +3b. Adjoint simulations +^^^^^^^^^^^^^^^^^^^^^^^ + +Now that we have all the required files for running an adjoint +simulation (*.adj waveforms and STATIONS_ADJOINT file), we can continue +with the SeisFlows3 Inversion workflow. No need to edit the Par_file or +anything like that, SeisFlows3 will take care of that under the hood. We +simply need to tell the workflow (via the parameters.yaml file) to +``resume_from`` the correct function. We can have a look at these +functions again: + +.. code:: ipython3 + + ! seisflows print tasks + + +.. parsed-literal:: + + SEISFLOWS WORKFLOW TASK LIST + //////////////////////////// + Task list for + + 1: evaluate_initial_misfit + 2: run_adjoint_simulations + 3: postprocess_event_kernels + 4: evaluate_gradient_from_kernels + 5: initialize_line_search + 6: perform_line_search + 7: finalize_iteration + + +.. code:: ipython3 + + # We'll stop just before the line search so that we can take a look at the files + # generated during the middle tasks + ! seisflows par stop_after evaluate_gradient_from_kernels + + +.. parsed-literal:: + + stop_after: evaluate_initial_misfit -> evaluate_gradient_from_kernels + + +.. code:: ipython3 + + # We can use the `seisflows submit` command to continue an active workflow + # The state file created during the first run will tell the workflow to resume from the stopped point in the workflow + ! seisflows submit + + +.. parsed-literal:: + + 2022-08-16 14:36:42 (D) | setting iteration==1 from state file + 2022-08-16 14:36:42 (I) | + ================================================================================ + SETTING UP INVERSION WORKFLOW + ================================================================================ + 2022-08-16 14:36:48 (D) | running setup for module 'system.Workstation' + 2022-08-16 14:36:51 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/sflog_002.txt + 2022-08-16 14:36:51 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/parameters_002.yaml + 2022-08-16 14:36:51 (D) | running setup for module 'solver.Specfem2D' + 2022-08-16 14:36:51 (I) | initializing 3 solver directories + 2022-08-16 14:36:51 (D) | running setup for module 'preprocess.Default' + 2022-08-16 14:36:52 (D) | running setup for module 'optimize.Gradient' + 2022-08-16 14:36:53 (I) | re-loading optimization module from checkpoint + 2022-08-16 14:36:54 (I) | re-loading optimization module from checkpoint + 2022-08-16 14:36:54 (I) | + //////////////////////////////////////////////////////////////////////////////// + RUNNING ITERATION 01 + //////////////////////////////////////////////////////////////////////////////// + 2022-08-16 14:36:54 (I) | + ================================================================================ + RUNNING INVERSION WORKFLOW + ================================================================================ + 2022-08-16 14:36:54 (I) | 'evaluate_initial_misfit' has already been run, skipping + 2022-08-16 14:36:54 (I) | + //////////////////////////////////////////////////////////////////////////////// + EVALUATING EVENT KERNELS W/ ADJOINT SIMULATIONS + //////////////////////////////////////////////////////////////////////////////// + 2022-08-16 14:36:54 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log' + 2022-08-16 14:37:05 (D) | renaming output event kernels: 'alpha' -> 'vp' + 2022-08-16 14:37:05 (D) | renaming output event kernels: 'beta' -> 'vs' + 2022-08-16 14:37:05 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log' + 2022-08-16 14:37:16 (D) | renaming output event kernels: 'alpha' -> 'vp' + 2022-08-16 14:37:16 (D) | renaming output event kernels: 'beta' -> 'vs' + 2022-08-16 14:37:18 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log' + 2022-08-16 14:37:29 (D) | renaming output event kernels: 'alpha' -> 'vp' + 2022-08-16 14:37:29 (D) | renaming output event kernels: 'beta' -> 'vs' + 2022-08-16 14:37:30 (I) | + //////////////////////////////////////////////////////////////////////////////// + GENERATING/PROCESSING MISFIT KERNEL + //////////////////////////////////////////////////////////////////////////////// + 2022-08-16 14:37:30 (I) | combining event kernels into single misfit kernel + 2022-08-16 14:37:31 (I) | scaling gradient to absolute model perturbations + 2022-08-16 14:37:32 (I) | stop workflow at `stop_after`: evaluate_gradient_from_kernels + + +-------------- + +The function **run_adjoint_simulations()** has run adjoint simulations +to generate event kernels. The functions **postprocess_event_kernels** +and **evaluate_gradient_from_kernels** will have summed and (optionally) +smoothed the kernels to recover the gradient, which will be used to +update our starting model. + + **NOTE**: Since we did not specify any smoothing lenghts + (PAR.SMOOTH_H and PAR.SMOOTH_V), no smoothing of the gradient has + occurred. + +Using the gradient-descent optimization algorithm, SeisFlows will now +compute a search direction that will be used in the line search to +search for a best fitting model which optimally reduces the objective +function. We can take a look at where SeisFlows has stored the +information relating to kernel generation and the optimization +computation. + +.. code:: ipython3 + + # Gradient evaluation files are stored here, the kernels are stored separately from the gradient incase + # the user wants to manually manipulate them + ! ls scratch/eval_grad + + +.. parsed-literal:: + + gradient kernels misfit_kernel model residuals.txt + + +.. code:: ipython3 + + # SeisFlows3 stores all kernels and gradient information as SPECFEM binary (.bin) files + ! ls scratch/eval_grad/gradient + + +.. parsed-literal:: + + proc000000_vp_kernel.bin proc000000_vs_kernel.bin + + +.. code:: ipython3 + + # Kernels are stored on a per-event basis, and summed together (sum/). If smoothing was performed, + # we would see both smoothed and unsmoothed versions of the misfit kernel + ! ls scratch/eval_grad/kernels + + +.. parsed-literal:: + + 001 002 003 + + +.. code:: ipython3 + + # We can see that some new values have been stored in prepartion for the line search, + # including g_new (current gradient) and p_new (current search direction). These are also + # stored as vector NumPy arrays (.npy files) + ! ls scratch/optimize + + +.. parsed-literal:: + + checkpoint.npz f_new.txt g_new.npz m_new.npz + + +.. code:: ipython3 + + g_new = np.load("scratch/optimize/g_new.npz") + print(g_new["vs_kernel"]) + + +.. parsed-literal:: + + [[-1.18126331e-12 2.40273470e-12 3.97045036e-11 ... 9.62017688e-11 + 4.21140102e-11 3.96825021e-12]] + + +-------------- + +3c. Line search and model update +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Let’s finish off the inversion by running through the line search, which +will generate new models using the gradient, evaluate the objective +function by running forward simulations, and comparing the evaluated +objective function with the value obtained in +**evalaute_initial_misfit**. + +Satisfactory reduction in the objective function will result in a +termination of the line search. We are using a bracketing line search +here `(Modrak et +al. 2018) `__, +which requires finding models which both increase and decrease the +misfit with respect to the initial evaluation. Therefore it takes +atleast two trial steps to complete the line search. + +.. code:: ipython3 + + ! seisflows par stop_after perform_line_search # We don't want to run the finalize_iteration argument so that we can explore the dir + + +.. parsed-literal:: + + stop_after: evaluate_gradient_from_kernels -> perform_line_search + + +.. code:: ipython3 + + ! seisflows submit + + +.. parsed-literal:: + + 2022-08-16 14:41:12 (D) | setting iteration==1 from state file + 2022-08-16 14:41:12 (I) | + ================================================================================ + SETTING UP INVERSION WORKFLOW + ================================================================================ + 2022-08-16 14:41:18 (D) | running setup for module 'system.Workstation' + 2022-08-16 14:41:21 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/sflog_003.txt + 2022-08-16 14:41:21 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/parameters_003.yaml + 2022-08-16 14:41:21 (D) | running setup for module 'solver.Specfem2D' + 2022-08-16 14:41:21 (I) | initializing 3 solver directories + 2022-08-16 14:41:22 (D) | running setup for module 'preprocess.Default' + 2022-08-16 14:41:24 (D) | running setup for module 'optimize.Gradient' + 2022-08-16 14:41:26 (I) | re-loading optimization module from checkpoint + 2022-08-16 14:41:28 (I) | re-loading optimization module from checkpoint + 2022-08-16 14:41:28 (I) | + //////////////////////////////////////////////////////////////////////////////// + RUNNING ITERATION 01 + //////////////////////////////////////////////////////////////////////////////// + 2022-08-16 14:41:28 (I) | + ================================================================================ + RUNNING INVERSION WORKFLOW + ================================================================================ + 2022-08-16 14:41:28 (I) | 'evaluate_initial_misfit' has already been run, skipping + 2022-08-16 14:41:28 (I) | 'run_adjoint_simulations' has already been run, skipping + 2022-08-16 14:41:28 (I) | 'postprocess_event_kernels' has already been run, skipping + 2022-08-16 14:41:28 (I) | 'evaluate_gradient_from_kernels' has already been run, skipping + 2022-08-16 14:41:28 (I) | initializing 'bracket'ing line search + 2022-08-16 14:41:28 (I) | enforcing max step length safeguard + 2022-08-16 14:41:28 (D) | step length(s) = 0.00E+00 + 2022-08-16 14:41:28 (D) | misfit val(s) = 1.28E-03 + 2022-08-16 14:41:28 (I) | try: first evaluation, attempt guess step length, alpha=9.08E+11 + 2022-08-16 14:41:28 (I) | try: applying initial step length safegaurd as alpha has exceeded maximum step length, alpha_new=1.44E+10 + 2022-08-16 14:41:28 (D) | overwriting initial step length, alpha_new=2.32E+09 + 2022-08-16 14:41:28 (I) | trial model 'm_try' parameters: + 2022-08-16 14:41:28 (I) | 5800.00 <= vp <= 5800.00 + 2022-08-16 14:41:28 (I) | 3244.51 <= vs <= 3790.00 + 2022-08-16 14:41:29 (I) | + LINE SEARCH STEP COUNT 01 + -------------------------------------------------------------------------------- + 2022-08-16 14:41:29 (I) | evaluating objective function for source 001 + 2022-08-16 14:41:29 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:41:33 (D) | quantifying misfit with 'Default' + 2022-08-16 14:41:33 (I) | evaluating objective function for source 002 + 2022-08-16 14:41:33 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:41:36 (D) | quantifying misfit with 'Default' + 2022-08-16 14:41:36 (I) | evaluating objective function for source 003 + 2022-08-16 14:41:36 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:41:40 (D) | quantifying misfit with 'Default' + 2022-08-16 14:41:40 (D) | misfit for trial model (f_try) == 8.65E-04 + 2022-08-16 14:41:40 (D) | step length(s) = 0.00E+00, 2.32E+09 + 2022-08-16 14:41:40 (D) | misfit val(s) = 1.28E-03, 8.65E-04 + 2022-08-16 14:41:40 (I) | try: misfit not bracketed, increasing step length using golden ratio, alpha=3.76E+09 + 2022-08-16 14:41:40 (I) | line search model 'm_try' parameters: + 2022-08-16 14:41:40 (I) | 5800.00 <= vp <= 5800.00 + 2022-08-16 14:41:40 (I) | 3086.61 <= vs <= 3969.23 + 2022-08-16 14:41:40 (I) | trial step unsuccessful. re-attempting line search + 2022-08-16 14:41:40 (I) | + LINE SEARCH STEP COUNT 02 + -------------------------------------------------------------------------------- + 2022-08-16 14:41:40 (I) | evaluating objective function for source 001 + 2022-08-16 14:41:40 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:41:44 (D) | quantifying misfit with 'Default' + 2022-08-16 14:41:44 (I) | evaluating objective function for source 002 + 2022-08-16 14:41:44 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:41:48 (D) | quantifying misfit with 'Default' + 2022-08-16 14:41:48 (I) | evaluating objective function for source 003 + 2022-08-16 14:41:48 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:41:52 (D) | quantifying misfit with 'Default' + 2022-08-16 14:41:52 (D) | misfit for trial model (f_try) == 1.73E-03 + 2022-08-16 14:41:52 (D) | step length(s) = 0.00E+00, 2.32E+09, 3.76E+09 + 2022-08-16 14:41:52 (D) | misfit val(s) = 1.28E-03, 8.65E-04, 1.73E-03 + 2022-08-16 14:41:52 (I) | try: bracket acceptable but step length unreasonable attempting to re-adjust step length alpha=1.59E+09 + 2022-08-16 14:41:52 (I) | line search model 'm_try' parameters: + 2022-08-16 14:41:52 (I) | 5800.00 <= vp <= 5800.00 + 2022-08-16 14:41:52 (I) | 3325.01 <= vs <= 3698.63 + 2022-08-16 14:41:52 (I) | trial step unsuccessful. re-attempting line search + 2022-08-16 14:41:52 (I) | + LINE SEARCH STEP COUNT 03 + -------------------------------------------------------------------------------- + 2022-08-16 14:41:52 (I) | evaluating objective function for source 001 + 2022-08-16 14:41:52 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:41:56 (D) | quantifying misfit with 'Default' + 2022-08-16 14:41:56 (I) | evaluating objective function for source 002 + 2022-08-16 14:41:56 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:42:00 (D) | quantifying misfit with 'Default' + 2022-08-16 14:42:00 (I) | evaluating objective function for source 003 + 2022-08-16 14:42:00 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:42:03 (D) | quantifying misfit with 'Default' + 2022-08-16 14:42:03 (D) | misfit for trial model (f_try) == 2.59E-03 + 2022-08-16 14:42:03 (D) | step length(s) = 0.00E+00, 1.59E+09, 2.32E+09, 3.76E+09 + 2022-08-16 14:42:03 (D) | misfit val(s) = 1.28E-03, 2.59E-03, 8.65E-04, 1.73E-03 + 2022-08-16 14:42:03 (I) | try: bracket acceptable but step length unreasonable attempting to re-adjust step length alpha=2.82E+09 + 2022-08-16 14:42:03 (I) | line search model 'm_try' parameters: + 2022-08-16 14:42:03 (I) | 5800.00 <= vp <= 5800.00 + 2022-08-16 14:42:03 (I) | 3189.77 <= vs <= 3852.13 + 2022-08-16 14:42:03 (I) | trial step unsuccessful. re-attempting line search + 2022-08-16 14:42:03 (I) | + LINE SEARCH STEP COUNT 04 + -------------------------------------------------------------------------------- + 2022-08-16 14:42:03 (I) | evaluating objective function for source 001 + 2022-08-16 14:42:03 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:42:07 (D) | quantifying misfit with 'Default' + 2022-08-16 14:42:07 (I) | evaluating objective function for source 002 + 2022-08-16 14:42:07 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:42:11 (D) | quantifying misfit with 'Default' + 2022-08-16 14:42:11 (I) | evaluating objective function for source 003 + 2022-08-16 14:42:11 (D) | running forward simulation with 'Specfem2D' + 2022-08-16 14:42:15 (D) | quantifying misfit with 'Default' + 2022-08-16 14:42:15 (D) | misfit for trial model (f_try) == 3.46E-03 + 2022-08-16 14:42:15 (D) | step length(s) = 0.00E+00, 1.59E+09, 2.32E+09, 2.82E+09, 3.76E+09 + 2022-08-16 14:42:15 (D) | misfit val(s) = 1.28E-03, 2.59E-03, 8.65E-04, 3.46E-03, 1.73E-03 + 2022-08-16 14:42:15 (I) | pass: bracket acceptable and step length reasonable. returning minimum line search misfit. + 2022-08-16 14:42:15 (I) | line search model 'm_try' parameters: + 2022-08-16 14:42:15 (I) | 5800.00 <= vp <= 5800.00 + 2022-08-16 14:42:15 (I) | 3244.51 <= vs <= 3790.00 + 2022-08-16 14:42:15 (I) | trial step successful. finalizing line search + 2022-08-16 14:42:15 (I) | + FINALIZING LINE SEARCH + -------------------------------------------------------------------------------- + 2022-08-16 14:42:15 (I) | writing optimization stats + 2022-08-16 14:42:15 (I) | renaming current (new) optimization vectors as previous model (old) + 2022-08-16 14:42:15 (I) | setting accepted trial model (try) as current model (new) + 2022-08-16 14:42:15 (I) | misfit of accepted trial model is f=8.645E-04 + 2022-08-16 14:42:15 (I) | resetting line search step count to 0 + 2022-08-16 14:42:15 (I) | stop workflow at `stop_after`: perform_line_search + + +From the log statements above, we can see that the SeisFlows line search +required 4 trial steps, where it modified values of Vs (shear-wave +velocity) until satisfactory reduction in the objective function was +met. This was the final step in the iteration, and so the finalization +of the line search made preparations for a subsequent iteration. + +.. code:: ipython3 + + # We can see that we have 'new' and 'old' values for each of the optimization values, + # representing the previous model (M00) and the current model (M01). + ! ls scratch/optimize + + +.. parsed-literal:: + + alpha.txt f_new.txt f_try.txt m_new.npz output_optim.txt + checkpoint.npz f_old.txt g_old.npz m_old.npz p_old.npz + + +.. code:: ipython3 + + # The stats/ directory contains text files describing the optimization/line search + ! cat scratch/optimize/output_optim.txt + + +.. parsed-literal:: + + step_count,step_length,gradient_norm_L1,gradient_norm_L2,misfit,if_restarted,slope,theta + 04,2.323E+09,9.243E-05,1.049E-06,1.279E-03,0,8.263E-13,0.000E+00 + + +4. Conclusions +~~~~~~~~~~~~~~ + +We’ve now seen how SeisFlows runs an **Inversion** workflow using the +**Specfem2D** solver on a **Workstation** system. More or less, this is +all you need to run SeisFlows with any combination of modules. The +specificities of a system or numerical solver are already handled +internally by SeisFlows, so if you want to use Specmfe3D_Cartesian as +your solver, you would only need to run +``seisflows par solver specfem3d`` at the beginning of your workflow +(you will also need to set up your Specfem3D models, similar to what we +did for Specfem2D here). To run on a slurm system like Chinook +(University of Alaska Fairbanks), you can run +``seisflows par system chinook``. diff --git a/docs/containers.rst b/docs/containers.rst index 350ab5bc..4def8e76 100644 --- a/docs/containers.rst +++ b/docs/containers.rst @@ -59,7 +59,15 @@ We can run SeisFlows through JupyterHub by opening the container through a port: .. code-block:: bash - docker run -p 8888:8888 ghcr.io/seisscoped/pyatoa:latest + docker run -p 8888:8888 \ + --mount type=bind,source=$(pwd),target=/home/scoped/work + ghcr.io/seisscoped/pyatoa:latest + +.. warning:: + If you do **not** use the ``--mount`` command, all progres will be lost + once you close the container. See the + `Docker bind mounts `__ + documentation for more information. To access the JupyterHub instance, open the URL that was output to the display in your favorite web browser. The URL will likely look something like @@ -86,40 +94,12 @@ in an empty directory to avoid muddling up the home directory. .. image:: images/container_2_example.png :align: center | -This example will download, configure and compile SPECFEM2D within your -JupyterHub instance, and then run a SeisFlows-Pyatoa-SPECFEM2D inversion -problem. See `the SPECFEM2D example docs page `__ -for a more thorough explanation of what's going on under the hood. - -.. warning:: - Once you close the JupyterHub instance, all progress you've made since - opening it will be lost. If you would like to save your progress, you can - use the ``--mount`` command to mount your local filesystem inside the - container. - -Aside: Mounting a local filesystem -""""""""""""""""""""""""""""""""""" +This example will download, configure and compile SPECFEM2D, and then run a +SeisFlows-Pyatoa-SPECFEM2D inversion problem. See `the SPECFEM2D example docs +page `__ for a more thorough explanation of what's +going on under the hood. -By default, JupyterHub does not provide explicit access to your local -filesystem. This is not ideal as we would usually like to save/view results. So -we often provide the container access to the local filesystem using the -``--mount`` flag. - -For example, if you already have SPECFEM2D downloaded and compiled on your local -filesystem, you can mount it to the container to avoid having to redo this -action. Or, as in the following code snippet, we bind our local filesystem's -working directory (WORKDIR) into the containers filesystem as -*/home/scoped/work* to save results. - -See the `Docker bind mounts `__ -documentation for more information. - -.. code-block:: bash - - WORKDIR=/Users/Chow/Work/scratch - docker run -p 8888:8888 \ - --mount type=bind,source=${WORKDIR},target=/home/scoped/work \ - ghcr.io/seisscoped/pyatoa:latest +A successful inversion From the command line diff --git a/docs/index.rst b/docs/index.rst index 36bdd286..d48fa4b6 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -27,7 +27,12 @@ optimization problems. `Pyatoa `__ See the `change log `__ for point-by-point changes from the - original codebase. + original codebase. The `Legacy SeisFlows codebase + `__ can still be + accessed along with its `documentation `__, but is no longer + supported by the developers. + .. warning:: @@ -117,6 +122,7 @@ installed manually to the same Conda environment. :caption: Examples specfem2d_example + 2D_example_walkthrough .. toctree:: :maxdepth: 1 diff --git a/docs/notebooks/2D_example_walkthrough.ipynb b/docs/notebooks/2D_example_walkthrough.ipynb new file mode 100644 index 00000000..1e553768 --- /dev/null +++ b/docs/notebooks/2D_example_walkthrough.ipynb @@ -0,0 +1,1616 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2D Example Walkthrough" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "The notebook below details a walkthrough of Example \\#1 shown in the `SPECFEM2D example \\#1 `__. This is meant for those who want to understand what is going on under the hood. You are welcome to follow along on your workstation. The following Table of Contents outlines the steps we will take in this tutorial:\n", + "\n", + ".. warning:: \n", + " Navigation links will not work outside of Jupyter. Please use the navigation bar to the left." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. __[Setup SPECFEM2D](#1.-Setup-SPECFEM2D)__ \n", + " a. [Download and compile codebase](#1a.-Download-and-compile-codebase*) \n", + " b. [Create a separate SPECFEM2D working directory](#1b.-Create-a-separate-SPECFEM2D-working-directory) \n", + " c. [Generate initial and target models](#1c.-Generate-initial-and-target-models) \n", + "2. __[Initialize SeisFlows (SF)](#2.-Initialize-SeisFlows-(SF))__ \n", + " a. [SeisFlows working directory and parameter file](#2a.-SF-working-directory-and-parameter-file) \n", + "3. __[Run SeisFlows](#2.-Run-SeisFlows)__ \n", + " a. [Forward simulations](#3a.-Forward-simulations) \n", + " b. [Exploring the SeisFlows directory structure](#3b.-Exploring-the-SF-directory-structure) \n", + " c. [Adjoint simulations](#3c.-Adjoint-simulations) \n", + " d. [Line search and model update](#3d.-Line-search-and-model-update) \n", + "4. __[Conclusions](#4.-Conclusions)__ " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Setup SPECFEM2D \n", + "#### 1a. Download and compile codebase (optional)\n", + "\n", + "> **NOTE**: If you have already downloaded and compiled SPECFEM2D, you can skip most of this subsection (1a). However you will need to edit the first two paths in the following cell (WORKDIR and SPECFEM2D_ORIGINAL), and execute the path structure defined in the cell.\n", + "\n", + "First we'll download and compile SPECFEM2D to generate the binaries necessary to run our simulations. We will then populate a new SPECFEM2D working directory that will be used by SeisFlows. We'll use to Python OS module to do our filesystem processes just to keep everything in Python, but this can easily be accomplished in bash." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import glob\n", + "import shutil\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# vvv USER MUST EDIT THE FOLLOWING PATHS vvv\n", + "WORKDIR = \"/home/bchow/Work/scratch\" \n", + "SPECFEM2D = \"/home/bchow/REPOSITORIES/specfem2d\"\n", + "# where WORKDIR: points to your own working directory\n", + "# and SPECFEM2D: points to an existing specfem2D repository if available (if not set as '')\n", + "# ^^^ USER MUST EDIT THE FOLLOWING PATHS ^^^\n", + "# ======================================================================================================\n", + "\n", + "# Distribute the necessary file structure of the SPECFEM2D repository that we will downloaded/reference\n", + "SPECFEM2D_ORIGINAL = os.path.join(WORKDIR, \"specfem2d\")\n", + "SPECFEM2D_BIN_ORIGINAL = os.path.join(SPECFEM2D_ORIGINAL, \"bin\")\n", + "SPECFEM2D_DATA_ORIGINAL = os.path.join(SPECFEM2D_ORIGINAL, \"DATA\")\n", + "TAPE_2007_EXAMPLE = os.path.join(SPECFEM2D_ORIGINAL, \"EXAMPLES\", \"Tape2007\")\n", + "\n", + "# The SPECFEM2D working directory that we will create separate from the downloaded repo\n", + "SPECFEM2D_WORKDIR = os.path.join(WORKDIR, \"specfem2d_workdir\")\n", + "SPECFEM2D_BIN = os.path.join(SPECFEM2D_WORKDIR, \"bin\")\n", + "SPECFEM2D_DATA = os.path.join(SPECFEM2D_WORKDIR, \"DATA\")\n", + "SPECFEM2D_OUTPUT = os.path.join(SPECFEM2D_WORKDIR, \"OUTPUT_FILES\")\n", + "\n", + "# Pre-defined locations of velocity models we will generate using the solver\n", + "SPECFEM2D_MODEL_INIT = os.path.join(SPECFEM2D_WORKDIR, \"OUTPUT_FILES_INIT\")\n", + "SPECFEM2D_MODEL_TRUE = os.path.join(SPECFEM2D_WORKDIR, \"OUTPUT_FILES_TRUE\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Existing SPECMFE2D respository found, symlinking to working directory\n" + ] + } + ], + "source": [ + "# Download SPECFEM2D from GitHub, devel branch for latest codebase OR symlink from existing repo\n", + "if not os.path.exists(WORKDIR):\n", + " os.makedirs(WORKDIR)\n", + "os.chdir(WORKDIR)\n", + "\n", + "if os.path.exists(\"specfem2d\"):\n", + " print(\"SPECFEM2D repository already found, you may skip this subsection\")\n", + " pass\n", + "elif os.path.exists(SPECFEM2D):\n", + " print(\"Existing SPECMFE2D respository found, symlinking to working directory\")\n", + " os.symlink(SPECFEM2D, \"./specfem2d\")\n", + "else:\n", + " print(\"Cloning respository from GitHub\")\n", + " ! git clone --recursive --branch devel https://github.com/geodynamics/specfem2d.git" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Compile SPECFEM2D to generate the Makefile\n", + "os.chdir(SPECFEM2D_ORIGINAL)\n", + "if not os.path.exists(\"./config.log\"):\n", + " os.system(\"./configure\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Run make to generate SPECFEM2D binaries\n", + "if not os.path.exists(\"bin\"):\n", + " os.system(\"make all\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/bchow/REPOSITORIES/specfem2d\n", + "xadj_seismogram\t\t xconvolve_source_timefunction xspecfem2D\n", + "xcheck_quality_external_mesh xmeshfem2D\t\t xsum_kernels\n", + "xcombine_sem\t\t xsmooth_sem\n" + ] + } + ], + "source": [ + "# Check out the binary files that have been created\n", + "os.chdir(SPECFEM2D_ORIGINAL)\n", + "! pwd\n", + "! ls bin/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1b. Create a separate SPECFEM2D working directory\n", + "\n", + "Next we'll create a new SPECFEM2D working directory, separate from the original repository. The intent here is to isolate the original SPECFEM2D repository from our working state, to protect it from things like accidental file deletions or manipulations. This is not a mandatory step for using SeisFlows, but it helps keep file structure clean in the long run, and is the SeisFlows3 dev team's preferred method of using SPECFEM. " + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + ".. note::\n", + " All SPECFEM2D/3D/3D_GLOBE need to run successfully are the bin/, DATA/, and OUTPUT_FILES/ directories. Everything else in the repository is not mandatory for running binaries." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial we will be using the [Tape2007 example problem](https://github.com/geodynamics/specfem2d/tree/devel/EXAMPLES/Tape2007) to define our __DATA/__ directory (last tested 8/15/22, bdba4389)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/bchow/Work/scratch/specfem2d_workdir\n", + "bin DATA\n" + ] + } + ], + "source": [ + "# Incase we've run this docs page before, delete the working directory before remaking\n", + "if os.path.exists(SPECFEM2D_WORKDIR):\n", + " shutil.rmtree(SPECFEM2D_WORKDIR)\n", + "\n", + "os.mkdir(SPECFEM2D_WORKDIR)\n", + "os.chdir(SPECFEM2D_WORKDIR)\n", + "\n", + "# Copy the binary files incase we update the source code. These can also be symlinked.\n", + "shutil.copytree(SPECFEM2D_BIN_ORIGINAL, \"bin\")\n", + "\n", + "# Copy the DATA/ directory because we will be making edits here frequently and it's useful to\n", + "# retain the original files for reference. We will be running one of the example problems: Tape2007\n", + "shutil.copytree(os.path.join(TAPE_2007_EXAMPLE, \"DATA\"), \"DATA\")\n", + "\n", + "! pwd\n", + "! ls" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -------------------------------------------------------------------------------\r\n", + " -------------------------------------------------------------------------------\r\n", + " D a t e : 16 - 08 - 2022 T i m e : 14:26:37\r\n", + " -------------------------------------------------------------------------------\r\n", + " -------------------------------------------------------------------------------\r\n", + "\r\n", + "see results in directory: OUTPUT_FILES/\r\n", + "\r\n", + "done\r\n", + "Tue Aug 16 02:26:37 PM AKDT 2022\r\n" + ] + } + ], + "source": [ + "# Run the Tape2007 example to make sure SPECFEM2D is working as expected\n", + "os.chdir(TAPE_2007_EXAMPLE)\n", + "! ./run_this_example.sh > output_log.txt\n", + "\n", + "assert(os.path.exists(\"OUTPUT_FILES/forward_image000004800.jpg\")), \\\n", + " (f\"Example did not run, the remainder of this docs page will likely not work.\"\n", + " f\"Please check the following directory: {TAPE_2007_EXAMPLE}\")\n", + "\n", + "! tail output_log.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "------------------------------------\n", + "Now we need to manually set up our SPECFEM2D working directory. As mentioned in the previous cell, the only required elements of this working directory are the following (these files will form the basis for how SeisFlows3 operates within the SPECFEM2D framework):\n", + "\n", + "1. __bin/__ directory containing SPECFEM2D binaries\n", + "2. __DATA/__ directory containing SOURCE and STATION files, as well as a SPECFEM2D Par_file\n", + "3. __OUTPUT_FILES/proc??????_*.bin__ files which define the starting (and target) models" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + ".. note:: \n", + " This file structure is the same for all versions of SPECFEM (2D/3D/3D_GLOBE)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "interfaces_Tape2007.dat\t\t SOURCE_003 SOURCE_012 SOURCE_021\r\n", + "model_velocity.dat_checker\t SOURCE_004 SOURCE_013 SOURCE_022\r\n", + "Par_file\t\t\t SOURCE_005 SOURCE_014 SOURCE_023\r\n", + "Par_file_Tape2007_132rec_checker SOURCE_006 SOURCE_015 SOURCE_024\r\n", + "Par_file_Tape2007_onerec\t SOURCE_007 SOURCE_016 SOURCE_025\r\n", + "proc000000_model_velocity.dat_input SOURCE_008 SOURCE_017 STATIONS\r\n", + "SOURCE\t\t\t\t SOURCE_009 SOURCE_018 STATIONS_checker\r\n", + "SOURCE_001\t\t\t SOURCE_010 SOURCE_019\r\n", + "SOURCE_002\t\t\t SOURCE_011 SOURCE_020\r\n" + ] + } + ], + "source": [ + "# First we will set the correct SOURCE and STATION files.\n", + "# This is the same task as shown in ./run_this_example.sh\n", + "os.chdir(SPECFEM2D_DATA)\n", + "\n", + "# Symlink source 001 as our main source\n", + "if os.path.exists(\"SOURCE\"):\n", + " os.remove(\"SOURCE\")\n", + "os.symlink(\"SOURCE_001\", \"SOURCE\")\n", + "\n", + "# Copy the correct Par_file so that edits do not affect the original file\n", + "if os.path.exists(\"Par_file\"):\n", + " os.remove(\"Par_file\")\n", + "shutil.copy(\"Par_file_Tape2007_onerec\", \"Par_file\")\n", + "\n", + "! ls" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1c. Generate initial and target models\n", + "\n", + "Since we're doing a synthetic-synthetic inversion, we need to manually set up the velocity models with which we generate our synthetic waveforms. The naming conventions for these models are:\n", + "\n", + "1. __MODEL_INIT:__ The initial or starting model. Used to generate the actual synthetic seismograms. This is considered M00.\n", + "2. __MODEL_TRUE:__ The target or true model. Used to generate 'data' (also synthetic). This is the reference model that our inversion is trying to resolve.\n", + "\n", + "The starting model is defined as a homogeneous halfspace uin the Tape2007 example problem. We will need to run both `xmeshfem2D` and `xspecfem2D` to generate the required velocity model database files. We will generate our target model by slightly perturbing the parameters of the initial model." + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + ".. note::\n", + " We can use the SeisFlows3 command line option `seisflows sempar` to directly edit the SPECFEM2D Par_file in the command line. This will work for the SPECFEM3D Par_file as well." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "setup_with_binary_database: 0 -> 1\n", + "SAVE_MODEL: default -> binary\n", + "save_ASCII_kernels: .true. -> .false.\n" + ] + } + ], + "source": [ + "os.chdir(SPECFEM2D_DATA)\n", + "\n", + "# Ensure that SPECFEM2D outputs the velocity model in the expected binary format\n", + "! seisflows sempar setup_with_binary_database 1 # allow creation of .bin files\n", + "! seisflows sempar save_model binary # output model in .bin database format\n", + "! seisflows sempar save_ascii_kernels .false. # output kernels in .bin format, not ASCII" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bin DATA OUTPUT_FILES\r\n" + ] + } + ], + "source": [ + "# SPECFEM requires that we create the OUTPUT_FILES directory before running\n", + "os.chdir(SPECFEM2D_WORKDIR)\n", + "\n", + "if os.path.exists(SPECFEM2D_OUTPUT):\n", + " shutil.rmtree(SPECFEM2D_OUTPUT)\n", + " \n", + "os.mkdir(SPECFEM2D_OUTPUT)\n", + "\n", + "! ls" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " **********************************************\n", + " **** Specfem 2-D Solver - serial version ****\n", + " **********************************************\n", + "\n", + " Running Git version of the code corresponding to commit cf89366717d9435985ba852ef1d41a10cee97884\n", + " dating From Date: Mon Nov 29 23:20:51 2021 -0800\n", + "\n", + "\n", + " NDIM = 2\n", + " -------------------------------------------------------------------------------\n", + " Program SPECFEM2D: \n", + " -------------------------------------------------------------------------------\n", + " -------------------------------------------------------------------------------\n", + " Tape-Liu-Tromp (GJI 2007)\n", + " -------------------------------------------------------------------------------\n", + " -------------------------------------------------------------------------------\n", + " D a t e : 16 - 08 - 2022 T i m e : 14:26:52\n", + " -------------------------------------------------------------------------------\n", + " -------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# GENERATE MODEL_INIT\n", + "os.chdir(SPECFEM2D_WORKDIR)\n", + "\n", + "# Run the mesher and solver to generate our initial model\n", + "! ./bin/xmeshfem2D > OUTPUT_FILES/mesher_log.txt\n", + "! ./bin/xspecfem2D > OUTPUT_FILES/solver_log.txt\n", + "\n", + "# Move the model files (*.bin) into the OUTPUT_FILES directory, where SeisFlows3 expects them\n", + "! mv DATA/*bin OUTPUT_FILES\n", + "\n", + "# Make sure we don't overwrite this initial model when creating our target model in the next step\n", + "! mv OUTPUT_FILES OUTPUT_FILES_INIT\n", + "\n", + "! head OUTPUT_FILES_INIT/solver_log.txt\n", + "! tail OUTPUT_FILES_INIT/solver_log.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-----------------------------\n", + "\n", + "Now we want to perturb the initial model to create our target model (__MODEL_TRUE__). The seisflows command line subargument `seisflows sempar velocity_model` will let us view and edit the velocity model. You can also do this manually by editing the Par_file directly. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VELOCITY_MODEL:\r\n", + "\r\n", + "1 1 2600.d0 5800.d0 3500.0d0 0 0 10.d0 10.d0 0 0 0 0 0 0\r\n", + "->\r\n", + "1 1 2600.d0 5900.d0 3550.0d0 0 0 10.d0 10.d0 0 0 0 0 0 0\r\n" + ] + } + ], + "source": [ + "# GENERATE MODEL_TRUE\n", + "os.chdir(SPECFEM2D_DATA)\n", + "\n", + "# Edit the Par_file by increasing velocities by ~10% \n", + "! seisflows sempar velocity_model '1 1 2600.d0 5900.d0 3550.0d0 0 0 10.d0 10.d0 0 0 0 0 0 0'" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " **********************************************\n", + " **** Specfem 2-D Solver - serial version ****\n", + " **********************************************\n", + "\n", + " Running Git version of the code corresponding to commit cf89366717d9435985ba852ef1d41a10cee97884\n", + " dating From Date: Mon Nov 29 23:20:51 2021 -0800\n", + "\n", + "\n", + " NDIM = 2\n", + " -------------------------------------------------------------------------------\n", + " Program SPECFEM2D: \n", + " -------------------------------------------------------------------------------\n", + " -------------------------------------------------------------------------------\n", + " Tape-Liu-Tromp (GJI 2007)\n", + " -------------------------------------------------------------------------------\n", + " -------------------------------------------------------------------------------\n", + " D a t e : 16 - 08 - 2022 T i m e : 14:26:52\n", + " -------------------------------------------------------------------------------\n", + " -------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# Re-run the mesher and solver to generate our target velocity model\n", + "os.chdir(SPECFEM2D_WORKDIR)\n", + "\n", + "# Make sure the ./OUTPUT_FILES directory exists since we moved the old one\n", + "if os.path.exists(SPECFEM2D_OUTPUT):\n", + " shutil.rmtree(SPECFEM2D_OUTPUT)\n", + "os.mkdir(SPECFEM2D_OUTPUT)\n", + "\n", + "# Run the binaries to generate MODEL_TRUE\n", + "! ./bin/xmeshfem2D > OUTPUT_FILES/mesher_log.txt\n", + "! ./bin/xspecfem2D > OUTPUT_FILES/solver_log.txt\n", + "\n", + "# Move all the relevant files into OUTPUT_FILES \n", + "! mv ./DATA/*bin OUTPUT_FILES\n", + "! mv OUTPUT_FILES OUTPUT_FILES_TRUE\n", + "\n", + "! head OUTPUT_FILES_INIT/solver_log.txt\n", + "! tail OUTPUT_FILES_INIT/solver_log.txt" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bin DATA OUTPUT_FILES_INIT OUTPUT_FILES_TRUE\r\n" + ] + } + ], + "source": [ + "# Great, we have all the necessary SPECFEM files to run our SeisFlows inversion!\n", + "! ls" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Initialize SeisFlows (SF)\n", + "In this Section we will look at a SeisFlows working directory, parameter file, and working state.\n", + "\n", + "#### 2a. SeisFlows working directory and parameter file\n", + "\n", + "As with SPECFEM, SeisFlows requires a parameter file (__parameters.yaml__) that controls how an automated workflow will proceed. Because SeisFlows is modular, there are a large number of potential parameters which may be present in a SeisFlows parameter file, as each sub-module may have its own set of unique parameters.\n", + "\n", + "In contrast to SPECFEM's method of listing all available parameters and leaving it up the User to determine which ones are relevant to them, SeisFlows dynamically builds its parameter file based on User inputs. In this subsection we will use the built-in SeisFlows command line tools to generate and populate the parameter file. " + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + ".. note::\n", + " See the `parameter file documentation page `__ for a more in depth exploration of this central SeisFlows file." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the previous section we saw the `sempar` command in action. We can use the `-h` or help flag to list all available SiesFlows3 command line commands." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "usage: seisflows [-h] [-w [WORKDIR]] [-p [PARAMETER_FILE]]\r\n", + " {setup,configure,swap,init,submit,resume,restart,clean,par,sempar,check,print,reset,debug,examples}\r\n", + " ...\r\n", + "\r\n", + "================================================================================\r\n", + "\r\n", + " SeisFlows: Waveform Inversion Package \r\n", + "\r\n", + "================================================================================\r\n", + "\r\n", + "optional arguments:\r\n", + " -h, --help show this help message and exit\r\n", + " -w [WORKDIR], --workdir [WORKDIR]\r\n", + " The SeisFlows working directory, default: cwd\r\n", + " -p [PARAMETER_FILE], --parameter_file [PARAMETER_FILE]\r\n", + " Parameters file, default: 'parameters.yaml'\r\n", + "\r\n", + "command:\r\n", + " Available SeisFlows arguments and their intended usages\r\n", + "\r\n", + " setup Setup working directory from scratch\r\n", + " configure Fill parameter file with defaults\r\n", + " swap Swap module parameters in an existing parameter file\r\n", + " init Initiate working environment\r\n", + " submit Submit initial workflow to system\r\n", + " resume Re-submit previous workflow to system\r\n", + " restart Remove current environment and submit new workflow\r\n", + " clean Remove files relating to an active working environment\r\n", + " par View and edit SeisFlows parameter file\r\n", + " sempar View and edit SPECFEM parameter file\r\n", + " check Check state of an active environment\r\n", + " print Print information related to an active environment\r\n", + " reset Reset modules within an active state\r\n", + " debug Start interactive debug environment\r\n", + " examples Look at and run pre-configured example problems\r\n", + "\r\n", + "'seisflows [command] -h' for more detailed descriptions of each command.\r\n" + ] + } + ], + "source": [ + "! seisflows -h" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creating parameter file: parameters.yaml\n", + "parameters.yaml sflog.txt specfem2d specfem2d_workdir\n" + ] + } + ], + "source": [ + "# The command 'setup' creates the 'parameters.yaml' file that controls all of SeisFlows\n", + "# the '-f' flag removes any exist 'parameters.yaml' file that might be in the directory\n", + "os.chdir(WORKDIR)\n", + "! seisflows setup -f\n", + "! ls" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# //////////////////////////////////////////////////////////////////////////////\r\n", + "#\r\n", + "# SeisFlows YAML Parameter File\r\n", + "#\r\n", + "# //////////////////////////////////////////////////////////////////////////////\r\n", + "#\r\n", + "# Modules correspond to the structure of the source code, and determine\r\n", + "# SeisFlows' behavior at runtime. Each module requires its own sub-parameters.\r\n", + "#\r\n", + "# .. rubric::\r\n", + "# - To determine available options for modules listed below, run:\r\n", + "# > seisflows print modules\r\n", + "# - To auto-fill with docstrings and default values (recommended), run:\r\n", + "# > seisflows configure\r\n", + "# - To set values as NoneType, use: null\r\n", + "# - To set values as infinity, use: inf\r\n", + "#\r\n", + "# MODULES\r\n", + "# ///////\r\n", + "# workflow (str): The types and order of functions for running SeisFlows\r\n", + "# system (str): Computer architecture of the system being used\r\n", + "# solver (str): External numerical solver to use for waveform simulations\r\n", + "# preprocess (str): Preprocessing schema for waveform data\r\n", + "# optimize (str): Optimization algorithm for the inverse problem\r\n", + "# ==============================================================================\r\n", + "workflow: forward\r\n", + "system: workstation\r\n", + "solver: specfem2d\r\n", + "preprocess: default\r\n", + "optimize: gradient\r\n" + ] + } + ], + "source": [ + "# Let's have a look at this file, which has not yet been populated\n", + "! cat parameters.yaml" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " SEISFLOWS MODULES \r\n", + " ///////////////// \r\n", + "'-': module, '*': class\r\n", + "\r\n", + "- workflow\r\n", + " * forward\r\n", + " * inversion\r\n", + " * migration\r\n", + "- system\r\n", + " * chinook\r\n", + " * cluster\r\n", + " * frontera\r\n", + " * lsf\r\n", + " * maui\r\n", + " * slurm\r\n", + " * workstation\r\n", + "- solver\r\n", + " * specfem\r\n", + " * specfem2d\r\n", + " * specfem3d\r\n", + " * specfem3d_globe\r\n", + "- preprocess\r\n", + " * default\r\n", + " * pyaflowa\r\n", + "- optimize\r\n", + " * LBFGS\r\n", + " * NLCG\r\n", + " * gradient\r\n" + ] + } + ], + "source": [ + "# We can use the `seisflows print modules` command to list out the available options \n", + "! seisflows print modules" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "workflow: forward -> inversion\n", + "# //////////////////////////////////////////////////////////////////////////////\n", + "#\n", + "# SeisFlows YAML Parameter File\n", + "#\n", + "# //////////////////////////////////////////////////////////////////////////////\n", + "#\n", + "# Modules correspond to the structure of the source code, and determine\n", + "# SeisFlows' behavior at runtime. Each module requires its own sub-parameters.\n", + "#\n", + "# .. rubric::\n", + "# - To determine available options for modules listed below, run:\n", + "# > seisflows print modules\n", + "# - To auto-fill with docstrings and default values (recommended), run:\n", + "# > seisflows configure\n", + "# - To set values as NoneType, use: null\n", + "# - To set values as infinity, use: inf\n", + "#\n", + "# MODULES\n", + "# ///////\n", + "# workflow (str): The types and order of functions for running SeisFlows\n", + "# system (str): Computer architecture of the system being used\n", + "# solver (str): External numerical solver to use for waveform simulations\n", + "# preprocess (str): Preprocessing schema for waveform data\n", + "# optimize (str): Optimization algorithm for the inverse problem\n", + "# ==============================================================================\n", + "workflow: inversion\n", + "system: workstation\n", + "solver: specfem2d\n", + "preprocess: default\n", + "optimize: gradient\n" + ] + } + ], + "source": [ + "# For this example, we can use most of the default modules, however we need to \n", + "# change the SOLVER module to let SeisFlows know we're using SPECFEM2D (as opposed to 3D)\n", + "! seisflows par workflow inversion\n", + "! cat parameters.yaml" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-------------------------\n", + "The `seisflows configure` command populates the parameter file based on the chosen modules. SeisFlows will attempt to fill in all parameters with reasonable default values. Docstrings above each module show descriptions and available options for each of these parameters. \n", + "\n", + "In the follownig cell we will use the `seisflows par` command to edit the parameters.yaml file directly, replacing some default parameters with our own values. Comments next to each evaluation describe the choice for each." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# //////////////////////////////////////////////////////////////////////////////\r\n", + "#\r\n", + "# SeisFlows YAML Parameter File\r\n", + "#\r\n", + "# //////////////////////////////////////////////////////////////////////////////\r\n", + "#\r\n", + "# Modules correspond to the structure of the source code, and determine\r\n", + "# SeisFlows' behavior at runtime. Each module requires its own sub-parameters.\r\n", + "#\r\n", + "# .. rubric::\r\n", + "# - To determine available options for modules listed below, run:\r\n", + "# > seisflows print modules\r\n", + "# - To auto-fill with docstrings and default values (recommended), run:\r\n", + "# > seisflows configure\r\n", + "# - To set values as NoneType, use: null\r\n", + "# - To set values as infinity, use: inf\r\n", + "#\r\n", + "# MODULES\r\n", + "# ///////\r\n", + "# workflow (str): The types and order of functions for running SeisFlows\r\n", + "# system (str): Computer architecture of the system being used\r\n", + "# solver (str): External numerical solver to use for waveform simulations\r\n", + "# preprocess (str): Preprocessing schema for waveform data\r\n", + "# optimize (str): Optimization algorithm for the inverse problem\r\n", + "# ==============================================================================\r\n", + "workflow: inversion\r\n", + "system: workstation\r\n", + "solver: specfem2d\r\n", + "preprocess: default\r\n", + "optimize: gradient\r\n", + "# =============================================================================\r\n", + "#\r\n", + "# Forward Workflow\r\n", + "# ----------------\r\n", + "# Run forward solver in parallel and (optionally) calculate\r\n", + "# data-synthetic misfit and adjoint sources.\r\n", + "#\r\n", + "# Parameters\r\n", + "# ----------\r\n", + "# :type modules: list of module\r\n", + "# :param modules: instantiated SeisFlows modules which should have been\r\n", + "# generated by the function `seisflows.config.import_seisflows` with a\r\n", + "# parameter file generated by seisflows.configure\r\n", + "# :type data_case: str\r\n", + "# :param data_case: How to address 'data' in the workflow, available options:\r\n", + "# 'data': real data will be provided by the user in\r\n", + "# `path_data/{source_name}` in the same format that the solver will\r\n", + "# produce synthetics (controlled by `solver.format`) OR\r\n", + "# synthetic': 'data' will be generated as synthetic seismograms using\r\n", + "# a target model provided in `path_model_true`. If None, workflow will\r\n" + ] + } + ], + "source": [ + "! seisflows configure\n", + "! head --lines=50 parameters.yaml" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ntask: 1 -> 3\n", + "materials: acoustic -> elastic\n", + "end: 1 -> 2\n", + "data_case: data -> synthetic\n", + "components: ZNE -> Y\n", + "step_count_max: 10 -> 5\n", + "path_specfem_bin: null -> /home/bchow/Work/scratch/specfem2d_workdir/bin\n", + "path_specfem_data: null -> /home/bchow/Work/scratch/specfem2d_workdir/DATA\n", + "path_model_init: null ->\n", + "/home/bchow/Work/scratch/specfem2d_workdir/OUTPUT_FILES_INIT\n", + "path_model_true: null ->\n", + "/home/bchow/Work/scratch/specfem2d_workdir/OUTPUT_FILES_TRUE\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# EDIT THE SEISFLOWS PARAMETER FILE\n", + "! seisflows par ntask 3 # set the number of sources/events to use\n", + "! seisflows par materials elastic # update Vp and Vs during inversion\n", + "! seisflows par end 2 # final iteration -- we will only run 1\n", + "! seisflows par data_case synthetic # synthetic-synthetic means we need both INIT and TRUE models\n", + "! seisflows par components Y # this default example creates Y-component seismograms\n", + "! seisflows par step_count_max 5 # limit the number of steps in the line search\n", + "\n", + "# Use Python syntax here to access path constants\n", + "os.system(f\"seisflows par path_specfem_bin {SPECFEM2D_BIN}\") # set path to SPECFEM2D binaries\n", + "os.system(f\"seisflows par path_specfem_data {SPECFEM2D_DATA}\") # set path to SEPCFEM2D DATA/\n", + "os.system(f\"seisflows par path_model_init {SPECFEM2D_MODEL_INIT}\") # set path to INIT model\n", + "os.system(f\"seisflows par path_model_true {SPECFEM2D_MODEL_TRUE}\") # set path to TRUE model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "------------------------------\n", + "One last thing, we will need to edit the SPECFEM2D Par_file parameter `MODEL` such that `xmeshfem2d` reads our pre-built velocity models (\\*.bin files) rather than the meshing parameters defined in the Par_file." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MODEL: default -> gll\r\n" + ] + } + ], + "source": [ + "os.chdir(SPECFEM2D_DATA)\n", + "! seisflows sempar model gll" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Run SeisFlows\n", + "\n", + "In this Section we will run SeisFlows to generate synthetic seismograms, kernels, a gradient, and an updated velocity model.\n", + "\n", + "#### 3a. Forward simulations\n", + "\n", + "SeisFlows is an automated workflow tool, such that once we run `seisflows submit` we should not need to intervene in the workflow. However the package does allow the User flexibility in how they want the workflow to behave.\n", + "\n", + "For example, we can run our workflow in stages by taking advantage of the `stop_after` parameter. As its name suggests, `stop_after` allows us to stop a workflow prematurely so that we may stop and look at results, or debug a failing workflow.\n", + "\n", + "The `seisflows print flow` command tells us what functions we can use for the `stop_after` parameter. " + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " SEISFLOWS WORKFLOW TASK LIST \r\n", + " //////////////////////////// \r\n", + "Task list for \r\n", + "\r\n", + "1: evaluate_initial_misfit\r\n", + "2: run_adjoint_simulations\r\n", + "3: postprocess_event_kernels\r\n", + "4: evaluate_gradient_from_kernels\r\n", + "5: initialize_line_search\r\n", + "6: perform_line_search\r\n", + "7: finalize_iteration\r\n" + ] + } + ], + "source": [ + "os.chdir(WORKDIR)\n", + "! seisflows print tasks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-----------------------------\n", + "In the Inversion workflow, the tasks listed are described as follows:\n", + "\n", + "1. __evaluate_initial_misfit:__ \n", + " a. Prepare data for inversion by either copying data from disk or generating 'synthetic data' with MODEL_TRUE \n", + " b. Call numerical solver to run forward simulations using MODEL_INIT, generating synthetics \n", + " c. Evaluate the objective function by performing waveform comparisons \n", + " d. Prepare `run_adjoint_simulations` step by generating adjoint sources and auxiliary files\n", + "2. __run_adjoint_simulations:__ Call numerical solver to run adjoint simulation, generating kernels\n", + "3. __postprocess_event_kernels:__ Combine all event kernels into a misfit kernel. \n", + "4. __evaluate_gradient_from_kernels:__ Smooth and mask the misfit kernel to create the gradient\n", + "4. __initialize_line_search:__ Call on the optimization library to scale the gradient by a step length to compute the search direction. Prepare file structure for line search.\n", + "5. __perform_line_search:__ Perform a line search by algorithmically scaling the gradient and evaluating the misfit function (forward simulations and misfit quantification) until misfit is acceptably reduced.\n", + "6. __finalize_iteration:__ Run any finalization steps such as saving traces, kernels, gradients and models to disk, setting up SeisFlows3 for any subsequent iterations. Clean the scratch/ directory in preparation for subsequent iterations\n", + "\n", + "Let's set the `stop_after` argument to __evaluate_initial_misfit__, this will halt the workflow after the intialization step. " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "stop_after: null -> evaluate_initial_misfit\r\n" + ] + } + ], + "source": [ + "! seisflows par stop_after evaluate_initial_misfit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-----------------------\n", + "Now let's run SeisFlows. There are two ways to do this: `submit` and `restart`\n", + "\n", + "1. `seisflows submit` is used to run new workflows and resume stopped or failed workflows.\n", + "2. The `restart` command is simply a convenience function that runs `clean` (to remove an active working state) and `submit` (to submit a fresh workflow). \n", + "\n", + "Since this is our first run, we'll use `seisflows submit`." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-08-16 14:32:48 (I) | \n", + "================================================================================\n", + " SETTING UP INVERSION WORKFLOW \n", + "================================================================================\n", + "2022-08-16 14:32:55 (D) | running setup for module 'system.Workstation'\n", + "2022-08-16 14:32:57 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/sflog_001.txt\n", + "2022-08-16 14:32:57 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/parameters_001.yaml\n", + "2022-08-16 14:32:57 (D) | running setup for module 'solver.Specfem2D'\n", + "2022-08-16 14:32:57 (I) | initializing 3 solver directories\n", + "2022-08-16 14:32:57 (D) | initializing solver directory source: 001\n", + "2022-08-16 14:33:04 (D) | linking source '001' as 'mainsolver'\n", + "2022-08-16 14:33:04 (D) | initializing solver directory source: 002\n", + "2022-08-16 14:33:09 (D) | initializing solver directory source: 003\n", + "2022-08-16 14:33:16 (D) | running setup for module 'preprocess.Default'\n", + "2022-08-16 14:33:16 (D) | running setup for module 'optimize.Gradient'\n", + "2022-08-16 14:33:17 (I) | no optimization checkpoint found, assuming first run\n", + "2022-08-16 14:33:17 (I) | re-loading optimization module from checkpoint\n", + "2022-08-16 14:33:17 (I) | \n", + "////////////////////////////////////////////////////////////////////////////////\n", + " RUNNING ITERATION 01 \n", + "////////////////////////////////////////////////////////////////////////////////\n", + "2022-08-16 14:33:17 (I) | \n", + "================================================================================\n", + " RUNNING INVERSION WORKFLOW \n", + "================================================================================\n", + "2022-08-16 14:33:17 (I) | \n", + "////////////////////////////////////////////////////////////////////////////////\n", + " EVALUATING MISFIT FOR INITIAL MODEL \n", + "////////////////////////////////////////////////////////////////////////////////\n", + "2022-08-16 14:33:17 (I) | checking initial model parameters\n", + "2022-08-16 14:33:17 (I) | 5800.00 <= vp <= 5800.00\n", + "2022-08-16 14:33:17 (I) | 2600.00 <= rho <= 2600.00\n", + "2022-08-16 14:33:17 (I) | 3500.00 <= vs <= 3500.00\n", + "2022-08-16 14:33:17 (I) | checking true/target model parameters\n", + "2022-08-16 14:33:17 (I) | 5900.00 <= vp <= 5900.00\n", + "2022-08-16 14:33:17 (I) | 2600.00 <= rho <= 2600.00\n", + "2022-08-16 14:33:17 (I) | 3550.00 <= vs <= 3550.00\n", + "2022-08-16 14:33:17 (I) | preparing observation data for source 001\n", + "2022-08-16 14:33:17 (I) | running forward simulation w/ target model for 001\n", + "2022-08-16 14:33:21 (I) | evaluating objective function for source 001\n", + "2022-08-16 14:33:21 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:33:25 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:33:25 (I) | preparing observation data for source 002\n", + "2022-08-16 14:33:25 (I) | running forward simulation w/ target model for 002\n", + "2022-08-16 14:33:29 (I) | evaluating objective function for source 002\n", + "2022-08-16 14:33:29 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:33:33 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:33:33 (I) | preparing observation data for source 003\n", + "2022-08-16 14:33:33 (I) | running forward simulation w/ target model for 003\n", + "2022-08-16 14:33:36 (I) | evaluating objective function for source 003\n", + "2022-08-16 14:33:36 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:33:40 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:33:40 (I) | stop workflow at `stop_after`: evaluate_initial_misfit\n" + ] + } + ], + "source": [ + "! seisflows submit " + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + ".. note::\n", + " For a detailed exploration of a SeisFlows working directory, see the `working directory `__ documentation page where we explain each of the files and directories that have been generated during this workflow. Below we just look at two files which are required for our adjoint simulation, the adjoint sources (.adj) and STATIONS_ADJOINT file" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " -48.0000000 0.0000000\r\n", + " -47.9400000 0.0000000\r\n", + " -47.8800000 0.0000000\r\n", + " -47.8200000 0.0000000\r\n", + " -47.7600000 0.0000000\r\n", + " -47.7000000 0.0000000\r\n", + " -47.6400000 0.0000000\r\n", + " -47.5800000 0.0000000\r\n", + " -47.5200000 0.0000000\r\n", + " -47.4600000 0.0000000\r\n" + ] + } + ], + "source": [ + "# The adjoint source is created in the same format as the synthetics (two-column ASCII) \n", + "! head scratch/solver/001/traces/adj/AA.S0001.BXY.adj" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3b. Adjoint simulations\n", + "\n", + "Now that we have all the required files for running an adjoint simulation (\\*.adj waveforms and STATIONS_ADJOINT file), we can continue with the SeisFlows3 Inversion workflow. No need to edit the Par_file or anything like that, SeisFlows3 will take care of that under the hood. We simply need to tell the workflow (via the parameters.yaml file) to `resume_from` the correct function. We can have a look at these functions again:" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " SEISFLOWS WORKFLOW TASK LIST \r\n", + " //////////////////////////// \r\n", + "Task list for \r\n", + "\r\n", + "1: evaluate_initial_misfit\r\n", + "2: run_adjoint_simulations\r\n", + "3: postprocess_event_kernels\r\n", + "4: evaluate_gradient_from_kernels\r\n", + "5: initialize_line_search\r\n", + "6: perform_line_search\r\n", + "7: finalize_iteration\r\n" + ] + } + ], + "source": [ + "! seisflows print tasks" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "stop_after: evaluate_initial_misfit -> evaluate_gradient_from_kernels\r\n" + ] + } + ], + "source": [ + "# We'll stop just before the line search so that we can take a look at the files \n", + "# generated during the middle tasks\n", + "! seisflows par stop_after evaluate_gradient_from_kernels" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-08-16 14:36:42 (D) | setting iteration==1 from state file\n", + "2022-08-16 14:36:42 (I) | \n", + "================================================================================\n", + " SETTING UP INVERSION WORKFLOW \n", + "================================================================================\n", + "2022-08-16 14:36:48 (D) | running setup for module 'system.Workstation'\n", + "2022-08-16 14:36:51 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/sflog_002.txt\n", + "2022-08-16 14:36:51 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/parameters_002.yaml\n", + "2022-08-16 14:36:51 (D) | running setup for module 'solver.Specfem2D'\n", + "2022-08-16 14:36:51 (I) | initializing 3 solver directories\n", + "2022-08-16 14:36:51 (D) | running setup for module 'preprocess.Default'\n", + "2022-08-16 14:36:52 (D) | running setup for module 'optimize.Gradient'\n", + "2022-08-16 14:36:53 (I) | re-loading optimization module from checkpoint\n", + "2022-08-16 14:36:54 (I) | re-loading optimization module from checkpoint\n", + "2022-08-16 14:36:54 (I) | \n", + "////////////////////////////////////////////////////////////////////////////////\n", + " RUNNING ITERATION 01 \n", + "////////////////////////////////////////////////////////////////////////////////\n", + "2022-08-16 14:36:54 (I) | \n", + "================================================================================\n", + " RUNNING INVERSION WORKFLOW \n", + "================================================================================\n", + "2022-08-16 14:36:54 (I) | 'evaluate_initial_misfit' has already been run, skipping\n", + "2022-08-16 14:36:54 (I) | \n", + "////////////////////////////////////////////////////////////////////////////////\n", + " EVALUATING EVENT KERNELS W/ ADJOINT SIMULATIONS \n", + "////////////////////////////////////////////////////////////////////////////////\n", + "2022-08-16 14:36:54 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log'\n", + "2022-08-16 14:37:05 (D) | renaming output event kernels: 'alpha' -> 'vp'\n", + "2022-08-16 14:37:05 (D) | renaming output event kernels: 'beta' -> 'vs'\n", + "2022-08-16 14:37:05 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log'\n", + "2022-08-16 14:37:16 (D) | renaming output event kernels: 'alpha' -> 'vp'\n", + "2022-08-16 14:37:16 (D) | renaming output event kernels: 'beta' -> 'vs'\n", + "2022-08-16 14:37:18 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log'\n", + "2022-08-16 14:37:29 (D) | renaming output event kernels: 'alpha' -> 'vp'\n", + "2022-08-16 14:37:29 (D) | renaming output event kernels: 'beta' -> 'vs'\n", + "2022-08-16 14:37:30 (I) | \n", + "////////////////////////////////////////////////////////////////////////////////\n", + " GENERATING/PROCESSING MISFIT KERNEL \n", + "////////////////////////////////////////////////////////////////////////////////\n", + "2022-08-16 14:37:30 (I) | combining event kernels into single misfit kernel\n", + "2022-08-16 14:37:31 (I) | scaling gradient to absolute model perturbations\n", + "2022-08-16 14:37:32 (I) | stop workflow at `stop_after`: evaluate_gradient_from_kernels\n" + ] + } + ], + "source": [ + "# We can use the `seisflows submit` command to continue an active workflow\n", + "# The state file created during the first run will tell the workflow to resume from the stopped point in the workflow\n", + "! seisflows submit " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----------------\n", + "The function __run_adjoint_simulations()__ has run adjoint simulations to generate event kernels. The functions __postprocess_event_kernels__ and __evaluate_gradient_from_kernels__ will have summed and (optionally) smoothed the kernels to recover the gradient, which will be used to update our starting model.\n", + "\n", + "> **NOTE**: Since we did not specify any smoothing lenghts (PAR.SMOOTH_H and PAR.SMOOTH_V), no smoothing of the gradient has occurred. \n", + "\n", + "Using the gradient-descent optimization algorithm, SeisFlows will now compute a search direction that will be used in the line search to search for a best fitting model which optimally reduces the objective function. We can take a look at where SeisFlows has stored the information relating to kernel generation and the optimization computation." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gradient kernels misfit_kernel model residuals.txt\r\n" + ] + } + ], + "source": [ + "# Gradient evaluation files are stored here, the kernels are stored separately from the gradient incase\n", + "# the user wants to manually manipulate them\n", + "! ls scratch/eval_grad" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "proc000000_vp_kernel.bin proc000000_vs_kernel.bin\r\n" + ] + } + ], + "source": [ + "# SeisFlows3 stores all kernels and gradient information as SPECFEM binary (.bin) files\n", + "! ls scratch/eval_grad/gradient" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "001 002 003\r\n" + ] + } + ], + "source": [ + "# Kernels are stored on a per-event basis, and summed together (sum/). If smoothing was performed, \n", + "# we would see both smoothed and unsmoothed versions of the misfit kernel\n", + "! ls scratch/eval_grad/kernels" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "checkpoint.npz\tf_new.txt g_new.npz m_new.npz\r\n" + ] + } + ], + "source": [ + "# We can see that some new values have been stored in prepartion for the line search,\n", + "# including g_new (current gradient) and p_new (current search direction). These are also\n", + "# stored as vector NumPy arrays (.npy files)\n", + "! ls scratch/optimize" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-1.18126331e-12 2.40273470e-12 3.97045036e-11 ... 9.62017688e-11\n", + " 4.21140102e-11 3.96825021e-12]]\n" + ] + } + ], + "source": [ + "g_new = np.load(\"scratch/optimize/g_new.npz\")\n", + "print(g_new[\"vs_kernel\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--------------------\n", + "#### 3c. Line search and model update\n", + "\n", + "Let's finish off the inversion by running through the line search, which will generate new models using the\n", + "gradient, evaluate the objective function by running forward simulations, and comparing the evaluated objective function with the value obtained in __evalaute_initial_misfit__. \n", + "\n", + "Satisfactory reduction in the objective function will result in a termination of the line search. We are using a bracketing line search here [(Modrak et al. 2018)](https://academic.oup.com/gji/article/206/3/1864/2583505), which requires finding models which both increase and decrease the misfit with respect to the initial evaluation. Therefore it takes atleast two trial steps to complete the line search." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "stop_after: evaluate_gradient_from_kernels -> perform_line_search\r\n" + ] + } + ], + "source": [ + "! seisflows par stop_after perform_line_search # We don't want to run the finalize_iteration argument so that we can explore the dir" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-08-16 14:41:12 (D) | setting iteration==1 from state file\n", + "2022-08-16 14:41:12 (I) | \n", + "================================================================================\n", + " SETTING UP INVERSION WORKFLOW \n", + "================================================================================\n", + "2022-08-16 14:41:18 (D) | running setup for module 'system.Workstation'\n", + "2022-08-16 14:41:21 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/sflog_003.txt\n", + "2022-08-16 14:41:21 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/parameters_003.yaml\n", + "2022-08-16 14:41:21 (D) | running setup for module 'solver.Specfem2D'\n", + "2022-08-16 14:41:21 (I) | initializing 3 solver directories\n", + "2022-08-16 14:41:22 (D) | running setup for module 'preprocess.Default'\n", + "2022-08-16 14:41:24 (D) | running setup for module 'optimize.Gradient'\n", + "2022-08-16 14:41:26 (I) | re-loading optimization module from checkpoint\n", + "2022-08-16 14:41:28 (I) | re-loading optimization module from checkpoint\n", + "2022-08-16 14:41:28 (I) | \n", + "////////////////////////////////////////////////////////////////////////////////\n", + " RUNNING ITERATION 01 \n", + "////////////////////////////////////////////////////////////////////////////////\n", + "2022-08-16 14:41:28 (I) | \n", + "================================================================================\n", + " RUNNING INVERSION WORKFLOW \n", + "================================================================================\n", + "2022-08-16 14:41:28 (I) | 'evaluate_initial_misfit' has already been run, skipping\n", + "2022-08-16 14:41:28 (I) | 'run_adjoint_simulations' has already been run, skipping\n", + "2022-08-16 14:41:28 (I) | 'postprocess_event_kernels' has already been run, skipping\n", + "2022-08-16 14:41:28 (I) | 'evaluate_gradient_from_kernels' has already been run, skipping\n", + "2022-08-16 14:41:28 (I) | initializing 'bracket'ing line search\n", + "2022-08-16 14:41:28 (I) | enforcing max step length safeguard\n", + "2022-08-16 14:41:28 (D) | step length(s) = 0.00E+00\n", + "2022-08-16 14:41:28 (D) | misfit val(s) = 1.28E-03\n", + "2022-08-16 14:41:28 (I) | try: first evaluation, attempt guess step length, alpha=9.08E+11\n", + "2022-08-16 14:41:28 (I) | try: applying initial step length safegaurd as alpha has exceeded maximum step length, alpha_new=1.44E+10\n", + "2022-08-16 14:41:28 (D) | overwriting initial step length, alpha_new=2.32E+09\n", + "2022-08-16 14:41:28 (I) | trial model 'm_try' parameters: \n", + "2022-08-16 14:41:28 (I) | 5800.00 <= vp <= 5800.00\n", + "2022-08-16 14:41:28 (I) | 3244.51 <= vs <= 3790.00\n", + "2022-08-16 14:41:29 (I) | \n", + "LINE SEARCH STEP COUNT 01\n", + "--------------------------------------------------------------------------------\n", + "2022-08-16 14:41:29 (I) | evaluating objective function for source 001\n", + "2022-08-16 14:41:29 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:41:33 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:41:33 (I) | evaluating objective function for source 002\n", + "2022-08-16 14:41:33 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:41:36 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:41:36 (I) | evaluating objective function for source 003\n", + "2022-08-16 14:41:36 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:41:40 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:41:40 (D) | misfit for trial model (f_try) == 8.65E-04\n", + "2022-08-16 14:41:40 (D) | step length(s) = 0.00E+00, 2.32E+09\n", + "2022-08-16 14:41:40 (D) | misfit val(s) = 1.28E-03, 8.65E-04\n", + "2022-08-16 14:41:40 (I) | try: misfit not bracketed, increasing step length using golden ratio, alpha=3.76E+09\n", + "2022-08-16 14:41:40 (I) | line search model 'm_try' parameters: \n", + "2022-08-16 14:41:40 (I) | 5800.00 <= vp <= 5800.00\n", + "2022-08-16 14:41:40 (I) | 3086.61 <= vs <= 3969.23\n", + "2022-08-16 14:41:40 (I) | trial step unsuccessful. re-attempting line search\n", + "2022-08-16 14:41:40 (I) | \n", + "LINE SEARCH STEP COUNT 02\n", + "--------------------------------------------------------------------------------\n", + "2022-08-16 14:41:40 (I) | evaluating objective function for source 001\n", + "2022-08-16 14:41:40 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:41:44 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:41:44 (I) | evaluating objective function for source 002\n", + "2022-08-16 14:41:44 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:41:48 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:41:48 (I) | evaluating objective function for source 003\n", + "2022-08-16 14:41:48 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:41:52 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:41:52 (D) | misfit for trial model (f_try) == 1.73E-03\n", + "2022-08-16 14:41:52 (D) | step length(s) = 0.00E+00, 2.32E+09, 3.76E+09\n", + "2022-08-16 14:41:52 (D) | misfit val(s) = 1.28E-03, 8.65E-04, 1.73E-03\n", + "2022-08-16 14:41:52 (I) | try: bracket acceptable but step length unreasonable attempting to re-adjust step length alpha=1.59E+09\n", + "2022-08-16 14:41:52 (I) | line search model 'm_try' parameters: \n", + "2022-08-16 14:41:52 (I) | 5800.00 <= vp <= 5800.00\n", + "2022-08-16 14:41:52 (I) | 3325.01 <= vs <= 3698.63\n", + "2022-08-16 14:41:52 (I) | trial step unsuccessful. re-attempting line search\n", + "2022-08-16 14:41:52 (I) | \n", + "LINE SEARCH STEP COUNT 03\n", + "--------------------------------------------------------------------------------\n", + "2022-08-16 14:41:52 (I) | evaluating objective function for source 001\n", + "2022-08-16 14:41:52 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:41:56 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:41:56 (I) | evaluating objective function for source 002\n", + "2022-08-16 14:41:56 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:42:00 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:42:00 (I) | evaluating objective function for source 003\n", + "2022-08-16 14:42:00 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:42:03 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:42:03 (D) | misfit for trial model (f_try) == 2.59E-03\n", + "2022-08-16 14:42:03 (D) | step length(s) = 0.00E+00, 1.59E+09, 2.32E+09, 3.76E+09\n", + "2022-08-16 14:42:03 (D) | misfit val(s) = 1.28E-03, 2.59E-03, 8.65E-04, 1.73E-03\n", + "2022-08-16 14:42:03 (I) | try: bracket acceptable but step length unreasonable attempting to re-adjust step length alpha=2.82E+09\n", + "2022-08-16 14:42:03 (I) | line search model 'm_try' parameters: \n", + "2022-08-16 14:42:03 (I) | 5800.00 <= vp <= 5800.00\n", + "2022-08-16 14:42:03 (I) | 3189.77 <= vs <= 3852.13\n", + "2022-08-16 14:42:03 (I) | trial step unsuccessful. re-attempting line search\n", + "2022-08-16 14:42:03 (I) | \n", + "LINE SEARCH STEP COUNT 04\n", + "--------------------------------------------------------------------------------\n", + "2022-08-16 14:42:03 (I) | evaluating objective function for source 001\n", + "2022-08-16 14:42:03 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:42:07 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:42:07 (I) | evaluating objective function for source 002\n", + "2022-08-16 14:42:07 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:42:11 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:42:11 (I) | evaluating objective function for source 003\n", + "2022-08-16 14:42:11 (D) | running forward simulation with 'Specfem2D'\n", + "2022-08-16 14:42:15 (D) | quantifying misfit with 'Default'\n", + "2022-08-16 14:42:15 (D) | misfit for trial model (f_try) == 3.46E-03\n", + "2022-08-16 14:42:15 (D) | step length(s) = 0.00E+00, 1.59E+09, 2.32E+09, 2.82E+09, 3.76E+09\n", + "2022-08-16 14:42:15 (D) | misfit val(s) = 1.28E-03, 2.59E-03, 8.65E-04, 3.46E-03, 1.73E-03\n", + "2022-08-16 14:42:15 (I) | pass: bracket acceptable and step length reasonable. returning minimum line search misfit.\n", + "2022-08-16 14:42:15 (I) | line search model 'm_try' parameters: \n", + "2022-08-16 14:42:15 (I) | 5800.00 <= vp <= 5800.00\n", + "2022-08-16 14:42:15 (I) | 3244.51 <= vs <= 3790.00\n", + "2022-08-16 14:42:15 (I) | trial step successful. finalizing line search\n", + "2022-08-16 14:42:15 (I) | \n", + "FINALIZING LINE SEARCH\n", + "--------------------------------------------------------------------------------\n", + "2022-08-16 14:42:15 (I) | writing optimization stats\n", + "2022-08-16 14:42:15 (I) | renaming current (new) optimization vectors as previous model (old)\n", + "2022-08-16 14:42:15 (I) | setting accepted trial model (try) as current model (new)\n", + "2022-08-16 14:42:15 (I) | misfit of accepted trial model is f=8.645E-04\n", + "2022-08-16 14:42:15 (I) | resetting line search step count to 0\n", + "2022-08-16 14:42:15 (I) | stop workflow at `stop_after`: perform_line_search\n" + ] + } + ], + "source": [ + "! seisflows submit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the log statements above, we can see that the SeisFlows line search required 4 trial steps, where it modified values of Vs (shear-wave velocity) until satisfactory reduction in the objective function was met. This was the final step in the iteration, and so the finalization of the line search made preparations for a subsequent iteration. " + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "alpha.txt\tf_new.txt f_try.txt m_new.npz output_optim.txt\r\n", + "checkpoint.npz\tf_old.txt g_old.npz m_old.npz p_old.npz\r\n" + ] + } + ], + "source": [ + "# We can see that we have 'new' and 'old' values for each of the optimization values,\n", + "# representing the previous model (M00) and the current model (M01).\n", + "! ls scratch/optimize" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step_count,step_length,gradient_norm_L1,gradient_norm_L2,misfit,if_restarted,slope,theta\r\n", + "04,2.323E+09,9.243E-05,1.049E-06,1.279E-03,0,8.263E-13,0.000E+00\r\n" + ] + } + ], + "source": [ + "# The stats/ directory contains text files describing the optimization/line search\n", + "! cat scratch/optimize/output_optim.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Conclusions\n", + "\n", + "We've now seen how SeisFlows runs an __Inversion__ workflow using the __Specfem2D__ solver on a __Workstation__ system. More or less, this is all you need to run SeisFlows with any combination of modules. The specificities of a system or numerical solver are already handled internally by SeisFlows, so if you want to use Specmfe3D_Cartesian as your solver, you would only need to run `seisflows par solver specfem3d` at the beginning of your workflow (you will also need to set up your Specfem3D models, similar to what we did for Specfem2D here). To run on a slurm system like Chinook (University of Alaska Fairbanks), you can run `seisflows par system chinook`. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/notebooks/specfem2d_example.ipynb b/docs/notebooks/specfem2d_example.ipynb index 3a85c76b..7ad00282 100644 --- a/docs/notebooks/specfem2d_example.ipynb +++ b/docs/notebooks/specfem2d_example.ipynb @@ -43,7 +43,7 @@ "metadata": {}, "source": [ ".. note::\n", - " Example number 1 is meant to FAIL during the line search of Iteration #2, after exceeding the maximum allowable line search step count. This is meant to illustrate line search behavior and allow the User to explore a working directory mid-workflow." + " Example number 1 is meant to **FAIL** during the line search of Iteration #2, after exceeding the maximum allowable line search step count. This is meant to illustrate line search behavior and allow the User to explore a working directory mid-workflow." ] }, { @@ -118,6 +118,58 @@ "! seisflows examples run 1 --specfem2d_repo path/to/specfem2d" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A successfully completed example problem will end with the following log messages:" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + ".. code:: bash\n", + " ...\n", + " 2022-08-25 17:29:16 (I) | 5800.00 <= vp <= 5800.00\n", + " 2022-08-25 17:29:16 (I) | 3236.17 <= vs <= 3802.01\n", + " 2022-08-25 17:29:16 (I) | trial step unsuccessful. re-attempting line search\n", + " 2022-08-25 17:29:16 (I) | \n", + " LINE SEARCH STEP COUNT 06\n", + " --------------------------------------------------------------------------------\n", + " 2022-08-25 17:29:16 (I) | evaluating objective function for source 001\n", + " 2022-08-25 17:29:16 (D) | running forward simulation with 'Specfem2D'\n", + " 2022-08-25 17:29:20 (D) | quantifying misfit with 'Default'\n", + " 2022-08-25 17:29:20 (I) | evaluating objective function for source 002\n", + " 2022-08-25 17:29:20 (D) | running forward simulation with 'Specfem2D'\n", + " 2022-08-25 17:29:24 (D) | quantifying misfit with 'Default'\n", + " 2022-08-25 17:29:24 (I) | evaluating objective function for source 003\n", + " 2022-08-25 17:29:24 (D) | running forward simulation with 'Specfem2D'\n", + " 2022-08-25 17:29:28 (D) | quantifying misfit with 'Default'\n", + " 2022-08-25 17:29:28 (D) | misfit for trial model (f_try) == 7.53E-03\n", + " 2022-08-25 17:29:28 (D) | step length(s) = 0.00E+00, 1.47E+08, 2.95E+08, 5.89E+08, 1.18E+09, 2.36E+09, 4.72E+09\n", + " 2022-08-25 17:29:28 (D) | misfit val(s) = 8.65E-04, 7.53E-03, 6.28E-03, 5.02E-03, 3.77E-03, 2.51E-03, 1.26E-03\n", + " 2022-08-25 17:29:28 (I) | fail: bracketing line search has failed to reduce the misfit before exceeding `step_count_max`=5\n", + " 2022-08-25 17:29:28 (D) | checking gradient/search direction angle, theta: 0.000\n", + " 2022-08-25 17:29:28 (C) | \n", + " ================================================================================\n", + " LINE SEARCH FAILED \n", + " ////////////////// \n", + " Line search has failed to reduce the misfit and has run out of fallback options.\n", + " Aborting inversion.\n", + " ================================================================================\n", + " \n", + " \n", + "Using the `working directory documentation page `__ you can figure out how to navigate around and look at the results of our small inversion problem. We will have a look at a few of the files and directories here." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -193,1599 +245,6 @@ "source": [ "! seisflows examples run 2 -r path/to/specfem2d" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "--------------\n", - "## Option 2: Manual run\n", - "\n", - "The notebook below details a walkthrough of Example \\#1 shown above. This is meant for those who want to understand what is going on under the hood. You are welcome to follow along on your workstation. The following Table of Contents outlines the steps we will take in this tutorial:\n", - "\n" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - ".. warning:: \n", - " Navigation links will not work outside of Jupyter. Please use the navigation bar to the left." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "1. __[Setup SPECFEM2D](#1.-Setup-SPECFEM2D)__ \n", - " a. [Download and compile codebase](#1a.-Download-and-compile-codebase*) \n", - " b. [Create a separate SPECFEM2D working directory](#1b.-Create-a-separate-SPECFEM2D-working-directory) \n", - " c. [Generate initial and target models](#1c.-Generate-initial-and-target-models) \n", - "2. __[Initialize SeisFlows (SF)](#2.-Initialize-SeisFlows-(SF))__ \n", - " a. [SeisFlows working directory and parameter file](#2a.-SF-working-directory-and-parameter-file) \n", - "3. __[Run SeisFlows](#2.-Run-SeisFlows)__ \n", - " a. [Forward simulations](#3a.-Forward-simulations) \n", - " b. [Exploring the SeisFlows directory structure](#3b.-Exploring-the-SF-directory-structure) \n", - " c. [Adjoint simulations](#3c.-Adjoint-simulations) \n", - " d. [Line search and model update](#3d.-Line-search-and-model-update) \n", - "4. __[Conclusions](#4.-Conclusions)__ " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Setup SPECFEM2D \n", - "#### 1a. Download and compile codebase (optional)\n", - "\n", - "> **NOTE**: If you have already downloaded and compiled SPECFEM2D, you can skip most of this subsection (1a). However you will need to edit the first two paths in the following cell (WORKDIR and SPECFEM2D_ORIGINAL), and execute the path structure defined in the cell.\n", - "\n", - "First we'll download and compile SPECFEM2D to generate the binaries necessary to run our simulations. We will then populate a new SPECFEM2D working directory that will be used by SeisFlows. We'll use to Python OS module to do our filesystem processes just to keep everything in Python, but this can easily be accomplished in bash." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import glob\n", - "import shutil\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# vvv USER MUST EDIT THE FOLLOWING PATHS vvv\n", - "WORKDIR = \"/home/bchow/Work/scratch\" \n", - "SPECFEM2D = \"/home/bchow/REPOSITORIES/specfem2d\"\n", - "# where WORKDIR: points to your own working directory\n", - "# and SPECFEM2D: points to an existing specfem2D repository if available (if not set as '')\n", - "# ^^^ USER MUST EDIT THE FOLLOWING PATHS ^^^\n", - "# ======================================================================================================\n", - "\n", - "# Distribute the necessary file structure of the SPECFEM2D repository that we will downloaded/reference\n", - "SPECFEM2D_ORIGINAL = os.path.join(WORKDIR, \"specfem2d\")\n", - "SPECFEM2D_BIN_ORIGINAL = os.path.join(SPECFEM2D_ORIGINAL, \"bin\")\n", - "SPECFEM2D_DATA_ORIGINAL = os.path.join(SPECFEM2D_ORIGINAL, \"DATA\")\n", - "TAPE_2007_EXAMPLE = os.path.join(SPECFEM2D_ORIGINAL, \"EXAMPLES\", \"Tape2007\")\n", - "\n", - "# The SPECFEM2D working directory that we will create separate from the downloaded repo\n", - "SPECFEM2D_WORKDIR = os.path.join(WORKDIR, \"specfem2d_workdir\")\n", - "SPECFEM2D_BIN = os.path.join(SPECFEM2D_WORKDIR, \"bin\")\n", - "SPECFEM2D_DATA = os.path.join(SPECFEM2D_WORKDIR, \"DATA\")\n", - "SPECFEM2D_OUTPUT = os.path.join(SPECFEM2D_WORKDIR, \"OUTPUT_FILES\")\n", - "\n", - "# Pre-defined locations of velocity models we will generate using the solver\n", - "SPECFEM2D_MODEL_INIT = os.path.join(SPECFEM2D_WORKDIR, \"OUTPUT_FILES_INIT\")\n", - "SPECFEM2D_MODEL_TRUE = os.path.join(SPECFEM2D_WORKDIR, \"OUTPUT_FILES_TRUE\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Existing SPECMFE2D respository found, symlinking to working directory\n" - ] - } - ], - "source": [ - "# Download SPECFEM2D from GitHub, devel branch for latest codebase OR symlink from existing repo\n", - "if not os.path.exists(WORKDIR):\n", - " os.makedirs(WORKDIR)\n", - "os.chdir(WORKDIR)\n", - "\n", - "if os.path.exists(\"specfem2d\"):\n", - " print(\"SPECFEM2D repository already found, you may skip this subsection\")\n", - " pass\n", - "elif os.path.exists(SPECFEM2D):\n", - " print(\"Existing SPECMFE2D respository found, symlinking to working directory\")\n", - " os.symlink(SPECFEM2D, \"./specfem2d\")\n", - "else:\n", - " print(\"Cloning respository from GitHub\")\n", - " ! git clone --recursive --branch devel https://github.com/geodynamics/specfem2d.git" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Compile SPECFEM2D to generate the Makefile\n", - "os.chdir(SPECFEM2D_ORIGINAL)\n", - "if not os.path.exists(\"./config.log\"):\n", - " os.system(\"./configure\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Run make to generate SPECFEM2D binaries\n", - "if not os.path.exists(\"bin\"):\n", - " os.system(\"make all\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/bchow/REPOSITORIES/specfem2d\n", - "xadj_seismogram\t\t xconvolve_source_timefunction xspecfem2D\n", - "xcheck_quality_external_mesh xmeshfem2D\t\t xsum_kernels\n", - "xcombine_sem\t\t xsmooth_sem\n" - ] - } - ], - "source": [ - "# Check out the binary files that have been created\n", - "os.chdir(SPECFEM2D_ORIGINAL)\n", - "! pwd\n", - "! ls bin/" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 1b. Create a separate SPECFEM2D working directory\n", - "\n", - "Next we'll create a new SPECFEM2D working directory, separate from the original repository. The intent here is to isolate the original SPECFEM2D repository from our working state, to protect it from things like accidental file deletions or manipulations. This is not a mandatory step for using SeisFlows, but it helps keep file structure clean in the long run, and is the SeisFlows3 dev team's preferred method of using SPECFEM. " - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - ".. note::\n", - " All SPECFEM2D/3D/3D_GLOBE need to run successfully are the bin/, DATA/, and OUTPUT_FILES/ directories. Everything else in the repository is not mandatory for running binaries." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this tutorial we will be using the [Tape2007 example problem](https://github.com/geodynamics/specfem2d/tree/devel/EXAMPLES/Tape2007) to define our __DATA/__ directory (last tested 8/15/22, bdba4389)." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/bchow/Work/scratch/specfem2d_workdir\n", - "bin DATA\n" - ] - } - ], - "source": [ - "# Incase we've run this docs page before, delete the working directory before remaking\n", - "if os.path.exists(SPECFEM2D_WORKDIR):\n", - " shutil.rmtree(SPECFEM2D_WORKDIR)\n", - "\n", - "os.mkdir(SPECFEM2D_WORKDIR)\n", - "os.chdir(SPECFEM2D_WORKDIR)\n", - "\n", - "# Copy the binary files incase we update the source code. These can also be symlinked.\n", - "shutil.copytree(SPECFEM2D_BIN_ORIGINAL, \"bin\")\n", - "\n", - "# Copy the DATA/ directory because we will be making edits here frequently and it's useful to\n", - "# retain the original files for reference. We will be running one of the example problems: Tape2007\n", - "shutil.copytree(os.path.join(TAPE_2007_EXAMPLE, \"DATA\"), \"DATA\")\n", - "\n", - "! pwd\n", - "! ls" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " -------------------------------------------------------------------------------\r\n", - " -------------------------------------------------------------------------------\r\n", - " D a t e : 16 - 08 - 2022 T i m e : 14:26:37\r\n", - " -------------------------------------------------------------------------------\r\n", - " -------------------------------------------------------------------------------\r\n", - "\r\n", - "see results in directory: OUTPUT_FILES/\r\n", - "\r\n", - "done\r\n", - "Tue Aug 16 02:26:37 PM AKDT 2022\r\n" - ] - } - ], - "source": [ - "# Run the Tape2007 example to make sure SPECFEM2D is working as expected\n", - "os.chdir(TAPE_2007_EXAMPLE)\n", - "! ./run_this_example.sh > output_log.txt\n", - "\n", - "assert(os.path.exists(\"OUTPUT_FILES/forward_image000004800.jpg\")), \\\n", - " (f\"Example did not run, the remainder of this docs page will likely not work.\"\n", - " f\"Please check the following directory: {TAPE_2007_EXAMPLE}\")\n", - "\n", - "! tail output_log.txt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "------------------------------------\n", - "Now we need to manually set up our SPECFEM2D working directory. As mentioned in the previous cell, the only required elements of this working directory are the following (these files will form the basis for how SeisFlows3 operates within the SPECFEM2D framework):\n", - "\n", - "1. __bin/__ directory containing SPECFEM2D binaries\n", - "2. __DATA/__ directory containing SOURCE and STATION files, as well as a SPECFEM2D Par_file\n", - "3. __OUTPUT_FILES/proc??????_*.bin__ files which define the starting (and target) models" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - ".. note:: \n", - " This file structure is the same for all versions of SPECFEM (2D/3D/3D_GLOBE)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "interfaces_Tape2007.dat\t\t SOURCE_003 SOURCE_012 SOURCE_021\r\n", - "model_velocity.dat_checker\t SOURCE_004 SOURCE_013 SOURCE_022\r\n", - "Par_file\t\t\t SOURCE_005 SOURCE_014 SOURCE_023\r\n", - "Par_file_Tape2007_132rec_checker SOURCE_006 SOURCE_015 SOURCE_024\r\n", - "Par_file_Tape2007_onerec\t SOURCE_007 SOURCE_016 SOURCE_025\r\n", - "proc000000_model_velocity.dat_input SOURCE_008 SOURCE_017 STATIONS\r\n", - "SOURCE\t\t\t\t SOURCE_009 SOURCE_018 STATIONS_checker\r\n", - "SOURCE_001\t\t\t SOURCE_010 SOURCE_019\r\n", - "SOURCE_002\t\t\t SOURCE_011 SOURCE_020\r\n" - ] - } - ], - "source": [ - "# First we will set the correct SOURCE and STATION files.\n", - "# This is the same task as shown in ./run_this_example.sh\n", - "os.chdir(SPECFEM2D_DATA)\n", - "\n", - "# Symlink source 001 as our main source\n", - "if os.path.exists(\"SOURCE\"):\n", - " os.remove(\"SOURCE\")\n", - "os.symlink(\"SOURCE_001\", \"SOURCE\")\n", - "\n", - "# Copy the correct Par_file so that edits do not affect the original file\n", - "if os.path.exists(\"Par_file\"):\n", - " os.remove(\"Par_file\")\n", - "shutil.copy(\"Par_file_Tape2007_onerec\", \"Par_file\")\n", - "\n", - "! ls" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 1c. Generate initial and target models\n", - "\n", - "Since we're doing a synthetic-synthetic inversion, we need to manually set up the velocity models with which we generate our synthetic waveforms. The naming conventions for these models are:\n", - "\n", - "1. __MODEL_INIT:__ The initial or starting model. Used to generate the actual synthetic seismograms. This is considered M00.\n", - "2. __MODEL_TRUE:__ The target or true model. Used to generate 'data' (also synthetic). This is the reference model that our inversion is trying to resolve.\n", - "\n", - "The starting model is defined as a homogeneous halfspace uin the Tape2007 example problem. We will need to run both `xmeshfem2D` and `xspecfem2D` to generate the required velocity model database files. We will generate our target model by slightly perturbing the parameters of the initial model." - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - ".. note::\n", - " We can use the SeisFlows3 command line option `seisflows sempar` to directly edit the SPECFEM2D Par_file in the command line. This will work for the SPECFEM3D Par_file as well." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "setup_with_binary_database: 0 -> 1\n", - "SAVE_MODEL: default -> binary\n", - "save_ASCII_kernels: .true. -> .false.\n" - ] - } - ], - "source": [ - "os.chdir(SPECFEM2D_DATA)\n", - "\n", - "# Ensure that SPECFEM2D outputs the velocity model in the expected binary format\n", - "! seisflows sempar setup_with_binary_database 1 # allow creation of .bin files\n", - "! seisflows sempar save_model binary # output model in .bin database format\n", - "! seisflows sempar save_ascii_kernels .false. # output kernels in .bin format, not ASCII" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "bin DATA OUTPUT_FILES\r\n" - ] - } - ], - "source": [ - "# SPECFEM requires that we create the OUTPUT_FILES directory before running\n", - "os.chdir(SPECFEM2D_WORKDIR)\n", - "\n", - "if os.path.exists(SPECFEM2D_OUTPUT):\n", - " shutil.rmtree(SPECFEM2D_OUTPUT)\n", - " \n", - "os.mkdir(SPECFEM2D_OUTPUT)\n", - "\n", - "! ls" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " **********************************************\n", - " **** Specfem 2-D Solver - serial version ****\n", - " **********************************************\n", - "\n", - " Running Git version of the code corresponding to commit cf89366717d9435985ba852ef1d41a10cee97884\n", - " dating From Date: Mon Nov 29 23:20:51 2021 -0800\n", - "\n", - "\n", - " NDIM = 2\n", - " -------------------------------------------------------------------------------\n", - " Program SPECFEM2D: \n", - " -------------------------------------------------------------------------------\n", - " -------------------------------------------------------------------------------\n", - " Tape-Liu-Tromp (GJI 2007)\n", - " -------------------------------------------------------------------------------\n", - " -------------------------------------------------------------------------------\n", - " D a t e : 16 - 08 - 2022 T i m e : 14:26:52\n", - " -------------------------------------------------------------------------------\n", - " -------------------------------------------------------------------------------\n" - ] - } - ], - "source": [ - "# GENERATE MODEL_INIT\n", - "os.chdir(SPECFEM2D_WORKDIR)\n", - "\n", - "# Run the mesher and solver to generate our initial model\n", - "! ./bin/xmeshfem2D > OUTPUT_FILES/mesher_log.txt\n", - "! ./bin/xspecfem2D > OUTPUT_FILES/solver_log.txt\n", - "\n", - "# Move the model files (*.bin) into the OUTPUT_FILES directory, where SeisFlows3 expects them\n", - "! mv DATA/*bin OUTPUT_FILES\n", - "\n", - "# Make sure we don't overwrite this initial model when creating our target model in the next step\n", - "! mv OUTPUT_FILES OUTPUT_FILES_INIT\n", - "\n", - "! head OUTPUT_FILES_INIT/solver_log.txt\n", - "! tail OUTPUT_FILES_INIT/solver_log.txt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "-----------------------------\n", - "\n", - "Now we want to perturb the initial model to create our target model (__MODEL_TRUE__). The seisflows command line subargument `seisflows sempar velocity_model` will let us view and edit the velocity model. You can also do this manually by editing the Par_file directly. " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "VELOCITY_MODEL:\r\n", - "\r\n", - "1 1 2600.d0 5800.d0 3500.0d0 0 0 10.d0 10.d0 0 0 0 0 0 0\r\n", - "->\r\n", - "1 1 2600.d0 5900.d0 3550.0d0 0 0 10.d0 10.d0 0 0 0 0 0 0\r\n" - ] - } - ], - "source": [ - "# GENERATE MODEL_TRUE\n", - "os.chdir(SPECFEM2D_DATA)\n", - "\n", - "# Edit the Par_file by increasing velocities by ~10% \n", - "! seisflows sempar velocity_model '1 1 2600.d0 5900.d0 3550.0d0 0 0 10.d0 10.d0 0 0 0 0 0 0'" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " **********************************************\n", - " **** Specfem 2-D Solver - serial version ****\n", - " **********************************************\n", - "\n", - " Running Git version of the code corresponding to commit cf89366717d9435985ba852ef1d41a10cee97884\n", - " dating From Date: Mon Nov 29 23:20:51 2021 -0800\n", - "\n", - "\n", - " NDIM = 2\n", - " -------------------------------------------------------------------------------\n", - " Program SPECFEM2D: \n", - " -------------------------------------------------------------------------------\n", - " -------------------------------------------------------------------------------\n", - " Tape-Liu-Tromp (GJI 2007)\n", - " -------------------------------------------------------------------------------\n", - " -------------------------------------------------------------------------------\n", - " D a t e : 16 - 08 - 2022 T i m e : 14:26:52\n", - " -------------------------------------------------------------------------------\n", - " -------------------------------------------------------------------------------\n" - ] - } - ], - "source": [ - "# Re-run the mesher and solver to generate our target velocity model\n", - "os.chdir(SPECFEM2D_WORKDIR)\n", - "\n", - "# Make sure the ./OUTPUT_FILES directory exists since we moved the old one\n", - "if os.path.exists(SPECFEM2D_OUTPUT):\n", - " shutil.rmtree(SPECFEM2D_OUTPUT)\n", - "os.mkdir(SPECFEM2D_OUTPUT)\n", - "\n", - "# Run the binaries to generate MODEL_TRUE\n", - "! ./bin/xmeshfem2D > OUTPUT_FILES/mesher_log.txt\n", - "! ./bin/xspecfem2D > OUTPUT_FILES/solver_log.txt\n", - "\n", - "# Move all the relevant files into OUTPUT_FILES \n", - "! mv ./DATA/*bin OUTPUT_FILES\n", - "! mv OUTPUT_FILES OUTPUT_FILES_TRUE\n", - "\n", - "! head OUTPUT_FILES_INIT/solver_log.txt\n", - "! tail OUTPUT_FILES_INIT/solver_log.txt" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "bin DATA OUTPUT_FILES_INIT OUTPUT_FILES_TRUE\r\n" - ] - } - ], - "source": [ - "# Great, we have all the necessary SPECFEM files to run our SeisFlows inversion!\n", - "! ls" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2. Initialize SeisFlows (SF)\n", - "In this Section we will look at a SeisFlows working directory, parameter file, and working state.\n", - "\n", - "#### 2a. SeisFlows working directory and parameter file\n", - "\n", - "As with SPECFEM, SeisFlows requires a parameter file (__parameters.yaml__) that controls how an automated workflow will proceed. Because SeisFlows is modular, there are a large number of potential parameters which may be present in a SeisFlows parameter file, as each sub-module may have its own set of unique parameters.\n", - "\n", - "In contrast to SPECFEM's method of listing all available parameters and leaving it up the User to determine which ones are relevant to them, SeisFlows dynamically builds its parameter file based on User inputs. In this subsection we will use the built-in SeisFlows command line tools to generate and populate the parameter file. " - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - ".. note::\n", - " See the `parameter file documentation page `__ for a more in depth exploration of this central SeisFlows file." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the previous section we saw the `sempar` command in action. We can use the `-h` or help flag to list all available SiesFlows3 command line commands." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "usage: seisflows [-h] [-w [WORKDIR]] [-p [PARAMETER_FILE]]\r\n", - " {setup,configure,swap,init,submit,resume,restart,clean,par,sempar,check,print,reset,debug,examples}\r\n", - " ...\r\n", - "\r\n", - "================================================================================\r\n", - "\r\n", - " SeisFlows: Waveform Inversion Package \r\n", - "\r\n", - "================================================================================\r\n", - "\r\n", - "optional arguments:\r\n", - " -h, --help show this help message and exit\r\n", - " -w [WORKDIR], --workdir [WORKDIR]\r\n", - " The SeisFlows working directory, default: cwd\r\n", - " -p [PARAMETER_FILE], --parameter_file [PARAMETER_FILE]\r\n", - " Parameters file, default: 'parameters.yaml'\r\n", - "\r\n", - "command:\r\n", - " Available SeisFlows arguments and their intended usages\r\n", - "\r\n", - " setup Setup working directory from scratch\r\n", - " configure Fill parameter file with defaults\r\n", - " swap Swap module parameters in an existing parameter file\r\n", - " init Initiate working environment\r\n", - " submit Submit initial workflow to system\r\n", - " resume Re-submit previous workflow to system\r\n", - " restart Remove current environment and submit new workflow\r\n", - " clean Remove files relating to an active working environment\r\n", - " par View and edit SeisFlows parameter file\r\n", - " sempar View and edit SPECFEM parameter file\r\n", - " check Check state of an active environment\r\n", - " print Print information related to an active environment\r\n", - " reset Reset modules within an active state\r\n", - " debug Start interactive debug environment\r\n", - " examples Look at and run pre-configured example problems\r\n", - "\r\n", - "'seisflows [command] -h' for more detailed descriptions of each command.\r\n" - ] - } - ], - "source": [ - "! seisflows -h" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "creating parameter file: parameters.yaml\n", - "parameters.yaml sflog.txt specfem2d specfem2d_workdir\n" - ] - } - ], - "source": [ - "# The command 'setup' creates the 'parameters.yaml' file that controls all of SeisFlows\n", - "# the '-f' flag removes any exist 'parameters.yaml' file that might be in the directory\n", - "os.chdir(WORKDIR)\n", - "! seisflows setup -f\n", - "! ls" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# //////////////////////////////////////////////////////////////////////////////\r\n", - "#\r\n", - "# SeisFlows YAML Parameter File\r\n", - "#\r\n", - "# //////////////////////////////////////////////////////////////////////////////\r\n", - "#\r\n", - "# Modules correspond to the structure of the source code, and determine\r\n", - "# SeisFlows' behavior at runtime. Each module requires its own sub-parameters.\r\n", - "#\r\n", - "# .. rubric::\r\n", - "# - To determine available options for modules listed below, run:\r\n", - "# > seisflows print modules\r\n", - "# - To auto-fill with docstrings and default values (recommended), run:\r\n", - "# > seisflows configure\r\n", - "# - To set values as NoneType, use: null\r\n", - "# - To set values as infinity, use: inf\r\n", - "#\r\n", - "# MODULES\r\n", - "# ///////\r\n", - "# workflow (str): The types and order of functions for running SeisFlows\r\n", - "# system (str): Computer architecture of the system being used\r\n", - "# solver (str): External numerical solver to use for waveform simulations\r\n", - "# preprocess (str): Preprocessing schema for waveform data\r\n", - "# optimize (str): Optimization algorithm for the inverse problem\r\n", - "# ==============================================================================\r\n", - "workflow: forward\r\n", - "system: workstation\r\n", - "solver: specfem2d\r\n", - "preprocess: default\r\n", - "optimize: gradient\r\n" - ] - } - ], - "source": [ - "# Let's have a look at this file, which has not yet been populated\n", - "! cat parameters.yaml" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " SEISFLOWS MODULES \r\n", - " ///////////////// \r\n", - "'-': module, '*': class\r\n", - "\r\n", - "- workflow\r\n", - " * forward\r\n", - " * inversion\r\n", - " * migration\r\n", - "- system\r\n", - " * chinook\r\n", - " * cluster\r\n", - " * frontera\r\n", - " * lsf\r\n", - " * maui\r\n", - " * slurm\r\n", - " * workstation\r\n", - "- solver\r\n", - " * specfem\r\n", - " * specfem2d\r\n", - " * specfem3d\r\n", - " * specfem3d_globe\r\n", - "- preprocess\r\n", - " * default\r\n", - " * pyaflowa\r\n", - "- optimize\r\n", - " * LBFGS\r\n", - " * NLCG\r\n", - " * gradient\r\n" - ] - } - ], - "source": [ - "# We can use the `seisflows print modules` command to list out the available options \n", - "! seisflows print modules" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "workflow: forward -> inversion\n", - "# //////////////////////////////////////////////////////////////////////////////\n", - "#\n", - "# SeisFlows YAML Parameter File\n", - "#\n", - "# //////////////////////////////////////////////////////////////////////////////\n", - "#\n", - "# Modules correspond to the structure of the source code, and determine\n", - "# SeisFlows' behavior at runtime. Each module requires its own sub-parameters.\n", - "#\n", - "# .. rubric::\n", - "# - To determine available options for modules listed below, run:\n", - "# > seisflows print modules\n", - "# - To auto-fill with docstrings and default values (recommended), run:\n", - "# > seisflows configure\n", - "# - To set values as NoneType, use: null\n", - "# - To set values as infinity, use: inf\n", - "#\n", - "# MODULES\n", - "# ///////\n", - "# workflow (str): The types and order of functions for running SeisFlows\n", - "# system (str): Computer architecture of the system being used\n", - "# solver (str): External numerical solver to use for waveform simulations\n", - "# preprocess (str): Preprocessing schema for waveform data\n", - "# optimize (str): Optimization algorithm for the inverse problem\n", - "# ==============================================================================\n", - "workflow: inversion\n", - "system: workstation\n", - "solver: specfem2d\n", - "preprocess: default\n", - "optimize: gradient\n" - ] - } - ], - "source": [ - "# For this example, we can use most of the default modules, however we need to \n", - "# change the SOLVER module to let SeisFlows know we're using SPECFEM2D (as opposed to 3D)\n", - "! seisflows par workflow inversion\n", - "! cat parameters.yaml" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "-------------------------\n", - "The `seisflows configure` command populates the parameter file based on the chosen modules. SeisFlows will attempt to fill in all parameters with reasonable default values. Docstrings above each module show descriptions and available options for each of these parameters. \n", - "\n", - "In the follownig cell we will use the `seisflows par` command to edit the parameters.yaml file directly, replacing some default parameters with our own values. Comments next to each evaluation describe the choice for each." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# //////////////////////////////////////////////////////////////////////////////\r\n", - "#\r\n", - "# SeisFlows YAML Parameter File\r\n", - "#\r\n", - "# //////////////////////////////////////////////////////////////////////////////\r\n", - "#\r\n", - "# Modules correspond to the structure of the source code, and determine\r\n", - "# SeisFlows' behavior at runtime. Each module requires its own sub-parameters.\r\n", - "#\r\n", - "# .. rubric::\r\n", - "# - To determine available options for modules listed below, run:\r\n", - "# > seisflows print modules\r\n", - "# - To auto-fill with docstrings and default values (recommended), run:\r\n", - "# > seisflows configure\r\n", - "# - To set values as NoneType, use: null\r\n", - "# - To set values as infinity, use: inf\r\n", - "#\r\n", - "# MODULES\r\n", - "# ///////\r\n", - "# workflow (str): The types and order of functions for running SeisFlows\r\n", - "# system (str): Computer architecture of the system being used\r\n", - "# solver (str): External numerical solver to use for waveform simulations\r\n", - "# preprocess (str): Preprocessing schema for waveform data\r\n", - "# optimize (str): Optimization algorithm for the inverse problem\r\n", - "# ==============================================================================\r\n", - "workflow: inversion\r\n", - "system: workstation\r\n", - "solver: specfem2d\r\n", - "preprocess: default\r\n", - "optimize: gradient\r\n", - "# =============================================================================\r\n", - "#\r\n", - "# Forward Workflow\r\n", - "# ----------------\r\n", - "# Run forward solver in parallel and (optionally) calculate\r\n", - "# data-synthetic misfit and adjoint sources.\r\n", - "#\r\n", - "# Parameters\r\n", - "# ----------\r\n", - "# :type modules: list of module\r\n", - "# :param modules: instantiated SeisFlows modules which should have been\r\n", - "# generated by the function `seisflows.config.import_seisflows` with a\r\n", - "# parameter file generated by seisflows.configure\r\n", - "# :type data_case: str\r\n", - "# :param data_case: How to address 'data' in the workflow, available options:\r\n", - "# 'data': real data will be provided by the user in\r\n", - "# `path_data/{source_name}` in the same format that the solver will\r\n", - "# produce synthetics (controlled by `solver.format`) OR\r\n", - "# synthetic': 'data' will be generated as synthetic seismograms using\r\n", - "# a target model provided in `path_model_true`. If None, workflow will\r\n" - ] - } - ], - "source": [ - "! seisflows configure\n", - "! head --lines=50 parameters.yaml" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ntask: 1 -> 3\n", - "materials: acoustic -> elastic\n", - "end: 1 -> 2\n", - "data_case: data -> synthetic\n", - "components: ZNE -> Y\n", - "step_count_max: 10 -> 5\n", - "path_specfem_bin: null -> /home/bchow/Work/scratch/specfem2d_workdir/bin\n", - "path_specfem_data: null -> /home/bchow/Work/scratch/specfem2d_workdir/DATA\n", - "path_model_init: null ->\n", - "/home/bchow/Work/scratch/specfem2d_workdir/OUTPUT_FILES_INIT\n", - "path_model_true: null ->\n", - "/home/bchow/Work/scratch/specfem2d_workdir/OUTPUT_FILES_TRUE\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# EDIT THE SEISFLOWS PARAMETER FILE\n", - "! seisflows par ntask 3 # set the number of sources/events to use\n", - "! seisflows par materials elastic # update Vp and Vs during inversion\n", - "! seisflows par end 2 # final iteration -- we will only run 1\n", - "! seisflows par data_case synthetic # synthetic-synthetic means we need both INIT and TRUE models\n", - "! seisflows par components Y # this default example creates Y-component seismograms\n", - "! seisflows par step_count_max 5 # limit the number of steps in the line search\n", - "\n", - "# Use Python syntax here to access path constants\n", - "os.system(f\"seisflows par path_specfem_bin {SPECFEM2D_BIN}\") # set path to SPECFEM2D binaries\n", - "os.system(f\"seisflows par path_specfem_data {SPECFEM2D_DATA}\") # set path to SEPCFEM2D DATA/\n", - "os.system(f\"seisflows par path_model_init {SPECFEM2D_MODEL_INIT}\") # set path to INIT model\n", - "os.system(f\"seisflows par path_model_true {SPECFEM2D_MODEL_TRUE}\") # set path to TRUE model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "------------------------------\n", - "One last thing, we will need to edit the SPECFEM2D Par_file parameter `MODEL` such that `xmeshfem2d` reads our pre-built velocity models (\\*.bin files) rather than the meshing parameters defined in the Par_file." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MODEL: default -> gll\r\n" - ] - } - ], - "source": [ - "os.chdir(SPECFEM2D_DATA)\n", - "! seisflows sempar model gll" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3. Run SeisFlows\n", - "\n", - "In this Section we will run SeisFlows to generate synthetic seismograms, kernels, a gradient, and an updated velocity model.\n", - "\n", - "#### 3a. Forward simulations\n", - "\n", - "SeisFlows is an automated workflow tool, such that once we run `seisflows submit` we should not need to intervene in the workflow. However the package does allow the User flexibility in how they want the workflow to behave.\n", - "\n", - "For example, we can run our workflow in stages by taking advantage of the `stop_after` parameter. As its name suggests, `stop_after` allows us to stop a workflow prematurely so that we may stop and look at results, or debug a failing workflow.\n", - "\n", - "The `seisflows print flow` command tells us what functions we can use for the `stop_after` parameter. " - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " SEISFLOWS WORKFLOW TASK LIST \r\n", - " //////////////////////////// \r\n", - "Task list for \r\n", - "\r\n", - "1: evaluate_initial_misfit\r\n", - "2: run_adjoint_simulations\r\n", - "3: postprocess_event_kernels\r\n", - "4: evaluate_gradient_from_kernels\r\n", - "5: initialize_line_search\r\n", - "6: perform_line_search\r\n", - "7: finalize_iteration\r\n" - ] - } - ], - "source": [ - "os.chdir(WORKDIR)\n", - "! seisflows print tasks" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "-----------------------------\n", - "In the Inversion workflow, the tasks listed are described as follows:\n", - "\n", - "1. __evaluate_initial_misfit:__ \n", - " a. Prepare data for inversion by either copying data from disk or generating 'synthetic data' with MODEL_TRUE \n", - " b. Call numerical solver to run forward simulations using MODEL_INIT, generating synthetics \n", - " c. Evaluate the objective function by performing waveform comparisons \n", - " d. Prepare `run_adjoint_simulations` step by generating adjoint sources and auxiliary files\n", - "2. __run_adjoint_simulations:__ Call numerical solver to run adjoint simulation, generating kernels\n", - "3. __postprocess_event_kernels:__ Combine all event kernels into a misfit kernel. \n", - "4. __evaluate_gradient_from_kernels:__ Smooth and mask the misfit kernel to create the gradient\n", - "4. __initialize_line_search:__ Call on the optimization library to scale the gradient by a step length to compute the search direction. Prepare file structure for line search.\n", - "5. __perform_line_search:__ Perform a line search by algorithmically scaling the gradient and evaluating the misfit function (forward simulations and misfit quantification) until misfit is acceptably reduced.\n", - "6. __finalize_iteration:__ Run any finalization steps such as saving traces, kernels, gradients and models to disk, setting up SeisFlows3 for any subsequent iterations. Clean the scratch/ directory in preparation for subsequent iterations\n", - "\n", - "Let's set the `stop_after` argument to __evaluate_initial_misfit__, this will halt the workflow after the intialization step. " - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "stop_after: null -> evaluate_initial_misfit\r\n" - ] - } - ], - "source": [ - "! seisflows par stop_after evaluate_initial_misfit" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "-----------------------\n", - "Now let's run SeisFlows. There are two ways to do this: `submit` and `restart`\n", - "\n", - "1. `seisflows submit` is used to run new workflows and resume stopped or failed workflows.\n", - "2. The `restart` command is simply a convenience function that runs `clean` (to remove an active working state) and `submit` (to submit a fresh workflow). \n", - "\n", - "Since this is our first run, we'll use `seisflows submit`." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-08-16 14:32:48 (I) | \n", - "================================================================================\n", - " SETTING UP INVERSION WORKFLOW \n", - "================================================================================\n", - "2022-08-16 14:32:55 (D) | running setup for module 'system.Workstation'\n", - "2022-08-16 14:32:57 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/sflog_001.txt\n", - "2022-08-16 14:32:57 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/parameters_001.yaml\n", - "2022-08-16 14:32:57 (D) | running setup for module 'solver.Specfem2D'\n", - "2022-08-16 14:32:57 (I) | initializing 3 solver directories\n", - "2022-08-16 14:32:57 (D) | initializing solver directory source: 001\n", - "2022-08-16 14:33:04 (D) | linking source '001' as 'mainsolver'\n", - "2022-08-16 14:33:04 (D) | initializing solver directory source: 002\n", - "2022-08-16 14:33:09 (D) | initializing solver directory source: 003\n", - "2022-08-16 14:33:16 (D) | running setup for module 'preprocess.Default'\n", - "2022-08-16 14:33:16 (D) | running setup for module 'optimize.Gradient'\n", - "2022-08-16 14:33:17 (I) | no optimization checkpoint found, assuming first run\n", - "2022-08-16 14:33:17 (I) | re-loading optimization module from checkpoint\n", - "2022-08-16 14:33:17 (I) | \n", - "////////////////////////////////////////////////////////////////////////////////\n", - " RUNNING ITERATION 01 \n", - "////////////////////////////////////////////////////////////////////////////////\n", - "2022-08-16 14:33:17 (I) | \n", - "================================================================================\n", - " RUNNING INVERSION WORKFLOW \n", - "================================================================================\n", - "2022-08-16 14:33:17 (I) | \n", - "////////////////////////////////////////////////////////////////////////////////\n", - " EVALUATING MISFIT FOR INITIAL MODEL \n", - "////////////////////////////////////////////////////////////////////////////////\n", - "2022-08-16 14:33:17 (I) | checking initial model parameters\n", - "2022-08-16 14:33:17 (I) | 5800.00 <= vp <= 5800.00\n", - "2022-08-16 14:33:17 (I) | 2600.00 <= rho <= 2600.00\n", - "2022-08-16 14:33:17 (I) | 3500.00 <= vs <= 3500.00\n", - "2022-08-16 14:33:17 (I) | checking true/target model parameters\n", - "2022-08-16 14:33:17 (I) | 5900.00 <= vp <= 5900.00\n", - "2022-08-16 14:33:17 (I) | 2600.00 <= rho <= 2600.00\n", - "2022-08-16 14:33:17 (I) | 3550.00 <= vs <= 3550.00\n", - "2022-08-16 14:33:17 (I) | preparing observation data for source 001\n", - "2022-08-16 14:33:17 (I) | running forward simulation w/ target model for 001\n", - "2022-08-16 14:33:21 (I) | evaluating objective function for source 001\n", - "2022-08-16 14:33:21 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-16 14:33:25 (D) | quantifying misfit with 'Default'\n", - "2022-08-16 14:33:25 (I) | preparing observation data for source 002\n", - "2022-08-16 14:33:25 (I) | running forward simulation w/ target model for 002\n", - "2022-08-16 14:33:29 (I) | evaluating objective function for source 002\n", - "2022-08-16 14:33:29 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-16 14:33:33 (D) | quantifying misfit with 'Default'\n", - "2022-08-16 14:33:33 (I) | preparing observation data for source 003\n", - "2022-08-16 14:33:33 (I) | running forward simulation w/ target model for 003\n", - "2022-08-16 14:33:36 (I) | evaluating objective function for source 003\n", - "2022-08-16 14:33:36 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-16 14:33:40 (D) | quantifying misfit with 'Default'\n", - "2022-08-16 14:33:40 (I) | stop workflow at `stop_after`: evaluate_initial_misfit\n" - ] - } - ], - "source": [ - "! seisflows submit " - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - ".. note::\n", - " For a detailed exploration of a SeisFlows working directory, see the `working directory `__ documentation page where we explain each of the files and directories that have been generated during this workflow. Below we just look at two files which are required for our adjoint simulation, the adjoint sources (.adj) and STATIONS_ADJOINT file" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " -48.0000000 0.0000000\r\n", - " -47.9400000 0.0000000\r\n", - " -47.8800000 0.0000000\r\n", - " -47.8200000 0.0000000\r\n", - " -47.7600000 0.0000000\r\n", - " -47.7000000 0.0000000\r\n", - " -47.6400000 0.0000000\r\n", - " -47.5800000 0.0000000\r\n", - " -47.5200000 0.0000000\r\n", - " -47.4600000 0.0000000\r\n" - ] - } - ], - "source": [ - "# The adjoint source is created in the same format as the synthetics (two-column ASCII) \n", - "! head scratch/solver/001/traces/adj/AA.S0001.BXY.adj" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 3b. Adjoint simulations\n", - "\n", - "Now that we have all the required files for running an adjoint simulation (\\*.adj waveforms and STATIONS_ADJOINT file), we can continue with the SeisFlows3 Inversion workflow. No need to edit the Par_file or anything like that, SeisFlows3 will take care of that under the hood. We simply need to tell the workflow (via the parameters.yaml file) to `resume_from` the correct function. We can have a look at these functions again:" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " SEISFLOWS WORKFLOW TASK LIST \r\n", - " //////////////////////////// \r\n", - "Task list for \r\n", - "\r\n", - "1: evaluate_initial_misfit\r\n", - "2: run_adjoint_simulations\r\n", - "3: postprocess_event_kernels\r\n", - "4: evaluate_gradient_from_kernels\r\n", - "5: initialize_line_search\r\n", - "6: perform_line_search\r\n", - "7: finalize_iteration\r\n" - ] - } - ], - "source": [ - "! seisflows print tasks" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "stop_after: evaluate_initial_misfit -> evaluate_gradient_from_kernels\r\n" - ] - } - ], - "source": [ - "# We'll stop just before the line search so that we can take a look at the files \n", - "# generated during the middle tasks\n", - "! seisflows par stop_after evaluate_gradient_from_kernels" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-08-16 14:36:42 (D) | setting iteration==1 from state file\n", - "2022-08-16 14:36:42 (I) | \n", - "================================================================================\n", - " SETTING UP INVERSION WORKFLOW \n", - "================================================================================\n", - "2022-08-16 14:36:48 (D) | running setup for module 'system.Workstation'\n", - "2022-08-16 14:36:51 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/sflog_002.txt\n", - "2022-08-16 14:36:51 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/parameters_002.yaml\n", - "2022-08-16 14:36:51 (D) | running setup for module 'solver.Specfem2D'\n", - "2022-08-16 14:36:51 (I) | initializing 3 solver directories\n", - "2022-08-16 14:36:51 (D) | running setup for module 'preprocess.Default'\n", - "2022-08-16 14:36:52 (D) | running setup for module 'optimize.Gradient'\n", - "2022-08-16 14:36:53 (I) | re-loading optimization module from checkpoint\n", - "2022-08-16 14:36:54 (I) | re-loading optimization module from checkpoint\n", - "2022-08-16 14:36:54 (I) | \n", - "////////////////////////////////////////////////////////////////////////////////\n", - " RUNNING ITERATION 01 \n", - "////////////////////////////////////////////////////////////////////////////////\n", - "2022-08-16 14:36:54 (I) | \n", - "================================================================================\n", - " RUNNING INVERSION WORKFLOW \n", - "================================================================================\n", - "2022-08-16 14:36:54 (I) | 'evaluate_initial_misfit' has already been run, skipping\n", - "2022-08-16 14:36:54 (I) | \n", - "////////////////////////////////////////////////////////////////////////////////\n", - " EVALUATING EVENT KERNELS W/ ADJOINT SIMULATIONS \n", - "////////////////////////////////////////////////////////////////////////////////\n", - "2022-08-16 14:36:54 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log'\n", - "2022-08-16 14:37:05 (D) | renaming output event kernels: 'alpha' -> 'vp'\n", - "2022-08-16 14:37:05 (D) | renaming output event kernels: 'beta' -> 'vs'\n", - "2022-08-16 14:37:05 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log'\n", - "2022-08-16 14:37:16 (D) | renaming output event kernels: 'alpha' -> 'vp'\n", - "2022-08-16 14:37:16 (D) | renaming output event kernels: 'beta' -> 'vs'\n", - "2022-08-16 14:37:18 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log'\n", - "2022-08-16 14:37:29 (D) | renaming output event kernels: 'alpha' -> 'vp'\n", - "2022-08-16 14:37:29 (D) | renaming output event kernels: 'beta' -> 'vs'\n", - "2022-08-16 14:37:30 (I) | \n", - "////////////////////////////////////////////////////////////////////////////////\n", - " GENERATING/PROCESSING MISFIT KERNEL \n", - "////////////////////////////////////////////////////////////////////////////////\n", - "2022-08-16 14:37:30 (I) | combining event kernels into single misfit kernel\n", - "2022-08-16 14:37:31 (I) | scaling gradient to absolute model perturbations\n", - "2022-08-16 14:37:32 (I) | stop workflow at `stop_after`: evaluate_gradient_from_kernels\n" - ] - } - ], - "source": [ - "# We can use the `seisflows submit` command to continue an active workflow\n", - "# The state file created during the first run will tell the workflow to resume from the stopped point in the workflow\n", - "! seisflows submit " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "----------------\n", - "The function __run_adjoint_simulations()__ has run adjoint simulations to generate event kernels. The functions __postprocess_event_kernels__ and __evaluate_gradient_from_kernels__ will have summed and (optionally) smoothed the kernels to recover the gradient, which will be used to update our starting model.\n", - "\n", - "> **NOTE**: Since we did not specify any smoothing lenghts (PAR.SMOOTH_H and PAR.SMOOTH_V), no smoothing of the gradient has occurred. \n", - "\n", - "Using the gradient-descent optimization algorithm, SeisFlows will now compute a search direction that will be used in the line search to search for a best fitting model which optimally reduces the objective function. We can take a look at where SeisFlows has stored the information relating to kernel generation and the optimization computation." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "gradient kernels misfit_kernel model residuals.txt\r\n" - ] - } - ], - "source": [ - "# Gradient evaluation files are stored here, the kernels are stored separately from the gradient incase\n", - "# the user wants to manually manipulate them\n", - "! ls scratch/eval_grad" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "proc000000_vp_kernel.bin proc000000_vs_kernel.bin\r\n" - ] - } - ], - "source": [ - "# SeisFlows3 stores all kernels and gradient information as SPECFEM binary (.bin) files\n", - "! ls scratch/eval_grad/gradient" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "001 002 003\r\n" - ] - } - ], - "source": [ - "# Kernels are stored on a per-event basis, and summed together (sum/). If smoothing was performed, \n", - "# we would see both smoothed and unsmoothed versions of the misfit kernel\n", - "! ls scratch/eval_grad/kernels" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "checkpoint.npz\tf_new.txt g_new.npz m_new.npz\r\n" - ] - } - ], - "source": [ - "# We can see that some new values have been stored in prepartion for the line search,\n", - "# including g_new (current gradient) and p_new (current search direction). These are also\n", - "# stored as vector NumPy arrays (.npy files)\n", - "! ls scratch/optimize" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-1.18126331e-12 2.40273470e-12 3.97045036e-11 ... 9.62017688e-11\n", - " 4.21140102e-11 3.96825021e-12]]\n" - ] - } - ], - "source": [ - "g_new = np.load(\"scratch/optimize/g_new.npz\")\n", - "print(g_new[\"vs_kernel\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "--------------------\n", - "#### 3c. Line search and model update\n", - "\n", - "Let's finish off the inversion by running through the line search, which will generate new models using the\n", - "gradient, evaluate the objective function by running forward simulations, and comparing the evaluated objective function with the value obtained in __evalaute_initial_misfit__. \n", - "\n", - "Satisfactory reduction in the objective function will result in a termination of the line search. We are using a bracketing line search here [(Modrak et al. 2018)](https://academic.oup.com/gji/article/206/3/1864/2583505), which requires finding models which both increase and decrease the misfit with respect to the initial evaluation. Therefore it takes atleast two trial steps to complete the line search." - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "stop_after: evaluate_gradient_from_kernels -> perform_line_search\r\n" - ] - } - ], - "source": [ - "! seisflows par stop_after perform_line_search # We don't want to run the finalize_iteration argument so that we can explore the dir" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-08-16 14:41:12 (D) | setting iteration==1 from state file\n", - "2022-08-16 14:41:12 (I) | \n", - "================================================================================\n", - " SETTING UP INVERSION WORKFLOW \n", - "================================================================================\n", - "2022-08-16 14:41:18 (D) | running setup for module 'system.Workstation'\n", - "2022-08-16 14:41:21 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/sflog_003.txt\n", - "2022-08-16 14:41:21 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/parameters_003.yaml\n", - "2022-08-16 14:41:21 (D) | running setup for module 'solver.Specfem2D'\n", - "2022-08-16 14:41:21 (I) | initializing 3 solver directories\n", - "2022-08-16 14:41:22 (D) | running setup for module 'preprocess.Default'\n", - "2022-08-16 14:41:24 (D) | running setup for module 'optimize.Gradient'\n", - "2022-08-16 14:41:26 (I) | re-loading optimization module from checkpoint\n", - "2022-08-16 14:41:28 (I) | re-loading optimization module from checkpoint\n", - "2022-08-16 14:41:28 (I) | \n", - "////////////////////////////////////////////////////////////////////////////////\n", - " RUNNING ITERATION 01 \n", - "////////////////////////////////////////////////////////////////////////////////\n", - "2022-08-16 14:41:28 (I) | \n", - "================================================================================\n", - " RUNNING INVERSION WORKFLOW \n", - "================================================================================\n", - "2022-08-16 14:41:28 (I) | 'evaluate_initial_misfit' has already been run, skipping\n", - "2022-08-16 14:41:28 (I) | 'run_adjoint_simulations' has already been run, skipping\n", - "2022-08-16 14:41:28 (I) | 'postprocess_event_kernels' has already been run, skipping\n", - "2022-08-16 14:41:28 (I) | 'evaluate_gradient_from_kernels' has already been run, skipping\n", - "2022-08-16 14:41:28 (I) | initializing 'bracket'ing line search\n", - "2022-08-16 14:41:28 (I) | enforcing max step length safeguard\n", - "2022-08-16 14:41:28 (D) | step length(s) = 0.00E+00\n", - "2022-08-16 14:41:28 (D) | misfit val(s) = 1.28E-03\n", - "2022-08-16 14:41:28 (I) | try: first evaluation, attempt guess step length, alpha=9.08E+11\n", - "2022-08-16 14:41:28 (I) | try: applying initial step length safegaurd as alpha has exceeded maximum step length, alpha_new=1.44E+10\n", - "2022-08-16 14:41:28 (D) | overwriting initial step length, alpha_new=2.32E+09\n", - "2022-08-16 14:41:28 (I) | trial model 'm_try' parameters: \n", - "2022-08-16 14:41:28 (I) | 5800.00 <= vp <= 5800.00\n", - "2022-08-16 14:41:28 (I) | 3244.51 <= vs <= 3790.00\n", - "2022-08-16 14:41:29 (I) | \n", - "LINE SEARCH STEP COUNT 01\n", - "--------------------------------------------------------------------------------\n", - "2022-08-16 14:41:29 (I) | evaluating objective function for source 001\n", - "2022-08-16 14:41:29 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-16 14:41:33 (D) | quantifying misfit with 'Default'\n", - "2022-08-16 14:41:33 (I) | evaluating objective function for source 002\n", - "2022-08-16 14:41:33 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-16 14:41:36 (D) | quantifying misfit with 'Default'\n", - "2022-08-16 14:41:36 (I) | evaluating objective function for source 003\n", - "2022-08-16 14:41:36 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-16 14:41:40 (D) | quantifying misfit with 'Default'\n", - "2022-08-16 14:41:40 (D) | misfit for trial model (f_try) == 8.65E-04\n", - "2022-08-16 14:41:40 (D) | step length(s) = 0.00E+00, 2.32E+09\n", - "2022-08-16 14:41:40 (D) | misfit val(s) = 1.28E-03, 8.65E-04\n", - "2022-08-16 14:41:40 (I) | try: misfit not bracketed, increasing step length using golden ratio, alpha=3.76E+09\n", - "2022-08-16 14:41:40 (I) | line search model 'm_try' parameters: \n", - "2022-08-16 14:41:40 (I) | 5800.00 <= vp <= 5800.00\n", - "2022-08-16 14:41:40 (I) | 3086.61 <= vs <= 3969.23\n", - "2022-08-16 14:41:40 (I) | trial step unsuccessful. re-attempting line search\n", - "2022-08-16 14:41:40 (I) | \n", - "LINE SEARCH STEP COUNT 02\n", - "--------------------------------------------------------------------------------\n", - "2022-08-16 14:41:40 (I) | evaluating objective function for source 001\n", - "2022-08-16 14:41:40 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-16 14:41:44 (D) | quantifying misfit with 'Default'\n", - "2022-08-16 14:41:44 (I) | evaluating objective function for source 002\n", - "2022-08-16 14:41:44 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-16 14:41:48 (D) | quantifying misfit with 'Default'\n", - "2022-08-16 14:41:48 (I) | evaluating objective function for source 003\n", - "2022-08-16 14:41:48 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-16 14:41:52 (D) | quantifying misfit with 'Default'\n", - "2022-08-16 14:41:52 (D) | misfit for trial model (f_try) == 1.73E-03\n", - "2022-08-16 14:41:52 (D) | step length(s) = 0.00E+00, 2.32E+09, 3.76E+09\n", - "2022-08-16 14:41:52 (D) | misfit val(s) = 1.28E-03, 8.65E-04, 1.73E-03\n", - "2022-08-16 14:41:52 (I) | try: bracket acceptable but step length unreasonable attempting to re-adjust step length alpha=1.59E+09\n", - "2022-08-16 14:41:52 (I) | line search model 'm_try' parameters: \n", - "2022-08-16 14:41:52 (I) | 5800.00 <= vp <= 5800.00\n", - "2022-08-16 14:41:52 (I) | 3325.01 <= vs <= 3698.63\n", - "2022-08-16 14:41:52 (I) | trial step unsuccessful. re-attempting line search\n", - "2022-08-16 14:41:52 (I) | \n", - "LINE SEARCH STEP COUNT 03\n", - "--------------------------------------------------------------------------------\n", - "2022-08-16 14:41:52 (I) | evaluating objective function for source 001\n", - "2022-08-16 14:41:52 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-16 14:41:56 (D) | quantifying misfit with 'Default'\n", - "2022-08-16 14:41:56 (I) | evaluating objective function for source 002\n", - "2022-08-16 14:41:56 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-16 14:42:00 (D) | quantifying misfit with 'Default'\n", - "2022-08-16 14:42:00 (I) | evaluating objective function for source 003\n", - "2022-08-16 14:42:00 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-16 14:42:03 (D) | quantifying misfit with 'Default'\n", - "2022-08-16 14:42:03 (D) | misfit for trial model (f_try) == 2.59E-03\n", - "2022-08-16 14:42:03 (D) | step length(s) = 0.00E+00, 1.59E+09, 2.32E+09, 3.76E+09\n", - "2022-08-16 14:42:03 (D) | misfit val(s) = 1.28E-03, 2.59E-03, 8.65E-04, 1.73E-03\n", - "2022-08-16 14:42:03 (I) | try: bracket acceptable but step length unreasonable attempting to re-adjust step length alpha=2.82E+09\n", - "2022-08-16 14:42:03 (I) | line search model 'm_try' parameters: \n", - "2022-08-16 14:42:03 (I) | 5800.00 <= vp <= 5800.00\n", - "2022-08-16 14:42:03 (I) | 3189.77 <= vs <= 3852.13\n", - "2022-08-16 14:42:03 (I) | trial step unsuccessful. re-attempting line search\n", - "2022-08-16 14:42:03 (I) | \n", - "LINE SEARCH STEP COUNT 04\n", - "--------------------------------------------------------------------------------\n", - "2022-08-16 14:42:03 (I) | evaluating objective function for source 001\n", - "2022-08-16 14:42:03 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-16 14:42:07 (D) | quantifying misfit with 'Default'\n", - "2022-08-16 14:42:07 (I) | evaluating objective function for source 002\n", - "2022-08-16 14:42:07 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-16 14:42:11 (D) | quantifying misfit with 'Default'\n", - "2022-08-16 14:42:11 (I) | evaluating objective function for source 003\n", - "2022-08-16 14:42:11 (D) | running forward simulation with 'Specfem2D'\n", - "2022-08-16 14:42:15 (D) | quantifying misfit with 'Default'\n", - "2022-08-16 14:42:15 (D) | misfit for trial model (f_try) == 3.46E-03\n", - "2022-08-16 14:42:15 (D) | step length(s) = 0.00E+00, 1.59E+09, 2.32E+09, 2.82E+09, 3.76E+09\n", - "2022-08-16 14:42:15 (D) | misfit val(s) = 1.28E-03, 2.59E-03, 8.65E-04, 3.46E-03, 1.73E-03\n", - "2022-08-16 14:42:15 (I) | pass: bracket acceptable and step length reasonable. returning minimum line search misfit.\n", - "2022-08-16 14:42:15 (I) | line search model 'm_try' parameters: \n", - "2022-08-16 14:42:15 (I) | 5800.00 <= vp <= 5800.00\n", - "2022-08-16 14:42:15 (I) | 3244.51 <= vs <= 3790.00\n", - "2022-08-16 14:42:15 (I) | trial step successful. finalizing line search\n", - "2022-08-16 14:42:15 (I) | \n", - "FINALIZING LINE SEARCH\n", - "--------------------------------------------------------------------------------\n", - "2022-08-16 14:42:15 (I) | writing optimization stats\n", - "2022-08-16 14:42:15 (I) | renaming current (new) optimization vectors as previous model (old)\n", - "2022-08-16 14:42:15 (I) | setting accepted trial model (try) as current model (new)\n", - "2022-08-16 14:42:15 (I) | misfit of accepted trial model is f=8.645E-04\n", - "2022-08-16 14:42:15 (I) | resetting line search step count to 0\n", - "2022-08-16 14:42:15 (I) | stop workflow at `stop_after`: perform_line_search\n" - ] - } - ], - "source": [ - "! seisflows submit" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the log statements above, we can see that the SeisFlows line search required 4 trial steps, where it modified values of Vs (shear-wave velocity) until satisfactory reduction in the objective function was met. This was the final step in the iteration, and so the finalization of the line search made preparations for a subsequent iteration. " - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "alpha.txt\tf_new.txt f_try.txt m_new.npz output_optim.txt\r\n", - "checkpoint.npz\tf_old.txt g_old.npz m_old.npz p_old.npz\r\n" - ] - } - ], - "source": [ - "# We can see that we have 'new' and 'old' values for each of the optimization values,\n", - "# representing the previous model (M00) and the current model (M01).\n", - "! ls scratch/optimize" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step_count,step_length,gradient_norm_L1,gradient_norm_L2,misfit,if_restarted,slope,theta\r\n", - "04,2.323E+09,9.243E-05,1.049E-06,1.279E-03,0,8.263E-13,0.000E+00\r\n" - ] - } - ], - "source": [ - "# The stats/ directory contains text files describing the optimization/line search\n", - "! cat scratch/optimize/output_optim.txt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4. Conclusions\n", - "\n", - "We've now seen how SeisFlows runs an __Inversion__ workflow using the __Specfem2D__ solver on a __Workstation__ system. More or less, this is all you need to run SeisFlows with any combination of modules. The specificities of a system or numerical solver are already handled internally by SeisFlows, so if you want to use Specmfe3D_Cartesian as your solver, you would only need to run `seisflows par solver specfem3d` at the beginning of your workflow (you will also need to set up your Specfem3D models, similar to what we did for Specfem2D here). To run on a slurm system like Chinook (University of Alaska Fairbanks), you can run `seisflows par system chinook`. " - ] } ], "metadata": { diff --git a/docs/specfem2d_example.rst b/docs/specfem2d_example.rst index 25c9ae46..bb054a45 100644 --- a/docs/specfem2d_example.rst +++ b/docs/specfem2d_example.rst @@ -35,7 +35,7 @@ Example #1 runs a 2 iteration inversion using SPECFEM2D, the default preprocessing module and a gradient descent optimization algorithm. .. note:: - Example number 1 is meant to FAIL during the line search of Iteration #2, after exceeding the maximum allowable line search step count. This is meant to illustrate line search behavior and allow the User to explore a working directory mid-workflow. + Example number 1 is meant to **FAIL** during the line search of Iteration #2, after exceeding the maximum allowable line search step count. This is meant to illustrate line search behavior and allow the User to explore a working directory mid-workflow. .. code:: ipython3 @@ -98,6 +98,10 @@ working directory. # The above commands are the same as the below ! seisflows examples run 1 --specfem2d_repo path/to/specfem2d +A successfully completed example problem will end with the following log +messages: + + Example #2 ~~~~~~~~~~ @@ -156,1358 +160,3 @@ You can run the example with the same command as shown for Example 1: .. code:: ipython3 ! seisflows examples run 2 -r path/to/specfem2d - --------------- - -Option 2: Manual run --------------------- - -The notebook below details a walkthrough of Example #1 shown above. This -is meant for those who want to understand what is going on under the -hood. You are welcome to follow along on your workstation. The following -Table of Contents outlines the steps we will take in this tutorial: - -.. warning:: - Navigation links will not work outside of Jupyter. Please use the navigation bar to the left. - -1. `Setup SPECFEM2D <#1.-Setup-SPECFEM2D>`__ - - a. `Download and compile - codebase <#1a.-Download-and-compile-codebase*>`__ - b. `Create a separate SPECFEM2D working - directory <#1b.-Create-a-separate-SPECFEM2D-working-directory>`__ - c. `Generate initial and target - models <#1c.-Generate-initial-and-target-models>`__ - -2. `Initialize SeisFlows (SF) <#2.-Initialize-SeisFlows-(SF)>`__ - - a. `SeisFlows working directory and parameter - file <#2a.-SF-working-directory-and-parameter-file>`__ - -3. `Run SeisFlows <#2.-Run-SeisFlows>`__ - - a. `Forward simulations <#3a.-Forward-simulations>`__ - b. `Exploring the SeisFlows directory - structure <#3b.-Exploring-the-SF-directory-structure>`__ - c. `Adjoint simulations <#3c.-Adjoint-simulations>`__ - d. `Line search and model - update <#3d.-Line-search-and-model-update>`__ - -4. `Conclusions <#4.-Conclusions>`__ - -1. Setup SPECFEM2D -~~~~~~~~~~~~~~~~~~ - -1a. Download and compile codebase (optional) -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - - **NOTE**: If you have already downloaded and compiled SPECFEM2D, you - can skip most of this subsection (1a). However you will need to edit - the first two paths in the following cell (WORKDIR and - SPECFEM2D_ORIGINAL), and execute the path structure defined in the - cell. - -First we’ll download and compile SPECFEM2D to generate the binaries -necessary to run our simulations. We will then populate a new SPECFEM2D -working directory that will be used by SeisFlows. We’ll use to Python OS -module to do our filesystem processes just to keep everything in Python, -but this can easily be accomplished in bash. - -.. code:: ipython3 - - import os - import glob - import shutil - import numpy as np - -.. code:: ipython3 - - # vvv USER MUST EDIT THE FOLLOWING PATHS vvv - WORKDIR = "/home/bchow/Work/scratch" - SPECFEM2D = "/home/bchow/REPOSITORIES/specfem2d" - # where WORKDIR: points to your own working directory - # and SPECFEM2D: points to an existing specfem2D repository if available (if not set as '') - # ^^^ USER MUST EDIT THE FOLLOWING PATHS ^^^ - # ====================================================================================================== - - # Distribute the necessary file structure of the SPECFEM2D repository that we will downloaded/reference - SPECFEM2D_ORIGINAL = os.path.join(WORKDIR, "specfem2d") - SPECFEM2D_BIN_ORIGINAL = os.path.join(SPECFEM2D_ORIGINAL, "bin") - SPECFEM2D_DATA_ORIGINAL = os.path.join(SPECFEM2D_ORIGINAL, "DATA") - TAPE_2007_EXAMPLE = os.path.join(SPECFEM2D_ORIGINAL, "EXAMPLES", "Tape2007") - - # The SPECFEM2D working directory that we will create separate from the downloaded repo - SPECFEM2D_WORKDIR = os.path.join(WORKDIR, "specfem2d_workdir") - SPECFEM2D_BIN = os.path.join(SPECFEM2D_WORKDIR, "bin") - SPECFEM2D_DATA = os.path.join(SPECFEM2D_WORKDIR, "DATA") - SPECFEM2D_OUTPUT = os.path.join(SPECFEM2D_WORKDIR, "OUTPUT_FILES") - - # Pre-defined locations of velocity models we will generate using the solver - SPECFEM2D_MODEL_INIT = os.path.join(SPECFEM2D_WORKDIR, "OUTPUT_FILES_INIT") - SPECFEM2D_MODEL_TRUE = os.path.join(SPECFEM2D_WORKDIR, "OUTPUT_FILES_TRUE") - -.. code:: ipython3 - - # Download SPECFEM2D from GitHub, devel branch for latest codebase OR symlink from existing repo - if not os.path.exists(WORKDIR): - os.makedirs(WORKDIR) - os.chdir(WORKDIR) - - if os.path.exists("specfem2d"): - print("SPECFEM2D repository already found, you may skip this subsection") - pass - elif os.path.exists(SPECFEM2D): - print("Existing SPECMFE2D respository found, symlinking to working directory") - os.symlink(SPECFEM2D, "./specfem2d") - else: - print("Cloning respository from GitHub") - ! git clone --recursive --branch devel https://github.com/geodynamics/specfem2d.git - - -.. parsed-literal:: - - Existing SPECMFE2D respository found, symlinking to working directory - - -.. code:: ipython3 - - # Compile SPECFEM2D to generate the Makefile - os.chdir(SPECFEM2D_ORIGINAL) - if not os.path.exists("./config.log"): - os.system("./configure") - -.. code:: ipython3 - - # Run make to generate SPECFEM2D binaries - if not os.path.exists("bin"): - os.system("make all") - -.. code:: ipython3 - - # Check out the binary files that have been created - os.chdir(SPECFEM2D_ORIGINAL) - ! pwd - ! ls bin/ - - -.. parsed-literal:: - - /home/bchow/REPOSITORIES/specfem2d - xadj_seismogram xconvolve_source_timefunction xspecfem2D - xcheck_quality_external_mesh xmeshfem2D xsum_kernels - xcombine_sem xsmooth_sem - - -1b. Create a separate SPECFEM2D working directory -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Next we’ll create a new SPECFEM2D working directory, separate from the -original repository. The intent here is to isolate the original -SPECFEM2D repository from our working state, to protect it from things -like accidental file deletions or manipulations. This is not a mandatory -step for using SeisFlows, but it helps keep file structure clean in the -long run, and is the SeisFlows3 dev team’s preferred method of using -SPECFEM. - -.. note:: - All SPECFEM2D/3D/3D_GLOBE need to run successfully are the bin/, DATA/, and OUTPUT_FILES/ directories. Everything else in the repository is not mandatory for running binaries. - -In this tutorial we will be using the `Tape2007 example -problem `__ -to define our **DATA/** directory (last tested 8/15/22, bdba4389). - -.. code:: ipython3 - - # Incase we've run this docs page before, delete the working directory before remaking - if os.path.exists(SPECFEM2D_WORKDIR): - shutil.rmtree(SPECFEM2D_WORKDIR) - - os.mkdir(SPECFEM2D_WORKDIR) - os.chdir(SPECFEM2D_WORKDIR) - - # Copy the binary files incase we update the source code. These can also be symlinked. - shutil.copytree(SPECFEM2D_BIN_ORIGINAL, "bin") - - # Copy the DATA/ directory because we will be making edits here frequently and it's useful to - # retain the original files for reference. We will be running one of the example problems: Tape2007 - shutil.copytree(os.path.join(TAPE_2007_EXAMPLE, "DATA"), "DATA") - - ! pwd - ! ls - - -.. parsed-literal:: - - /home/bchow/Work/scratch/specfem2d_workdir - bin DATA - - -.. code:: ipython3 - - # Run the Tape2007 example to make sure SPECFEM2D is working as expected - os.chdir(TAPE_2007_EXAMPLE) - ! ./run_this_example.sh > output_log.txt - - assert(os.path.exists("OUTPUT_FILES/forward_image000004800.jpg")), \ - (f"Example did not run, the remainder of this docs page will likely not work." - f"Please check the following directory: {TAPE_2007_EXAMPLE}") - - ! tail output_log.txt - - -.. parsed-literal:: - - ------------------------------------------------------------------------------- - ------------------------------------------------------------------------------- - D a t e : 16 - 08 - 2022 T i m e : 14:26:37 - ------------------------------------------------------------------------------- - ------------------------------------------------------------------------------- - - see results in directory: OUTPUT_FILES/ - - done - Tue Aug 16 02:26:37 PM AKDT 2022 - - --------------- - -Now we need to manually set up our SPECFEM2D working directory. As -mentioned in the previous cell, the only required elements of this -working directory are the following (these files will form the basis for -how SeisFlows3 operates within the SPECFEM2D framework): - -1. **bin/** directory containing SPECFEM2D binaries -2. **DATA/** directory containing SOURCE and STATION files, as well as a - SPECFEM2D Par_file -3. \__OUTPUT_FILES/proc??????_*.bin_\_ files which define the starting - (and target) models - -.. note:: - This file structure is the same for all versions of SPECFEM (2D/3D/3D_GLOBE) - -.. code:: ipython3 - - # First we will set the correct SOURCE and STATION files. - # This is the same task as shown in ./run_this_example.sh - os.chdir(SPECFEM2D_DATA) - - # Symlink source 001 as our main source - if os.path.exists("SOURCE"): - os.remove("SOURCE") - os.symlink("SOURCE_001", "SOURCE") - - # Copy the correct Par_file so that edits do not affect the original file - if os.path.exists("Par_file"): - os.remove("Par_file") - shutil.copy("Par_file_Tape2007_onerec", "Par_file") - - ! ls - - -.. parsed-literal:: - - interfaces_Tape2007.dat SOURCE_003 SOURCE_012 SOURCE_021 - model_velocity.dat_checker SOURCE_004 SOURCE_013 SOURCE_022 - Par_file SOURCE_005 SOURCE_014 SOURCE_023 - Par_file_Tape2007_132rec_checker SOURCE_006 SOURCE_015 SOURCE_024 - Par_file_Tape2007_onerec SOURCE_007 SOURCE_016 SOURCE_025 - proc000000_model_velocity.dat_input SOURCE_008 SOURCE_017 STATIONS - SOURCE SOURCE_009 SOURCE_018 STATIONS_checker - SOURCE_001 SOURCE_010 SOURCE_019 - SOURCE_002 SOURCE_011 SOURCE_020 - - -1c. Generate initial and target models -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Since we’re doing a synthetic-synthetic inversion, we need to manually -set up the velocity models with which we generate our synthetic -waveforms. The naming conventions for these models are: - -1. **MODEL_INIT:** The initial or starting model. Used to generate the - actual synthetic seismograms. This is considered M00. -2. **MODEL_TRUE:** The target or true model. Used to generate ‘data’ - (also synthetic). This is the reference model that our inversion is - trying to resolve. - -The starting model is defined as a homogeneous halfspace uin the -Tape2007 example problem. We will need to run both ``xmeshfem2D`` and -``xspecfem2D`` to generate the required velocity model database files. -We will generate our target model by slightly perturbing the parameters -of the initial model. - -.. note:: - We can use the SeisFlows3 command line option `seisflows sempar` to directly edit the SPECFEM2D Par_file in the command line. This will work for the SPECFEM3D Par_file as well. - -.. code:: ipython3 - - os.chdir(SPECFEM2D_DATA) - - # Ensure that SPECFEM2D outputs the velocity model in the expected binary format - ! seisflows sempar setup_with_binary_database 1 # allow creation of .bin files - ! seisflows sempar save_model binary # output model in .bin database format - ! seisflows sempar save_ascii_kernels .false. # output kernels in .bin format, not ASCII - - -.. parsed-literal:: - - setup_with_binary_database: 0 -> 1 - SAVE_MODEL: default -> binary - save_ASCII_kernels: .true. -> .false. - - -.. code:: ipython3 - - # SPECFEM requires that we create the OUTPUT_FILES directory before running - os.chdir(SPECFEM2D_WORKDIR) - - if os.path.exists(SPECFEM2D_OUTPUT): - shutil.rmtree(SPECFEM2D_OUTPUT) - - os.mkdir(SPECFEM2D_OUTPUT) - - ! ls - - -.. parsed-literal:: - - bin DATA OUTPUT_FILES - - -.. code:: ipython3 - - # GENERATE MODEL_INIT - os.chdir(SPECFEM2D_WORKDIR) - - # Run the mesher and solver to generate our initial model - ! ./bin/xmeshfem2D > OUTPUT_FILES/mesher_log.txt - ! ./bin/xspecfem2D > OUTPUT_FILES/solver_log.txt - - # Move the model files (*.bin) into the OUTPUT_FILES directory, where SeisFlows3 expects them - ! mv DATA/*bin OUTPUT_FILES - - # Make sure we don't overwrite this initial model when creating our target model in the next step - ! mv OUTPUT_FILES OUTPUT_FILES_INIT - - ! head OUTPUT_FILES_INIT/solver_log.txt - ! tail OUTPUT_FILES_INIT/solver_log.txt - - -.. parsed-literal:: - - - ********************************************** - **** Specfem 2-D Solver - serial version **** - ********************************************** - - Running Git version of the code corresponding to commit cf89366717d9435985ba852ef1d41a10cee97884 - dating From Date: Mon Nov 29 23:20:51 2021 -0800 - - - NDIM = 2 - ------------------------------------------------------------------------------- - Program SPECFEM2D: - ------------------------------------------------------------------------------- - ------------------------------------------------------------------------------- - Tape-Liu-Tromp (GJI 2007) - ------------------------------------------------------------------------------- - ------------------------------------------------------------------------------- - D a t e : 16 - 08 - 2022 T i m e : 14:26:52 - ------------------------------------------------------------------------------- - ------------------------------------------------------------------------------- - - --------------- - -Now we want to perturb the initial model to create our target model -(**MODEL_TRUE**). The seisflows command line subargument -``seisflows sempar velocity_model`` will let us view and edit the -velocity model. You can also do this manually by editing the Par_file -directly. - -.. code:: ipython3 - - # GENERATE MODEL_TRUE - os.chdir(SPECFEM2D_DATA) - - # Edit the Par_file by increasing velocities by ~10% - ! seisflows sempar velocity_model '1 1 2600.d0 5900.d0 3550.0d0 0 0 10.d0 10.d0 0 0 0 0 0 0' - - -.. parsed-literal:: - - VELOCITY_MODEL: - - 1 1 2600.d0 5800.d0 3500.0d0 0 0 10.d0 10.d0 0 0 0 0 0 0 - -> - 1 1 2600.d0 5900.d0 3550.0d0 0 0 10.d0 10.d0 0 0 0 0 0 0 - - -.. code:: ipython3 - - # Re-run the mesher and solver to generate our target velocity model - os.chdir(SPECFEM2D_WORKDIR) - - # Make sure the ./OUTPUT_FILES directory exists since we moved the old one - if os.path.exists(SPECFEM2D_OUTPUT): - shutil.rmtree(SPECFEM2D_OUTPUT) - os.mkdir(SPECFEM2D_OUTPUT) - - # Run the binaries to generate MODEL_TRUE - ! ./bin/xmeshfem2D > OUTPUT_FILES/mesher_log.txt - ! ./bin/xspecfem2D > OUTPUT_FILES/solver_log.txt - - # Move all the relevant files into OUTPUT_FILES - ! mv ./DATA/*bin OUTPUT_FILES - ! mv OUTPUT_FILES OUTPUT_FILES_TRUE - - ! head OUTPUT_FILES_INIT/solver_log.txt - ! tail OUTPUT_FILES_INIT/solver_log.txt - - -.. parsed-literal:: - - - ********************************************** - **** Specfem 2-D Solver - serial version **** - ********************************************** - - Running Git version of the code corresponding to commit cf89366717d9435985ba852ef1d41a10cee97884 - dating From Date: Mon Nov 29 23:20:51 2021 -0800 - - - NDIM = 2 - ------------------------------------------------------------------------------- - Program SPECFEM2D: - ------------------------------------------------------------------------------- - ------------------------------------------------------------------------------- - Tape-Liu-Tromp (GJI 2007) - ------------------------------------------------------------------------------- - ------------------------------------------------------------------------------- - D a t e : 16 - 08 - 2022 T i m e : 14:26:52 - ------------------------------------------------------------------------------- - ------------------------------------------------------------------------------- - - -.. code:: ipython3 - - # Great, we have all the necessary SPECFEM files to run our SeisFlows inversion! - ! ls - - -.. parsed-literal:: - - bin DATA OUTPUT_FILES_INIT OUTPUT_FILES_TRUE - - -2. Initialize SeisFlows (SF) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -In this Section we will look at a SeisFlows working directory, parameter -file, and working state. - -2a. SeisFlows working directory and parameter file -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -As with SPECFEM, SeisFlows requires a parameter file -(**parameters.yaml**) that controls how an automated workflow will -proceed. Because SeisFlows is modular, there are a large number of -potential parameters which may be present in a SeisFlows parameter file, -as each sub-module may have its own set of unique parameters. - -In contrast to SPECFEM’s method of listing all available parameters and -leaving it up the User to determine which ones are relevant to them, -SeisFlows dynamically builds its parameter file based on User inputs. In -this subsection we will use the built-in SeisFlows command line tools to -generate and populate the parameter file. - -.. note:: - See the `parameter file documentation page `__ for a more in depth exploration of this central SeisFlows file. - -In the previous section we saw the ``sempar`` command in action. We can -use the ``-h`` or help flag to list all available SiesFlows3 command -line commands. - -.. code:: ipython3 - - ! seisflows -h - - -.. parsed-literal:: - - usage: seisflows [-h] [-w [WORKDIR]] [-p [PARAMETER_FILE]] - {setup,configure,swap,init,submit,resume,restart,clean,par,sempar,check,print,reset,debug,examples} - ... - - ================================================================================ - - SeisFlows: Waveform Inversion Package - - ================================================================================ - - optional arguments: - -h, --help show this help message and exit - -w [WORKDIR], --workdir [WORKDIR] - The SeisFlows working directory, default: cwd - -p [PARAMETER_FILE], --parameter_file [PARAMETER_FILE] - Parameters file, default: 'parameters.yaml' - - command: - Available SeisFlows arguments and their intended usages - - setup Setup working directory from scratch - configure Fill parameter file with defaults - swap Swap module parameters in an existing parameter file - init Initiate working environment - submit Submit initial workflow to system - resume Re-submit previous workflow to system - restart Remove current environment and submit new workflow - clean Remove files relating to an active working environment - par View and edit SeisFlows parameter file - sempar View and edit SPECFEM parameter file - check Check state of an active environment - print Print information related to an active environment - reset Reset modules within an active state - debug Start interactive debug environment - examples Look at and run pre-configured example problems - - 'seisflows [command] -h' for more detailed descriptions of each command. - - -.. code:: ipython3 - - # The command 'setup' creates the 'parameters.yaml' file that controls all of SeisFlows - # the '-f' flag removes any exist 'parameters.yaml' file that might be in the directory - os.chdir(WORKDIR) - ! seisflows setup -f - ! ls - - -.. parsed-literal:: - - creating parameter file: parameters.yaml - parameters.yaml sflog.txt specfem2d specfem2d_workdir - - -.. code:: ipython3 - - # Let's have a look at this file, which has not yet been populated - ! cat parameters.yaml - - -.. parsed-literal:: - - # ////////////////////////////////////////////////////////////////////////////// - # - # SeisFlows YAML Parameter File - # - # ////////////////////////////////////////////////////////////////////////////// - # - # Modules correspond to the structure of the source code, and determine - # SeisFlows' behavior at runtime. Each module requires its own sub-parameters. - # - # .. rubric:: - # - To determine available options for modules listed below, run: - # > seisflows print modules - # - To auto-fill with docstrings and default values (recommended), run: - # > seisflows configure - # - To set values as NoneType, use: null - # - To set values as infinity, use: inf - # - # MODULES - # /////// - # workflow (str): The types and order of functions for running SeisFlows - # system (str): Computer architecture of the system being used - # solver (str): External numerical solver to use for waveform simulations - # preprocess (str): Preprocessing schema for waveform data - # optimize (str): Optimization algorithm for the inverse problem - # ============================================================================== - workflow: forward - system: workstation - solver: specfem2d - preprocess: default - optimize: gradient - - -.. code:: ipython3 - - # We can use the `seisflows print modules` command to list out the available options - ! seisflows print modules - - -.. parsed-literal:: - - SEISFLOWS MODULES - ///////////////// - '-': module, '*': class - - - workflow - * forward - * inversion - * migration - - system - * chinook - * cluster - * frontera - * lsf - * maui - * slurm - * workstation - - solver - * specfem - * specfem2d - * specfem3d - * specfem3d_globe - - preprocess - * default - * pyaflowa - - optimize - * LBFGS - * NLCG - * gradient - - -.. code:: ipython3 - - # For this example, we can use most of the default modules, however we need to - # change the SOLVER module to let SeisFlows know we're using SPECFEM2D (as opposed to 3D) - ! seisflows par workflow inversion - ! cat parameters.yaml - - -.. parsed-literal:: - - workflow: forward -> inversion - # ////////////////////////////////////////////////////////////////////////////// - # - # SeisFlows YAML Parameter File - # - # ////////////////////////////////////////////////////////////////////////////// - # - # Modules correspond to the structure of the source code, and determine - # SeisFlows' behavior at runtime. Each module requires its own sub-parameters. - # - # .. rubric:: - # - To determine available options for modules listed below, run: - # > seisflows print modules - # - To auto-fill with docstrings and default values (recommended), run: - # > seisflows configure - # - To set values as NoneType, use: null - # - To set values as infinity, use: inf - # - # MODULES - # /////// - # workflow (str): The types and order of functions for running SeisFlows - # system (str): Computer architecture of the system being used - # solver (str): External numerical solver to use for waveform simulations - # preprocess (str): Preprocessing schema for waveform data - # optimize (str): Optimization algorithm for the inverse problem - # ============================================================================== - workflow: inversion - system: workstation - solver: specfem2d - preprocess: default - optimize: gradient - - --------------- - -The ``seisflows configure`` command populates the parameter file based -on the chosen modules. SeisFlows will attempt to fill in all parameters -with reasonable default values. Docstrings above each module show -descriptions and available options for each of these parameters. - -In the follownig cell we will use the ``seisflows par`` command to edit -the parameters.yaml file directly, replacing some default parameters -with our own values. Comments next to each evaluation describe the -choice for each. - -.. code:: ipython3 - - ! seisflows configure - ! head --lines=50 parameters.yaml - - -.. parsed-literal:: - - # ////////////////////////////////////////////////////////////////////////////// - # - # SeisFlows YAML Parameter File - # - # ////////////////////////////////////////////////////////////////////////////// - # - # Modules correspond to the structure of the source code, and determine - # SeisFlows' behavior at runtime. Each module requires its own sub-parameters. - # - # .. rubric:: - # - To determine available options for modules listed below, run: - # > seisflows print modules - # - To auto-fill with docstrings and default values (recommended), run: - # > seisflows configure - # - To set values as NoneType, use: null - # - To set values as infinity, use: inf - # - # MODULES - # /////// - # workflow (str): The types and order of functions for running SeisFlows - # system (str): Computer architecture of the system being used - # solver (str): External numerical solver to use for waveform simulations - # preprocess (str): Preprocessing schema for waveform data - # optimize (str): Optimization algorithm for the inverse problem - # ============================================================================== - workflow: inversion - system: workstation - solver: specfem2d - preprocess: default - optimize: gradient - # ============================================================================= - # - # Forward Workflow - # ---------------- - # Run forward solver in parallel and (optionally) calculate - # data-synthetic misfit and adjoint sources. - # - # Parameters - # ---------- - # :type modules: list of module - # :param modules: instantiated SeisFlows modules which should have been - # generated by the function `seisflows.config.import_seisflows` with a - # parameter file generated by seisflows.configure - # :type data_case: str - # :param data_case: How to address 'data' in the workflow, available options: - # 'data': real data will be provided by the user in - # `path_data/{source_name}` in the same format that the solver will - # produce synthetics (controlled by `solver.format`) OR - # synthetic': 'data' will be generated as synthetic seismograms using - # a target model provided in `path_model_true`. If None, workflow will - - -.. code:: ipython3 - - # EDIT THE SEISFLOWS PARAMETER FILE - ! seisflows par ntask 3 # set the number of sources/events to use - ! seisflows par materials elastic # update Vp and Vs during inversion - ! seisflows par end 2 # final iteration -- we will only run 1 - ! seisflows par data_case synthetic # synthetic-synthetic means we need both INIT and TRUE models - ! seisflows par components Y # this default example creates Y-component seismograms - ! seisflows par step_count_max 5 # limit the number of steps in the line search - - # Use Python syntax here to access path constants - os.system(f"seisflows par path_specfem_bin {SPECFEM2D_BIN}") # set path to SPECFEM2D binaries - os.system(f"seisflows par path_specfem_data {SPECFEM2D_DATA}") # set path to SEPCFEM2D DATA/ - os.system(f"seisflows par path_model_init {SPECFEM2D_MODEL_INIT}") # set path to INIT model - os.system(f"seisflows par path_model_true {SPECFEM2D_MODEL_TRUE}") # set path to TRUE model - - -.. parsed-literal:: - - ntask: 1 -> 3 - materials: acoustic -> elastic - end: 1 -> 2 - data_case: data -> synthetic - components: ZNE -> Y - step_count_max: 10 -> 5 - path_specfem_bin: null -> /home/bchow/Work/scratch/specfem2d_workdir/bin - path_specfem_data: null -> /home/bchow/Work/scratch/specfem2d_workdir/DATA - path_model_init: null -> - /home/bchow/Work/scratch/specfem2d_workdir/OUTPUT_FILES_INIT - path_model_true: null -> - /home/bchow/Work/scratch/specfem2d_workdir/OUTPUT_FILES_TRUE - - - - -.. parsed-literal:: - - 0 - - - --------------- - -One last thing, we will need to edit the SPECFEM2D Par_file parameter -``MODEL`` such that ``xmeshfem2d`` reads our pre-built velocity models -(*.bin files) rather than the meshing parameters defined in the -Par_file. - -.. code:: ipython3 - - os.chdir(SPECFEM2D_DATA) - ! seisflows sempar model gll - - -.. parsed-literal:: - - MODEL: default -> gll - - -3. Run SeisFlows -~~~~~~~~~~~~~~~~ - -In this Section we will run SeisFlows to generate synthetic seismograms, -kernels, a gradient, and an updated velocity model. - -3a. Forward simulations -^^^^^^^^^^^^^^^^^^^^^^^ - -SeisFlows is an automated workflow tool, such that once we run -``seisflows submit`` we should not need to intervene in the workflow. -However the package does allow the User flexibility in how they want the -workflow to behave. - -For example, we can run our workflow in stages by taking advantage of -the ``stop_after`` parameter. As its name suggests, ``stop_after`` -allows us to stop a workflow prematurely so that we may stop and look at -results, or debug a failing workflow. - -The ``seisflows print flow`` command tells us what functions we can use -for the ``stop_after`` parameter. - -.. code:: ipython3 - - os.chdir(WORKDIR) - ! seisflows print tasks - - -.. parsed-literal:: - - SEISFLOWS WORKFLOW TASK LIST - //////////////////////////// - Task list for - - 1: evaluate_initial_misfit - 2: run_adjoint_simulations - 3: postprocess_event_kernels - 4: evaluate_gradient_from_kernels - 5: initialize_line_search - 6: perform_line_search - 7: finalize_iteration - - --------------- - -In the Inversion workflow, the tasks listed are described as follows: - -1. **evaluate_initial_misfit:** - - a. Prepare data for inversion by either copying data from disk or - generating ‘synthetic data’ with MODEL_TRUE - b. Call numerical solver to run forward simulations using MODEL_INIT, - generating synthetics - c. Evaluate the objective function by performing waveform comparisons - d. Prepare ``run_adjoint_simulations`` step by generating adjoint - sources and auxiliary files - -2. **run_adjoint_simulations:** Call numerical solver to run adjoint - simulation, generating kernels -3. **postprocess_event_kernels:** Combine all event kernels into a - misfit kernel. -4. **evaluate_gradient_from_kernels:** Smooth and mask the misfit kernel - to create the gradient -5. **initialize_line_search:** Call on the optimization library to scale - the gradient by a step length to compute the search direction. - Prepare file structure for line search. -6. **perform_line_search:** Perform a line search by algorithmically - scaling the gradient and evaluating the misfit function (forward - simulations and misfit quantification) until misfit is acceptably - reduced. -7. **finalize_iteration:** Run any finalization steps such as saving - traces, kernels, gradients and models to disk, setting up SeisFlows3 - for any subsequent iterations. Clean the scratch/ directory in - preparation for subsequent iterations - -Let’s set the ``stop_after`` argument to **evaluate_initial_misfit**, -this will halt the workflow after the intialization step. - -.. code:: ipython3 - - ! seisflows par stop_after evaluate_initial_misfit - - -.. parsed-literal:: - - stop_after: null -> evaluate_initial_misfit - - --------------- - -Now let’s run SeisFlows. There are two ways to do this: ``submit`` and -``restart`` - -1. ``seisflows submit`` is used to run new workflows and resume stopped - or failed workflows. -2. The ``restart`` command is simply a convenience function that runs - ``clean`` (to remove an active working state) and ``submit`` (to - submit a fresh workflow). - -Since this is our first run, we’ll use ``seisflows submit``. - -.. code:: ipython3 - - ! seisflows submit - - -.. parsed-literal:: - - 2022-08-16 14:32:48 (I) | - ================================================================================ - SETTING UP INVERSION WORKFLOW - ================================================================================ - 2022-08-16 14:32:55 (D) | running setup for module 'system.Workstation' - 2022-08-16 14:32:57 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/sflog_001.txt - 2022-08-16 14:32:57 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/parameters_001.yaml - 2022-08-16 14:32:57 (D) | running setup for module 'solver.Specfem2D' - 2022-08-16 14:32:57 (I) | initializing 3 solver directories - 2022-08-16 14:32:57 (D) | initializing solver directory source: 001 - 2022-08-16 14:33:04 (D) | linking source '001' as 'mainsolver' - 2022-08-16 14:33:04 (D) | initializing solver directory source: 002 - 2022-08-16 14:33:09 (D) | initializing solver directory source: 003 - 2022-08-16 14:33:16 (D) | running setup for module 'preprocess.Default' - 2022-08-16 14:33:16 (D) | running setup for module 'optimize.Gradient' - 2022-08-16 14:33:17 (I) | no optimization checkpoint found, assuming first run - 2022-08-16 14:33:17 (I) | re-loading optimization module from checkpoint - 2022-08-16 14:33:17 (I) | - //////////////////////////////////////////////////////////////////////////////// - RUNNING ITERATION 01 - //////////////////////////////////////////////////////////////////////////////// - 2022-08-16 14:33:17 (I) | - ================================================================================ - RUNNING INVERSION WORKFLOW - ================================================================================ - 2022-08-16 14:33:17 (I) | - //////////////////////////////////////////////////////////////////////////////// - EVALUATING MISFIT FOR INITIAL MODEL - //////////////////////////////////////////////////////////////////////////////// - 2022-08-16 14:33:17 (I) | checking initial model parameters - 2022-08-16 14:33:17 (I) | 5800.00 <= vp <= 5800.00 - 2022-08-16 14:33:17 (I) | 2600.00 <= rho <= 2600.00 - 2022-08-16 14:33:17 (I) | 3500.00 <= vs <= 3500.00 - 2022-08-16 14:33:17 (I) | checking true/target model parameters - 2022-08-16 14:33:17 (I) | 5900.00 <= vp <= 5900.00 - 2022-08-16 14:33:17 (I) | 2600.00 <= rho <= 2600.00 - 2022-08-16 14:33:17 (I) | 3550.00 <= vs <= 3550.00 - 2022-08-16 14:33:17 (I) | preparing observation data for source 001 - 2022-08-16 14:33:17 (I) | running forward simulation w/ target model for 001 - 2022-08-16 14:33:21 (I) | evaluating objective function for source 001 - 2022-08-16 14:33:21 (D) | running forward simulation with 'Specfem2D' - 2022-08-16 14:33:25 (D) | quantifying misfit with 'Default' - 2022-08-16 14:33:25 (I) | preparing observation data for source 002 - 2022-08-16 14:33:25 (I) | running forward simulation w/ target model for 002 - 2022-08-16 14:33:29 (I) | evaluating objective function for source 002 - 2022-08-16 14:33:29 (D) | running forward simulation with 'Specfem2D' - 2022-08-16 14:33:33 (D) | quantifying misfit with 'Default' - 2022-08-16 14:33:33 (I) | preparing observation data for source 003 - 2022-08-16 14:33:33 (I) | running forward simulation w/ target model for 003 - 2022-08-16 14:33:36 (I) | evaluating objective function for source 003 - 2022-08-16 14:33:36 (D) | running forward simulation with 'Specfem2D' - 2022-08-16 14:33:40 (D) | quantifying misfit with 'Default' - 2022-08-16 14:33:40 (I) | stop workflow at `stop_after`: evaluate_initial_misfit - - -.. note:: - For a detailed exploration of a SeisFlows working directory, see the `working directory `__ documentation page where we explain each of the files and directories that have been generated during this workflow. Below we just look at two files which are required for our adjoint simulation, the adjoint sources (.adj) and STATIONS_ADJOINT file - -.. code:: ipython3 - - # The adjoint source is created in the same format as the synthetics (two-column ASCII) - ! head scratch/solver/001/traces/adj/AA.S0001.BXY.adj - - -.. parsed-literal:: - - -48.0000000 0.0000000 - -47.9400000 0.0000000 - -47.8800000 0.0000000 - -47.8200000 0.0000000 - -47.7600000 0.0000000 - -47.7000000 0.0000000 - -47.6400000 0.0000000 - -47.5800000 0.0000000 - -47.5200000 0.0000000 - -47.4600000 0.0000000 - - -3b. Adjoint simulations -^^^^^^^^^^^^^^^^^^^^^^^ - -Now that we have all the required files for running an adjoint -simulation (*.adj waveforms and STATIONS_ADJOINT file), we can continue -with the SeisFlows3 Inversion workflow. No need to edit the Par_file or -anything like that, SeisFlows3 will take care of that under the hood. We -simply need to tell the workflow (via the parameters.yaml file) to -``resume_from`` the correct function. We can have a look at these -functions again: - -.. code:: ipython3 - - ! seisflows print tasks - - -.. parsed-literal:: - - SEISFLOWS WORKFLOW TASK LIST - //////////////////////////// - Task list for - - 1: evaluate_initial_misfit - 2: run_adjoint_simulations - 3: postprocess_event_kernels - 4: evaluate_gradient_from_kernels - 5: initialize_line_search - 6: perform_line_search - 7: finalize_iteration - - -.. code:: ipython3 - - # We'll stop just before the line search so that we can take a look at the files - # generated during the middle tasks - ! seisflows par stop_after evaluate_gradient_from_kernels - - -.. parsed-literal:: - - stop_after: evaluate_initial_misfit -> evaluate_gradient_from_kernels - - -.. code:: ipython3 - - # We can use the `seisflows submit` command to continue an active workflow - # The state file created during the first run will tell the workflow to resume from the stopped point in the workflow - ! seisflows submit - - -.. parsed-literal:: - - 2022-08-16 14:36:42 (D) | setting iteration==1 from state file - 2022-08-16 14:36:42 (I) | - ================================================================================ - SETTING UP INVERSION WORKFLOW - ================================================================================ - 2022-08-16 14:36:48 (D) | running setup for module 'system.Workstation' - 2022-08-16 14:36:51 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/sflog_002.txt - 2022-08-16 14:36:51 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/parameters_002.yaml - 2022-08-16 14:36:51 (D) | running setup for module 'solver.Specfem2D' - 2022-08-16 14:36:51 (I) | initializing 3 solver directories - 2022-08-16 14:36:51 (D) | running setup for module 'preprocess.Default' - 2022-08-16 14:36:52 (D) | running setup for module 'optimize.Gradient' - 2022-08-16 14:36:53 (I) | re-loading optimization module from checkpoint - 2022-08-16 14:36:54 (I) | re-loading optimization module from checkpoint - 2022-08-16 14:36:54 (I) | - //////////////////////////////////////////////////////////////////////////////// - RUNNING ITERATION 01 - //////////////////////////////////////////////////////////////////////////////// - 2022-08-16 14:36:54 (I) | - ================================================================================ - RUNNING INVERSION WORKFLOW - ================================================================================ - 2022-08-16 14:36:54 (I) | 'evaluate_initial_misfit' has already been run, skipping - 2022-08-16 14:36:54 (I) | - //////////////////////////////////////////////////////////////////////////////// - EVALUATING EVENT KERNELS W/ ADJOINT SIMULATIONS - //////////////////////////////////////////////////////////////////////////////// - 2022-08-16 14:36:54 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log' - 2022-08-16 14:37:05 (D) | renaming output event kernels: 'alpha' -> 'vp' - 2022-08-16 14:37:05 (D) | renaming output event kernels: 'beta' -> 'vs' - 2022-08-16 14:37:05 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log' - 2022-08-16 14:37:16 (D) | renaming output event kernels: 'alpha' -> 'vp' - 2022-08-16 14:37:16 (D) | renaming output event kernels: 'beta' -> 'vs' - 2022-08-16 14:37:18 (I) | running SPECFEM executable bin/xspecfem2D, log to 'adj_solver.log' - 2022-08-16 14:37:29 (D) | renaming output event kernels: 'alpha' -> 'vp' - 2022-08-16 14:37:29 (D) | renaming output event kernels: 'beta' -> 'vs' - 2022-08-16 14:37:30 (I) | - //////////////////////////////////////////////////////////////////////////////// - GENERATING/PROCESSING MISFIT KERNEL - //////////////////////////////////////////////////////////////////////////////// - 2022-08-16 14:37:30 (I) | combining event kernels into single misfit kernel - 2022-08-16 14:37:31 (I) | scaling gradient to absolute model perturbations - 2022-08-16 14:37:32 (I) | stop workflow at `stop_after`: evaluate_gradient_from_kernels - - --------------- - -The function **run_adjoint_simulations()** has run adjoint simulations -to generate event kernels. The functions **postprocess_event_kernels** -and **evaluate_gradient_from_kernels** will have summed and (optionally) -smoothed the kernels to recover the gradient, which will be used to -update our starting model. - - **NOTE**: Since we did not specify any smoothing lenghts - (PAR.SMOOTH_H and PAR.SMOOTH_V), no smoothing of the gradient has - occurred. - -Using the gradient-descent optimization algorithm, SeisFlows will now -compute a search direction that will be used in the line search to -search for a best fitting model which optimally reduces the objective -function. We can take a look at where SeisFlows has stored the -information relating to kernel generation and the optimization -computation. - -.. code:: ipython3 - - # Gradient evaluation files are stored here, the kernels are stored separately from the gradient incase - # the user wants to manually manipulate them - ! ls scratch/eval_grad - - -.. parsed-literal:: - - gradient kernels misfit_kernel model residuals.txt - - -.. code:: ipython3 - - # SeisFlows3 stores all kernels and gradient information as SPECFEM binary (.bin) files - ! ls scratch/eval_grad/gradient - - -.. parsed-literal:: - - proc000000_vp_kernel.bin proc000000_vs_kernel.bin - - -.. code:: ipython3 - - # Kernels are stored on a per-event basis, and summed together (sum/). If smoothing was performed, - # we would see both smoothed and unsmoothed versions of the misfit kernel - ! ls scratch/eval_grad/kernels - - -.. parsed-literal:: - - 001 002 003 - - -.. code:: ipython3 - - # We can see that some new values have been stored in prepartion for the line search, - # including g_new (current gradient) and p_new (current search direction). These are also - # stored as vector NumPy arrays (.npy files) - ! ls scratch/optimize - - -.. parsed-literal:: - - checkpoint.npz f_new.txt g_new.npz m_new.npz - - -.. code:: ipython3 - - g_new = np.load("scratch/optimize/g_new.npz") - print(g_new["vs_kernel"]) - - -.. parsed-literal:: - - [[-1.18126331e-12 2.40273470e-12 3.97045036e-11 ... 9.62017688e-11 - 4.21140102e-11 3.96825021e-12]] - - --------------- - -3c. Line search and model update -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Let’s finish off the inversion by running through the line search, which -will generate new models using the gradient, evaluate the objective -function by running forward simulations, and comparing the evaluated -objective function with the value obtained in -**evalaute_initial_misfit**. - -Satisfactory reduction in the objective function will result in a -termination of the line search. We are using a bracketing line search -here `(Modrak et -al. 2018) `__, -which requires finding models which both increase and decrease the -misfit with respect to the initial evaluation. Therefore it takes -atleast two trial steps to complete the line search. - -.. code:: ipython3 - - ! seisflows par stop_after perform_line_search # We don't want to run the finalize_iteration argument so that we can explore the dir - - -.. parsed-literal:: - - stop_after: evaluate_gradient_from_kernels -> perform_line_search - - -.. code:: ipython3 - - ! seisflows submit - - -.. parsed-literal:: - - 2022-08-16 14:41:12 (D) | setting iteration==1 from state file - 2022-08-16 14:41:12 (I) | - ================================================================================ - SETTING UP INVERSION WORKFLOW - ================================================================================ - 2022-08-16 14:41:18 (D) | running setup for module 'system.Workstation' - 2022-08-16 14:41:21 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/sflog_003.txt - 2022-08-16 14:41:21 (D) | copying par/log file to: /home/bchow/Work/scratch/logs/parameters_003.yaml - 2022-08-16 14:41:21 (D) | running setup for module 'solver.Specfem2D' - 2022-08-16 14:41:21 (I) | initializing 3 solver directories - 2022-08-16 14:41:22 (D) | running setup for module 'preprocess.Default' - 2022-08-16 14:41:24 (D) | running setup for module 'optimize.Gradient' - 2022-08-16 14:41:26 (I) | re-loading optimization module from checkpoint - 2022-08-16 14:41:28 (I) | re-loading optimization module from checkpoint - 2022-08-16 14:41:28 (I) | - //////////////////////////////////////////////////////////////////////////////// - RUNNING ITERATION 01 - //////////////////////////////////////////////////////////////////////////////// - 2022-08-16 14:41:28 (I) | - ================================================================================ - RUNNING INVERSION WORKFLOW - ================================================================================ - 2022-08-16 14:41:28 (I) | 'evaluate_initial_misfit' has already been run, skipping - 2022-08-16 14:41:28 (I) | 'run_adjoint_simulations' has already been run, skipping - 2022-08-16 14:41:28 (I) | 'postprocess_event_kernels' has already been run, skipping - 2022-08-16 14:41:28 (I) | 'evaluate_gradient_from_kernels' has already been run, skipping - 2022-08-16 14:41:28 (I) | initializing 'bracket'ing line search - 2022-08-16 14:41:28 (I) | enforcing max step length safeguard - 2022-08-16 14:41:28 (D) | step length(s) = 0.00E+00 - 2022-08-16 14:41:28 (D) | misfit val(s) = 1.28E-03 - 2022-08-16 14:41:28 (I) | try: first evaluation, attempt guess step length, alpha=9.08E+11 - 2022-08-16 14:41:28 (I) | try: applying initial step length safegaurd as alpha has exceeded maximum step length, alpha_new=1.44E+10 - 2022-08-16 14:41:28 (D) | overwriting initial step length, alpha_new=2.32E+09 - 2022-08-16 14:41:28 (I) | trial model 'm_try' parameters: - 2022-08-16 14:41:28 (I) | 5800.00 <= vp <= 5800.00 - 2022-08-16 14:41:28 (I) | 3244.51 <= vs <= 3790.00 - 2022-08-16 14:41:29 (I) | - LINE SEARCH STEP COUNT 01 - -------------------------------------------------------------------------------- - 2022-08-16 14:41:29 (I) | evaluating objective function for source 001 - 2022-08-16 14:41:29 (D) | running forward simulation with 'Specfem2D' - 2022-08-16 14:41:33 (D) | quantifying misfit with 'Default' - 2022-08-16 14:41:33 (I) | evaluating objective function for source 002 - 2022-08-16 14:41:33 (D) | running forward simulation with 'Specfem2D' - 2022-08-16 14:41:36 (D) | quantifying misfit with 'Default' - 2022-08-16 14:41:36 (I) | evaluating objective function for source 003 - 2022-08-16 14:41:36 (D) | running forward simulation with 'Specfem2D' - 2022-08-16 14:41:40 (D) | quantifying misfit with 'Default' - 2022-08-16 14:41:40 (D) | misfit for trial model (f_try) == 8.65E-04 - 2022-08-16 14:41:40 (D) | step length(s) = 0.00E+00, 2.32E+09 - 2022-08-16 14:41:40 (D) | misfit val(s) = 1.28E-03, 8.65E-04 - 2022-08-16 14:41:40 (I) | try: misfit not bracketed, increasing step length using golden ratio, alpha=3.76E+09 - 2022-08-16 14:41:40 (I) | line search model 'm_try' parameters: - 2022-08-16 14:41:40 (I) | 5800.00 <= vp <= 5800.00 - 2022-08-16 14:41:40 (I) | 3086.61 <= vs <= 3969.23 - 2022-08-16 14:41:40 (I) | trial step unsuccessful. re-attempting line search - 2022-08-16 14:41:40 (I) | - LINE SEARCH STEP COUNT 02 - -------------------------------------------------------------------------------- - 2022-08-16 14:41:40 (I) | evaluating objective function for source 001 - 2022-08-16 14:41:40 (D) | running forward simulation with 'Specfem2D' - 2022-08-16 14:41:44 (D) | quantifying misfit with 'Default' - 2022-08-16 14:41:44 (I) | evaluating objective function for source 002 - 2022-08-16 14:41:44 (D) | running forward simulation with 'Specfem2D' - 2022-08-16 14:41:48 (D) | quantifying misfit with 'Default' - 2022-08-16 14:41:48 (I) | evaluating objective function for source 003 - 2022-08-16 14:41:48 (D) | running forward simulation with 'Specfem2D' - 2022-08-16 14:41:52 (D) | quantifying misfit with 'Default' - 2022-08-16 14:41:52 (D) | misfit for trial model (f_try) == 1.73E-03 - 2022-08-16 14:41:52 (D) | step length(s) = 0.00E+00, 2.32E+09, 3.76E+09 - 2022-08-16 14:41:52 (D) | misfit val(s) = 1.28E-03, 8.65E-04, 1.73E-03 - 2022-08-16 14:41:52 (I) | try: bracket acceptable but step length unreasonable attempting to re-adjust step length alpha=1.59E+09 - 2022-08-16 14:41:52 (I) | line search model 'm_try' parameters: - 2022-08-16 14:41:52 (I) | 5800.00 <= vp <= 5800.00 - 2022-08-16 14:41:52 (I) | 3325.01 <= vs <= 3698.63 - 2022-08-16 14:41:52 (I) | trial step unsuccessful. re-attempting line search - 2022-08-16 14:41:52 (I) | - LINE SEARCH STEP COUNT 03 - -------------------------------------------------------------------------------- - 2022-08-16 14:41:52 (I) | evaluating objective function for source 001 - 2022-08-16 14:41:52 (D) | running forward simulation with 'Specfem2D' - 2022-08-16 14:41:56 (D) | quantifying misfit with 'Default' - 2022-08-16 14:41:56 (I) | evaluating objective function for source 002 - 2022-08-16 14:41:56 (D) | running forward simulation with 'Specfem2D' - 2022-08-16 14:42:00 (D) | quantifying misfit with 'Default' - 2022-08-16 14:42:00 (I) | evaluating objective function for source 003 - 2022-08-16 14:42:00 (D) | running forward simulation with 'Specfem2D' - 2022-08-16 14:42:03 (D) | quantifying misfit with 'Default' - 2022-08-16 14:42:03 (D) | misfit for trial model (f_try) == 2.59E-03 - 2022-08-16 14:42:03 (D) | step length(s) = 0.00E+00, 1.59E+09, 2.32E+09, 3.76E+09 - 2022-08-16 14:42:03 (D) | misfit val(s) = 1.28E-03, 2.59E-03, 8.65E-04, 1.73E-03 - 2022-08-16 14:42:03 (I) | try: bracket acceptable but step length unreasonable attempting to re-adjust step length alpha=2.82E+09 - 2022-08-16 14:42:03 (I) | line search model 'm_try' parameters: - 2022-08-16 14:42:03 (I) | 5800.00 <= vp <= 5800.00 - 2022-08-16 14:42:03 (I) | 3189.77 <= vs <= 3852.13 - 2022-08-16 14:42:03 (I) | trial step unsuccessful. re-attempting line search - 2022-08-16 14:42:03 (I) | - LINE SEARCH STEP COUNT 04 - -------------------------------------------------------------------------------- - 2022-08-16 14:42:03 (I) | evaluating objective function for source 001 - 2022-08-16 14:42:03 (D) | running forward simulation with 'Specfem2D' - 2022-08-16 14:42:07 (D) | quantifying misfit with 'Default' - 2022-08-16 14:42:07 (I) | evaluating objective function for source 002 - 2022-08-16 14:42:07 (D) | running forward simulation with 'Specfem2D' - 2022-08-16 14:42:11 (D) | quantifying misfit with 'Default' - 2022-08-16 14:42:11 (I) | evaluating objective function for source 003 - 2022-08-16 14:42:11 (D) | running forward simulation with 'Specfem2D' - 2022-08-16 14:42:15 (D) | quantifying misfit with 'Default' - 2022-08-16 14:42:15 (D) | misfit for trial model (f_try) == 3.46E-03 - 2022-08-16 14:42:15 (D) | step length(s) = 0.00E+00, 1.59E+09, 2.32E+09, 2.82E+09, 3.76E+09 - 2022-08-16 14:42:15 (D) | misfit val(s) = 1.28E-03, 2.59E-03, 8.65E-04, 3.46E-03, 1.73E-03 - 2022-08-16 14:42:15 (I) | pass: bracket acceptable and step length reasonable. returning minimum line search misfit. - 2022-08-16 14:42:15 (I) | line search model 'm_try' parameters: - 2022-08-16 14:42:15 (I) | 5800.00 <= vp <= 5800.00 - 2022-08-16 14:42:15 (I) | 3244.51 <= vs <= 3790.00 - 2022-08-16 14:42:15 (I) | trial step successful. finalizing line search - 2022-08-16 14:42:15 (I) | - FINALIZING LINE SEARCH - -------------------------------------------------------------------------------- - 2022-08-16 14:42:15 (I) | writing optimization stats - 2022-08-16 14:42:15 (I) | renaming current (new) optimization vectors as previous model (old) - 2022-08-16 14:42:15 (I) | setting accepted trial model (try) as current model (new) - 2022-08-16 14:42:15 (I) | misfit of accepted trial model is f=8.645E-04 - 2022-08-16 14:42:15 (I) | resetting line search step count to 0 - 2022-08-16 14:42:15 (I) | stop workflow at `stop_after`: perform_line_search - - -From the log statements above, we can see that the SeisFlows line search -required 4 trial steps, where it modified values of Vs (shear-wave -velocity) until satisfactory reduction in the objective function was -met. This was the final step in the iteration, and so the finalization -of the line search made preparations for a subsequent iteration. - -.. code:: ipython3 - - # We can see that we have 'new' and 'old' values for each of the optimization values, - # representing the previous model (M00) and the current model (M01). - ! ls scratch/optimize - - -.. parsed-literal:: - - alpha.txt f_new.txt f_try.txt m_new.npz output_optim.txt - checkpoint.npz f_old.txt g_old.npz m_old.npz p_old.npz - - -.. code:: ipython3 - - # The stats/ directory contains text files describing the optimization/line search - ! cat scratch/optimize/output_optim.txt - - -.. parsed-literal:: - - step_count,step_length,gradient_norm_L1,gradient_norm_L2,misfit,if_restarted,slope,theta - 04,2.323E+09,9.243E-05,1.049E-06,1.279E-03,0,8.263E-13,0.000E+00 - - -4. Conclusions -~~~~~~~~~~~~~~ - -We’ve now seen how SeisFlows runs an **Inversion** workflow using the -**Specfem2D** solver on a **Workstation** system. More or less, this is -all you need to run SeisFlows with any combination of modules. The -specificities of a system or numerical solver are already handled -internally by SeisFlows, so if you want to use Specmfe3D_Cartesian as -your solver, you would only need to run -``seisflows par solver specfem3d`` at the beginning of your workflow -(you will also need to set up your Specfem3D models, similar to what we -did for Specfem2D here). To run on a slurm system like Chinook -(University of Alaska Fairbanks), you can run -``seisflows par system chinook``. diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 60e4d7c1..c4f1c874 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -918,6 +918,8 @@ def examples(self, run=None, choice=None, specfem2d_repo=None, **kwargs): specfem2d_repo = os.path.expanduser( os.path.abspath(specfem2d_repo) ) + os.chdir(self._args.workdir) # run example in working dir. + # $ python /path/to/example.py run path/to/specfem2d subprocess.run(f"python {fid} {arg2} {specfem2d_repo}", shell=True, check=False) From a3889d576a68dafd7292e6273cf6bd78d0ba7c62 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Sun, 28 Aug 2022 15:57:33 -0800 Subject: [PATCH 142/195] Feature plot2d (#134) * moved read and write fortran binary files into their own separate functions in the SPECFEM tools, rather than hiding them inside the Model class. * specfem Model class now reads and writes coordinates if working with SPECFEM2D * adding 2d plotting capabilites for seisflows to plot kernels and models specfem2d solver now exports X and Z coordinate files to disk which can be used by the Model class to plot sections also changed some naming schema of output models and gradients that get written to output directory * seisflows now has function which plots SPECFEM2D model, kernels, gradients bugfix specfem.Model had some conflicts when model parameters were called X or Z, which occurred during the Rosenbrock tests feature: kernels now only save updated quantities rather than kernels for all quantities that are created during the adjoint simulation * bug fix plot 2d model naming --- seisflows/seisflows.py | 52 +++++++- seisflows/solver/specfem.py | 25 ++-- seisflows/solver/specfem2d.py | 34 +++++- seisflows/tools/array.py | 2 - seisflows/tools/graphics.py | 23 ++-- seisflows/tools/specfem.py | 207 ++++++++++++++++++++++++-------- seisflows/workflow/inversion.py | 27 ++++- seisflows/workflow/migration.py | 10 +- 8 files changed, 301 insertions(+), 79 deletions(-) diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 60e4d7c1..2e9266b5 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -27,7 +27,7 @@ from seisflows.tools import unix, msg from seisflows.tools.config import load_yaml, custom_import, import_seisflows from seisflows.tools.specfem import (getpar, setpar, getpar_vel_model, - setpar_vel_model) + setpar_vel_model, Model) def sfparser(): @@ -230,6 +230,20 @@ def _format_action(self, action): # check.add_argument("args", type=str, nargs="*", # help="Generic arguments passed to check functions") # ========================================================================= + plot2d = subparser.add_parser( + "plot2d", formatter_class=argparse.RawDescriptionHelpFormatter, + description="""Plots model/kernels/gradient files located in the output/ + directory. ONLY available for SPECFEM2D models.""", + help="Plot 2D figures of models/kernels/gradients.") + + plot2d.add_argument("name", type=str, nargs="?", + help="Name of directory in the output/ directory") + plot2d.add_argument("parameter", type=str, nargs="?", + help="Name of parameter to plot from `name`. E.g., 'vs', " + "'vp' etc.") + plot2d.add_argument("-c", "--cmap", type=str, nargs="?", + help="colormap to be passed to PyPlot") + # ========================================================================= print_ = subparser.add_parser( "print", formatter_class=argparse.RawDescriptionHelpFormatter, description=""" @@ -297,7 +311,7 @@ def _format_action(self, action): # ========================================================================= # Defines all arguments/functions that expect a sub-argument subparser_dict = {"check": check, "par": par, "inspect": inspect, - "sempar": sempar, "clean": clean, + "sempar": sempar, "clean": clean, "plot2d": plot2d, "restart": restart, "print": print_, "reset": reset, "examples": examples, "swap": swap} if parser.parse_args().command in subparser_dict: @@ -956,6 +970,40 @@ def print(self, choice=None, **kwargs): acceptable_args[choice](*self._args.args, **kwargs) + def plot2d(self, name, parameter, cmap=None, **kwargs): + """ + Plot model, gradient or kernels in the PATH.OUTPUT + + :type name: str + :param name: Name of directory in the output/ directory + :type parameter: str + :param parameter: Name of parameter to plot from `name`, e.g., 'vs', + 'vp' etc. + :type cmap: str + :param cmap: optional colormap parameter to be passed to Pyplot + """ + # Figure out which models/gradients/kernels we can actually plot + _, output_dir, _ = getpar(key="path_output", + file=self._args.parameter_file, + delim=":") + acceptable_names = [ + os.path.basename(_) for _ in glob(os.path.join(output_dir, "*")) + ] + assert(name in acceptable_names), ( + f"`seisflows plot 2d` can only plot {acceptable_names}" + ) + # Grab model_init to use its coordinates + # name of directory for model_init is defined by solver.specfem.setup() + base_model = Model(path=os.path.join(output_dir, "MODEL_INIT")) + assert(base_model.coordinates is not None), \ + f"`MODEL_INIT` does not have any available 2D coordinates" + + # Now read in the actual updated values and update the model + plot_model = Model(path=os.path.join(output_dir, name)) + plot_model.coordinates = base_model.coordinates + # plot2d has internal check for acceptable parameter value + plot_model.plot2d(parameter=parameter, show=True, cmap=cmap) + def reset(self, choice=None, **kwargs): """ Mid-level function to wrap lower level reset functions diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 9f562bdb..3484ac09 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -402,12 +402,17 @@ def setup(self): """ self._initialize_working_directories() - # Export the initial model to the SeisFlows output directory - dst = os.path.join(self.path.output, "MODEL_INIT", "") - unix.mkdir(dst) - for key in self._parameters: - src = glob(os.path.join(self.path.model_init, f"*{key}{self._ext}")) - unix.cp(src, dst) + # Export the initial and target models to the SeisFlows output directory + # Copy ALL files with relevant extension, just incase + for name, model in zip(["MODEL_INIT", "MODEL_TRUE"], + [self.path.model_init, self.path.model_true]): + dst = os.path.join(self.path.output, name) + if not os.path.exists(dst): + unix.mkdir(dst) + for par in self._parameters: + src = glob(os.path.join(self.path.model_init, + f"*{par}{self._ext}")) + unix.cp(src, dst) def forward_simulation(self, executables=None, save_traces=False, export_traces=False, **kwargs): @@ -533,11 +538,15 @@ def adjoint_simulation(self, executables=None, save_kernels=False, # Save and export the kernels to user-defined locations if export_kernels: unix.mkdir(export_kernels) - unix.cp(src=glob(f"*_kernel{self._ext}"), dst=export_kernels) + for par in self._parameters: + unix.cp(src=glob(f"*{par}_kernel{self._ext}"), + dst=export_kernels) if save_kernels: unix.mkdir(save_kernels) - unix.mv(src=glob(f"*_kernel{self._ext}"), dst=save_kernels) + for par in self._parameters: + unix.mv(src=glob(f"*{par}_kernel{self._ext}"), dst=save_kernels) + def combine(self, input_path, output_path, parameters=None): """ diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index c0f8b761..7aa4daa1 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -48,8 +48,31 @@ def __init__(self, source_prefix="SOURCE", multiples=False, **kwargs): elif self.materials.upper() == "ELASTIC": self._parameters += ["vp", "vs"] + # @property + # def model_files(self): + # """ + # Return a list of paths to model files AND coordinates, which can be + # used by SeisFlows to plot SPECFEM2D models and gradients. + # + # :rtype: list + # :return: a list of full paths to model files that matches the internal + # list of solver parameters + # """ + # _model_files = super().model_files + # + # # Append coordinates 'x' and 'z' files to current list of model files + # for parameter in ["x", "z"]: + # _model_files += glob(os.path.join(self.path.mainsolver, + # self.model_databases, + # f"*{parameter}{self._ext}")) + # return _model_files + def setup(self): - """Setup the SPECFEM2D solver interface in a SeisFlows workflow""" + """ + Setup the SPECFEM2D solver interface in a SeisFlows workflow + Append coordinate files to exported model files so that we can use + them for plotting later + """ source_file = os.path.join(self.path.specfem_data, self.source_prefix) self._f0 = getpar(key="f0", file=source_file)[1] @@ -61,6 +84,15 @@ def setup(self): super().setup() + # Copy in coordinate files to the Model definition so we can plot + for name, model in zip(["MODEL_INIT", "MODEL_TRUE"], + [self.path.model_init, self.path.model_true]): + dst = os.path.join(self.path.output, name) + for par in ["x", "z"]: + src = glob(os.path.join(self.path.model_init, + f"*{par}{self._ext}")) + unix.cp(src, dst) + def smooth(self, input_path, output_path, parameters=None, span_h=0., span_v=0., use_gpu=False): """ diff --git a/seisflows/tools/array.py b/seisflows/tools/array.py index 69352ac3..77b421d2 100644 --- a/seisflows/tools/array.py +++ b/seisflows/tools/array.py @@ -2,8 +2,6 @@ """ Tools useful for array manipulation in Seisflows """ -import os - import numpy as np import scipy.signal as _signal import scipy.interpolate as _interp diff --git a/seisflows/tools/graphics.py b/seisflows/tools/graphics.py index b4660619..1d1233e5 100644 --- a/seisflows/tools/graphics.py +++ b/seisflows/tools/graphics.py @@ -1,6 +1,6 @@ #!/usr/bin/env python3 """ -Visualization tools for Seisflows +Basic visualization tools for SeisFlows to visualize waveforms, models, etc. """ import numpy as np import matplotlib.pyplot as plt @@ -9,25 +9,28 @@ from obspy.core.stream import Stream -def plot_gll(x, y, z): +def plot_2D_contour(x, z, data, cmap="viridis"): """ - Plots values on 2D unstructured GLL mesh + Plots values of a SPECEFM2D model on an unstructured grid + :type x: np.array :param x: x values of GLL mesh - :type y: np.array - :param y: y values of GLL mesh :type z: np.array :param z: z values of GLL mesh + :type data: np.array + :param data: D """ - r = (max(x) - min(x))/(max(y) - min(y)) + # Figure out aspect ratio of the figure + r = (max(x) - min(x))/(max(z) - min(z)) rx = r/np.sqrt(1 + r**2) ry = 1/np.sqrt(1 + r**2) - f = plt.figure(figsize=(10*rx, 10*ry)) - p = plt.tricontourf(x, y, z, 125) - plt.axis('image') + f = plt.figure(figsize=(10 * rx, 10 * ry)) + p = plt.tricontourf(x, z, data, levels=125, cmap=cmap) + cbar = plt.colorbar(p, shrink=0.8, pad=0.025) # , format="%.2f") + plt.axis("image") - return f, p + return f, p, cbar def plot_vector(t, v, xlabel='', ylabel='', title=''): diff --git a/seisflows/tools/specfem.py b/seisflows/tools/specfem.py index f285baf8..b7674a81 100644 --- a/seisflows/tools/specfem.py +++ b/seisflows/tools/specfem.py @@ -3,6 +3,7 @@ i.e., SPECFEM2D/3D/3D_GLOBE """ import os +import matplotlib.pyplot as plt import numpy as np from copy import deepcopy from glob import glob @@ -10,6 +11,8 @@ from seisflows.tools.config import Dict from seisflows.tools import unix, msg from seisflows.tools.math import poissons_ratio +from seisflows.tools.graphics import plot_2D_contour + class Model: @@ -49,6 +52,7 @@ def __init__(self, path=None, fmt="", parameters=None): self.path = path self.fmt = fmt self.model = None + self.coordinates = None self._parameters = parameters self._ngll = None self._nproc = None @@ -57,7 +61,8 @@ def __init__(self, path=None, fmt="", parameters=None): if self.path and os.path.exists(path): # Read existing model from a previously saved .npz file if os.path.splitext(path)[-1] == ".npz": - self.model, self._ngll, self.fmt = self.load(file=self.path) + self.model, self.coordinates, self._ngll, self.fmt = \ + self.load(file=self.path) _first_key = list(self.model.keys())[0] self._nproc = len(self.model[_first_key]) # Read a SPECFEM model from its native output files @@ -67,6 +72,11 @@ def __init__(self, path=None, fmt="", parameters=None): self._nproc, self.available_parameters = \ self._get_nproc_parameters() self.model = self.read(parameters=parameters) + # Coordinates are only useful for SPECFEM2D models + try: + self.coordinates = self.read_coordinates_specfem2d() + except AssertionError: + pass # .sorted() enforces parameter order every time, otherwise things # can get screwy if keys returns different each time @@ -199,6 +209,44 @@ def read(self, parameters=None): return parameter_dict + def read_coordinates_specfem2d(self): + """ + Attempt to read coordinate files from the given model definition. + This is only really useful for SPECFEM2D, where we can plot the + model, kernel and gradient using matplotlib. + + .. warning + This will NOT work for SPECEFM3D. When you get to 3D, the + coordinates don't match up one-to-one with the model values so you + need external viewing software (e.g., ParaView) to plot. + + :rtype: Dict + :return: a dictioanary with the X and Z coordinates read in from + a SPECFEM2D model, if applicable + """ + coordinates = {"x": [], "z": []} + if self.fmt == ".bin": + coordinates["x"] = self._read_model_fortran_binary(parameter="x") + coordinates["z"] = self._read_model_fortran_binary(parameter="z") + elif self.fmt == ".dat": + fids = glob(os.path.join(self.path, + self.fnfmt(val="*", ext=".dat"))) + for fid in sorted(fids): + coordinates["x"].append(np.loadtxt(fid).T[:, 0]) + coordinates["z"].append(np.loadtxt(fid).T[:, 0]) + + # Internal check for parameter validity by checking length of coord + # against length of model. If they're not the same, then it's not + # useful to store coordinates because they can't be used for plotting + assert(len(coordinates["x"]) == len(coordinates["z"])), \ + f"coordinate arrays do not match in length" + # Assuming all model parameters have the same length + assert(len(coordinates["x"]) == + len(self.model[list(self.model.keys())[0]])), \ + f"length of coordinates array does not match model length" + + return coordinates + def merge(self, parameter=None): """ Convert dictionary representation `model` to vector representation `m` @@ -307,6 +355,11 @@ def save(self, path): .npz array so that it can be loaded in at a later time for future use """ model = self.split() + if self.coordinates: + # Incase we have model parameters called 'x' or 'z', rename for save + model["x_coord"] = self.coordinates["x"] + model["z_coord"] = self.coordinates["z"] + np.savez(file=path, fmt=self.fmt, **model) def load(self, file): @@ -321,17 +374,24 @@ def load(self, file): :return: (Model Dictionary, ngll points for each slice, file format) """ model = Dict() + coords = Dict() ngll = [] data = np.load(file=file) for i, key in enumerate(data.files): if key == "fmt": continue - model[key] = data[key] - if not ngll: - for array in model[key]: - ngll.append(len(array)) + # Special case where we are using SPECFEM2D and carry around coords + elif "coord" in key: + coords[key[0]] = data[key] # drop '_coord' suffix from `save` + else: + model[key] = data[key] + # Assign the number of GLL points per slice. Only needs to happen + # once because all model values should have same points/slice + if not ngll: + for array in model[key]: + ngll.append(len(array)) - return model, ngll, str(data["fmt"]) + return model, coords, ngll, str(data["fmt"]) def update(self, model=None, vector=None): """ @@ -344,6 +404,64 @@ def update(self, model=None, vector=None): elif vector is not None: self.model = self.split(vector=vector) + def plot2d(self, parameter, cmap=None, show=True, save=None): + """ + Plot internal model parameters as a 2D contour plot. Kwargs are passed + to underlying matplotlib.pylpot.tricontourf function. + + .. warning:: + This is only available for SPECFEM2D models. SPECFEM3D model + coordinates do not match the model vectors (because the grids are + irregular) and cannot be visualized like this. + + :type parameter: str + :param parameter: chosen internal parameter value to plot. + :type cmap: str + :param cmap: colormap which match available matplotlib colormap. + If None, will choose default colormap based on parameter choice. + :type show: bool + :param show: show the figure after plotting + :type save: str + :param save: if not None, full path to figure to save the output image + """ + assert(parameter in self._parameters), \ + f"chosen `parameter` must be in {self._parameters}" + + assert(self.coordinates is not None), ( + f"`plot2d` function requires model coordinates which are only " + f"available for solver SPECFEM2D" + ) + + # Choose default colormap based on parameter values + if cmap is None: + if "kernel" in parameter: + cmap = "seismic_r" + else: + cmap = "Spectral" + + # 'Merge' the coordinate matrices to get a vector representation + x, z = np.array([]), np.array([]) + for iproc in range(self.nproc): + x = np.append(x, self.coordinates["x"][iproc]) + z = np.append(z, self.coordinates["z"][iproc]) + data = self.merge(parameter=parameter) + + f, p, cbar = plot_2D_contour(x=x, z=z, data=data, cmap=cmap) + + # Set some figure labels based on information we know here + ax = plt.gca() + ax.set_xlabel("X [m]") + ax.set_ylabel("Z [m]") + ax.set_title(f"{parameter.title()}_min = {data.min()};\n" + f"{parameter.title()}_max = {data.max()};\n" + f"{parameter.title()}_mean = {data.mean()}; ") + cbar.ax.set_ylabel(parameter.title(), rotation=270, labelpad=15) + + if save: + plt.savefig(save) + if show: + plt.show() + def _get_nproc_parameters(self): """ Get the number of processors and the available parameters from a list of @@ -404,29 +522,12 @@ def _read_model_fortran_binary(self, parameter): :rtype: np.array :return: vector of model values for given `parameter` """ - def _read(filename): - """Read a single slice (e.g., proc000000_vs.bin) binary file""" - nbytes = os.path.getsize(filename) - with open(filename, 'rb') as file: - # read size of record - file.seek(0) - n = np.fromfile(file, dtype='int32', count=1)[0] - - if n == nbytes-8: - file.seek(4) - data = np.fromfile(file, dtype='float32') - return data[:-1] - else: - file.seek(0) - data = np.fromfile(file, dtype='float32') - return data - array = [] fids = glob(os.path.join( self.path, self.fnfmt(val=parameter, ext=".bin")) ) for fid in sorted(fids): # make sure were going in numerical order - array.append(_read(fid)) + array.append(read_fortran_binary(fid)) array = np.array(array) @@ -485,13 +586,8 @@ def _write_model_fortran_binary(self, path): for i, data in enumerate(self.model[parameter]): filename = self.fnfmt(i=i, val=parameter, ext=".bin") filepath = os.path.join(path, filename) - buffer = np.array([4 * len(data)], dtype="int32") - data = data.astype("float32") - with open(filepath, 'wb') as f: - buffer.tofile(f) - data.tofile(f) - buffer.tofile(f) + write_fortran_binary(arr=data, filename=filepath) def check_source_names(path_specfem_data, source_prefix, ntask=None): @@ -732,46 +828,57 @@ def setpar_vel_model(file, model): setpar(key="nbmodels", val=len(model), file=file) -def _read(filename): +def read_fortran_binary(filename): """ - Legacy code: Reads Fortran style binary data into numpy array. + Reads Fortran-style unformatted binary data into numpy array. .. note:: - Has been rewritten into the Model class but left here if useful + The FORTRAN runtime system embeds the record boundaries in the data by + inserting an INTEGER*4 byte count at the beginning and end of each + unformatted sequential record during an unformatted sequential WRITE. + see: https://docs.oracle.com/cd/E19957-01/805-4939/6j4m0vnc4/index.html + + :type filename: str + :param filename: full path to the Fortran unformatted binary file to read + :rtype: np.array + :return: numpy array with data with data read in as type Float32 """ nbytes = os.path.getsize(filename) - with open(filename, 'rb') as file: + with open(filename, "rb") as file: # read size of record file.seek(0) - n = np.fromfile(file, dtype='int32', count=1)[0] + n = np.fromfile(file, dtype="int32", count=1)[0] - if n == nbytes-8: + if n == nbytes - 8: file.seek(4) - data = np.fromfile(file, dtype='float32') + data = np.fromfile(file, dtype="float32") return data[:-1] else: file.seek(0) - data = np.fromfile(file, dtype='float32') + data = np.fromfile(file, dtype="float32") return data -def _write(v, filename): +def write_fortran_binary(arr, filename): """ - Legacy code: Writes Fortran style binary files - Data are written as single precision floating point numbers - - .. note:: - Has been rewritten into the Model class but left here if useful + Writes Fortran style binary files. Data are written as single precision + floating point numbers. .. note:: FORTRAN unformatted binaries are bounded by an INT*4 byte count. This function mimics that behavior by tacking on the boundary data. https://docs.oracle.com/cd/E19957-01/805-4939/6j4m0vnc4/index.html + + :type arr: np.array + :param arr: data array to write as Fortran binary + :type filename: str + :param filename: full path to file that should be written in format + unformatted Fortran binary """ - n = np.array([4 * len(v)], dtype='int32') - v = np.array(v, dtype='float32') + buffer = np.array([4 * len(arr)], dtype="int32") + data = np.array(arr, dtype="float32") - with open(filename, 'wb') as file: - n.tofile(file) - v.tofile(file) - n.tofile(file) + with open(filename, "wb") as file: + buffer.tofile(file) + data.tofile(file) + buffer.tofile(file) diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index edc156cf..91d71419 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -241,6 +241,11 @@ def evaluate_initial_misfit(self): """ if self.iteration == 1: super().evaluate_initial_misfit() + + # Expose the initial model to the optimization library + model = Model(self.path.model_init, + parameters=self.solver._parameters) + self.optimize.save_vector(name="m_new", m=model) else: # Thrifty inversion SKIPS initial misfit evaluation, re-using final # model from previous line search. Can only happen mid-workflow @@ -280,10 +285,21 @@ def evaluate_gradient_from_kernels(self): """ super().evaluate_gradient_from_kernels() - model = Model(os.path.join(self.path.eval_grad, "model"), - parameters=self.solver._parameters) - self.optimize.save_vector(name="m_new", m=model) - + # Rename kernels (K) and gradient (G) output files by iteration number + # so they don't get overwritten by future iterations. + src = os.path.join(self.path.output, "kernels") + dst = os.path.join(self.path.output, f"KERNELS_{self.iteration:0>2}") + if os.path.exists(src): + logger.debug(f"{src} -> {dst}") + unix.mv(src, dst) + + src = os.path.join(self.path.output, "gradient") + dst = os.path.join(self.path.output, f"GRADIENT_{self.iteration:0>2}") + if os.path.exists(src): + logger.debug(f"{src} -> {dst}") + unix.mv(src, dst) + + # Expose the gradient to the optimization library gradient = Model(path=os.path.join(self.path.eval_grad, "gradient")) self.optimize.save_vector(name="g_new", m=gradient) @@ -417,7 +433,7 @@ def finalize_iteration(self): if self.export_model: model = self.optimize.load_vector("m_new") model.write(path=os.path.join(self.path.output, - f"M{self.iteration:0>2}"), + f"MODEL_{self.iteration:0>2}"), ) # Update optimization @@ -439,6 +455,7 @@ def finalize_iteration(self): self.preprocess.finalize() + def _update_thrifty_status(self): """ Determine if line search forward simulation can be carried over to the diff --git a/seisflows/workflow/migration.py b/seisflows/workflow/migration.py index cf065dcc..637b4b89 100644 --- a/seisflows/workflow/migration.py +++ b/seisflows/workflow/migration.py @@ -54,7 +54,7 @@ class Migration(Forward): """ __doc__ = Forward.__doc__ + __doc__ - def __init__(self, modules=None, path_mask=None, export_gradient=False, + def __init__(self, modules=None, path_mask=None, export_gradient=True, export_kernels=False, **kwargs): """ Instantiate Migration-specific parameters @@ -198,3 +198,11 @@ def evaluate_gradient_from_kernels(self): gradient.update(vector=gradient.vector * mask.vector) gradient.write(path=os.path.join(self.path.eval_grad, "gradient")) + + # Export gradient to disk + if self.export_gradient: + logger.info("exporting gradient to disk") + src = os.path.join(self.path.eval_grad, "gradient") + dst = os.path.join(self.path.output, "gradient") + unix.cp(src, dst) + From 5942760f3612c8513a576552eaa03b53425a69a9 Mon Sep 17 00:00:00 2001 From: bch0w Date: Sun, 28 Aug 2022 16:34:51 -0800 Subject: [PATCH 143/195] bugfix seisflows plot2d picking up non model directories for plotting pyaflowa now finalizes its scratch directory into output/pyaflowa, rather than directly into output --- seisflows/preprocess/pyaflowa.py | 6 +++--- seisflows/seisflows.py | 30 ++++++++++++++++++++++-------- 2 files changed, 25 insertions(+), 11 deletions(-) diff --git a/seisflows/preprocess/pyaflowa.py b/seisflows/preprocess/pyaflowa.py index 3dda7d37..6bde4b62 100644 --- a/seisflows/preprocess/pyaflowa.py +++ b/seisflows/preprocess/pyaflowa.py @@ -579,19 +579,19 @@ def finalize(self): if self.export_datasets: src = glob(os.path.join(self.path._datasets, "*.h5")) src += glob(os.path.join(self.path._datasets, "*.csv")) # inspector - dst = os.path.join(self.path.output, "datasets", "") + dst = os.path.join(self.path.output, "pyaflowa", "datasets", "") unix.mkdir(dst) unix.cp(src, dst) if self.export_figures: src = glob(os.path.join(self.path._figures, "*.pdf")) - dst = os.path.join(self.path.output, "figures", "") + dst = os.path.join(self.path.output, "pyaflowa", "figures", "") unix.mkdir(dst) unix.cp(src, dst) if self.export_log_files: src = glob(os.path.join(self.path._logs, "*.log")) - dst = os.path.join(self.path.output, "logs", "") + dst = os.path.join(self.path.output, "pyaflowa", "logs", "") unix.mkdir(dst) unix.cp(src, dst) diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 99ef1e37..23045eb5 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -243,6 +243,8 @@ def _format_action(self, action): "'vp' etc.") plot2d.add_argument("-c", "--cmap", type=str, nargs="?", help="colormap to be passed to PyPlot") + plot2d.add_argument("-s", "--savefig", type=str, nargs="?", default=None, + help="optional name and path to save figure") # ========================================================================= print_ = subparser.add_parser( "print", formatter_class=argparse.RawDescriptionHelpFormatter, @@ -972,7 +974,8 @@ def print(self, choice=None, **kwargs): acceptable_args[choice](*self._args.args, **kwargs) - def plot2d(self, name, parameter, cmap=None, **kwargs): + def plot2d(self, name=None, parameter=None, cmap=None, savefig=None, + **kwargs): """ Plot model, gradient or kernels in the PATH.OUTPUT @@ -983,17 +986,27 @@ def plot2d(self, name, parameter, cmap=None, **kwargs): 'vp' etc. :type cmap: str :param cmap: optional colormap parameter to be passed to Pyplot + :type savefig: str + :param savefig: optional name and path of filename to save figure + to disk """ # Figure out which models/gradients/kernels we can actually plot _, output_dir, _ = getpar(key="path_output", file=self._args.parameter_file, delim=":") - acceptable_names = [ - os.path.basename(_) for _ in glob(os.path.join(output_dir, "*")) - ] - assert(name in acceptable_names), ( - f"`seisflows plot 2d` can only plot {acceptable_names}" - ) + # Assuming only models/kernels/gradients have the format *_* in output + acceptable_names = sorted([ + os.path.basename(_) for _ in glob(os.path.join(output_dir, "*_*")) + ]) + if name is None: + print(msg.cli(f"Available models/gradients/kernels", + items=sorted(acceptable_names), header="Plot2D") + ) + sys.exit(0) + else: + assert(name in acceptable_names), ( + f"`seisflows plot 2d` can only plot {acceptable_names}" + ) # Grab model_init to use its coordinates # name of directory for model_init is defined by solver.specfem.setup() base_model = Model(path=os.path.join(output_dir, "MODEL_INIT")) @@ -1004,7 +1017,8 @@ def plot2d(self, name, parameter, cmap=None, **kwargs): plot_model = Model(path=os.path.join(output_dir, name)) plot_model.coordinates = base_model.coordinates # plot2d has internal check for acceptable parameter value - plot_model.plot2d(parameter=parameter, show=True, cmap=cmap) + plot_model.plot2d(parameter=parameter, cmap=cmap, show=True, + save=savefig) def reset(self, choice=None, **kwargs): """ From 1c6d4f3006fa22e0651e49899851dbc702a271f9 Mon Sep 17 00:00:00 2001 From: bch0w Date: Sun, 28 Aug 2022 16:59:33 -0800 Subject: [PATCH 144/195] added plot2d to command line tool docs page --- docs/command_line_tool.rst | 118 ++++++++++++ .../command_line_tool_39_0.png | Bin 0 -> 66983 bytes docs/notebooks/command_line_tool.ipynb | 173 +++++++++++++++++- 3 files changed, 289 insertions(+), 2 deletions(-) create mode 100644 docs/images/command_line_tool_files/command_line_tool_39_0.png diff --git a/docs/command_line_tool.rst b/docs/command_line_tool.rst index b22c5a34..7afc0894 100644 --- a/docs/command_line_tool.rst +++ b/docs/command_line_tool.rst @@ -532,3 +532,121 @@ defines. -h, --help show this help message and exit -f, --force Skip the clean warning check statement + +Plotting +~~~~~~~~ + +seisflows plot2d +^^^^^^^^^^^^^^^^ + +``plot2d`` allows you to quickly plot SPECFEM2D models, kernels and +gradients which have been exported to disk during a SeisFlows workflow. +From a SeisFlows working directory the format for running ``plot2d`` is +provided in the help message. + +.. code:: ipython3 + + # a directory where we have run example #2 + %cd ~/Work/scratch/ + ! ls + + +.. parsed-literal:: + + /home/bchow/Work/scratch + logs parameters.yaml sflog.txt specfem2d + output scratch sfstate.txt specfem2d_workdir + + +.. code:: ipython3 + + ! seisflows plot2d -h + + +.. parsed-literal:: + + usage: seisflows plot2d [-h] [-c [CMAP]] [-s [SAVEFIG]] [name] [parameter] + + Plots model/kernels/gradient files located in the output/ + directory. ONLY available for SPECFEM2D models. + + positional arguments: + name Name of directory in the output/ directory + parameter Name of parameter to plot from `name`. E.g., 'vs', + 'vp' etc. + + optional arguments: + -h, --help show this help message and exit + -c [CMAP], --cmap [CMAP] + colormap to be passed to PyPlot + -s [SAVEFIG], --savefig [SAVEFIG] + optional name and path to save figure + + +Running ``plot2d`` without any arguments will print out a list of +available directories you can plot + +.. code:: ipython3 + + ! seisflows plot2d + + +.. parsed-literal:: + + PLOT2D + ////// + Available models/gradients/kernels + + GRADIENT_01 + MODEL_01 + MODEL_INIT + MODEL_TRUE + + +Users will also have to choose which parameter they would like to plot, +which is defined by the available parameters in the underlying model. +Incorrect choices will throw an AssertionError which will tell you what +parameters are available to plot. + +.. code:: ipython3 + + ! seisflows plot2d GRADIENT_01 + + +.. parsed-literal:: + + Traceback (most recent call last): + File "/home/bchow/miniconda3/envs/docs/bin/seisflows", line 33, in + sys.exit(load_entry_point('seisflows', 'console_scripts', 'seisflows')()) + File "/home/bchow/REPOSITORIES/seisflows/seisflows/seisflows.py", line 1298, in main + sf() + File "/home/bchow/REPOSITORIES/seisflows/seisflows/seisflows.py", line 410, in __call__ + getattr(self, self._args.command)(**vars(self._args)) + File "/home/bchow/REPOSITORIES/seisflows/seisflows/seisflows.py", line 1021, in plot2d + save=savefig) + File "/home/bchow/REPOSITORIES/seisflows/seisflows/tools/specfem.py", line 428, in plot2d + f"chosen `parameter` must be in {self._parameters}" + AssertionError: chosen `parameter` must be in ['vp_kernel', 'vs_kernel'] + + +.. code:: ipython3 + + ! seisflows plot2d GRADIENT_01 vs_kernel --savefig gradient_01_vs_kernel.png + + +.. parsed-literal:: + + Figure(707.107x707.107) + + +.. code:: ipython3 + + from IPython.display import Image + Image(filename='gradient_01_vs_kernel.png') + + + + +.. image:: images/command_line_tool_files/command_line_tool_39_0.png + + diff --git 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\"/home/bchow/REPOSITORIES/seisflows/seisflows/seisflows.py\", line 410, in __call__\r\n", + " getattr(self, self._args.command)(**vars(self._args))\r\n", + " File \"/home/bchow/REPOSITORIES/seisflows/seisflows/seisflows.py\", line 1021, in plot2d\r\n", + " save=savefig)\r\n", + " File \"/home/bchow/REPOSITORIES/seisflows/seisflows/tools/specfem.py\", line 428, in plot2d\r\n", + " f\"chosen `parameter` must be in {self._parameters}\"\r\n", + "AssertionError: chosen `parameter` must be in ['vp_kernel', 'vs_kernel']\r\n" + ] + } + ], + "source": [ + "! seisflows plot2d GRADIENT_01" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Figure(707.107x707.107)\r\n" + ] + } + ], + "source": [ + "! seisflows plot2d GRADIENT_01 vs_kernel --savefig gradient_01_vs_kernel.png" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "Image(filename='gradient_01_vs_kernel.png') " + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -669,7 +838,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.12" } }, "nbformat": 4, From 1b76e32ea410c3ab965e157587ee20686fb5cef2 Mon Sep 17 00:00:00 2001 From: bch0w Date: Sun, 28 Aug 2022 18:10:14 -0800 Subject: [PATCH 145/195] updated specfem2d example with what user should expect when finishing example problem #1 --- .../specfem2d_example_15_0.png | Bin 0 -> 67022 bytes docs/notebooks/specfem2d_example.ipynb | 113 +++++++++++++++++- .../specfem2d_example_15_0.png | Bin 0 -> 67022 bytes docs/specfem2d_example.rst | 97 +++++++++++++++ 4 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zmSU=wMuif-ef5yU$6vmn3|ZcW)RP~XO+V#CAW~0<Hn=2-PU^I3GTm1rG_hg-PKXwpka%rcBv8lQTdAR{q(3siEjXQQiwKR<)_wvB9dG z3?YvA-ey&Cx&2q~X{ReOM3Uq3LfUIXI9fbe3{->EiY1%6Vp#VV_7pSieBM3to)H%# zVqRP3SQ`__ you can figure out how to navigate around and look at the results of our small inversion problem. We will have a look at a few of the files and directories here." + "Using the `working directory documentation page `__ you can figure out how to navigate around and look at the results of our small inversion problem. We will have a look at a few of the files and directories here. I've run the example problem in a scratch directory but your output directory should look the same." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/bchow/Work/scratch\n", + "logs\tparameters.yaml sflog.txt specfem2d\r\n", + "output\tscratch\t\t sfstate.txt specfem2d_workdir\r\n" + ] + } + ], + "source": [ + "%cd ~/Work/scratch\n", + "! ls" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the `output/` directory, we can see the updated model from our first iteration (MODEL_01) and the gradient that was used to create it (GRADIENT_01). The 2nd iteration produced a gradient (GRADIENT_02), but was unable to succesfully reduce the misfit during the line search, which is why we don't have a MODEL_02." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GRADIENT_01 GRADIENT_02 MODEL_01 MODEL_INIT\tMODEL_TRUE\n", + "\n", + "proc000000_vp.bin proc000000_vs.bin\n" + ] + } + ], + "source": [ + "! ls output\n", + "! echo\n", + "! ls output/MODEL_01" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because we're working with SPECFEM2D, we can plot the models and gradients that were created during our workflow using the `seisflows plot2d` command. If we use the `--savefig` option we can also save the output .png files to disk." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Figure(707.107x707.107)\r\n" + ] + } + ], + "source": [ + "! seisflows plot2d GRADIENT_01 vs_kernel --savefig i02_gradient_vs_kernel.png" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Because this docs page was made in a Jupyter Notebook, we need to use IPython to open the resulting .png\n", + "from IPython.display import Image\n", + "Image(filename='i02_gradient_vs_kernel.png') " + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Have a look at the `working directory documentation page `__ for more detailed explanations of how to navigate the SeisFlows working directory." + ] }, { "cell_type": "markdown", @@ -181,7 +280,9 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", diff --git a/docs/notebooks/specfem2d_example_files/specfem2d_example_15_0.png b/docs/notebooks/specfem2d_example_files/specfem2d_example_15_0.png new file mode 100644 index 0000000000000000000000000000000000000000..25325e27bd2c61df45c76363f1f69b8ab008d987 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We will have a look at a few of the files and directories here. I've run the example problem in a scratch directory but your output directory should look the same. + +.. code:: ipython3 + + %cd ~/Work/scratch + ! ls + + +.. parsed-literal:: + + /home/bchow/Work/scratch + logs parameters.yaml sflog.txt specfem2d + output scratch sfstate.txt specfem2d_workdir + + +In the ``output/`` directory, we can see the updated model from our +first iteration (MODEL_01) and the gradient that was used to create it +(GRADIENT_01). The 2nd iteration produced a gradient (GRADIENT_02), but +was unable to succesfully reduce the misfit during the line search, +which is why we don’t have a MODEL_02. + +.. code:: ipython3 + + ! ls output + ! echo + ! ls output/MODEL_01 + + +.. parsed-literal:: + + GRADIENT_01 GRADIENT_02 MODEL_01 MODEL_INIT MODEL_TRUE + + proc000000_vp.bin proc000000_vs.bin + + +Because we’re working with SPECFEM2D, we can plot the models and +gradients that were created during our workflow using the +``seisflows plot2d`` command. If we use the ``--savefig`` option we can +also save the output .png files to disk. + +.. code:: ipython3 + + ! seisflows plot2d GRADIENT_01 vs_kernel --savefig i02_gradient_vs_kernel.png + + +.. parsed-literal:: + + Figure(707.107x707.107) + + +.. code:: ipython3 + + # Because this docs page was made in a Jupyter Notebook, we need to use IPython to open the resulting .png + from IPython.display import Image + Image(filename='i02_gradient_vs_kernel.png') + + + + +.. image:: images/specfem2d_example_files/specfem2d_example_15_0.png + + + +Have a look at the `working directory documentation page `__ for more detailed explanations of how to navigate the SeisFlows working directory. Example #2 ~~~~~~~~~~ From 75243790eadcc826d18e754538cc73a65811df26 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Mon, 29 Aug 2022 13:04:24 -0800 Subject: [PATCH 146/195] bugfix: specfem2d smoothing was not providing smoothing lengths (h and v) properly to underlying smoothing function --- seisflows/solver/specfem.py | 4 ++-- seisflows/solver/specfem2d.py | 23 ++--------------------- 2 files changed, 4 insertions(+), 23 deletions(-) diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 3484ac09..551979e1 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -628,8 +628,8 @@ def smooth(self, input_path, output_path, parameters=None, span_h=None, if span_v is None: span_v = self.smooth_v - logger.info(f"smoothing {parameters} with horizontal Gaussian " - f"{span_h}m and vertical Gaussian {span_v}m") + logger.debug(f"smoothing {parameters} with horizontal Gaussian " + f"{span_h}m and vertical Gaussian {span_v}m") if not os.path.exists(output_path): unix.mkdir(output_path) diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index 7aa4daa1..1fb6bc6b 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -48,25 +48,6 @@ def __init__(self, source_prefix="SOURCE", multiples=False, **kwargs): elif self.materials.upper() == "ELASTIC": self._parameters += ["vp", "vs"] - # @property - # def model_files(self): - # """ - # Return a list of paths to model files AND coordinates, which can be - # used by SeisFlows to plot SPECFEM2D models and gradients. - # - # :rtype: list - # :return: a list of full paths to model files that matches the internal - # list of solver parameters - # """ - # _model_files = super().model_files - # - # # Append coordinates 'x' and 'z' files to current list of model files - # for parameter in ["x", "z"]: - # _model_files += glob(os.path.join(self.path.mainsolver, - # self.model_databases, - # f"*{parameter}{self._ext}")) - # return _model_files - def setup(self): """ Setup the SPECFEM2D solver interface in a SeisFlows workflow @@ -93,8 +74,8 @@ def setup(self): f"*{par}{self._ext}")) unix.cp(src, dst) - def smooth(self, input_path, output_path, parameters=None, span_h=0., - span_v=0., use_gpu=False): + def smooth(self, input_path, output_path, parameters=None, span_h=None, + span_v=None, use_gpu=False): """ Specfem2D requires additional model parameters in directory to perform the xsmooth_sem task. This function will copy these files into the From 2913cba922d01c186c6c2f8a58b48f3e75a1fcad Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Mon, 29 Aug 2022 13:26:13 -0800 Subject: [PATCH 147/195] updated notebook convert function to do some bookkeeping after creating the .rst files from the .ipynb notebooks --- docs/notebooks/convert.py | 56 ++++++++++++++---- .../specfem2d_example_15_0.png | Bin 67022 -> 0 bytes docs/specfem2d_example.rst | 6 +- 3 files changed, 47 insertions(+), 15 deletions(-) delete mode 100644 docs/notebooks/specfem2d_example_files/specfem2d_example_15_0.png diff --git a/docs/notebooks/convert.py b/docs/notebooks/convert.py index 9a91ec81..d6bc780d 100644 --- a/docs/notebooks/convert.py +++ b/docs/notebooks/convert.py @@ -13,9 +13,9 @@ Copied and edited from Pyflex documentation """ -import io import os import sys +import shutil import glob @@ -24,29 +24,61 @@ def convert_nb(nbname): Open up a Jupyter notebook, execute the code inside and save to a new notebook, and then convert the executed notebook to a .rst file for docs. """ - # Remove file extension nbname = os.path.splitext(nbname)[0] filename = f"{nbname}.ipynb" rst_filename = filename.replace("ipynb", "rst") - # Run nbconvert to execute a notebook, allowing errors through and saving - # the new, executed notebook in place - - # !!! For SeisFlows documentation, we don't want to be running notebooks - # os.system(f"jupyter nbconvert --to notebook " - # f"--execute {filename} --inplace") - # os.system("rm -rf ./index_files") - # Run nbconvert to convert executed notebook to RST format for docs page os.system(f"jupyter nbconvert --to rst {filename}") - os.system(f"rm ../{rst_filename}") - os.system(f"mv {rst_filename} ..") + + # Overwrite any existing .rst file with newly created one + rst_path = f"../{rst_filename}" + if os.path.exists(rst_path): + os.remove(rst_path) + os.rename(rst_filename, rst_path) + + +def adjust_nb(nbname): + """ + Since the notebooks are not stored in the same directory, their images + will point at the wrong directory. Just make sure that we adjust the + path and then move the created images to the correct location. Probably + an easier way to do this but for this scale this should work. + """ + nbname = os.path.splitext(nbname)[0] + filename = f"{nbname}.ipynb" + rst_filename = filename.replace("ipynb", "rst") + rst_path = f"../{rst_filename}" + + # Move the created images files to a new directory + nb_files_path_old = f"{nbname}_files" + nb_files_path_new = f"../images/{nb_files_path_old}" + + if os.path.exists(nb_files_path_old): + if os.path.exists(nb_files_path_new): + shutil.rmtree(nb_files_path_new) + os.rename(nb_files_path_old, nb_files_path_new) + + with open(rst_path, "r") as f: + rst_file = f.readlines() + + # Check if any images in the converted notebook + for i, line in enumerate(rst_file[:]): + if ".. image::" in line: + # If so, replace with correct image location + new_line = line.replace(".. image:: ", ".. image:: images/") + rst_file[i] = new_line + + with open(rst_path, "w") as f: + f.writelines(rst_file) if __name__ == "__main__": if sys.argv[1:]: for nbname in sys.argv[1:]: 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zmSU=wMuif-ef5yU$6vmn3|ZcW)RP~XO+V#CAW~0<Hn=2-PU^I3GTm1rG_hg-PKXwpka%rcBv8lQTdAR{q(3siEjXQQiwKR<)_wvB9dG z3?YvA-ey&Cx&2q~X{ReOM3Uq3LfUIXI9fbe3{->EiY1%6Vp#VV_7pSieBM3to)H%# zVqRP3SQ`__ you .. parsed-literal:: /home/bchow/Work/scratch - logs parameters.yaml sflog.txt specfem2d - output scratch sfstate.txt specfem2d_workdir + logs parameters.yaml sflog.txt specfem2d + output scratch sfstate.txt specfem2d_workdir In the ``output/`` directory, we can see the updated model from our @@ -181,7 +181,7 @@ also save the output .png files to disk. .. parsed-literal:: - Figure(707.107x707.107) + Figure(707.107x707.107) .. code:: ipython3 From 39130db2727996402a6997d1c8cabf3a4bfc6d77 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Mon, 29 Aug 2022 14:31:26 -0800 Subject: [PATCH 148/195] plot2d now plots zero midpoint for gradient/kernel figures --- seisflows/tools/graphics.py | 21 ++++++++++++++++++--- seisflows/tools/specfem.py | 8 +++++--- 2 files changed, 23 insertions(+), 6 deletions(-) diff --git a/seisflows/tools/graphics.py b/seisflows/tools/graphics.py index 1d1233e5..2d041fb3 100644 --- a/seisflows/tools/graphics.py +++ b/seisflows/tools/graphics.py @@ -9,9 +9,9 @@ from obspy.core.stream import Stream -def plot_2D_contour(x, z, data, cmap="viridis"): +def plot_2d_contour(x, z, data, cmap="viridis", zero_midpoint=False): """ - Plots values of a SPECEFM2D model on an unstructured grid + Plots values of a SPECEFM2D model/gradient on an unstructured grid :type x: np.array :param x: x values of GLL mesh @@ -19,14 +19,29 @@ def plot_2D_contour(x, z, data, cmap="viridis"): :param z: z values of GLL mesh :type data: np.array :param data: D + :type cmap: str + :param cmap: matplotlib colormap to be applied to the contour plot. Defaults + to 'viridis' + :type zero_midpoint: bool + :param zero_midpoint: set 0 as the midpoint for the colorbar. Useful for + diverging colorscales (e.g., for gradients), where the neutral color + (e.g., white) is set at value=0 """ # Figure out aspect ratio of the figure r = (max(x) - min(x))/(max(z) - min(z)) rx = r/np.sqrt(1 + r**2) ry = 1/np.sqrt(1 + r**2) + # Assign zero as the midpoint for things like gradients + if zero_midpoint: + abs_max_val = max(abs(data)) + vmin = -1 * abs_max_val + vmax = abs_max_val + else: + vmin, vmax = None, None + f = plt.figure(figsize=(10 * rx, 10 * ry)) - p = plt.tricontourf(x, z, data, levels=125, cmap=cmap) + p = plt.tricontourf(x, z, data, levels=125, cmap=cmap, vmin=vmin, vmax=vmax) cbar = plt.colorbar(p, shrink=0.8, pad=0.025) # , format="%.2f") plt.axis("image") diff --git a/seisflows/tools/specfem.py b/seisflows/tools/specfem.py index b7674a81..dfe70801 100644 --- a/seisflows/tools/specfem.py +++ b/seisflows/tools/specfem.py @@ -11,8 +11,7 @@ from seisflows.tools.config import Dict from seisflows.tools import unix, msg from seisflows.tools.math import poissons_ratio -from seisflows.tools.graphics import plot_2D_contour - +from seisflows.tools.graphics import plot_2d_contour class Model: @@ -436,8 +435,10 @@ def plot2d(self, parameter, cmap=None, show=True, save=None): if cmap is None: if "kernel" in parameter: cmap = "seismic_r" + zero_midpoint = True else: cmap = "Spectral" + zero_midpoint = False # 'Merge' the coordinate matrices to get a vector representation x, z = np.array([]), np.array([]) @@ -446,7 +447,8 @@ def plot2d(self, parameter, cmap=None, show=True, save=None): z = np.append(z, self.coordinates["z"][iproc]) data = self.merge(parameter=parameter) - f, p, cbar = plot_2D_contour(x=x, z=z, data=data, cmap=cmap) + f, p, cbar = plot_2d_contour(x=x, z=z, data=data, cmap=cmap, + zero_midpoint=zero_midpoint) # Set some figure labels based on information we know here ax = plt.gca() From 7de6d46b87813182a9bf8a491331b59b74bcaf86 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Mon, 29 Aug 2022 14:46:43 -0800 Subject: [PATCH 149/195] bugfix: step_count_max was exceeded by 1 step count, changed a '<=' logic to '<' to fix this --- seisflows/plugins/line_search/backtrack.py | 2 +- seisflows/plugins/line_search/bracket.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/seisflows/plugins/line_search/backtrack.py b/seisflows/plugins/line_search/backtrack.py index b637b1fa..a86db90b 100644 --- a/seisflows/plugins/line_search/backtrack.py +++ b/seisflows/plugins/line_search/backtrack.py @@ -82,7 +82,7 @@ def calculate_step_length(self): f"successful w/ alpha={alpha}") status = "PASS" # If misfit continually increases, decrease step length - elif step_count <= self.step_count_max: + elif step_count < self.step_count_max: slope = gtp[-1] / gtg[-1] alpha = parabolic_backtrack(f0=f[0], g0=slope, x1=x[1], f1=f[1], b1=0.1, b2=0.5) diff --git a/seisflows/plugins/line_search/bracket.py b/seisflows/plugins/line_search/bracket.py index c4fc6f02..9ddbce82 100644 --- a/seisflows/plugins/line_search/bracket.py +++ b/seisflows/plugins/line_search/bracket.py @@ -179,13 +179,13 @@ def calculate_step_length(self): f"alpha={alpha:.2E}") status = "TRY" # If misfit continues to step down, increase step length - elif step_count <= self.step_count_max and all(f <= f[0]): + elif step_count < self.step_count_max and all(f <= f[0]): alpha = 1.618034 * x[-1] # 1.618034 is the 'golden ratio' logger.info(f"try: misfit not bracketed, increasing step length " f"using golden ratio, alpha={alpha:.2E}") status = "TRY" # If misfit increases, reduce step length by backtracking - elif step_count <= self.step_count_max: + elif step_count < self.step_count_max: slope = gtp[-1] / gtg[-1] alpha = parabolic_backtrack(f0=f[0], g0=slope, x1=x[1], f1=f[1], b1=0.1, b2=0.5) From e7e1745988f8db054f93fdc0398ac2daea09c179 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Mon, 29 Aug 2022 16:08:01 -0800 Subject: [PATCH 150/195] refactored SEISFLOWS EXAMPLES ////////////////// Example options where is either the example name or corresponding index, provided below. 'seisflows examples ': print example description 'seisflows examples setup ': setup example but don't run workflow 'seisflows examples run ': setup and run example 1: ex1_homogeneous_halfspace 2: ex2_hh_w_pyatoa 3: ex3_fwd_solver command to not use subprocess but rather import directly from package, makes it less confusing and also allows user to select the number of stations, events and iterations for each example problem reworked some of the example problems to avoid using __main__ for better compatibility with new cli tool example 1 now only uses 1 station, 1 event and 1 iteration to show off a kernel, but allows users to choose beyond default example 2 defaults to 4 events, 2 iterations, X stations started example 3, which will be a mass forward solver --- docs/notebooks/specfem2d_example.ipynb | 52 +++---- ...ersion.py => ex1_homogeneous_halfspace.py} | 0 ...version_w_pyatoa.py => ex2_hh_w_pyatoa.py} | 92 +++++------ seisflows/examples/ex3_fwd_solver.py | 140 +++++++++++++++++ seisflows/examples/sfexample2d.py | 100 +++++++----- seisflows/seisflows.py | 144 ++++++++++-------- 6 files changed, 341 insertions(+), 187 deletions(-) rename seisflows/examples/{ex1_specfem2d_workstation_inversion.py => ex1_homogeneous_halfspace.py} (100%) rename seisflows/examples/{ex2_specfem2d_workstation_inversion_w_pyatoa.py => ex2_hh_w_pyatoa.py} (60%) create mode 100644 seisflows/examples/ex3_fwd_solver.py diff --git a/docs/notebooks/specfem2d_example.ipynb b/docs/notebooks/specfem2d_example.ipynb index b4787448..0a236ac0 100644 --- a/docs/notebooks/specfem2d_example.ipynb +++ b/docs/notebooks/specfem2d_example.ipynb @@ -6,44 +6,31 @@ "source": [ "# Specfem2D Workstation Example\n", "\n", - "To demonstrate the inversion capabilities of SeisFlows, we will run a __Specfem2D synthetic-synthetic example__ on a __local machine__ (tested on a Linux workstation running CentOS 7, and an Apple Laptop running macOS 10.14.6). Many of the setup steps here may be unique to our OS and workstation, but hopefully they may serve as templates for new Users wanting to explore SeisFlows. \n", + "SeisFlows comes with some __Specfem2D synthetic examples__ to showcase the package. These examples are meant to be run on a __local machine__ (tested on a Linux workstation running CentOS 7, and an Apple Laptop running macOS 10.14.6).\n", "\n", - "The numerical solver we will use is: [SPECFEM2D](https://geodynamics.org/cig/software/specfem2d/). We'll also be working in our `seisflows` [Conda](https://docs.conda.io/en/latest/) environment, see the installation documentation page for instructions on how to install and activate the required Conda environment.\n", + "The numerical solver we will use is: [SPECFEM2D](https://geodynamics.org/cig/software/specfem2d/). We'll also be working in our `seisflows` [Conda](https://docs.conda.io/en/latest/) environment, see the installation documentation page for instructions on how to install and activate the required Conda environment. \n", "\n", "-----------------------------------" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Option 1: Automated run\n", - "\n", - "We have set up this example to run using a single command line argument. The following command will run an example script which will (1) download and compile SPECFEM2D, (2) setup a SPECFEM2D working directory to generate initial and target models, and (3) Run a SeisFlows inversion. " - ] - }, { "cell_type": "raw", "metadata": {}, "source": [ ".. warning:: \n", - " If you do not have a compiled version of SPECFEM2D, then this example will attempt to automatically download and compile SPECFEM2D. This step may fail if you do not have software required by SPECFEM2D, if there are issues with the SPECFEM2D repository itself, or if the configuration and compiling steps fail. If you run any issues, it is recommended that you manually install and compile SPECFEM2D, and directly provide its path to this example problem using the -r or --specfem2d_repo flags (shown below)." + " If you do not have a compiled version of SPECFEM2D, then each example will attempt to automatically download and compile SPECFEM2D. This step may fail if you do not have software required by SPECFEM2D, if there are issues with the SPECFEM2D repository itself, or if the configuration and compiling steps fail. If you run any issues, it is recommended that you manually install and compile SPECFEM2D, and directly provide its path to this example problem using the -r or --specfem2d_repo flags (shown below)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Example \\#1\n", - "Example \\#1 runs a 2 iteration inversion using SPECFEM2D, the default preprocessing module and a gradient descent optimization algorithm." - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - ".. note::\n", - " Example number 1 is meant to **FAIL** during the line search of Iteration #2, after exceeding the maximum allowable line search step count. This is meant to illustrate line search behavior and allow the User to explore a working directory mid-workflow." + "## Example \\#1: Simple, default inversion\n", + "Example \\#1 runs a 1-iteration synthetic inversion with 1 event and 1 station.\n", + "\n", + "The starting model (MODEL_INIT) and target model (MODEL_TRUE) are used to generate synthetics and data, respectively. Both models are homogeneous halfspace models with slightly varying P- and S-wave velocity values. Only Vp and Vs are updated during the example.\n", + "\n", + "Misfit during Example \\#1 is defined by a 'waveform' misfit using the default preprocessing module. It also uses a gradient-descent optimization algorithm paired with a bracketing line search. No smoothing/regularization is applied to the gradient." ] }, { @@ -103,7 +90,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "You can either setup and run the example in separate tasks using the `examples setup` and `submit` commands. or directly run the example after setup using the `examples run` command. Use the `-r` or `--specfem2d_repo` flag to point SeisFlows at an existing SPECFEM2D/ repository (with compiled binaries). If not given, SeisFlows will automatically download, configure and compile SPECFEM2D in your current working directory." + "### Running the example\n", + "\n", + "You can either setup and run the example in separate tasks using the `seisflows examples setup` and `seisflows submit` commands, or by directly running the example after setup using the `examples run` command (illustrated below). \n", + "\n", + "Use the `-r` or `--specfem2d_repo` flag to point SeisFlows at an existing SPECFEM2D/ repository (with compiled binaries) if available. If not given, SeisFlows will automatically download, configure and compile SPECFEM2D in your current working directory." ] }, { @@ -112,10 +103,11 @@ "metadata": {}, "outputs": [], "source": [ - "! seisflows examples setup 1 -r path/to/specfem2d\n", + "! seisflows examples setup 1 -r ${PATH_TO_SPECFEM2D}\n", "! seisflows submit\n", + "\n", "# The above commands are the same as the below\n", - "! seisflows examples run 1 --specfem2d_repo path/to/specfem2d" + "! seisflows examples run 1 --specfem2d_repo ${PATH_TO_SPECFEM2D}" ] }, { @@ -162,12 +154,14 @@ " EXAMPLE COMPLETED SUCCESFULLY\n", "\n", " \n", - "Using the `working directory documentation page `__ you can figure out how to navigate around and look at the results of our small inversion problem. We will have a look at a few of the files and directories here. I've run the example problem in a scratch directory but your output directory should look the same." + "Using the `working directory documentation page `__ you can figure out how to navigate around and look at the results of this small inversion problem. \n", + "\n", + "We will have a look at a few of the files and directories here. I've run the example problem in a scratch directory but your output directory should look the same." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -350,7 +344,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -364,7 +358,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.12" + "version": "3.7.6" } }, "nbformat": 4, diff --git a/seisflows/examples/ex1_specfem2d_workstation_inversion.py b/seisflows/examples/ex1_homogeneous_halfspace.py similarity index 100% rename from seisflows/examples/ex1_specfem2d_workstation_inversion.py rename to seisflows/examples/ex1_homogeneous_halfspace.py diff --git a/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py b/seisflows/examples/ex2_hh_w_pyatoa.py similarity index 60% rename from seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py rename to seisflows/examples/ex2_hh_w_pyatoa.py index fcae1abf..f8ea5c03 100644 --- a/seisflows/examples/ex2_specfem2d_workstation_inversion_w_pyatoa.py +++ b/seisflows/examples/ex2_hh_w_pyatoa.py @@ -2,9 +2,9 @@ """ SEISFLOWS SPECFEM2D WORKSTATION EXAMPLE 2 -This example will run two iterations of an inversion to assess misfit between -a homogeneous halfspace model and a checkerboard model using 2 events and -5 receivers. +This example will run N iterations of an inversion to assess misfit between +a homogeneous halfspace model and a checkerboard model using X events and +Y receivers. N, X and Y are all user-selectable. .. note:: See Example 1 docstring for more information @@ -26,7 +26,8 @@ class SFPyatoaEx2D(SFExample2D): advantage of the default SPECFEM2D stuff, onyl changes the generation of MODEL TRUE, the number of stations, and the setup of the parameter file. """ - def __init__(self, ntask=2, niter=2, nsta=5, specfem2d_repo=None): + def __init__(self, ntask=None, niter=None, nsta=None, method="run", + specfem2d_repo=None): """ Overload init and attempt to import Pyatoa before running example, overload the default number of tasks to 2, and add a new init parameter @@ -45,12 +46,10 @@ def __init__(self, ntask=2, niter=2, nsta=5, specfem2d_repo=None): contain binary executables. If not given, SPECFEM2D will be downloaded configured and compiled automatically. """ - super().__init__(ntask=ntask, niter=niter, - specfem2d_repo=specfem2d_repo) - self.nsta = nsta - # -1 because it represents index but we need to talk in terms of count - assert(1 <= self.nsta <= 131), \ - f"number of stations must be between 1 and 131, not {self.nsta}" + # Setting default values for ntask, niter, nsta here vvv + super().__init__(ntask=ntask or 4, niter=niter or 2, nsta=nsta or 32, + method=method, specfem2d_repo=specfem2d_repo) + # Make sure that Pyatoa has been installed before running try: import pyatoa @@ -62,6 +61,31 @@ def __init__(self, ntask=2, niter=2, nsta=5, specfem2d_repo=None): ) sys.exit(-1) + def print_dialogue(self): + """ + Print help/system dialogue message that explains the setup of th + this workflow + """ + print(msg.ascii_logo_small) + print(msg.cli( + f"This is a [SPECFEM2D] [WORKSTATION] example, which will " + f"run an inversion to assess misfit between a starting homogeneous " + f"halfspace model and a target checkerboard model. This " + f"example problem uses the [PYAFLOWA] preprocessing " + f"module and the [LBFGS] optimization algorithm. " + f"[{self.ntask} events, {self.nsta} stations, {self.niter} " + f"iterations]. " + f"The tasks involved include: ", + items=["1. (optional) Download, configure, compile SPECFEM2D", + "2. Set up a SPECFEM2D working directory", + "3. Generate starting model from 'Tape2007' example", + "4. Generate target model w/ perturbed starting model", + "5. Set up a SeisFlows working directory", + "6. Run the inversion workflow"], + header="seisflows example 2", + border="=") + ) + def setup_specfem2d_for_model_true(self): """ Overwrites MODEL TRUE creation from EX1 @@ -113,52 +137,4 @@ def setup_seisflows_working_directory(self): self.sf.par("path_model_init", self.workdir_paths.model_init) self.sf.par("path_model_true", self.workdir_paths.model_true) - def finalize_specfem2d_par_file(self): - """ - Final changes to the SPECFEM2D Par_file before running SeisFlows. - Par_file will be used to control all the child specfem2d directories. - Need to tell them to read models from .bin files, and to use existing - station files rather than create them from the Par_file - """ - print("> EX2: Finalizing SPECFEM2D Par_file for SeisFlows inversion") - cd(self.workdir_paths.data) - self.sf.sempar("model", "gll") # GLL so SPECFEM reads .bin files - self.sf.sempar("use_existing_stations", ".true.") # Use STATIONS file - # Assign STATIONS_checker file which has 132 stations - rm("STATIONS") - - # Only write the first 10 lines to get 10 stations in inversion - with open("STATIONS_checker", "r") as f: - lines = f.readlines() - - print(f"> EX2: Using {self.nsta} stations in this inversion workflow") - with open("STATIONS", "w") as f: - f.writelines(lines[:self.nsta]) - - -if __name__ == "__main__": - print(msg.ascii_logo_small) - print(msg.cli( - f"This is a [SPECFEM2D] [WORKSTATION] example, which will " - f"run an inversion to assess misfit between a homogeneous halfspace " - f"and checkerboard model using Pyatoa for misfit quantification " - f"[2 events, 5 stations, 1 iterations]. The tasks involved include: ", - items=["1. (optional) Download, configure, compile SPECFEM2D", - "2. Set up a SPECFEM2D working directory", - "3. Generate starting model from Tape2007 example", - "4. Generate target model w/ perturbed starting model", - "5. Set up a SeisFlows working directory", - f"6. Run an inversion workflow. The line search is expected to " - f"attempt 2 evaluations (i01s02)"], - header="seisflows example 2", - border="=") - ) - - # Dynamically traverse sys.argv to get user-input command line. Cannot - # use argparser here because we're being called by SeisFlows CLI tool which - # is occupying argparser - if len(sys.argv) > 2: - _, _, specfem2d_repo = sys.argv - sfex2d = SFPyatoaEx2D(specfem2d_repo=specfem2d_repo) - sfex2d.main() diff --git a/seisflows/examples/ex3_fwd_solver.py b/seisflows/examples/ex3_fwd_solver.py new file mode 100644 index 00000000..f8ea5c03 --- /dev/null +++ b/seisflows/examples/ex3_fwd_solver.py @@ -0,0 +1,140 @@ +#!/usr/bin/env python3 +""" + SEISFLOWS SPECFEM2D WORKSTATION EXAMPLE 2 + +This example will run N iterations of an inversion to assess misfit between +a homogeneous halfspace model and a checkerboard model using X events and +Y receivers. N, X and Y are all user-selectable. + +.. note:: + See Example 1 docstring for more information + +.. rubric:: + $ seisflows examples run 2 +""" +import os +import sys + +from seisflows.tools import msg +from seisflows.tools.unix import cd, rm, ln +from seisflows.examples.sfexample2d import SFExample2D + + +class SFPyatoaEx2D(SFExample2D): + """ + A class for running SeisFlows examples. Overloads Example 1 to take + advantage of the default SPECFEM2D stuff, onyl changes the generation of + MODEL TRUE, the number of stations, and the setup of the parameter file. + """ + def __init__(self, ntask=None, niter=None, nsta=None, method="run", + specfem2d_repo=None): + """ + Overload init and attempt to import Pyatoa before running example, + overload the default number of tasks to 2, and add a new init parameter + `nsta` which chooses the number of stations, between 1 and 132 + + :type ntask: int + :param ntask: number of events to use in inversion, between 1 and 25. + defaults to 3 + :type niter: int + :param niter: number of iterations to run. defaults to 2 + :type nsta: int + :param nsta: number of stations to include in inversion, between 1 and + 131 + :type specfem2d_repo: str + :param specfem2d_repo: path to the SPECFEM2D directory which should + contain binary executables. If not given, SPECFEM2D will be + downloaded configured and compiled automatically. + """ + # Setting default values for ntask, niter, nsta here vvv + super().__init__(ntask=ntask or 4, niter=niter or 2, nsta=nsta or 32, + method=method, specfem2d_repo=specfem2d_repo) + + # Make sure that Pyatoa has been installed before running + try: + import pyatoa + except ModuleNotFoundError: + print(msg.cli("Module Pyatoa not found but is required for this " + "example. Please install Pyatoa and rerun this " + "example.", header="module not found error", + border="=") + ) + sys.exit(-1) + + def print_dialogue(self): + """ + Print help/system dialogue message that explains the setup of th + this workflow + """ + print(msg.ascii_logo_small) + print(msg.cli( + f"This is a [SPECFEM2D] [WORKSTATION] example, which will " + f"run an inversion to assess misfit between a starting homogeneous " + f"halfspace model and a target checkerboard model. This " + f"example problem uses the [PYAFLOWA] preprocessing " + f"module and the [LBFGS] optimization algorithm. " + f"[{self.ntask} events, {self.nsta} stations, {self.niter} " + f"iterations]. " + f"The tasks involved include: ", + items=["1. (optional) Download, configure, compile SPECFEM2D", + "2. Set up a SPECFEM2D working directory", + "3. Generate starting model from 'Tape2007' example", + "4. Generate target model w/ perturbed starting model", + "5. Set up a SeisFlows working directory", + "6. Run the inversion workflow"], + header="seisflows example 2", + border="=") + ) + + def setup_specfem2d_for_model_true(self): + """ + Overwrites MODEL TRUE creation from EX1 + + Make some adjustments to the parameter file to create the final velocity + model. This function assumes it is running from inside the + SPECFEM2D/DATA directory + """ + cd(self.workdir_paths.data) + assert(os.path.exists("Par_file")), f"I cannot find the Par_file!" + + print("> EX: Updating SPECFEM2D to set checkerboard model as " + "MODEL_TRUE") + self.sf.sempar("model", "legacy") # read model_velocity.dat_checker + rm("proc000000_model_velocity.dat_input") + ln("model_velocity.dat_checker", "proc000000_model_velocity.dat_input") + + def setup_seisflows_working_directory(self): + """ + Create and set the SeisFlows parameter file, making sure all required + parameters are set correctly for this example problem + """ + cd(self.cwd) + + print("> EX2: Setting SeisFlows parameters for Pyatao preprocessing") + self.sf.setup(force=True) # Force will delete existing parameter file + self.sf.par("workflow", "inversion") + self.sf.par("preprocess", "pyaflowa") + self.sf.par("optimize", "LBFGS") + self.sf.configure() + + self.sf.par("end", 1) # only 1 iteration + self.sf.par("ntask", self.ntask) # 3 sources for this example + self.sf.par("materials", "elastic") # how velocity model parameterized + self.sf.par("density", False) # update density or keep constant + self.sf.par("data_format", "ascii") # output synthetic seismograms + self.sf.par("data_case", "synthetic") # synthetic-synthetic inversion + self.sf.par("attenuation", False) + self.sf.par("components", "Y") + + # PYATOA preprocessing parameters + self.sf.par("unit_output", "DISP") + self.sf.par("min_period", 10) # filter bounds define window selection + self.sf.par("max_period", 200) + # self.sf.par("pyflex_preset", "") # To turn off windowing completely + + self.sf.par("path_specfem_bin", self.workdir_paths.bin) + self.sf.par("path_specfem_data", self.workdir_paths.data) + self.sf.par("path_model_init", self.workdir_paths.model_init) + self.sf.par("path_model_true", self.workdir_paths.model_true) + + diff --git a/seisflows/examples/sfexample2d.py b/seisflows/examples/sfexample2d.py index b5f00e36..d645ba52 100644 --- a/seisflows/examples/sfexample2d.py +++ b/seisflows/examples/sfexample2d.py @@ -44,7 +44,8 @@ class SFExample2D: A class for running SeisFlows examples. Simplifies calls structure so that multiple example runs can benefit from the code written here """ - def __init__(self, ntask=3, niter=2, specfem2d_repo=None): + def __init__(self, ntask=None, niter=None, nsta=None, method="run", + specfem2d_repo=None): """ Set path structure which is used to navigate around SPECFEM repositories and the example working directory @@ -54,6 +55,9 @@ def __init__(self, ntask=3, niter=2, specfem2d_repo=None): defaults to 3 :type niter: int :param niter: number of iterations to run. defaults to 2 + :type nsta: int + :param nsta: number of stations to include in inversion, between 1 and + 131 :type specfem2d_repo: str :param specfem2d_repo: path to the SPECFEM2D directory which should contain binary executables. If not given, SPECFEM2D will be @@ -63,21 +67,47 @@ def __init__(self, ntask=3, niter=2, specfem2d_repo=None): self.sem2d_paths, self.workdir_paths = self.define_dir_structures( cwd=self.cwd, specfem2d_repo=specfem2d_repo ) - self.ntask = ntask + self.ntask = ntask or 1 assert(1 <= self.ntask <= 25), \ f"number of tasks/events must be between 1 and 25, not {self.ntask}" - self.niter = niter + self.niter = niter or 1 assert(1 <= self.niter <= np.inf), \ f"number of iterations must be between 1 and inf, not {self.niter}" + self.nsta = nsta or 1 + # -1 because it represents index but we need to talk in terms of count + assert(1 <= self.nsta <= 131), \ + f"number of stations must be between 1 and 131, not {self.nsta}" # This bool information is provided by the User running 'setup' or 'run' - self.run_example = bool(sys.argv[1] == "run") + self.run_example = bool(method == "run") # Command line tool to use $ seisflows from inside Python # Zero out sys.argv to ensure that no arguments are given to the CLI sys.argv = [sys.argv[0]] self.sf = SeisFlows() + def print_dialogue(self): + """ + Print help/system dialogue message that explains the setup of th + this workflow + """ + print(msg.ascii_logo_small) + print(msg.cli( + f"This is a [SPECFEM2D] [WORKSTATION] example, which will " + f"run an inversion to assess misfit between two homogeneous " + f"halfspace models with slightly different velocities. " + f"[{self.ntask} events, 1 station, {self.niter} iterations]. " + f"The tasks involved include: ", + items=["1. (optional) Download, configure, compile SPECFEM2D", + "2. Set up a SPECFEM2D working directory", + "3. Generate starting model from 'Tape2007' example", + "4. Generate target model w/ perturbed starting model", + "5. Set up a SeisFlows working directory", + "6. Run the inversion workflow"], + header="seisflows example 1", + border="=") + ) + @staticmethod def define_dir_structures(cwd, specfem2d_repo, ex="Tape2007"): """ @@ -91,15 +121,10 @@ def define_dir_structures(cwd, specfem2d_repo, ex="Tape2007"): :type ex: str :type ex: The name of the example problem inside SPECFEM2D/EXAMPLES """ - if not os.path.exists(specfem2d_repo): + if specfem2d_repo is None or not os.path.exists(specfem2d_repo): print(f"No existing SPECFEM2D repo given, default to: " f"{cwd}/specfem2d") specfem2d_repo = os.path.join(cwd, "specfem2d") - else: - assert(os.path.exists(specfem2d_repo)), ( - f"User supplied SPECFEM2D directory '{specfem2d_repo}' " - f"does not exist, please check your path and try again." - ) # This defines required structures from the SPECFEM2D repository sem2d = { @@ -133,6 +158,11 @@ def download_specfem2d(self): print(f"Downloading SPECFEM2D with command: {cmd}") subprocess.run(cmd, shell=True, check=True) + assert self.sem2d_paths.repo, ( + f"User supplied SPECFEM2D directory '{self.sem2d_paths.repo}' " + f"does not exist, please check your path and try again." + ) + def configure_specfem2d_and_make_binaries(self): """ Run ./configure within the SPECFEM2D repo directory. @@ -297,6 +327,8 @@ def setup_seisflows_working_directory(self): self.sf.par("data_case", "synthetic") # synthetic-synthetic inversion self.sf.par("components", "Y") # only Y component seismograms avail. self.sf.par("attenuation", False) + self.sf.par("misfit", "traveltime") # cross-correlation phase measure + self.sf.par("adjoint", "traveltime") # cross-correlation phase measure self.sf.par("path_specfem_bin", self.workdir_paths.bin) self.sf.par("path_specfem_data", self.workdir_paths.data) @@ -305,12 +337,26 @@ def setup_seisflows_working_directory(self): def finalize_specfem2d_par_file(self): """ - Last minute changes to get the SPECFEM2D Par_file in the correct format - for running SeisFlows. Setting model type to read from .bin GLL files - change number of stations etc. + Final changes to the SPECFEM2D Par_file before running SeisFlows. + Par_file will be used to control all the child specfem2d directories. + Need to tell them to read models from .bin files, and to use existing + station files rather than create them from the Par_file """ + print("> Finalizing SPECFEM2D Par_file for SeisFlows inversion") + cd(self.workdir_paths.data) self.sf.sempar("model", "gll") # GLL so SPECFEM reads .bin files + self.sf.sempar("use_existing_stations", ".true.") # Use STATIONS file + + # Assign STATIONS_checker file which has 132 stations + rm("STATIONS") + + with open("STATIONS_checker", "r") as f: + lines = f.readlines() + + print(f"> Using {self.nsta} stations in this inversion workflow") + with open("STATIONS", "w") as f: + f.writelines(lines[:self.nsta]) def run_sf_example(self): """ @@ -324,6 +370,7 @@ def main(self): Setup the example and then optionally run the actual seisflows workflow """ print(msg.cli("EXAMPLE SETUP", border="=")) + # Step 1: Download and configure SPECFEM2D, make binaries. Optional self.download_specfem2d() self.configure_specfem2d_and_make_binaries() @@ -349,30 +396,3 @@ def main(self): self.run_sf_example() print(msg.cli("EXAMPLE COMPLETED SUCCESFULLY")) - -if __name__ == "__main__": - print(msg.ascii_logo_small) - print(msg.cli( - f"This is a [SPECFEM2D] [WORKSTATION] example, which will " - f"run an inversion to assess misfit between two homogeneous halfspace " - f"models with slightly different velocities. [3 events, 1 station, 2 " - f"iterations]. The inversion is expected to fail after the 5th line " - f"search step count of the 2nd iteration. The tasks involved include: ", - items=["1. (optional) Download, configure, compile SPECFEM2D", - "2. Set up a SPECFEM2D working directory", - "3. Generate starting model from Tape2007 example", - "4. Generate target model w/ perturbed starting model", - "5. Set up a SeisFlows working directory", - "6. Run an inversion workflow"], - header="seisflows example 1", - border="=") - ) - - # Dynamically traverse sys.argv to get user-input command line. Cannot - # use argparser here because we're being called by SeisFlows CLI tool which - # is occupying argparser. Call looks something like: - # $ python /path/to/example.py run path/to/specfem2d - if len(sys.argv) > 2: - _, _, specfem2d_repo = sys.argv - sfex2d = SFExample2D(specfem2d_repo=specfem2d_repo) - sfex2d.main() diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 23045eb5..e483a254 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -19,7 +19,6 @@ import warnings import argparse import traceback -import subprocess from glob import glob from IPython import embed @@ -297,19 +296,41 @@ def _format_action(self, action): numerical solver """ ) - examples.add_argument("run", type=str, nargs="?", default=None, - help="Run your choice of example problem") - examples.add_argument("choice", type=str, nargs="?", default=None, - help="Name of the specific example problem to run") + examples.add_argument("method", type=str, nargs="?", default=None, + help="Method for running the example problem. If not" + "provided, simply prints out the list of " + "available example problems. If given as an " + "integer value, will print out the help message " + "for the given example. If 'run', will run the " + "example. If 'setup' will simply setup the " + "example working directory but will not execute " + "`seisflows submit`") + examples.add_argument("choice", type=int, nargs="?", default=None, + help="If `method` in ['setup', 'run'], integer" + "value corresponding to the given example " + "problem which can listed using `seisflows " + "examples`") examples.add_argument("-r", "--specfem2d_repo", type=str, nargs="?", default=None, - help= "path to the SPECFEM2D directory which should " - "contain binary executables. If not given, " - "assumes directory is called 'specfem2d/' in " - "the current working directory. If that dir " - "is not found, SPECFEM2D will be downloaded, " - "configured and compiled automatically in the " - "current working directory.") + help="path to the SPECFEM2D directory which should " + "contain binary executables. If not given, " + "assumes directory is called 'specfem2d/' in " + "the current working directory. If that dir " + "is not found, SPECFEM2D will be downloaded, " + "configured and compiled automatically in the " + "current working directory.") + examples.add_argument("--nsta", type=int, nargs="?", default=None, + help="User-defined number of stations to use for " + "the example problem (1 <= NSTA <= 131). If " + "not given, each example has its own default.") + examples.add_argument("--ntask", type=int, nargs="?", default=None, + help="User-defined number of events to use for " + "the example problem (1 <= NTASK <= 25). If " + "not given, each example has its own default.") + examples.add_argument("--niter", type=int, nargs="?", default=None, + help="User-defined number of iterations to run for " + "the example problem (1 <= NITER <= inf). If " + "not given, each example has its own default.") # ========================================================================= # Defines all arguments/functions that expect a sub-argument subparser_dict = {"check": check, "par": par, "inspect": inspect, @@ -871,9 +892,10 @@ def par(self, parameter, value=None, skip_print=False, **kwargs): if not skip_print: print(msg.cli(f"{key}: {cur_val} -> {value}")) - def examples(self, run=None, choice=None, specfem2d_repo=None, **kwargs): + def examples(self, method=None, choice=None, specfem2d_repo=None, + nsta=None, nevent=None, niter=None, **kwargs): """ - List or run a SeisFlows example problem + List or run a SeisFlows example problems USAGE @@ -887,8 +909,8 @@ def examples(self, run=None, choice=None, specfem2d_repo=None, **kwargs): seisflows examples run 1 - :type run: bool - :param run: if True, run an example of choice `choice` + :type method: bool + :param method: if True, run an example of choice `choice` :type choice: str :param choice: The choice of example, must match the given tag or file name that is assigned to it @@ -897,6 +919,43 @@ def examples(self, run=None, choice=None, specfem2d_repo=None, **kwargs): contain binary executables. If not given, SPECFEM2D will be downloaded configured and compiled automatically. """ + # e.g., $ seisflows examples + if method is None: + self._print_examples() + sys.exit(0) + # e.g., $ seisflows examples 1 + elif method and choice is None: + try: + choice = int(method) + except ValueError: + print(f"`method` argument must be 'run', 'setup' or an integer " + f"value corresponding to one of the available examples") + sys.exit(0) + + # Allow the examples to be dynamically recovered based on user choice + if choice == 1: + from seisflows.examples.ex1_homogeneous_halfspace \ + import SFExample2D as Example + elif choice == 2: + from seisflows.examples.ex2_hh_w_pyatoa \ + import SFPyatoaEx2D as Example + else: + print(f"no SeisFlows example matching given number: {choice}") + sys.exit(0) + + # Run or setup example, or just print system dialogue + example = Example(specfem2d_repo=specfem2d_repo, method=method, + nsta=nsta, ntask=nevent, niter=niter) + example.print_dialogue() + + # e.g., $ seisflows examples run 1 + if method in ["setup", "run"]: + example.main() + + @staticmethod + def _print_examples(): + """Simply print a list of available examples which match the format + ex_?*.py""" # Gather all the available examples in the repository examples_dir = os.path.join(ROOT_DIR, "examples") examples_list = [] @@ -906,51 +965,16 @@ def examples(self, run=None, choice=None, specfem2d_repo=None, **kwargs): example_name = os.path.splitext(os.path.basename(fid))[0] examples_list.append((i+1, example_name, fid)) - arg1, arg2 = None, None - if run: - # Case 1: seisflows examples 1 OR seisflows examples ex1_... - if choice is None: - arg1 = run - arg2 = "" - # Case 2: seisflows examples run 1 OR seisflows examples run ex1_... - elif run in ["run", "setup"]: - arg1 = choice - arg2 = f"{run}" # space so that we do $ python ex.py run - if arg1: - # Allow for matching against index (int) and name (str) - try: - arg1 = int(arg1) - except ValueError: - pass - - for ex_tup in examples_list: - j, exname, fid = ex_tup - if arg1 in [j, exname]: - print(f"{run.capitalize()} example: {exname}") - # Set default value for SPECFEM2D repository and make - # sure paths are fully expanded to avoid any pathing error - if specfem2d_repo is None: - specfem2d_repo = os.path.join(os.getcwd(), "specfem2d") - specfem2d_repo = os.path.expanduser( - os.path.abspath(specfem2d_repo) - ) - os.chdir(self._args.workdir) # run example in working dir. - - # $ python /path/to/example.py run path/to/specfem2d - subprocess.run(f"python {fid} {arg2} {specfem2d_repo}", - shell=True, check=False) - return - - # Default behavior is to just print this help dialogue items = [f"{j}: {exname}" for j, exname, fid in examples_list] - print(msg.cli("Example options where is either the " - "example name or corresponding index, provided below.", - items=[ - "'seisflows examples ': print example description", - "'seisflows examples setup ': setup example but " - "don't run workflow", - "'seisflows examples run ': setup and run example" - ], + print(msg.cli( + "Example options where is either the example name " + "or corresponding index, provided below.", + items=[ + "'seisflows examples ': print example description", + "'seisflows examples setup ': setup example but " + "don't run workflow 'seisflows examples run ': " + "setup and run example" + ], header="seisflows examples" )) print(msg.cli(items=items)) From b3e2573e05c4f89ea0443dc39a7f7e6b8ce6827d Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Mon, 29 Aug 2022 16:20:01 -0800 Subject: [PATCH 151/195] bugfix pyaflowa wasnt writing adjoint sources for misfit ==0 added log message to adjoint simulations to state what source theyre running for --- seisflows/preprocess/pyaflowa.py | 2 +- seisflows/workflow/migration.py | 2 ++ 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/seisflows/preprocess/pyaflowa.py b/seisflows/preprocess/pyaflowa.py index 6bde4b62..cdaa20fd 100644 --- a/seisflows/preprocess/pyaflowa.py +++ b/seisflows/preprocess/pyaflowa.py @@ -516,7 +516,7 @@ def _quantify_misfit_station(self, config, station_code, # Write out the .adj adjoint source files for solver to discover. # Write empty adjoint sources for components with no adjoint sources - if mgmt.stats.misfit and save_adjsrcs: + if mgmt.stats.misfit is not None and save_adjsrcs: mgmt.write_adjsrcs(path=save_adjsrcs, write_blanks=True) # Wait until the very end to write to the HDF5 file, then do it diff --git a/seisflows/workflow/migration.py b/seisflows/workflow/migration.py index 637b4b89..25593c02 100644 --- a/seisflows/workflow/migration.py +++ b/seisflows/workflow/migration.py @@ -114,6 +114,8 @@ def run_adjoint_simulation(): else: export_kernels = False + logger.info(f"running adjoint simulation for source " + f"{self.solver.source_name}") # Run adjoint simulations on system. Make kernels discoverable in # path `eval_grad`. Optionally export those kernels self.solver.adjoint_simulation( From e76cc39dd29cbbf677a094c3a5cbc7a3fe573c1e Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Mon, 29 Aug 2022 17:38:33 -0800 Subject: [PATCH 152/195] bugfix solver.specfem setup not copying correct model files added smoothing to example 2 working on example 3, added to cli tool --- seisflows/examples/ex2_hh_w_pyatoa.py | 8 +- seisflows/examples/ex3_fwd_solver.py | 106 +++++++++++--------------- seisflows/examples/sfexample2d.py | 2 +- seisflows/seisflows.py | 2 + seisflows/solver/specfem.py | 10 +-- seisflows/solver/specfem2d.py | 3 +- 6 files changed, 56 insertions(+), 75 deletions(-) diff --git a/seisflows/examples/ex2_hh_w_pyatoa.py b/seisflows/examples/ex2_hh_w_pyatoa.py index f8ea5c03..2faaa5c4 100644 --- a/seisflows/examples/ex2_hh_w_pyatoa.py +++ b/seisflows/examples/ex2_hh_w_pyatoa.py @@ -29,9 +29,7 @@ class SFPyatoaEx2D(SFExample2D): def __init__(self, ntask=None, niter=None, nsta=None, method="run", specfem2d_repo=None): """ - Overload init and attempt to import Pyatoa before running example, - overload the default number of tasks to 2, and add a new init parameter - `nsta` which chooses the number of stations, between 1 and 132 + Overload init and attempt to import Pyatoa before running example. :type ntask: int :param ntask: number of events to use in inversion, between 1 and 25. @@ -110,7 +108,7 @@ def setup_seisflows_working_directory(self): """ cd(self.cwd) - print("> EX2: Setting SeisFlows parameters for Pyatao preprocessing") + print("> EX2: Setting SeisFlows parameters for Pyatoa preprocessing") self.sf.setup(force=True) # Force will delete existing parameter file self.sf.par("workflow", "inversion") self.sf.par("preprocess", "pyaflowa") @@ -125,6 +123,8 @@ def setup_seisflows_working_directory(self): self.sf.par("data_case", "synthetic") # synthetic-synthetic inversion self.sf.par("attenuation", False) self.sf.par("components", "Y") + self.sf.par("smooth_h", 5000.) + self.sf.par("smooth_v", 5000.) # PYATOA preprocessing parameters self.sf.par("unit_output", "DISP") diff --git a/seisflows/examples/ex3_fwd_solver.py b/seisflows/examples/ex3_fwd_solver.py index f8ea5c03..6324a6f0 100644 --- a/seisflows/examples/ex3_fwd_solver.py +++ b/seisflows/examples/ex3_fwd_solver.py @@ -1,37 +1,31 @@ #!/usr/bin/env python3 """ - SEISFLOWS SPECFEM2D WORKSTATION EXAMPLE 2 + SEISFLOWS SPECFEM2D WORKSTATION EXAMPLE 3 -This example will run N iterations of an inversion to assess misfit between -a homogeneous halfspace model and a checkerboard model using X events and -Y receivers. N, X and Y are all user-selectable. +This example will run a number of forward simulations and misfit quantification. +This is useful for generating a large number of synthetics through a given model .. note:: See Example 1 docstring for more information .. rubric:: - $ seisflows examples run 2 + $ seisflows examples run 3 """ -import os -import sys - from seisflows.tools import msg -from seisflows.tools.unix import cd, rm, ln +from seisflows.tools.unix import cd from seisflows.examples.sfexample2d import SFExample2D -class SFPyatoaEx2D(SFExample2D): +class SFFwdEx2D(SFExample2D): """ A class for running SeisFlows examples. Overloads Example 1 to take advantage of the default SPECFEM2D stuff, onyl changes the generation of MODEL TRUE, the number of stations, and the setup of the parameter file. """ - def __init__(self, ntask=None, niter=None, nsta=None, method="run", - specfem2d_repo=None): + def __init__(self, ntask=None, nsta=None, method="run", specfem2d_repo=None, + **kwargs): """ - Overload init and attempt to import Pyatoa before running example, - overload the default number of tasks to 2, and add a new init parameter - `nsta` which chooses the number of stations, between 1 and 132 + Overloads init of the base problem :type ntask: int :param ntask: number of events to use in inversion, between 1 and 25. @@ -47,20 +41,9 @@ def __init__(self, ntask=None, niter=None, nsta=None, method="run", downloaded configured and compiled automatically. """ # Setting default values for ntask, niter, nsta here vvv - super().__init__(ntask=ntask or 4, niter=niter or 2, nsta=nsta or 32, + super().__init__(ntask=ntask or 25, niter=1, nsta=nsta or 131, method=method, specfem2d_repo=specfem2d_repo) - # Make sure that Pyatoa has been installed before running - try: - import pyatoa - except ModuleNotFoundError: - print(msg.cli("Module Pyatoa not found but is required for this " - "example. Please install Pyatoa and rerun this " - "example.", header="module not found error", - border="=") - ) - sys.exit(-1) - def print_dialogue(self): """ Print help/system dialogue message that explains the setup of th @@ -69,40 +52,21 @@ def print_dialogue(self): print(msg.ascii_logo_small) print(msg.cli( f"This is a [SPECFEM2D] [WORKSTATION] example, which will " - f"run an inversion to assess misfit between a starting homogeneous " - f"halfspace model and a target checkerboard model. This " - f"example problem uses the [PYAFLOWA] preprocessing " - f"module and the [LBFGS] optimization algorithm. " + f"run forward simulations generate synthetic seismograms through " + f"a given starting model. This example uses no preprocessing or " + f"optimization modules" f"[{self.ntask} events, {self.nsta} stations, {self.niter} " f"iterations]. " f"The tasks involved include: ", items=["1. (optional) Download, configure, compile SPECFEM2D", "2. Set up a SPECFEM2D working directory", "3. Generate starting model from 'Tape2007' example", - "4. Generate target model w/ perturbed starting model", - "5. Set up a SeisFlows working directory", - "6. Run the inversion workflow"], + "4. Set up a SeisFlows working directory", + "5. Run the forward simulation workflow"], header="seisflows example 2", border="=") ) - def setup_specfem2d_for_model_true(self): - """ - Overwrites MODEL TRUE creation from EX1 - - Make some adjustments to the parameter file to create the final velocity - model. This function assumes it is running from inside the - SPECFEM2D/DATA directory - """ - cd(self.workdir_paths.data) - assert(os.path.exists("Par_file")), f"I cannot find the Par_file!" - - print("> EX: Updating SPECFEM2D to set checkerboard model as " - "MODEL_TRUE") - self.sf.sempar("model", "legacy") # read model_velocity.dat_checker - rm("proc000000_model_velocity.dat_input") - ln("model_velocity.dat_checker", "proc000000_model_velocity.dat_input") - def setup_seisflows_working_directory(self): """ Create and set the SeisFlows parameter file, making sure all required @@ -112,12 +76,11 @@ def setup_seisflows_working_directory(self): print("> EX2: Setting SeisFlows parameters for Pyatao preprocessing") self.sf.setup(force=True) # Force will delete existing parameter file - self.sf.par("workflow", "inversion") - self.sf.par("preprocess", "pyaflowa") - self.sf.par("optimize", "LBFGS") + self.sf.par("workflow", "forward") + self.sf.par("preprocess", "null") + self.sf.par("optimize", "null") self.sf.configure() - self.sf.par("end", 1) # only 1 iteration self.sf.par("ntask", self.ntask) # 3 sources for this example self.sf.par("materials", "elastic") # how velocity model parameterized self.sf.par("density", False) # update density or keep constant @@ -126,15 +89,32 @@ def setup_seisflows_working_directory(self): self.sf.par("attenuation", False) self.sf.par("components", "Y") - # PYATOA preprocessing parameters - self.sf.par("unit_output", "DISP") - self.sf.par("min_period", 10) # filter bounds define window selection - self.sf.par("max_period", 200) - # self.sf.par("pyflex_preset", "") # To turn off windowing completely - self.sf.par("path_specfem_bin", self.workdir_paths.bin) self.sf.par("path_specfem_data", self.workdir_paths.data) self.sf.par("path_model_init", self.workdir_paths.model_init) - self.sf.par("path_model_true", self.workdir_paths.model_true) - + def main(self): + """ + Setup the example and then optionally run the actual seisflows workflow + """ + print(msg.cli("EXAMPLE SETUP", border="=")) + + # Step 1: Download and configure SPECFEM2D, make binaries. Optional + self.download_specfem2d() + self.configure_specfem2d_and_make_binaries() + # Step 2: Create a working directory and generate initial/final models + self.create_specfem2d_working_directory() + # Step 2a: Generate MODEL_INIT, rearrange consequent directory structure + print(msg.cli("GENERATING INITIAL MODEL", border="=")) + self.setup_specfem2d_for_model_init() + self.run_xspecfem2d_binaries() + self.cleanup_xspecfem2d_run(choice="INIT") + # Step 3: Prepare Par_file and directory for MODEL_TRUE generation + self.setup_seisflows_working_directory() + self.finalize_specfem2d_par_file() + print(msg.cli("COMPLETE EXAMPLE SETUP", border="=")) + # Step 4: Run the workflwo + if self.run_example: + print(msg.cli("RUNNING SEISFLOWS FORWARD WORKFLOW", border="=")) + self.run_sf_example() + print(msg.cli("EXAMPLE COMPLETED SUCCESFULLY", border="=")) diff --git a/seisflows/examples/sfexample2d.py b/seisflows/examples/sfexample2d.py index d645ba52..3ad3f660 100644 --- a/seisflows/examples/sfexample2d.py +++ b/seisflows/examples/sfexample2d.py @@ -394,5 +394,5 @@ def main(self): if self.run_example: print(msg.cli("RUNNING SEISFLOWS INVERSION WORKFLOW", border="=")) self.run_sf_example() - print(msg.cli("EXAMPLE COMPLETED SUCCESFULLY")) + print(msg.cli("EXAMPLE COMPLETED SUCCESFULLY", border="=")) diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index e483a254..7b4c38ea 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -939,6 +939,8 @@ def examples(self, method=None, choice=None, specfem2d_repo=None, elif choice == 2: from seisflows.examples.ex2_hh_w_pyatoa \ import SFPyatoaEx2D as Example + elif choice == 3: + from seisflows.examples.ex3_fwd_solver import SFFwdEx2D as Example else: print(f"no SeisFlows example matching given number: {choice}") sys.exit(0) diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 551979e1..d32199dd 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -398,20 +398,20 @@ def setup(self): Prepares solver scratch directories for an impending workflow. Sets up directory structure expected by SPECFEM and copies or generates - seismic data to be inverted or migrated + seismic data to be inverted or migrated. + + Exports INIT/STARTING and TRUE/TARGET models to disk (output/ dir.) """ self._initialize_working_directories() # Export the initial and target models to the SeisFlows output directory - # Copy ALL files with relevant extension, just incase for name, model in zip(["MODEL_INIT", "MODEL_TRUE"], [self.path.model_init, self.path.model_true]): - dst = os.path.join(self.path.output, name) + dst = os.path.join(self.path.output, name, "") if not os.path.exists(dst): unix.mkdir(dst) for par in self._parameters: - src = glob(os.path.join(self.path.model_init, - f"*{par}{self._ext}")) + src = glob(os.path.join(model, f"*{par}{self._ext}")) unix.cp(src, dst) def forward_simulation(self, executables=None, save_traces=False, diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index 1fb6bc6b..8bb4e009 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -70,8 +70,7 @@ def setup(self): [self.path.model_init, self.path.model_true]): dst = os.path.join(self.path.output, name) for par in ["x", "z"]: - src = glob(os.path.join(self.path.model_init, - f"*{par}{self._ext}")) + src = glob(os.path.join(model, f"*{par}{self._ext}")) unix.cp(src, dst) def smooth(self, input_path, output_path, parameters=None, span_h=None, From c893c99b2a030845556fe8507ed2e5cd1da82c05 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Mon, 29 Aug 2022 18:16:50 -0800 Subject: [PATCH 153/195] updating specfem2d example problems to match the new examples 1 and 2 --- .../specfem2d_example_13_1.png | Bin 0 -> 34117 bytes .../specfem2d_example_15_0.png | Bin 67022 -> 0 bytes .../specfem2d_example_15_1.png | Bin 0 -> 67656 bytes .../specfem2d_example_17_1.png | Bin 0 -> 58836 bytes .../specfem2d_example_28_1.png | Bin 0 -> 217588 bytes .../specfem2d_example_30_1.png | Bin 0 -> 79493 bytes .../specfem2d_example_31_1.png | Bin 0 -> 81881 bytes docs/notebooks/specfem2d_example.ipynb | 464 ++++++++++++++---- docs/specfem2d_example.rst | 374 ++++++++++---- 9 files changed, 656 insertions(+), 182 deletions(-) create mode 100644 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z9Gp9Xj$Kl)v<(a0gyDNKLJ{*Wq>%2lKELOak~2uZEhZi(V1qse`W$t3D0U#e3McJS z^*ln_LDA9Q+kXNo+a1d7;U1S~r)*-Vo{tD3>qYR0wM6$GU4F9MN*E5=L|dWzs@l4X zT>bdwI9z?}G7{4fHk_H686)o1fDauc37Bd{$8foaDB;_m)9Usg-vzN%O0_=Y^X_FA zYpzW`M;s!qwmJJJ3oCj8ruKXlxEG2#NGdw_WAFV221~YO#dHF csdr!I$UGFBPTq1ulY*b0^bB-!HXjWBJC4d5hX4Qo literal 0 HcmV?d00001 diff --git a/docs/notebooks/specfem2d_example.ipynb b/docs/notebooks/specfem2d_example.ipynb index 0a236ac0..110f695d 100644 --- a/docs/notebooks/specfem2d_example.ipynb +++ b/docs/notebooks/specfem2d_example.ipynb @@ -21,16 +21,25 @@ " If you do not have a compiled version of SPECFEM2D, then each example will attempt to automatically download and compile SPECFEM2D. This step may fail if you do not have software required by SPECFEM2D, if there are issues with the SPECFEM2D repository itself, or if the configuration and compiling steps fail. If you run any issues, it is recommended that you manually install and compile SPECFEM2D, and directly provide its path to this example problem using the -r or --specfem2d_repo flags (shown below)." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import Image # To display .png files in the notebook" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example \\#1: Simple, default inversion\n", - "Example \\#1 runs a 1-iteration synthetic inversion with 1 event and 1 station.\n", + "Example \\#1 runs a 1-iteration synthetic inversion with 1 event and 1 station, used to illustrate misfit kernels in adjoint tomography.\n", "\n", "The starting model (MODEL_INIT) and target model (MODEL_TRUE) are used to generate synthetics and data, respectively. Both models are homogeneous halfspace models with slightly varying P- and S-wave velocity values. Only Vp and Vs are updated during the example.\n", "\n", - "Misfit during Example \\#1 is defined by a 'waveform' misfit using the default preprocessing module. It also uses a gradient-descent optimization algorithm paired with a bracketing line search. No smoothing/regularization is applied to the gradient." + "Misfit during Example \\#1 is defined by a 'traveltime' misfit using the default preprocessing module. It also uses a gradient-descent optimization algorithm paired with a bracketing line search. No smoothing/regularization is applied to the gradient." ] }, { @@ -42,43 +51,41 @@ "name": "stdout", "output_type": "stream", "text": [ - "1 example: ex1_specfem2d_workstation_inversion\n", - "\n", - " @@@@@@@@@@ \n", - " .@@@@. .%&( %@. \n", - " @@@@ @@@@ &@@@@@@ ,%@ \n", - " @@@@ @@@, /@@ @ \n", - " @@@ @@@@ @@@ @ \n", - " @@@@ @@@@ @@@ @ @ \n", - " @@@ @@@@ ,@@@ @ @ \n", - " @@@@ @@@@ @@@@ @@ @ @\n", - " @@@@ @@@@@ @@@@@ @@@ @@ @\n", - " @@@@ @@@@@ @@@@@@@@@@@@@@ @@ @\n", - " @@@@ @@@@@@ @@@& @@@ @ \n", - " @@@@@ @@@@@@@@ %@@@@# @@ \n", - " @@@@# @@@@@@@@@@@@@@@@@ @@ \n", - " &@@@@@ @@@@( @@& \n", - " @@@@@@@ /@@@@ \n", - " @@@@@@@@@@@@@@@@@\n", - " @@@@@@@@@@ \n", - "\n", - "\n", - "================================================================================\n", - " SEISFLOWS EXAMPLE 1 \n", - " /////////////////// \n", - "This is a [SPECFEM2D] [WORKSTATION] example, which will run an inversion to\n", - "assess misfit between two homogeneous halfspace models with slightly different\n", - "velocities. [3 events, 1 station, 2 iterations]. The inversion is expected to\n", - "fail after the 5th line search step count of the 2nd iteration. The tasks\n", - "involved include:\n", - "\n", - "1. (optional) Download, configure, compile SPECFEM2D\n", - "2. Set up a SPECFEM2D working directory\n", - "3. Generate starting model from Tape2007 example\n", - "4. Generate target model w/ perturbed starting model\n", - "5. Set up a SeisFlows working directory\n", - "6. Run an inversion workflow\n", - "================================================================================\n" + "No existing SPECFEM2D repo given, default to: /home/bchow/REPOSITORIES/seisflows/docs/notebooks/specfem2d\r\n", + "\r\n", + " @@@@@@@@@@ \r\n", + " .@@@@. .%&( %@. \r\n", + " @@@@ @@@@ &@@@@@@ ,%@ \r\n", + " @@@@ @@@, /@@ @ \r\n", + " @@@ @@@@ @@@ @ \r\n", + " @@@@ @@@@ @@@ @ @ \r\n", + " @@@ @@@@ ,@@@ @ @ \r\n", + " @@@@ @@@@ @@@@ @@ @ @\r\n", + " @@@@ @@@@@ @@@@@ @@@ @@ @\r\n", + " @@@@ @@@@@ @@@@@@@@@@@@@@ @@ @\r\n", + " @@@@ @@@@@@ @@@& @@@ @ \r\n", + " @@@@@ @@@@@@@@ %@@@@# @@ \r\n", + " @@@@# @@@@@@@@@@@@@@@@@ @@ \r\n", + " &@@@@@ @@@@( @@& \r\n", + " @@@@@@@ /@@@@ \r\n", + " @@@@@@@@@@@@@@@@@\r\n", + " @@@@@@@@@@ \r\n", + "\r\n", + "\r\n", + "================================================================================\r\n", + " SEISFLOWS EXAMPLE 1 \r\n", + " /////////////////// \r\n", + "This is a [SPECFEM2D] [WORKSTATION] example, which will run an inversion to\r\n", + "assess misfit between two homogeneous halfspace models with slightly different\r\n", + "velocities. [1 events, 1 station, 1 iterations]. The tasks involved include:\r\n", + "\r\n", + "1. (optional) Download, configure, compile SPECFEM2D\r\n", + "2. Set up a SPECFEM2D working directory\r\n", + "3. Generate starting model from 'Tape2007' example\r\n", + "4. Generate target model w/ perturbed starting model\r\n", + "5. Set up a SeisFlows working directory\r\n", + "6. Run the inversion workflow\r\n", + "================================================================================\r\n" ] } ], @@ -123,35 +130,37 @@ "source": [ ".. code:: bash\n", "\n", - " ...\n", - " 2022-08-25 17:29:16 (I) | 5800.00 <= vp <= 5800.00\n", - " 2022-08-25 17:29:16 (I) | 3236.17 <= vs <= 3802.01\n", - " 2022-08-25 17:29:16 (I) | trial step unsuccessful. re-attempting line search\n", - " 2022-08-25 17:29:16 (I) | \n", - " LINE SEARCH STEP COUNT 06\n", + " LINE SEARCH STEP COUNT 02\n", " --------------------------------------------------------------------------------\n", - " 2022-08-25 17:29:16 (I) | evaluating objective function for source 001\n", - " 2022-08-25 17:29:16 (D) | running forward simulation with 'Specfem2D'\n", - " 2022-08-25 17:29:20 (D) | quantifying misfit with 'Default'\n", - " 2022-08-25 17:29:20 (I) | evaluating objective function for source 002\n", - " 2022-08-25 17:29:20 (D) | running forward simulation with 'Specfem2D'\n", - " 2022-08-25 17:29:24 (D) | quantifying misfit with 'Default'\n", - " 2022-08-25 17:29:24 (I) | evaluating objective function for source 003\n", - " 2022-08-25 17:29:24 (D) | running forward simulation with 'Specfem2D'\n", - " 2022-08-25 17:29:28 (D) | quantifying misfit with 'Default'\n", - " 2022-08-25 17:29:28 (D) | misfit for trial model (f_try) == 7.53E-03\n", - " 2022-08-25 17:29:28 (D) | step length(s) = 0.00E+00, 1.47E+08, 2.95E+08, 5.89E+08, 1.18E+09, 2.36E+09, 4.72E+09\n", - " 2022-08-25 17:29:28 (D) | misfit val(s) = 8.65E-04, 7.53E-03, 6.28E-03, 5.02E-03, 3.77E-03, 2.51E-03, 1.26E-03\n", - " 2022-08-25 17:29:28 (I) | fail: bracketing line search has failed to reduce the misfit before exceeding `step_count_max`=5\n", - " 2022-08-25 17:29:28 (D) | checking gradient/search direction angle, theta: 0.000\n", - " 2022-08-25 17:29:28 (C) | \n", - " ================================================================================\n", - " LINE SEARCH FAILED \n", - " ////////////////// \n", - " Line search has failed to reduce the misfit and has run out of fallback options.\n", - " Aborting inversion.\n", - " ================================================================================\n", - " EXAMPLE COMPLETED SUCCESFULLY\n", + " 2022-08-29 15:50:58 (I) | evaluating objective function for source 001\n", + " 2022-08-29 15:50:58 (D) | running forward simulation with 'Specfem2D'\n", + " 2022-08-29 15:51:03 (D) | quantifying misfit with 'Default'\n", + " 2022-08-29 15:51:03 (D) | misfit for trial model (f_try) == 4.61E-01\n", + " 2022-08-29 15:51:03 (D) | step length(s) = 0.00E+00, 4.78E+09, 7.73E+09\n", + " 2022-08-29 15:51:03 (D) | misfit val(s) = 1.04E+00, 2.30E-01, 4.61E-01\n", + " 2022-08-29 15:51:03 (I) | pass: bracket acceptable and step length reasonable. returning minimum line search misfit.\n", + " 2022-08-29 15:51:03 (I) | line search model 'm_try' parameters: \n", + " 2022-08-29 15:51:03 (I) | 5800.00 <= vp <= 5800.00\n", + " 2022-08-29 15:51:03 (I) | 3431.53 <= vs <= 3790.00\n", + " 2022-08-29 15:51:03 (I) | trial step successful. finalizing line search\n", + " 2022-08-29 15:51:03 (I) | \n", + " FINALIZING LINE SEARCH\n", + " --------------------------------------------------------------------------------\n", + " 2022-08-29 15:51:03 (I) | writing optimization stats\n", + " 2022-08-29 15:51:03 (I) | renaming current (new) optimization vectors as previous model (old)\n", + " 2022-08-29 15:51:03 (I) | setting accepted trial model (try) as current model (new)\n", + " 2022-08-29 15:51:03 (I) | misfit of accepted trial model is f=2.304E-01\n", + " 2022-08-29 15:51:03 (I) | resetting line search step count to 0\n", + " 2022-08-29 15:51:03 (I) | \n", + " CLEANING WORKDIR FOR NEXT ITERATION\n", + " --------------------------------------------------------------------------------\n", + " 2022-08-29 15:51:05 (I) | thrifty inversion encountering first iteration, defaulting to standard inversion workflow\n", + " 2022-08-29 15:51:06 (I) | \n", + " ////////////////////////////////////////////////////////////////////////////////\n", + " COMPLETE ITERATION 01 \n", + " ////////////////////////////////////////////////////////////////////////////////\n", + " 2022-08-29 15:51:06 (I) | setting current iteration to: 2\n", + "\n", "\n", " \n", "Using the `working directory documentation page `__ you can figure out how to navigate around and look at the results of this small inversion problem. \n", @@ -161,21 +170,21 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "/home/bchow/Work/scratch\n", + "/home/bchow/Work/scratch/example_1\n", "logs\tparameters.yaml sflog.txt specfem2d\r\n", "output\tscratch\t\t sfstate.txt specfem2d_workdir\r\n" ] } ], "source": [ - "%cd ~/Work/scratch\n", + "%cd ~/Work/scratch/example_1\n", "! ls" ] }, @@ -183,19 +192,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In the `output/` directory, we can see the updated model from our first iteration (MODEL_01) and the gradient that was used to create it (GRADIENT_01). The 2nd iteration produced a gradient (GRADIENT_02), but was unable to succesfully reduce the misfit during the line search, which is why we don't have a MODEL_02." + "In the `output/` directory, we can see our starting model (MODEL_INIT), our target model (MODEL_TRUE) and the updated model from our first iteration (MODEL_01) alongside the gradient that was used to create it (GRADIENT_01). " ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "GRADIENT_01 GRADIENT_02 MODEL_01 MODEL_INIT\tMODEL_TRUE\n", + "GRADIENT_01 MODEL_01 MODEL_INIT MODEL_TRUE\n", "\n", "proc000000_vp.bin proc000000_vs.bin\n" ] @@ -211,12 +220,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Because we're working with SPECFEM2D, we can plot the models and gradients that were created during our workflow using the `seisflows plot2d` command. If we use the `--savefig` option we can also save the output .png files to disk." + "Because we're working with SPECFEM2D, we can plot the models and gradients that were created during our workflow using the `seisflows plot2d` command. If we use the `--savefig` option we can also save the output .png files to disk. Because this docs page was made in a Jupyter Notebook, we need to use the IPython Image class to open the resulting .png file.\n", + "\n", + "This figure shows the starting homogeneous halfspace model in Vs." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -225,10 +236,29 @@ "text": [ "Figure(707.107x707.107)\r\n" ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "! seisflows plot2d GRADIENT_01 vs_kernel --savefig i02_gradient_vs_kernel.png" + "! seisflows plot2d MODEL_INIT vs --savefig m_init_vs.png\n", + "Image(filename='m_init_vs.png') " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we see the gradient created during the adjoint simulation." ] }, { @@ -238,9 +268,16 @@ "scrolled": true }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Figure(707.107x707.107)\r\n" + ] + }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "" ] @@ -251,29 +288,74 @@ } ], "source": [ - "# Because this docs page was made in a Jupyter Notebook, we need to use IPython to open the resulting .png\n", - "from IPython.display import Image\n", - "Image(filename='i02_gradient_vs_kernel.png') " + "! seisflows plot2d GRADIENT_01 vs_kernel --savefig g_01_vs.png\n", + "Image(filename='g_01_vs.png') " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally we see the updated model, which is the sum of the initial model, and a scaled gradient." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Figure(707.107x707.107)\r\n" + ] + }, + { + "data": { + "image/png": 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AIHbu3CmEKFyZhLQQ1HZR3OnTpwUAMXPmTLevv2fPnqJq1aqqY3/++ac8aw5ABAUFiQULFoiIiAjRtWtXt+eU3lNnwXXBggUCgFi/fr3bc0mkn7P333/f48cQEdniAjryOQEBARg2bBiSkpJw6dIlLF++HCEhIRg8eLDd2Mcffxy7d+9GamoqNm3aBCEE+vTpg3/++acUrtx7DAaDR7eEhIQinb9169bQarU4deoUAODatWu4fPky3nzzTYSHh8u3tWvXIjMzE+Hh4RgxYoTLc0oLGKVzFichBAD7xXk5OTkYMGAAduzYgQ0bNqBr1652jz18+DAAoE2bNqrjlSpVQkxMDI4ePaoa26hRIwQFBanGSo+Vxt51110ICAhwuPDzyJEjcls76Zw6nQ733HOPatydd96JKlWqqJ7f1eu3fe0xMTHYs2cPLly4gN9//x1XrlzB4MGDce3aNdx///1uz9m2bVsAcPr9W7ZsGSIjI+VFeZ5w9n0iIioM9hkmnxQfH48PPvgA8+bNw+bNmzFq1CgEBgY6HR8UFIRevXohNzcXAwYMwLFjx+ReseXRgQMHPBpXt27dIp1/165dsFgsiImJAQBERUVhx44dduPmzp2LXbt2YcuWLYiIiHB5zmvXruHIkSPo2LFjka7JU3l5efj0008REREhXz9gDcIDBw7E999/jy+//BI9evRw+Pjo6GgAwN69e1U/I9evX8epU6dUATo6OhpHjx5FRkaGavORPXv2AABq1KgBwNpBo2/fvvjyyy/xxhtvyH2Cz507hx07duC5555TndNsNuPAgQNo166dfPzUqVO4fv26fE5nzpw5g59//hkPPvigw/urV6+O6tWrAwD+85//ICgoCPHx8S7PCUD+/ivfU8kvv/yC33//HVOmTHHYgcSZjz/+GADQvn17jx9DRGSLYZh8UuvWrdGsWTO88847EEI4/Md8zJgxCAgIQMeOHVGtWjUkJydjzpw5CAsLs5v1K29at27tlfN88803WLJkCfr164fatWsjLy8Pv/zyC9555x3ExMTgiSeeAGBt5+VoZ7iVK1dCp9Op7ktNTUW3bt0wfPhw1KtXDwEBATh16hQWLFiAnJwczJgxQ3WOX375BWfPngVg3flNCIEvvvgCgHWGVQqkH3/8MUaPHo3ly5fjscceAwA8//zzyMvLQ8eOHREVFYXz589j4cKFOHz4MFasWKFqA/avf/0LW7ZswbRp01ClShXs3btXvi80NBSNGjUCADz88MOYPn06nn76aVy4cAH33HMPLl26hHnz5uHWrVuqDVQmTZqEAQMGoFu3bnjuuecQERGBvXv3Ys6cOWjUqJGqzd8rr7yCNm3aoE+fPnjppZfkTTciIiIwefJkedzjjz+Ot99+G4MGDcJ//vMf1K9fH3///Tdmz56NoKAg1Y5tDz74IO6//340a9YMoaGhOHLkCN544w1oNBq89tprqvf5jTfeQFRUFGrVqoXLly/js88+w4YNG7Bq1So5HAPWzUkuX76M+++/H9WrV8fNmzeRlJSEJUuWYPDgwWjVqpXdz8GyZcsAwGmoXrNmDb788kv07t0btWvXxs2bN/H5558jMTERo0aNQvPmzVXjNRoNOnXqhJ07dzo8HxGRSmnWaBCVJqlGsVGjRg7vT0hIEF26dBGRkZHCaDSK6OhoMWTIEFWnB0+gDNYMe8uJEyfEv/71L1G7dm3h7+8v/P39RYMGDcS///1vu84FjjhaQJednS2eeOIJ0bBhQ7mNVo0aNcSjjz6qWnynPAc86IYhtRFTHlu2bJlo27atqFy5stDr9SI8PFz06NFDfPvtt3bP4+w54KCN16VLl8T48eNFTEyM8Pf3F9HR0aJ3795iz549duf9/vvvRffu3UVUVJQICAgQd999t5g8ebLDxYu//PKL6Nq1qwgMDBShoaFiwIABdht2CGGt742LixN16tQRfn5+olatWmLo0KF279+kSZNEo0aNREhIiNDr9SI6Olo8+uij4uTJk3bnfOWVV8Rdd90l/Pz8RKVKlUTPnj3FDz/8YDdu48aN4sEHHxSRkZFCr9eL4OBg0bZtW/Huu+86XJAo1fDff//9dvdJ9uzZI7p27SqioqKEwWAQgYGBok2bNuL9999XbboihLU+HU4WmBIROaIRIr/oioiIqJzbvHkz+vTpg99++w1NmzYt7cshonKAqw6IiKjC2LFjBx555BEGYSLyGGeGiYrIZDK5vF+r1XKVOxERURnHf6mJishdW7LRo0eX9iUSERGRG+wmQVRE7tqTuWsVRkRERKWPZRJERERE5LNYJkFUDg0cOBABAQG4efOm0zEjRoyAwWDA5cuXS+7CKpjMzEw88sgjqF+/PkJCQhAUFITGjRvj9ddfR2Zmpmps586dodFonN6Sk5Plsbm5uZg+fTrq1q0Lo9GI2rVr4+WXX0ZWVpbdNeTl5eGVV15BnTp14OfnhwYNGmDhwoUev4aMjAxMmjQJ0dHR8Pf3R4sWLZCYmFj0N4WIqIJhmQRRORQfH48NGzZgzZo1GDdunN39qampWL9+Pfr06YPIyMhSuMKKIS8vD0IIPP/886hbty60Wi1++OEHvPrqq9i5cye2b98uj33//feRlpamevytW7fQs2dPtGrVClFRUfLxYcOGYfPmzZg+fTratGmDPXv24PXXX8exY8ewceNG1TnGjRuHVatW4bXXXkObNm3w7bff4tlnn0V6ejqmTp3q9jU8/PDDOHDgAObOnYu7774ba9aswbBhw2CxWDB8+PDbfIeIiCqAUuxxTERFZDKZRHR0tGjVqpXD+xcvXiwAiK+//rqEr8w3TJkyRQAQp0+fdjlu5cqVAoBYunSpfGzPnj0CgHjzzTdVY2fPni0AiK1bt8rHjh49KjQajZg9e7Zq7JgxY0RAQIDbjU02bdokAIg1a9aojnfr1k1ER0cLk8nk8vFERL6AZRJE5ZBOp8PIkSNx8OBBHDlyxO7+FStWoFq1aqrtfBcvXozmzZsjODgYISEhaNCggUczi0oajQbjx4/HihUrUL9+fQQEBKB169bYu3cvhBCYN28e6tati+DgYDzwwAP466+/VI/ftm0b+vfvjxo1asDf3x8xMTEYO3Ysrl27Jo/Jzs5Gy5YtERMTg9TUVPl4cnIyoqKi0LlzZ5jN5kJdt7fdcccdAAC93vWHa8uWLUNwcDCGDh0qH/v5558BAA899JBqbJ8+fQAA69atk49t2LABQgg8/vjjqrGPP/44srKykJSU5PL5169fj+DgYAwePNju8RcvXsS+fftcPp6IyBcwDBOVU6NHj4ZGo8Hy5ctVx48fP479+/dj5MiR0Ol0AIDExESMGzcOnTp1wvr167FhwwY899xzdnWvnvjmm2+wdOlSzJ07F2vXrkV6ejp69+6NyZMn4+eff8aiRYvw0Ucf4fjx4xg0aBCEYo3u6dOnERsbi8WLF2Pr1q2YPn069u3bh3vvvRd5eXkAAH9/f3z22We4cuWK3J7OYrFgxIgREEJg7dq18utyxmQyeXQTHq4fFkLAZDIhLS0NSUlJePPNNzFs2DDUqlXL6WP+/PNP/Pjjj3jkkUcQHBwsH8/NzQUA+Pn5qcZLX//+++/ysaNHj+KOO+5QlVgAQLNmzeT7XTl69CgaNmxoF9odPX7lypXQaDRYuXKly3MSEVU0rBkmKqdiYmJw//33Y/Xq1XjjjTdgMBgAQA7Hyj7HP//8MypVqoR3331XPta1a9ciPW9OTg62bt2KoKAgANbZ4gEDBmDHjh349ddfodFoAABXr17FpEmTcPToUXk3sKeeeko+jxACHTp0QOfOnVG7dm1s2bIF/fr1AwDUq1cPS5cuxdChQ7FgwQLcuHEDO3fuRFJSEqpVq+by+s6ePYu6det69Fp27NiBzp07ux336aefYtiwYfLXjz/+OD766COXj1m2bBkAa323UqNGjQBYvyfK6/zpp58AANevX5ePXb9+HZUrV7Y7d1BQEIxGo2qsI9evX8edd95pd1w6p/LxWq0WOp2OG8UQkc9hGCYqx+Lj4/HYY49h48aNGDRoEEwmE1avXo377rsP9erVk8e1bdsWixYtwrBhw/DII4+gY8eORe6D3KVLFzkIA0DDhg0BAL169ZKDsPL4P//8I4fhK1euYPr06di0aRMuXrwIi8Uijz9x4oQchgFgyJAh2LlzJ/7973/DbDZj6tSp6Natm9vri46OdtsDWlK/fn2PxvXo0QMHDhxAeno69uzZg//973+4fv061q9f7zA8mkwmJCQkoHHjxmjfvr3qvl69eiEmJgYvvvgiIiMj0aZNG+zduxdTp051GEaV76ktV/cV9vGPPfYYHnvsMbfnIyKqaBiGicqxf/3rX5gwYQJWrFiBQYMGYfPmzbh8+TL+97//qcbFxcXBZDJhyZIlGDRoECwWC9q0aYPXX3/do4CpZDtTaTQaXR7Pzs4GYC116N69Oy5evIj//ve/aNq0KYKCgmCxWNC+fXuHbcVGjx6NxYsXw2g0YuLEiR5dn9FoRIsWLTwa667cQhIeHo7WrVsDsP4ycNddd+GRRx7BV199hYEDB9qN37x5M5KTk/Hiiy86vL4tW7YgLi4O3bt3B2Cd6Z09ezZee+01VK9eXR5bpUoVHD582O4cmZmZyM3NdThrrFSlShWHs8c3btwAYP89IyLyRfw8jKgcCwgIwLBhw5CUlIRLly5h+fLlCAkJsVswBVg/2t+9ezdSU1OxadMmCCHQp08f/PPPPyVyrUePHsVvv/2GefPmYcKECejcuTPatGmDKlWqOByfmZmJuLg43H333QgICMATTzzh0fOcPXvW7VbZ0m3Xrl1Fei1t27YFAJw6dcrh/cuWLYPRaERcXJzD+2NiYrBnzx5cuHABv//+O65cuYLBgwfj2rVruP/+++VxTZs2xdWrV1U9igHIiyabNGni8jqbNm2KEydOwGQyFenxRES+gGGYqJyLj4+H2WzGvHnzsHnzZjzyyCMIDAx0Oj4oKAi9evXCtGnTkJubi2PHjpXIdUofydsuHPvwww8djn/qqadw7tw5fPnll1i2bBk2btyIt99+2+3zSGUSntxatWpVpNeyY8cOANZQays5ORmbN2/GgAEDnAZ9SfXq1dG0aVMEBgZi3rx5CAoKUtUY9+/fHxqNBgkJCarHrVy5EgEBAejZs6fL8w8cOBAZGRmqDhUAkJCQgOjoaLRr187l44mIfAHLJIjKudatW6NZs2Z45513IISwW7AFAGPGjEFAQAA6duyIatWqITk5GXPmzEFYWBjatGlTItfZoEED3HXXXXjppZcghEDlypXx9ddfY9u2bXZjly5ditWrV2PFihVo3LgxGjdujPHjx+PFF19Ex44d5ZlZR4xGo1zScLs+/PBD/Pjjj+jevTtq1qyJzMxM/Pjjj1i4cCE6dOiA/v372z0mISEBJpPJ5Uz2G2+8gaioKNSqVQuXL1/GZ599hg0bNmDVqlWqMonGjRsjPj4eM2bMgE6nQ5s2bbB161Z89NFHeP3111VlDq+++ipeffVVfPfdd+jUqRMAa31yt27d8PTTTyMtLQ0xMTFYu3YtkpKSsHr1alWZyMqVK/H4449jxYoVGDVqlBfePSKi8oFhmKgCiI+Px7PPPotGjRo5nO277777sHLlSnz22WdISUlBREQE7r33Xnz88cdyz9ziZjAY8PXXX+PZZ5/F2LFjodfr8eCDD2L79u2qFmVHjhzBxIkTMXLkSFUomz9/Pvbs2YOhQ4fi0KFDqFSpUrFfc9OmTfHNN9/g5ZdfxrVr16DX61GvXj1MnToVzz//vMM+w8uXL0edOnXw4IMPOj1vdnY2Xn31VVy4cAEBAQFo3749du7cifvuu89u7Pvvv4/q1atj4cKFSE5ORp06dbBgwQJMmDBBNc5iscBsNtu1i/vyyy8xbdo0TJ8+HTdu3ECDBg2wdu1aPPLII6pxGRkZAOC2WwcRUUWjEZ422iQiogpryJAhOHPmjMedOIiIKgrODBMR+TghBHbu3InVq1eX9qUQEZU4zgwTkV23AVtarZabMRARUYXEf92IyG0LMuVudkRERBUJyySIyG2daFF3qyMiIirrWCZBRERERD6LZRJERERE5LMYhqncGDhwIAICAnDz5k2nY0aMGAGDwYDLly+X3IX5sPPnz2PgwIG48847ERQUhLCwMLRs2RKLFi2yW5Q3c+ZMaDQau5u/v7/DcycmJqJFixbw9/dHdHQ0Jk2aJPfCVcrIyMCkSZMQHR0Nf39/tGjRAomJiR5d//bt29GtWzdER0fDz88PVatWxQMPPIDNmzfbjf3mm2/w2GOPoWnTpjAYDPKOeracvU7pZntt69atQ8eOHVG5cmVUqlQJbdu2xapVq1xe9+XLl1GlShVoNBp88cUXLscuXboUGo0GwcHBdve9++67aN++PSIiIuDn54datWrhkUcecbgr4aVLlzBq1ChUrVoV/v7+aNasGZYtW+bwOa9cuYJRo0YhIiICgYGBiI2NxXfffWc3LicnB/PmzUOTJk0QFBSEyMhI9OrVC7t373Z43qNHj2Lw4MG444474Ofnhzp16mDcuHF24z755BO0bNkS/v7+iIiIwPDhw3H+/HmX7xMR+TBBVE58/fXXAoB47733HN5/8+ZNERAQIAYMGFDCV+a7Tpw4IR577DGxfPlysX37drF582Yxfvx4AUDEx8erxs6YMUMAEElJSWLPnj3ybd++fXbnXb16tQAgnnjiCfH999+LDz74QISFhYlu3brZje3WrZuoVKmS+OCDD8T3338vnnjiCQFAfPLJJ26vPzExUTz77LMiMTFR7Ny5U3z55Zeie/fuAoBYtWqVauzo0aNFvXr1xJAhQ0SrVq2Es78+z58/r3p90q1JkyYiICBApKSkyGOXLVsmAIhBgwaJzZs3iy1btohHHnlEABBvvfWW0+seNGiQiI6OFgDE559/7nTchQsXRFhYmIiOjhZBQUF290+fPl3MnDlTrF+/XuzcuVMsX75c3H333SIoKEj88ccf8ribN2+KO++8U9SoUUOsWLFCJCUliZEjRwoA4s0331SdMzs7WzRp0kTUqFFDrF69WmzdulX0799f6PV6sXPnTtXYuLg4odVqxbRp08R3330nPv/8c9GqVSuh1+vtfi6+//57ERAQILp37y6++OILsXPnTvHxxx+L5557TjXu3XfflX92kpKSxNKlS0W1atVE7dq1xY0bN5y+V0TkuxiGqdwwmUwiOjpatGrVyuH9ixcvFgDE119/XcJXRraGDBki9Hq9yM7Olo9JYfjq1asuH2symUS1atVE9+7dVcc/+eQTAUBs3rxZPrZp0yYBQKxZs0Y1tlu3biI6OlqYTKZCX3tubq6oXr26uO+++1THzWaz/OdnnnnGaRh25MyZM0Kj0YhHH31Udbxjx46idu3aqnNbLBbRoEED0axZM4fn+uKLL0RwcLBISEhwG4b79Okj+vbtK0aOHOkwDDty/PhxAUD897//lY/NmTNHABC//PKLamz37t1FUFCQKuC/9957AoDYvXu3fCwvL080atRItG3bVj6WnZ0tdDqd3Xty8eJFAUBMnDhRPpaZmSmqVasmevfuLSwWi9Nrz87OFmFhYaJv376q47t37xYAxNSpUz16D4jIt7BMgsoNnU6HkSNH4uDBgzhy5Ijd/StWrEC1atXQq1cv+djixYvRvHlzBAcHIyQkBA0aNMDUqVML9bwajQbjx4/HihUrUL9+fQQEBKB169bYu3cvhBCYN28e6tati+DgYDzwwAP466+/7M6xfft2dO3aFaGhoQgMDETHjh3tPjb+66+/8Pjjj6NevXoIDAxE9erV0bdvX7vXunPnTmg0GqxduxbTpk1DdHQ0QkND8eCDD+LkyZOFem3F5Y477oBWq4VOpyv0Y/fu3YtLly7h8ccfVx0fPHgwgoODsX79evnY+vXrERwcjMGDB6vGPv7447h48SL27dtX6Oc3GAyoVKmS3VbLt9Nnefny5RBC4IknnrB7ruDgYNW5NRoNQkNDHZaP3LhxA8888wxmzZql2sLakdWrV2PXrl14//33C3Wt0vbcytf/888/IzIyEq1atVKN7dOnDzIzM5GUlCQfW79+PerXr4/Y2Fj5mF6vx6OPPor9+/fj//7v/wAU9K4OCwtTnTM0NBRarVb1+j///HNcunQJ//73v52WpwDWMorU1FQ89NBDquOxsbGoXLky1q1b5+nbQEQ+hGGYypXRo0dDo9Fg+fLlquPHjx/H/v37MXLkSDmAJSYmYty4cejUqRPWr1+PDRs24LnnnkNmZmahn/ebb77B0qVLMXfuXKxduxbp6eno3bs3Jk+ejJ9//hmLFi3CRx99hOPHj2PQoEEQiiYtq1evRvfu3REaGoqEhAR89tlnqFy5Mnr06KEKxBcvXkSVKlUwd+5cJCUl4b333oNer0e7du0chtypU6fin3/+wdKlS/HRRx/hzz//RN++fWE2m12+FiEETCaTRzdPSedMSUnBp59+ipUrV2Ly5Ml2gRIAmjZtCp1Oh8jISDz22GM4d+6c6v6jR48CAJo1a6Y6bjAY0KBBA/l+aWzDhg3tnkd6rHKsKxaLBSaTCRcvXsSMGTNw6tQpTJ482aPHenLulStXIiYmBp06dVLdN2HCBJw4cQKzZs3C1atXce3aNcyfPx8HDx7ECy+8YHeuiRMnom7duhg/frzL57xy5QomTZqEuXPnokaNGm6v0Ww2IycnB3/88QeeeOIJVK1aVfXLSG5uLvz8/OweJx37/fff5WNHjx61+94BBd8TqR7ZYDBg3LhxSEhIwIYNG5CWloazZ89izJgxCAsLw5gxY+TH/vDDD/J13nvvvTAajQgPD8ewYcNw8eJF1XUqr8v2Wv/8809kZ2fLx0aNGgWNRoOzZ8+6fY+IqAIr1XlpoiLo1KmTiIiIELm5ufKxyZMnCwDi1KlT8rHx48eLSpUq3fbzARBRUVEiIyNDPrZhwwYBQLRo0UL1se0777wjAIjff/9dCGH9eLdy5cp2H9uazWbRvHlz1cfGtkwmk8jNzRX16tVT1UXu2LFDABAPPfSQavxnn30mAIg9e/a4fD3S4z25nTlzxu37I0TBx+gAhEajEdOmTbMb8/HHH4tZs2aJzZs3i++//17MnTtXVK5cWURGRooLFy7I42bNmiUAiEuXLtmdo3v37uLuu++Wv65Xr57o0aOH3Tjpo/bZs2d7dP09evSQrz80NFR8+eWXLscXpkxiy5YtAoCYM2eOw/s3bNggwsLC5OcPCAgQq1evthv3zTffCIPBII4cOSKEKPg+OiqTGDRokOjQoYP8s+muTMLPz09+/rvvvlscP35cdf+kSZOEVqsV//zzj+p4XFycACCefPJJ+ZjBYBBjx461ew6pVEFZ0mKxWMT06dOFVquVn79WrVri0KFDqsdK359KlSqJKVOmyHXkVapUETExMSIzM1MIIcT169eFVqu1q1f/66+/5PNfvHhRPj569Gih0+nE2bNnnb43RFTxcWaYyp34+Hhcu3YNGzduBGDdSnj16tW47777UK9ePXlc27ZtcfPmTQwbNgxfffUVrl27VuTn7NKlC4KCguSvGzZsCADo1auX6mNb6fg///wDANi9ezdu3LiBkSNHqmZcLRYLevbsiQMHDsgz1SaTCbNnz0ajRo1gNBqh1+thNBrx559/4sSJE3bX1K9fP9XX0syb9NzOtGrVCgcOHPDoFh0d7dH7M2rUKBw4cADffvstpkyZgnnz5mHChAmqMXFxcZg6dSp69eqFLl264MUXX8SWLVtw9epVvPHGG3bndPZxuO1xVx+bu7pPaeHChdi/fz+++uor9OjRA0OHDsXatWs9eqw7y5Ytg16vx6hRo+zuS0pKwqOPPoqHH34YW7ZswbZt2/DEE09g1KhRWLFihTwuNTUVY8eOxYsvvogmTZq4fL5169bh66+/xpIlSzx+/bt378aePXuwevVqhISEoEuXLqqOEk8++SQMBgNGjBiBY8eO4fr163jvvffw6aefArAvIfH0ezJr1izMnz8fM2fOxI4dO/DVV1+hfv366NatGw4dOiSPs1gsAIChQ4fif//7H7p06YKxY8di2bJl+Ouvv7BmzRoAQOXKlTFixAh8/PHH+PDDD3Hjxg38/vvvGDFihPyJkfJaly1bBpPJhNq1a3v0PhFRBVXaaZyosG7duiXCwsJE7969hRBCfPXVVwKAWLlypd3Y5cuXi9jYWKHT6YRGoxFt27YVW7duLdTzARDPPPOM6tiZM2cEADFv3jzVcdvZOqkrgqvbuXPnhBBCTJgwQWi1WvHyyy+LpKQksW/fPnHgwAHRvHlz0alTJ6fPYXtNK1ascPl6LBaLyMvL8+hWVHPnzhUAxK+//up2bIMGDVQz5B988IEAII4dO2Y3tnXr1iI2Nlb+un379qJNmzZ2444ePSoAiA8//LBI19+zZ08RHh6uWtim5OnM8NWrV4XRaBT9+/e3u89isYhq1arZzfALIcRjjz0mgoKC5E8jnnnmGVGnTh2RnJwsUlJSREpKitxdJSEhQaSkpAiLxSLS09NFZGSkmDx5sjwuJSVFDBs2TF7opvyEw5G0tDRRtWpV0a9fP9XxzZs3i5o1a8o/tzVr1hQLFy4UAMRrr70mj4uKihKDBw+2O+8333wjAIhvv/1WCGFdqKfRaOz+H8rNzRUxMTGic+fO8jGpw4btjH1WVpbQaDTi6aeflo9lZGSIRx99VJ5t1mq1YuTIkaJfv37Cz8/vtn6uiahi4swwlTsBAQEYNmwYkpKScOnSJSxfvhwhISF2i6gA60Kq3bt3IzU1FZs2bYIQAn369HE7e+ot0jbGCxcudDr7GhkZCcBaW/zYY49h9uzZ6NGjB9q2bYvWrVvf1oy2I7t27YLBYPDoVtRayrZt2wIATp065XasEEI1W9e0aVMAsFs4aDKZ8Mcff6hmRps2bYoTJ07Y1TdLj3U3i+rq+lNSUnD16tUiPV6yatUq5Obm2i2cA6y9gi9duiS/V0pt2rRBZmam/P4fPXoUZ8+eRVRUFMLDwxEeHo6+ffsCAEaOHInw8HCkpqbi2rVruHz5Mt588015XHh4ONauXYvMzEyEh4djxIgRLq9ZWmhq+73r1asX/vnnH5w6dQrHjx/HmTNnUKVKFQDA/fffL49r2rSpwwWutt+T3377DUIItGnTRjXOYDCgefPmqnpvRzXISsqfn6CgIKxatQrXrl3Db7/9hsuXL2PlypU4efIkOnTo4LCOnYh8G/9WoHIpPj4eH3zwAebNm4fNmzdj1KhRCAwMdDo+KCgIvXr1Qm5uLgYMGIBjx46VyEejHTt2RKVKlXD8+HG3i540Go3dwp9Nmzbh//7v/xATE+O1a5LKJDzhaZmErR07dgCA2+veu3cv/vzzT0ycOFE+1q5dO1SrVg0rV67E0KFD5eNffPEFMjIy8PDDD8vHBg4ciCVLlmDdunWqsQkJCYiOjka7du0Kfe1CCOzatQuVKlWSw15RLVu2DNHR0aoOJ5Lw8HD4+/tj7969dvft2bMHWq0W1apVAwC88847dpvNHD58GM899xxmzpyJTp06ITg4GP7+/vJ7rzR37lzs2rULW7ZskX9Bc+batWs4cuQIOnbsaHefRqORS5Fyc3OxYMECtGjRQhWGBw4ciHHjxmHfvn3y+y+VMrVr107+mZL+u3fvXtXCwpycHPz666+qhX8DBw7EtGnTsGXLFgwcOFA+vmXLFggh0L59e7trlX4RAICNGzfi5MmT+N///ufytRORb2IYpnKpdevWaNasGd555x0IIRAfH283ZsyYMQgICEDHjh1RrVo1JCcnY86cOQgLC7ObjSouwcHBWLhwIUaOHIkbN27gX//6F6pWrYqrV6/it99+w9WrV7F48WIA1jZVK1euRIMGDdCsWTMcPHgQ8+bN86gbQGGEhISgdevWXjnXjBkzcPnyZdx///2oXr06bt68iaSkJCxZsgSDBw9WteJq3rw5Hn30UTRs2BD+/v7Yv38/5s2bh6ioKEyZMkUep9Pp8MYbbyAuLg5jx47FsGHD8Oeff2LKlCno1q0bevbsKY/t1asXunXrhqeffhppaWmIiYnB2rVrkZSUhNWrV6tau8XHxyMhIQGnT5+WfxHq378/mjdvjhYtWqBKlSq4ePEiVq5ciV27dsndPCT//POP/EvE6dOnAUDe/a1OnTp27+m+fftw7NgxTJ061WGLOT8/P4wbNw5vvfUWHnvsMQwdOhQ6nQ4bNmzAmjVrEB8fj8qVKwMAWrRo4fR70LhxY3Tu3BmAtYWZ9GellStXQqfTqe5LTU1Ft27dMHz4cNSrVw8BAQE4deoUFixYgJycHMyYMUN1jgkTJqBz586oUqUK/v77b7z77ru4cOECdu3apRo3evRovPfeexg8eDDmzp2LqlWr4v3338fJkyexfft2edy9996LNm3aYObMmbh16xbuv/9+pKamYuHChThz5oxqF74GDRrgmWeewfvvv4+QkBD06tULp06dwn/+8x+0bNkSQ4YMkceuW7cOFy9eRMOGDZGdnY2dO3diwYIFeOqpp9C/f3/VtY4aNQoJCQk4c+YM6tSp4/Q9JqIKrjRrNIhux4IFCwQA0ahRI4f3JyQkiC5duojIyEhhNBpFdHS0GDJkiNzpwVO4jZphya5du0Tv3r1F5cqVhcFgENWrVxe9e/dWjUtJSRHx8fGiatWqIjAwUNx7773ixx9/FJ06dfJqzbA3bdy4UTz44IMiMjJS6PV6ERwcLNq2bSveffddu9rMRx55RMTExIigoCBhMBhE7dq1xVNPPaVa3a+0Zs0a0axZM2E0GkVUVJSYOHGiSE9PtxuXnp4uJk6cKKKiooTRaBTNmjUTa9eutRsn7Zim7JDxv//9T7Rp00aEh4cLnU4nqlSpInr06CG++eYbu8evWLHCad33yJEj7caPGTNGaDQacfr0aafvn9lsFkuWLBGtW7cWlSpVEqGhoaJly5Zi0aJFqm4pjrjqJuHotdt2k8jOzhZPPPGEaNiwoQgODhZ6vV7UqFFDPProow7rtfv37y+qVasmDAaDiIqKEqNGjXLahSE5OVk89thjonLlysLf31+0b99ebNu2zW7czZs3xbRp00TDhg1FYGCgqFq1qujcubNqYxWJyWQSc+fOFTExMcJgMIhq1aqJp59+WrXhhxBCrF+/XrRo0UIEBQWJgIAA0bp1a7Fs2TKHm3UMGjTIbldAIvI9GiEUDVGJiIh8RFRUFOLi4jBv3rzSvhQiKkUMw0RE5HOOHTuG2NhY/P33327rqImoYmMYJp/lboc1abtYIiIiqrj4Lz35LHdtxUaPHl3al0hERETFjN0kyGe5ay/Gj06JiIgqPpZJEBEREZHPYpkEEREREfkslkkUI4vFgosXLyIkJAQajaa0L4eIiKhUCCGQnp6O6OhoLkymModhuBhdvHgRNWvWLO3LICIiKhPOnz/v9V01iW4Xw3AxCgkJAQCciLsXIUa+1URE5JvSc01ouOon+d9ForKECa0YSaURIUY9QhmGiYjIx7FkkMoiFu4QERERkc9iGCYiIiIin8UwTEREREQ+i2GYiIiIiHwWV3URERFRmZWdnY3c3Fyvn9doNMLf39/r56Xyh2GYiIiIyqTs7GyEV4pCdk6q188dFRWFM2fOMBATwzARERGVTbm5ucjOScXAngtg0Ad47bx5piysT3oWubm5DMPEMExERERlm0EfAKPBe2GYSIkL6IiIiIjIZ3FmmIiIiMo0i0EDs8F783cWcCc8KsCZYSIiIiLyWQzDREREROSzWCZBREREZZpJr4VG7735O5PgXCAV4E8DEREREfkshmEiIiIi8lkMw0REREQuLF68GM2aNUNoaChCQ0MRGxuLLVu2yPdrNBqHt3nz5sljkpOTERcXh6ioKAQFBeGee+7BF198oXqelJQUxMXFISwsDGFhYYiLi8PNmzdL6mX6LNYMExERUZlm1muh9WJrNXMha4Zr1KiBuXPnIiYmBgCQkJCA/v3749ChQ2jcuDEuXbqkGr9lyxbEx8dj0KBB8rG4uDikpqZi48aNiIiIwJo1azB06FD88ssvaNmyJQBg+PDhuHDhApKSkgAATz75JOLi4vD111/fzsslNxiGiYiIiFzo27ev6utZs2Zh8eLF2Lt3Lxo3boyoqCjV/V999RW6dOmCO++8Uz62Z88eLF68GG3btgUA/Oc//8Hbb7+NX3/9FS1btsSJEyeQlJSEvXv3ol27dgCAJUuWIDY2FidPnkT9+vWL+VX6LpZJEBEREXnIbDYjMTERmZmZiI2Ntbv/8uXL2LRpE+Lj41XH7733Xnz66ae4ceMGLBYLEhMTkZOTg86dOwOwhuWwsDA5CANA+/btERYWht27dxfra/J1nBkmIiIin5SWlqb62s/PD35+fg7HHjlyBLGxscjOzkZwcDDWr1+PRo0a2Y1LSEhASEgIHn74YdXxTz/9FEOHDkWVKlWg1+sRGBiI9evX46677gJgrSmuWrWq3fmqVq2K5OTkor5E8gBnhomIiKhMM+l1Xr8BQM2aNeXFamFhYZgzZ47Ta6hfvz4OHz6MvXv34umnn8bIkSNx/Phxu3HLly/HiBEj4O/vrzr+n//8BykpKdi+fTt++eUXPP/88xg8eDCOHDkij9Fo7LeJFkI4PE7ew5lhIiIi8knnz59HaGio/LWzWWEAMBqN8gK61q1b48CBA1iwYAE+/PBDecyPP/6IkydP4tNPP1U99vTp01i0aBGOHj2Kxo0bAwCaN2+OH3/8Ee+99x4++OADREVF4fLly3bPe/XqVURGRt7W6yTXODNMREREPklqlSbdXIVhW0II5OTkqI4tW7YMrVq1QvPmzVXHb926BQDQatWxS6fTwWKxAABiY2ORmpqK/fv3y/fv27cPqamp6NChQ6FeFxUOZ4aJiIioTDMbSre12tSpU9GrVy/UrFkT6enpSExMxM6dO+UWaIC1/vjzzz/Hm2++aff4Bg0aICYmBmPHjsX8+fNRpUoVbNiwAdu2bcM333wDAGjYsCF69uyJMWPGyLPNTz75JPr06cNOEsWMYZiIiIjIhcuXLyMuLg6XLl1CWFgYmjVrhqSkJHTr1k0ek5iYCCEEhg0bZvd4g8GAzZs346WXXkLfvn2RkZGBmJgYJCQk4KGHHpLHffLJJ5g4cSK6d+8OAOjXrx8WLVpU/C/Qx2mEEKK0L6KiSktLQ1hYGC7Ed0aokb93EBGRb0rLNaHGsp1ITU1V1ei6fVz+v6PdR6+GwRjotevJy72FrcsfLfT1UMXEmmEiIiIi8lmcriQiIqIyzaLXwqz33vydxcK5QCrAnwYiIiIi8lkMw0RERETks1gmQURERGWaSa8FvNhazcQyCVLgTwMRERER+SyGYSIiIiLyWQzDREREROSzWDNMREREZZpZr4XGi63VzGbOBVIB/jQQERERkc9iGCYiIiIin8UwTEREREQ+izXDREREVKbl+mlh8dN57XwmzgWSAn8aiIiIiMhnMQwTERERkc9imQQRERGVaWadl1urmTgXSAX400BEREREPothmIiIiIh8FsMwEREREfks1gwTERFRmWYxaGE2eG/+zsLtmEmBPw1ERERE5LMYhomIiIjIZ7FMgoiIiMo0i967rdUsXjwXlX/8aSAiIiIin8UwTEREREQ+i2GYiIiIiHwWa4aJiIioTNMZLNAbLN47odmL56JyjzPDREREROSzGIaJiIiIyGcxDBMRERGRz2LNMBEREZVpep0Fer0X63x1rBmmApwZJiIiIiKfxTBMRERERD6LZRJERERUpum93VrNxDIJKsCZYSIiIiLyWQzDREREROSzGIaJiIiIXFi8eDGaNWuG0NBQhIaGIjY2Flu2bJHv12g0Dm/z5s2zO5cQAr169YJGo8GGDRtU982aNQsdOnRAYGAgKlWqVMyviiSsGSYiIqIyTa8X3m2tpheFGl6jRg3MnTsXMTExAICEhAT0798fhw4dQuPGjXHp0iXV+C1btiA+Ph6DBg2yO9c777wDjUbj8Hlyc3MxePBgxMbGYtmyZYW6Rio6hmEiIiIiF/r27av6etasWVi8eDH27t2Lxo0bIyoqSnX/V199hS5duuDOO+9UHf/tt9/w1ltv4cCBA6hWrZrd87zyyisAgJUrV3r3BZBLDMNERETkk9LS0lRf+/n5wc/Pz+VjzGYzPv/8c2RmZiI2Ntbu/suXL2PTpk1ISEhQHb916xaGDRuGRYsW2YVnKl0Mw0RERFSmGfzMMPiZvXY+jcV6rpo1a6qOz5gxAzNnznT4mCNHjiA2NhbZ2dkIDg7G+vXr0ahRI7txCQkJCAkJwcMPP6w6/txzz6FDhw7o37+/d14EeQ3DMBEREfmk8+fPIzQ0VP7a1axw/fr1cfjwYdy8eRPr1q3DyJEjsWvXLrtAvHz5cowYMQL+/v7ysY0bN+L777/HoUOHvP8i6LaVWjeJOXPmQKPRYNKkSfIxIQRmzpyJ6OhoBAQEoHPnzjh27JjqcTk5OZgwYQIiIiIQFBSEfv364cKFC6oxKSkpiIuLQ1hYGMLCwhAXF4ebN2+qxpw7dw59+/ZFUFAQIiIiMHHiROTm5qrGHDlyBJ06dUJAQACqV6+OV199FUIUruieiIiIyiapO4R0cxWGjUYjYmJi0Lp1a8yZMwfNmzfHggULVGN+/PFHnDx5Ek888YTq+Pfff4/Tp0+jUqVK0Ov10Outc5GDBg1C586dvf66qHBKJQwfOHAAH330EZo1a6Y6/sYbb+Ctt97CokWLcODAAURFRaFbt25IT0+Xx0yaNAnr169HYmIifvrpJ2RkZKBPnz4wmws+Phk+fDgOHz6MpKQkJCUl4fDhw4iLi5PvN5vN6N27NzIzM/HTTz8hMTER69atw+TJk+UxaWlp6NatG6Kjo3HgwAEsXLgQ8+fPx1tvvVWM7wwRERGVB0II5OTkqI4tW7YMrVq1QvPmzVXHX3rpJfz+++84fPiwfAOAt99+GytWrCipSyYnSrxMIiMjAyNGjMCSJUvw+uuvy8eFEHjnnXcwbdo0uc4mISEBkZGRWLNmDcaOHYvU1FQsW7YMq1atwoMPPggAWL16NWrWrInt27ejR48eOHHiBJKSkrB37160a9cOALBkyRLExsbi5MmTqF+/PrZu3Yrjx4/j/PnziI6OBgC8+eabGDVqFGbNmoXQ0FB88sknyM7OxsqVK+Hn54cmTZrg1KlTeOutt/D88887bYtCRERE3mXQW2Dw5nbMeYU719SpU9GrVy/UrFkT6enpSExMxM6dO5GUlCSPSUtLw+eff44333zT7vFRUVEOF83VqlULdevWlb8+d+4cbty4gXPnzsFsNsuhOSYmBsHBwYW6ZvJcic8MP/PMM+jdu7ccZiVnzpxBcnIyunfvLh/z8/NDp06dsHv3bgDAwYMHkZeXpxoTHR2NJk2ayGP27NmDsLAwOQgDQPv27REWFqYa06RJEzkIA0CPHj2Qk5ODgwcPymM6deqk+sikR48euHjxIs6ePevwteXk5CAtLU11IyIiovLt8uXLiIuLQ/369dG1a1fs27cPSUlJ6NatmzwmMTERQggMGzasyM8zffp0tGzZEjNmzEBGRgZatmyJli1b4pdffvHGyyAnSnRmODExEb/++isOHDhgd19ycjIAIDIyUnU8MjIS//zzjzzGaDQiPDzcboz0+OTkZFStWtXu/FWrVlWNsX2e8PBwGI1G1Zg6derYPY90n/I3OcmcOXPkHoFERERUMXiyAcaTTz6JJ5980uNzOlqDtHLlSvYYLgUlNjN8/vx5PPvss1i9erVqhaUt2/IDIYTbkgTbMY7Ge2OM9IPr7HpefvllpKamyrfz58+7vG4iIiIiKl0lFoYPHjyIK1euoFWrVvJKyl27duHdd9+FXq9XzboqXblyRb4vKioKubm5SElJcTnm8uXLds9/9epV1Rjb50lJSUFeXp7LMVeuXAFgP3st8fPzs1uZSkRERLdHr7d4/UYkKbEw3LVrVxw5ckS1krJ169YYMWIEDh8+jDvvvBNRUVHYtm2b/Jjc3Fzs2rULHTp0AAC0atUKBoNBNebSpUs4evSoPCY2NhapqanYv3+/PGbfvn1ITU1VjTl69KhqL/GtW7fCz88PrVq1ksf88MMPqnZrW7duRXR0tF35BBERERGVTyVWMxwSEoImTZqojgUFBaFKlSry8UmTJmH27NmoV68e6tWrh9mzZyMwMBDDhw8HAISFhSE+Ph6TJ09GlSpVULlyZbzwwgto2rSpvCCvYcOG6NmzJ8aMGYMPP/wQgLWOp0+fPqhfvz4AoHv37mjUqBHi4uIwb9483LhxAy+88ALGjBkjz+YOHz4cr7zyCkaNGoWpU6fizz//xOzZszF9+nR2kiAiIiKqIMrUDnRTpkxBVlYWxo0bh5SUFLRr1w5bt25FSEiIPObtt9+GXq/HkCFDkJWVha5du2LlypXQ6XTymE8++QQTJ06Uu07069cPixYtku/X6XTYtGkTxo0bh44dOyIgIADDhw/H/Pnz5TFhYWHYtm0bnnnmGbRu3Rrh4eF4/vnn8fzzz5fAO0FEREQSvUFAb/DeplfCi+ei8k8juKVasUlLS0NYWBguxHdGqLFM/d5BRERUYtJyTaixbCdSU1MLtZ5G+nd04JpVMAQGeu168m7dwvrhcYW+HqqYSm07ZiIiIiKi0sYwTEREREQ+i5/dExERUZnm7XZogq3VSIEzw0RERETksxiGiYiIiMhnsUyCiIiIyjSd3gK9wXulDRaWSZACZ4aJiIiIyGcxDBMRERGRz2IYJiIiIiKfxZphIiIiKtO83VqNNcOkxJlhIiIiIvJZDMNERERE5LMYhomIiIjIZ7FmmIiIiMo0vcHLfYa9eC4q/zgzTEREREQ+i2GYiIiIiHwWyySIyKeZzRoAgE4nSvlKiMgZg9Z68xbBqUBSYBgmogpJCrmeYBAmIvJdDMNEVO55GnwZeomIyBbDMBGVOwy/RETkLQzDRFQueBKAGX6JKiZ/HeCn8975tF48F5V/DMNEVCaV5uyvVl+6odpi8rzemYiIbg/DMBGVObZBuLhmfEs79Drj7LoYkomIvI9hmIjKBLNZA51OFEsQLquht7BcvQ4GZarIDBrvtlaz8H8XUmAYJqJSpQy/yj8XNQRXlOBbWI5eNwMyEZF7DMNEVKLc1QIXJQT7agB2R/m+MBgTETnGMExEpeJ2yx8YgAvH9v1iOCYismIYJqJiJ9UDSzj7W/oYjqk8MeisN2+xsLUaKTAME1GxsC2HKMz2yBIG4JLDcExEvophmIi8ylXo9XRGmCG49EnfA4ZiIqrovNiohIh8mdmscRqEdTrhNghr9UK+VRQVIUhWxO8LUWEtXrwYzZo1Q2hoKEJDQxEbG4stW7bI92s0Goe3efPmyWNycnIwYcIEREREICgoCP369cOFCxfk+8+ePYv4+HjUrVsXAQEBuOuuuzBjxgzk5uaW6Gv1RZwZJqLb5igEV4RZYG+E2ds9R1l6fzhbTKXFoAWM3uwzXMhz1ahRA3PnzkVMTAwAICEhAf3798ehQ4fQuHFjXLp0STV+y5YtiI+Px6BBg+RjkyZNwtdff43ExERUqVIFkydPRp8+fXDw4EHodDr88ccfsFgs+PDDDxETE4OjR49izJgxyMzMxPz582/7NZNzGiFE2fmbtoJJS0tDWFgYLsR3RqiRv3dQxeNqJtidshLyynOwKwvvYXl+/6jkpOWaUGPZTqSmpiI0NNTzx+X/O/qfH5bAPzjQa9eTnXELr98/ptDXo1S5cmXMmzcP8fHxdvcNGDAA6enp+O677wAAqampuOOOO7Bq1SoMHToUAHDx4kXUrFkTmzdvRo8ePRw+x7x587B48WL8/fffRbpG8gzLJIio0ByVREilEJ6WQ5QGi0ljdyvPysLrKQuBnKio0tLSVLecnBy3jzGbzUhMTERmZiZiY2Pt7r98+TI2bdqkCskHDx5EXl4eunfvLh+Ljo5GkyZNsHv3bqfPlZqaisqVKxfyVVFhcbqSiAqlqCURpRGaynvYLQpHr7m433tu7kHFzaAVMGi993Nszj9XzZo1VcdnzJiBmTNnOnzMkSNHEBsbi+zsbAQHB2P9+vVo1KiR3biEhASEhITg4Ycflo8lJyfDaDQiPDxcNTYyMhLJyckOn+/06dNYuHAh3nzzzcK8NCoChmEi8pij2WBXSjoAM4g5Zvu+FOf3hcGYypPz58+ryiT8/Pycjq1fvz4OHz6MmzdvYt26dRg5ciR27dplF4iXL1+OESNGwN/f3+3zCyGg0dj/f3Lx4kX07NkTgwcPxhNPPFGIV0RFwTBMRG4VZTa4pIIwA1fhlVQ45oI7Kuuk7hCeMBqN8gK61q1b48CBA1iwYAE+/PBDecyPP/6IkydP4tNPP1U9NioqCrm5uUhJSVHNDl+5cgUdOnRQjb148SK6dOmC2NhYfPTRR0V9aVQIrBkmokJxVxdcEjXBFaXmt6wo7veTdcVUEQkh7GqMly1bhlatWqF58+aq461atYLBYMC2bdvkY5cuXcLRo0dVYfj//u//0LlzZ9xzzz1YsWIFtFrGtJLAmWEicki5hXJZqQlm+C1+yvfYm99TzhLT7TCWcmu1qVOnolevXqhZsybS09ORmJiInTt3IikpSR6TlpaGzz//3GGNb1hYGOLj4zF58mRUqVIFlStXxgsvvICmTZviwQcfBGCdEe7cuTNq1aqF+fPn4+rVq/Ljo6KiivZCySMMw0SkUthtk0tiFphKR3EEY9YUU3l0+fJlxMXF4dKlSwgLC0OzZs2QlJSEbt26yWMSExMhhMCwYcMcnuPtt9+GXq/HkCFDkJWVha5du2LlypXQ6XQAgK1bt+Kvv/7CX3/9hRo1aqgeyy64xYt9hosR+wxTeVPY2uDiCsIMSWWbt7/v/H5XfLfbZ3jO7o+83mf45Q5P3lafYao4mNCICEDhOkUwBPs26fvk7dlifv/JGb3Wugudt5hYiksKDMNEPq4szAYzBJVP3i6jYCgmotLAMEzkwwoThBmCyRVvzhYzFBNRSWIYJvJRyiBc0j2DGXIqruLqRkFEVFwYhol8lE4nVO3THGEIpttxu7PF7DxBEgNrhqkY8ceByIeVVBDmBhm+zRvff84yE1Fx4cwwkQ+RSiNKqiyCAZiUbreEgrXERFQcODNM5EPcbaUMMAhTybid2WLOEhORN3FmmIgAMART6fB2z2KqmLy9HbOZU4GkwB8HogrM062VGYSptBV2plirF/KNiOh2cGaYqIKSgnBJdIzwhRDs6H3yhddd0ooyU6zVC34viKjIGIaJKiBPewj7ehC+3dfPgFx8ChuKubiuYvPTAv46753Pws/FSYFhmKiCKakgXN5CR0l9nM5Q5l0Wk4azxERUrBiGiSoQBuECpVFLWh7el/KIs8REVJwYhokqoOIKwmU9XBRXAC7rr9tXFLZPMWeJicgTDMNEFUhx9hCu6KGior++iqaw5RNUvnl7O2ZvnovKP4ZhIh9R0YJwYV9PWXwNdHs8mSnm7DARucMwTFTBVZT64KK+jrJy/VS8XM0Us4aYiFxhGCYqx9z1EL4dZSk4FCUIl6Xrp5LhrnSCobj8spZJeO/vOpZJkBLDMFE55W53ufJeT8kATEXhqPOE7TGWThCREsMwUTkkBeGKOCtcmBDMQEPOuPvZYCAmIgnDMFE540lpRHmsr+WCOCppDMREBDAME5UrFTEIMwQTkTt6rYDeizXD3jwXlX8sISeqQCpyELaYNAzCdFsc/QyV99p6Irp9nBkmKifczQqXpyDMumAqTYXd3pmIKjaGYaJyoLg6R5TlIMwQTMWNP2NEBDAME5V57jpHlKcg7Imyel1EVHr0WuHVPsOsGSYl1gwTlWEMwkRERMWLM8NEZVhx9BEu6cDpSWBnCCYiotLCMExUTpX1HdoYgonIWwxeLpPw5rmo/GOZBJGPYBAmIiKyxzBMVA6V940qytr1EBGR72KZBFE5Utb7orq7PoZgIiIqaxiGiSq4kgqgDMJEVFy4HTMVJ5ZJEJUTZXnBHIMwERGVVwzDROUAgzAREVHxYBgmqoDKQhC2mDRlLgibzZpC34io9Emt1bx5K4zFixejWbNmCA0NRWhoKGJjY7FlyxbVmBMnTqBfv34ICwtDSEgI2rdvj3PnzqnG7NmzBw888ACCgoJQqVIldO7cGVlZWfL9/fr1Q61ateDv749q1aohLi4OFy9eLPobRx5hGCYq48rqojl3Qbgs8EawZTgmoho1amDu3Ln45Zdf8Msvv+CBBx5A//79cezYMQDA6dOnce+996JBgwbYuXMnfvvtN/z3v/+Fv7+/fI49e/agZ8+e6N69O/bv348DBw5g/Pjx0GoLoliXLl3w2Wef4eTJk1i3bh1Onz6Nf/3rXyX+en2NRghRNv+lrQDS0tIQFhaGC/GdEWrkWkUqvPJYHlGaQbg0wmpx7BJIVNGk5ZpQY9lOpKamIjQ01PPH5f87+t1f7yEoJMBr15OZnoWuMc8U+nqUKleujHnz5iE+Ph6PPPIIDAYDVq1a5XR8+/bt0a1bN7z22mseP8fGjRsxYMAA5OTkwGAwFOk6yT3ODBNVIL4ahEtz1pYzxkTlV1pamuqWk5Pj9jFmsxmJiYnIzMxEbGwsLBYLNm3ahLvvvhs9evRA1apV0a5dO2zYsEF+zJUrV7Bv3z5UrVoVHTp0QGRkJDp16oSffvrJ6fPcuHEDn3zyCTp06MAgXMwYhonKqLK4sYZWL8pUEC5LQbQsXQtRRWPUCfh58WbM/0SnZs2aCAsLk29z5sxxeg1HjhxBcHAw/Pz88NRTT2H9+vVo1KgRrly5goyMDMydOxc9e/bE1q1bMXDgQDz88MPYtWsXAODvv/8GAMycORNjxoxBUlIS7rnnHnTt2hV//vmn6nlefPFFBAUFoUqVKjh37hy++uqrYnpXScLP7onotpTmbHBhFOU6i1KmYjZrWDpBVE6cP39eVSbh5+fndGz9+vVx+PBh3Lx5E+vWrcPIkSOxa9cuVKpUCQDQv39/PPfccwCAFi1aYPfu3fjggw/QqVMnWCwWAMDYsWPx+OOPAwBatmyJ7777DsuXL1eF8H//+9+Ij4/HP//8g1deeQWPPfYYvvnmG2g0/GW7uJTYzLC7lZhCCMycORPR0dEICAhA586d5cJ0SU5ODiZMmICIiAgEBQWhX79+uHDhgmpMSkoK4uLi5N/y4uLicPPmTdWYc+fOoW/fvggKCkJERAQmTpyI3Nxc1ZgjR46gU6dOCAgIQPXq1fHqq6+C5dVUUsrqrHBpPK8tT2ZgpU4WyltRFPXxnCUmKh+kTCLdXIVho9GImJgYtG7dGnPmzEHz5s2xYMECREREQK/Xo1GjRqrxDRs2lLtJVKtWDQBcjpFERETg7rvvRrdu3ZCYmIjNmzdj79693ni55ESJhWF3KzHfeOMNvPXWW1i0aBEOHDiAqKgodOvWDenp6fI5Jk2ahPXr1yMxMRE//fQTMjIy0KdPH5jNZnnM8OHDcfjwYSQlJSEpKQmHDx9GXFycfL/ZbEbv3r2RmZmJn376CYmJiVi3bh0mT54sj0lLS0O3bt0QHR2NAwcOYOHChZg/fz7eeuutEninyNeVxSBcFhQmBHt0PpNGvnmiqKGYiComIQRycnJgNBrRpk0bnDx5UnX/qVOnULt2bQBAnTp1EB0d7XKMs+cA4FEtMxVdiZVJ9O3bV/X1rFmzsHjxYuzduxeNGjXCO++8g2nTpuHhhx8GACQkJCAyMhJr1qzB2LFjkZqaimXLlmHVqlV48MEHAQCrV69GzZo1sX37dvTo0QMnTpxAUlIS9u7di3bt2gEAlixZgtjYWJw8eRL169fH1q1bcfz4cZw/fx7R0dEAgDfffBOjRo3CrFmzEBoaik8++QTZ2dlYuXIl/Pz80KRJE5w6dQpvvfUWnn/+eX5UQWVGaS6YK8kQ7kmodHQ9ngZd5TidB5uIFOYXFpZNEN0+PQT0Gi9ux4zCnWvq1Kno1asXatasifT0dCQmJmLnzp1ISkoCYC1tGDp0KO6//3506dIFSUlJ+Prrr7Fz504AgEajwb///W/MmDEDzZs3R4sWLZCQkIA//vgDX3zxBQBg//792L9/P+69916Eh4fj77//xvTp03HXXXchNjbWa6+d7JXKAjrblZhnzpxBcnIyunfvLo/x8/NDp06dsHv3bgDAwYMHkZeXpxoTHR2NJk2ayGP27NmDsLAwOQgD1lYmYWFhqjFNmjSRgzAA9OjRAzk5OTh48KA8plOnTqqPS3r06IGLFy/i7NmzTl9XTk6O3cpUosIoqz2FbZWlIOxoxrYwM752z1cMr40zxETl2+XLlxEXF4f69euja9eu2LdvH5KSktCtWzcAwMCBA/HBBx/gjTfeQNOmTbF06VKsW7cO9957r3yOSZMm4eWXX8Zzzz2H5s2b47vvvsO2bdtw1113AQACAgLw5ZdfomvXrqhfvz5Gjx6NJk2aYNeuXS7LN+j2legCuiNHjiA2NhbZ2dkIDg6WV2JKQTUyMlI1PjIyEv/88w8AIDk5GUajEeHh4XZjkpOT5TFVq1a1e96qVauqxtg+T3h4OIxGo2pMnTp17J5Huq9u3boOX9+cOXPwyiuvuH0fiLyhtMojykoQ9nQm2OLkHFoHs7XuZoWB8vMLCxF5z7Jly9yOGT16NEaPHu1yzEsvvYSXXnrJ4X1NmzbF999/X6Tro9tTomHY2UpMiW35gRDCbUmC7RhH470xRqrbcXU9L7/8Mp5//nn567S0NNSsWdPl9RNJykPIKqtBuLhCcHn4nhD5gqJsoezufESSEg3D0kpMAGjdujUOHDiABQsW4MUXXwRgnXWVVlwC1ibV0oxsVFQUcnNzkZKSopodvnLlCjp06CCPuXz5st3zXr16VXWeffv2qe5PSUlBXl6eaow0S6x8HsB+9lrJz8+PH2VQiSjpWeGSfj5nQdhdCHYXgJWh11HQlWp7pTpfb5Q3sF6YiKhsK9VNN6SVmHXr1kVUVBS2bdsm35ebm4tdu3bJQbdVq1YwGAyqMZcuXcLRo0flMbGxsUhNTcX+/fvlMfv27UNqaqpqzNGjR3Hp0iV5zNatW+Hn54dWrVrJY3744QdVu7WtW7ciOjrarnyCyBsKMwNZ0btHFDUIS7Q6oboZ/Cww+Off/BzfjMGAMRjQBWigC9DA4GeBVm99rE4nbutGRERlW4nNDLtaianRaDBp0iTMnj0b9erVQ7169TB79mwEBgZi+PDhAICwsDDEx8dj8uTJqFKlCipXrowXXngBTZs2lbtLNGzYED179sSYMWPw4YcfAgCefPJJ9OnTB/Xr1wcAdO/eHY0aNUJcXBzmzZuHGzdu4IUXXsCYMWPkxtvDhw/HK6+8glGjRmHq1Kn4888/MXv2bEyfPp2dJMjr+FF8gdsNwhLbGWCdzrpznsZg/f1f468HDM7nAjR5loI/Z5sgFF87uh4iIiq/SiwMSysxL126hLCwMDRr1ky1EnPKlCnIysrCuHHjkJKSgnbt2mHr1q0ICQmRz/H2229Dr9djyJAhyMrKQteuXbFy5UrodDp5zCeffIKJEyfKXSf69euHRYsWyffrdDps2rQJ48aNQ8eOHREQEIDhw4dj/vz58piwsDBs27YNzzzzDFq3bo3w8HA8//zzqnpgotJQkUNYUYKws7IIoOCXDIOfBRqDFhp/A2DQQmPQFYTi/P/ahl3pa5FnhsZghCbPApFtKjg3GI6JSpLeyzXDetYMk4JGcFu1YpOWloawsDBciO+MUCN3viZ7LI8o4CgMexKEDX7qYKqcCdZFBlmDsEELTaBBDr8w6qDxs/4SLdILyqEKQrAFyDND5Fnyb2ZAui8/FHO2mMhzabkm1Fi2E6mpqartj90+Lv/f0cPnFyAkNMBr15OeloUWNZ8t9PVQxcSERkSlzpOFaq5mhJWBWKsX0IYYoQ3zgy4iAJoQIzQBRsDfD9AXfIok/VkTngtk5+/uZDLDkpELDQDkmmG5ZQJu5QGA3KJfAz1EtsluVlmrFwzERETlEMMwUSnhrLCVJ+URLhfL6QvqgaVaYF1EILSV/KCNCAaCA6EJCACCAwC9g7/ysnOA7FyI9AzrOf1zAJMZIivXWjscaCgIxHlma6lFfiC2ux4GYqJiodcKr5Y2sEyClBiGiajMcRUopVlhrU6oFspp/PXQhvlZZ4Qjg6whODwMqBQCBAQB/qHQGAIg8rLUJ8y9BeRkQBMcAGRkQeh1QHYuNCYzNEEGIDPPPhDnP5+jQExEROULwzBRKeCssFVhyyMAdc9gqf2ZNsQIXUQAtHcEQlspAIgIh+aOykDYHdAYAoDgCGQjGxZhhn9wOLQZKepQbPAHTGbA3whk5R9XllQ4wCBMRFQxMAwTUam4nfIIZRDWRQZZg3Blf2juqGQNwVWqQhMaCUtACPIsuci1pCE97xrScnUINV5GiH8EgkNqAunXrLXAFhOg1QOm69AEBECYzNZwnGudBUb+bLA0KyyR6oUr8i8sREQVHcMwUQnjrPDtMfhb5G4R2sr+0EUEQFc9FKgUYi2LqFYHIjwa6Xk3cDPzb/yd5oc8iwZ5lkBkmbQINZpRKzgFlfxSEOAXCn1AKPx1wdCkXARw3fokJrMcfAs6SuQv0stvsybyLOrg7maWmxtwEBUdt2Om4sQwTERlhqvwL9UHK4OwNtBgreuV6oOrVIUm5A5kmlJxLTsVf9wMwNEULa5maWDQAgYdUNVfh7TcAFTxNyHCPx3VAg0I1IdBWEz5C+eygIxbEDnWbhLiVh4st/LsQrDZrN60w9G1K3/xsQ3LDMdERGUDwzBRGVWRZ4U9qRUGrAHYbNLIQVjqHKENMUJj0FkXzFUKgCZE6hoRBpNOi5zcTNwyWcNqnkWT/1/geg5QNxgI0FsQoLcgUG9BgD4UyM4AstKA7Fwg4xYsN7JgSc2xhuE8M0R6LsxZIv/atfL3xt1OeLachePCBGNXnyxU5J8ZIqLiwjBMVIK49XLhKYOwTpcfhAMN0Abq5VlhBAcAfsGAXzByLVnItWQh02REVn4gzjID6Zl6ZKQZcCX0Fu6vlo0Qox4BusoIFH4QN05D/N8l4FoKLFcyYL5mDcMi2wRzloDZrIEl/1xmk0buaKEMwzoH31uLWSMv+LNV1GBsMWmc/hwpjzMYExF5hmGYqAyqyEHG022XlaQgrAvQAAYttIF69aywvx80AWGAfzBMuVeRZ9Egy6RFeh6Qa7HOCt+84YfUm344Vy0LV7P1qBEUCaNFA3H1NHApGUi+BtOFdIhbeTBdSIfFpEFejhZmkxYWs0a96Uf+n015GugNwuH1S8FUObPtijzerPFKCQWDMVUkWo0OWo3rDi+FPR+RhGGYqIRwVrholNsra/wN0Bh01uJfo65gVzmDP2Dwh0WYYYEZJosGeRYNcvPXvGVn6ZGZYUB2lh5ZOVpkmbSwwAxx9R/gUjLEpSswnbkJ04V0mLMEsjP1cgC2mDQw5dkHYUmukxAMRVC2e006IYdrKSgrZ3zdBWJXs8PO3kNH105ERAzDRGUOA4uaHIQNWuvub9LNL39mR9EPWGvKQ6jhjvyvMmDQGmHU6lE9MA83I1NwLkODAJ21ZlivMQLpqRBXr8P8f2kwX8uy7mCXlWc3EwxYvy/OtoRWlkIoZ4LlTsT5wVgKsY5mi3V6YReIpdfvSGEDMcBQTETkCMMwUQngrLCVpwvnAPV7Jm21rDHoCsKwQf0xp8hOh8ZsghZAJUMwAoPCEGK8jDtDM+Gn08JPF4QsUxrScnUwaAX0ZgssKWmwXMuAJTUH2jA/iDwLzNesEVZavOeIFISlUCn9Vw6b+bXCjoKxo790lTPFtq/f1SxxUQKx6joZiqmcYJkEFSeGYaIyhOHESgpr0qwwoJFnheUSCb2uYFY4LxsA8tuj5QIADMZA3BFWA/DXA7duQlz7P4QBiDIarNsyp18FbqZBZOZBE2iwnudWHjKuW/9s8LfIs7VKyiBsyrMJonmA3lBwv1Yv7IJxbv5x5V++0n3KccqA7WqWuKiBWDq3dA4iIl/FMExEpc5dGNP4O/+rSqRnQmMyA8FmORTDZAZSr0NcPQ9k50KkpMJy4Tp0Te8E/I0Q/n6AyQxNzWrQBQcCGbes13ExFQb/bITW1kBkm5F9UwqkGuRmWbtJ6PQCeTnqHsN5JqH6s0Gf/3ryw7GjwOpqpljiqHTC24EYsIZiBmIi8lUMw0TFjCUS7mltZkIBm1phQC6L0Bi01m2STWYgO8e6UUbGLSA9A9Dn/5Vmsm6ggYxbEFm5MF++BWPcQGQEGqHV6KDX5P9Xa4Tm1k2ItMtAZiq0lW7gjqrBsFzJgOlSJvyRBUOWCXk5WmsIztbClKeBNj/86uFgdhgF4digL7hfD3UolmaAnYViV7PExRWIpfMQEfkShmGiMsLXQ4gyyNmFPUPBTKzIs0Bj0ELkmaHR54diQJ7dBSBvmmFOzYFIz4WxcQTgFwyLyLLerzEDAjBZcqE3+APGQMBiguYOQJisdcd6AGaDFppbedCl50KbYYZWJ6DNnxW2mDTIgxZ6aKA3WEOvcobYUUgGXIdiT2eJnZVN3G4gBhiKqWzSQgctvFgz7MVzUfnHMExEZZLTUJdnhsjTQtzIhibIrLpLZOZZt0tOzbH2C07JQ3amDjh2DdpfdiKkQWPAlAtNQBgsofldJ7RaaPxDAJ0BQquHJjwHwmSGFgCMOmhu5sBi0MGILOh0Zuj0Qp4lBgCzzroJhx75O915EIgByOOVHNUTK3kyS+yNQCydn4GYiHwBwzBRMfI0lDB0uJFnsS6cyyfyrA2Exc2c/PvN1hB8Kw/Is8CUkpe/YYYOOr2Axl8Py9kr0ObXCSMgDFqNDhZhBnR6a59is8k6QxwcCE12rjUQV1JfhuZWHrTpuarvqylPI88SG/WA3qSVQ7AyDDsKxo4CMQC7WWJd/kI81deKWeLiDMTS+YiIKiqGYSIqUxy2Ecsvk5BCsBTNpK+lEGxJz5V3jsvO1MHgZ7EGOqnMIr/OWORlWduw6fL/CtRaA7HGYoIw+AP+Rus2z3odlEvlRH6vY8MN60I9qWxCCotSezRlyLUNwaY8IXecUNYT2/K0lthZ2YRtDfbt4CwxlTatVg+d1uDF8+V57VxU/jEME1GZpVHVCpsVf7aojon0XJizBMxmLfKytcjLD6gWvXXWVNWT2GQGcm9ZO09Y9NYgDFhniLX6/NnhbGj0OiAjCwKQA7FQXI8UiAHAoi/Ynlkqn3AXiOXz5C+yczdLLG3WATgOxIDjbhMsmyAico1hmKiYsESi8JxuQZwffgFAKI6JbBNEniU//FpDsBRG7eQq6ovNJnUY1tkEYr9g69cmMzQIlgOxBVDNFBvTcwFYFN9D66jCBmIlZ7PEnvxlzbIJIqLCYxgmojLBURCWO0dkmwp6DeeHYAAOZ4NN+VsfOyS1XLOYrKUSCLAGY+UMMQDojUCeHsjvRwy9DvD3g7aSOhBbABiRC3OWRfEk1kBsMWlg1mmg1WuALA0Meg3yTMJp2YTUn9jZLLFt2YRUR+ysbKK4AjHAWWIiqlgYhomo1DmdEVaQAjBgDckWkwZms7UswWK2LmLLzdJCqxfOA7HJDJGXZ92kw2Ky3rR6ayAGCv4LWBfVWUyAXgdNQIA8I60NNtsFYh1y8/+UP0axSQcA6A3q2WopACsX2kmBWH6Mh4vrgMLVETMQU3nE1mpUnBiGiYoBSyQc0+kKQpqnpNlh6c8A7IJwXrYWuVnagppaV++/yWTdrCPgFgQAjSGgIBBbTOqx2vzZYVgX7cmBGPYzxOpArIUxwCIHdMACU44yHDuY+VUsrHMXim+njpiBmIhIjWGYiEqNJzPCgH0Itpg0MJs0chA25RV8LTHBOrlrfbxZPofMbAJMuQWBWEmrt/7taDEBRsXxvDxAbwb8jXIg1uRZ5D/bBmLrqaTXqK44VgZe+Zql7hJOyiakwCu9Pk9miIHiD8TSOYmIyiOGYaJS4qvhwdMArCS3LjMXtDBTBmFlqQRgrad1yGQGsnOtt+D8RXSA40AMFMwY63X55RL+UJ5ZCwC5BWUTtoFYq7NeT0HJhAVavUb1vXe0kM7TbhMmAHqD49nZkgzEAGeJiaj8Yhgm8jJvBgxfZhuszIqwazFbZ4KlDS88IfIssGTkQutvtNYNZ+cA/igIxMoFdLblEhK9DhqDAcJkAvR6QG+GJsSoKptQB2LpCFQ1xIDrThO2x5WBWGuzAYdtIFZu0MFATBWFVqODVuPFmmEvnovKP4ZhIirzlHXGZqlEwqQul3AWwsxmDfR5FkDRp1juKmEyA0Z9wWI6V/T5f13aLKhTN1UreiAGCsJvbo6A0U+9MYfyMY4CsfU+K9sd6wAwEBMROeGkIScRFSeGhcKzDb7KOmHp/ZSCo9nZ+5trLgjBJrM1FEsh2JSrHit9LfUe1ufPJMn/1ReUTxh0gFEHjUGbf9NBE2KELkADg58FOr2Awc8CY4D1ptML6+51euuiOUP+ph3SAjopCMuXkv+6pNZs0i8C0gx5wesv+AUBgByIlWMA2C1i9PbPIz8doYpm8eLFaNasGUJDQxEaGorY2Fhs2bJFNebEiRPo168fwsLCEBISgvbt2+PcuXMAgBs3bmDChAmoX78+AgMDUatWLUycOBGpqakOny8nJwctWrSARqPB4cOHi/vl+TzODBNRmWa7YE4Oejb/lQKjxWTtvQsUhGJpcw6RZ7EJw/k3ZFtX27mZHIZeL/cd1gAF5RL+yvnf/D/fAuCvhw4mSDPEFrP1uowBFnXrNRR0kVC2XHNEWninN9jPhstt5fK/VvYilt8bzhBTOVTaZRI1atTA3LlzERMTAwBISEhA//79cejQITRu3BinT5/Gvffei/j4eLzyyisICwvDiRMn4O9vXcV78eJFXLx4EfPnz0ejRo3wzz//4KmnnsLFixfxxRdf2D3flClTEB0djd9+++32Xyy5xTBM5EWcESsZpjz1zKer8GgNZAX3izwzNCaTtW5Yr7PODut16h7Druh1+QEact2w9c9maPx0QK4ZwqCDNlARjvNyYbDpMmEMsDjcvrm4ArH8Z0X7OdtexMURiKXzEpVnffv2VX09a9YsLF68GHv37kXjxo0xbdo0PPTQQ3jjjTfkMXfeeaf85yZNmmDdunXy13fddRdmzZqFRx99FCaTCXp9QRzbsmULtm7dinXr1tnNPlPxcBiGf//990KfqFGjRqpvJhE5xmDgOeXH+bYlAWaT+yCsJM0MixwzNAFmdd2wyawsuHV/svxSCXl2OP+YxqADpAV1twCNwQIEGqzxNz0XWrPID8VSAAYsDnoKOwrEyj8rN+goSiB2xduBmKiiMZvN+Pzzz5GZmYnY2FhYLBZs2rQJU6ZMQY8ePXDo0CHUrVsXL7/8MgYMGOD0PKmpqQgNDVVlp8uXL2PMmDHYsGEDAgMDS+DVEOAkDEt1KkJ49heiVqvFqVOnVL8FERHdDikIK2tggYK62IJa4YJ6WsDxhhbWIGy2LqLLzZ/JlXajk2aHAfXfiFIoVs4Y620+WjWZFQvrrOUSGuRCGHUFZRO38iAMWmhDjDAgF3nQqgJxUUombHesK+oMsbOSCW/jDDGVVWlpaaqv/fz84Ofn53DskSNHEBsbi+zsbAQHB2P9+vVo1KgRkpOTkZGRgblz5+L111/H//73PyQlJeHhhx/Gjh070KlTJ7tzXb9+Ha+99hrGjh0rHxNCYNSoUXjqqafQunVrnD171quvlZxzOgWyb98+3HHHHW5PIIRAkyZNvHpRRESAfXhyFqYcbWChHGsxadSrhZUL6OS64XzWrd08K5tQ1Q5LC+us5RIC1g05YNBCG2iA5VYeNAZrELYNxLYzsYUpmQAKwrG3ArGylMKbWEdMRVVcNcM1a9ZUHZ8xYwZmzpzp8DH169fH4cOHcfPmTaxbtw4jR47Erl27UKlSJQBA//798dxzzwGwTiru3r0bH3zwgV0YTktLQ+/evdGoUSPMmDFDPr5w4UKkpaXh5Zdf9tKrJE85DMOdOnVCTEyM/A125/7770dAgIOG9UQ+hB8tFy/l7LCyRMJhEDYX7MJmNmlg8AMglUnkzxJrTCbApCuYHVbO+lpM1kDsjDQbrKwdlu/TQRNgBJBrnYUONAC38uQSCqTnQpd/zVIg1huE3do9TwOxaqc6g7qWGri9GeLiKJlgIKay5Pz58wgNDZW/djYrDABGo1FeQNe6dWscOHAACxYswMKFC6HX69GoUSPV+IYNG+Knn35SHUtPT0fPnj3lmWWDwSDf9/3332Pv3r1219C6dWuMGDECCQkJRX6d5JrDv+137NhRqJNs3rzZKxdDVNExBHjGtq+wMtypu0g4LiEw2vzNZjZroMs2QQQabOqGnc0O69z3HZbGIb92GFCVTSjDr7yg7haAEKO1B3GW9dq1JgGDvzUUFyYQu9q62bYPcVkqmZCuh/8vUFkgtUorCiEEcnJyYDQa0aZNG5w8eVJ1/6lTp1C7dm3567S0NPTo0QN+fn7YuHGj3GlC8u677+L111+Xv7548SJ69OiBTz/9FO3atSvSNZJnuOKNiMoN2y4Srtj22pVJdcNyEFbMDivZfq26EOc71Mn3SyUUUv2wtKAOKGi5liUAfwvysrVuA7H81DYzxMr6YXeBGCj4ZcJVIFYqrgV1DMRUGFrooIUXyyQKea6pU6eiV69eqFmzJtLT05GYmIidO3ciKSkJAPDvf/8bQ4cOxf33348uXbogKSkJX3/9NXbu3AnAOiPcvXt33Lp1C6tXr0ZaWppcr3zHHXdAp9OhVq1aqucMDg4GYO08UaNGjdt8xeSK2zAshMAXX3yBHTt24MqVK7BYLKr7v/zyy2K7OCIiSVGCkylPA0P+5Iu8iA76glIJvc3ssCPKUKwsi1B0kQAczw4DUNcP55dMCIMWGuihzbNu7GHJD5tavYAeBe2OpbCqnBV2VDLhaSD2dOvmkiiXkF4vAzGVB5cvX0ZcXBwuXbqEsLAwNGvWDElJSejWrRsAYODAgfjggw8wZ84cTJw4EfXr18e6detw7733AgAOHjyIffv2AYBcaiE5c+YM6tSpU6Kvh9TchuFnn30WH330Ebp06YLIyEhoNPyLi8gW64W9z7aLhMTRMWVQdDTeYtIAirphDWAtlTC4mB2WNtRwFpIBdc9h5TH5HGZruQRQ0MVCsaBOm19GYVBs25wHrRyItaaC2VzptUklE8rXLf3ZWSBWvm+OArF0PwMxkWPLli1zO2b06NEYPXq0w/s6d+7scYcuSZ06dQr9GCoat2F49erV+PLLL/HQQw+VxPUQVVj8B7/onAVgaXti5TGDXqPahU56vNRRQigCsfqENrPD8oYchew77Oy4Mb9swmD9r8aQP2Ns0EILC7T5u9NJu9TZ9iC2DcSOOAvEAKDVCaeBWHl/aQRiIqLS5PZv+bCwMPYPJqJS42jxnMePtRkvsk3QBBrkumFVqYQ+f3ZY+QBngdhZzbAtRdcJZbmENQQX1A8DJrsOE4B9D2KgiF0mXARioOCXjdIKxJwdJne0Gq2XW6tp3Q8in+H2p2HmzJl45ZVXkJWVVRLXQ0Q+zuzB4jhPWBSbdkjnlWqFlbvRqTtK2IRcaabY0/CrJLVr0+vl3ek0fjpoDFr1LdAAjb8eugBr6NTqBbQ6AZ0+/8/6/D/bdHgw6AvqiVWXrAjHeTYzy7a/WEgbmADqrh22M/G235PiCK6ccSai0uJ2Znjw4MFYu3Ytqlatijp16qh64gHAr7/+WmwXR1RRcNardKk6SuRZZ2RFngUaT2aHgYIZYg9oDIb8hXT2O9c5KpcA4HRBHQCnPYilkhBPNuZwtCmHNPtrgrTPiLCbIQbAlmtEFZDZbMY333yDZcuWYePGjaV9OaXObRgeNWoUDh48iEcffZQL6IioTHDWYxhQLzBTznBaTBpYdAV1w5r8zhIAVG3WkH9mDaBolWZ23WpNolxQZ1daYT1uVy4B+/ph293plB0m5KeCOgArA7HTHsQOArF1vEYOxID1lwetYjc6Z7vUcUEdUfly8uRJLF++HB9//DGuXr2KLl26lPYllQluw/CmTZvw7bffyu1BiEiNH++WDKnHsGdjCzbeMOVpoNVroNVZd6IT2SbAoIXUYg05io4PjkKvdMxdIFYEYY3BAJGXZ38/UDDrnN9dQuSZoTHkV6zl1w87W1DnqgexsiRC2W3CUSC2nvf2N+VgIKaSUlzbMfuCzMxMfPbZZ1i2bBl2796NunXrYty4cRg1apTddtS+ym0YrlmzZpF3ZyEiKi22HSUAa5DTSq3V8iyAbakE4Hx2GFDM+noWilXHlPLMgItyCWcL6qRArJzxtu1B7Iy7lmvclIOoYtm7dy+WLVuGzz77DGazGYMGDcKsWbPQqVOn0r60MsdtGH7zzTcxZcoUfPDBB2wKTUS3zTZ0FSeL2VoWYDFpYNGr64alUgmRp82fHXYyA+woALvqPZxPrh12MF6aiXZaLqGoHwYK+g1br10jlzcAzluuOaohlgKx9DjAdYcJT3sQFxcGYqKi69ixIypVqoQ33ngDI0aMkHe0I3tuw/Cjjz6KW7du4a677kJgYKDdArobN24U28URVQT8x9z7tHoB5LkfZ0uqG9ZILdZsSbvH5YdXax2voeA+wLPaYVsOHlOw8YdW8VwWxZ+t9cOA+wV10s+YtKBO9ZIUpRFAQWBW1g8r64NdLahzFoiLs/8wAzEBLJMoih49emDbtm2YNWsWzp8/j8cffxx33XVXaV9WmeQ2DL/zzjslcBlE5AuUgakkQ45UN2zwKzgmpFlhZakEYNc1osiL6STKrZttyIvppAMGRbdLB/XDgPsFdcoOE9bXLhyWUXjaYQJQL6hTYiAmKrs2b96MCxcuICEhAcuXL8ecOXNw33334fHHH8eQIUMQEBBQ2pdYZrgNwyNHjiyJ6yAi8hpp9tOot37Er6yP1erz64YdlkroHM4Ay+3WXAViu93r3NQNoyAEC8Da8s1N/TDgfkGdMhA742pBnW2HCelYadYPE1HR1KhRA9OmTcO0adOwY8cOLF++HE8//TQmTpyIIUOGYPTo0YiNjS3tyyx1DjfdSEtLK9RJ0tPTvXIxRORbiiM4KWtllbOJtptO2MmfHS44kWJDDuUx2/tdkEssbNus5W/EIW3GYR1r3YQDBmljDh1g0ELjr7duvOFgQw6pvldiPVZQMgHYzwZLbDfkAKyBWNq6WnmfcsGe9GflZiYlgSGb6PZ06dIFq1atwqVLlzB37lz8+uuv6NixY2lfVpngMAyHh4fjypUrHp+kevXq+Pvvv712UURU8dxumLENfnqDBga9xmUHBaBgEZ38dX7YE9km1W50ABQ70tmHXLlVmu19HoTigovWOZ4h9tNZu0soyiRUgdiglQOxTu9+hzrb90Rv0KjKJiR5poISCotN4JV2qFMds9mhzlEgLs5yBgZiH2Y2ef9Wwc2aNQszZsyQv05KSkL//v0xbdo0DBs2DAcPHuTGafkclkkIIbB06VKPVx7m2fbTJPIR7v5xZp1j2WI2FdQNCwelEhqjLr/NmoMd5/R6a7mEwVCoumGHPYfzzyePQX4QhzUEK+uIRZ4Zmvz6YZiE6mfObNK4XVAHwGXJBAC7conCLKiTr4X1w0Rlytq1azF79mwAwPXr1zFo0CAMGzYMe/bswTPPPINPPvkELVq0KN2LLCMchuFatWphyZIlHp8kKirKrssEEZGkNGb0bOuGTXkaGPMDnVavDmvKnsPw09nX/wLWcKwMxIWlXEjnoKbYdjGdu3ZrgGf1wwBcbtvsqP+ws/phdzvUqa6NgZioVJ09exaNGzcGYN1A7e6778bSpUtx6NAh9OjRo5SvrmxxGIbPnj1bwpdBRL6qMMFGOTt5O5Qt1qQyBADyjnQArLPDgHpBXX4gtjteGE62bLYNwdbrsW29VtBuDVD3H/Zky2aJbXcJ2/7DykAMeL4hh23/YS6oI6+xmAGLF0sbLB6WNpVjAQEByM7OBgBs374d3bt3BwBUqVIFGRkZpXlpZY7bbhJEROWZtBOdcvMN5cf7yg04YNBat0m2nR2W5Adf1eyws006HHEzO2xH0V0CgKrdmtls8zoAwGbLamsbNsct12zZ9h+23aGOG3IQlS/33XcfXn75ZfTp0wdffPEFvvvuOwDAX3/9xW2YbThcQEdEVFbZBUC43oYYgGqW05zfHcFs1sgL5wDYLaRTdZZQyg+zqjpgk/OFdwDsyyqU4dmms4RyMZ2yuwQAubuExqCVu0soF8/pDZ4tqHNFuaDOYrMwzpRXsBjR0YI6CRfUEZW+t99+G9euXcOUKVMwfvx4uYVaVlYWpk6dWspXV7ZwZpiIilVxBBZpptcZ213XAHXdMOCkVEJaSAe4nR0uMlezw3qdXbmEFNCV5RLIht1COumXBEflEs62bLZVmP7D7uqHS2pBHRE5Vrt2bezevdvueO/evUvhaso2hmEiKnXF9ZG3chGdxLZUQtVVwqAt+K9t7bBEUe9b5MV0gOPaYQfh27a7BABVuQSg3q4ZcB6IAcdbNjuiXFAHFMz+Oqoflu53tSFHcWG5hI/wdjs0H2itRp5jmQRRMeE/0N4h9dS1O56/wYS08MuWo803bHvpKj/Ol3oOW/9cUD6h2nxDtemGg3KJorAN2y7KJWSKcgkAqtIIqf+wLUcbcjhiG5SdlUtIWC5BROWd0zDctWtXfPnll04feO3aNdx5553FclFE5LuKuviqsHXD8vH8DTgAqcVafuDNNTuuHVbWBisCcZFCsaPaYQUpEMtf5wdjaTMOAKrd6ZS/NLirH/YkENvWDyvfN2X9MAC7wOwIAzERlUVOw/COHTswZMgQ1e4lSmazGf/880+xXRgRkTLcOdp+uLDMillN5c50Up9h6c/KxXQAHG/NXEiqcgq77Zl16j8rtmm2PtZ2q2brX93S7LAUiAHYbdesDMSuAqOj/sO2x91t16wMzCW9XTMRUVG5LJNYvHgxFixYgIEDB7InHRGVCYWdAVRuOSxRdkSwLZWwPkg9O+yQzeyw9fG3WzKhDskOyyUU9xWmXEIZiB3NDrudWfdgu2bbP3O7ZvIai8n7N6J8LsNw//79sWfPHhw/fhyxsbH4+++/S+q6iMo8/sNbemxriItSN6wMbHalErBptZZndlw7XIhA7DQoS7PCDmaHHVLMDhemXELJNhA7e/+clUtIXLVbs8VATERlldsFdA0bNsT+/ftRs2ZNtGnTBtu3by+J6yKiCuB2gom7x9rOcnpCCmrKUgmJbamELNfNzLADUg1xkWqJPZkdvs1yCfmpPHjvXJVLSBzVD9vODgMsmSCissmjbhJhYWHYtGkTxowZg4ceeghvv/12cV8XEVGhFSoYOyiVENkmVamE3SYcjmqHHcwO23IYiJWht7CzwwqelksUpn7YFUflEsr7XJVLODuPt3F2uAKSWqt580aUz2mfYY1GY/f13Llz0bJlS8THx+P7778v9osjKq/YVs27tDph17arsEx5Aka9orbVYD2ncsMInQHy9syANWiKPEvBJhyqE9psnKHXWY/ZLo4rCpuNOWw34rD9s/S17WYcAJyWLQCOt2t2xHYzDunnW/ne2W7XLN2v3BDEVnH2JGb/YSLylNOZYSEc/wU1dOhQ/PTTTzhy5EixXRQR+R7VYi+deuGX9F9XHSU8aRVmsfloH3DQVQLwbHZYPpHz+mG33AVnJ63WHG7VjKKVSxS2/3BhyyXkP7O7BBGVUS5bq1WuXNnhfS1atMDBgwexYsWKYrswIiJn7BeJud6aWclhQLMplQDUdcOqGmLVyZ0E36K0YLMtlbApo1C2WrOlXEznSbkEgCL1H1a6nXIJLqYjorLE6bREp06dXD6wSpUqeOyxx7x+QUREnlJ+TF8YjkolLCYNLLqCUgkoSyQMWmubtfzHy88mBVe5TMLsUZ2vW6qtmnWqmWiNn851uUR+cHdVLmFbQuBpSYFtuYR0XpZLULHzdjs0tlYjBW7HTERlVlECkrNZYqnfsLNSCcA6YynyLPJCOrs2a446S3hzdtiWB7PDrrZqLmp3CU9mh5XvJcsliKg8YxgmogrD44/4He2UZtv5wFGbNRSidhgofCB2NqusV+5Gp261VnBca1cuISlMdwl3HTlsF9m5KpeQjtn+meUSRFSWeGHZMxGRd7j6SFs1gwnABMXH8IquCIA10NnWCjtiMWnsSiV0OmEtj/C3LqTTGLTWhXT5j7HrLCGXM+gdl0vIXSE8/OtW1Z3CbNdZQhW2jU46TeQvANQYtNBCHebddZeQ3v/CdJeQzmtXLoGCIKq83xmWS5AzwmyCMN/mDo825yOScGaYiMo8Z7Wmth/zO+MqGDsqlQDUC+lczg7LJyriP66FCcn55NlhJ2wX0ykDprfLJQA4LZeQ7vN0dpiIqDQwDBNRheTo435l3bCnpRK2bdYcdpZwVy4B3GaHCQcbdEic7Eyn3KoZgGqrZmUglu93shlHYbZqlkjlEtIvGvIxs/p9lu9juQQRlSKGYSIqk3SKPriF4elspsRZEJYW0kl/Vsk1u54ddhWIbUOxo5BcmNphN2wX09m+n9LssJKn21w76z2s5MliOoAzxFS2LV68GM2aNUNoaChCQ0MRGxuLLVu2yPePGjUKGo1GdWvfvr3qHMnJyYiLi0NUVBSCgoJwzz334IsvvlCNqVOnjt15XnrppRJ5jb6MNcNEVK5IJRN6ALk27cGUdcOFYcrTwKgXsJit7b+kYOaozZoAVAvXCk7ipK2ao+OF2pjDde2w21ZrADRSlbWpoMew6ims98qkwCrVXruqH1ZS1vwq67ml+5TPLbVac1QnzNphsiO83FpNFO5cNWrUwNy5cxETEwMASEhIQP/+/XHo0CE0btwYANCzZ0/V/gtGo1F1jri4OKSmpmLjxo2IiIjAmjVrMHToUPzyyy9o2bKlPO7VV1/FmDFj5K+Dg4ML/fKocBiGiajc0OqEXb9ciauFYQBUi70A+3BmyQ9nUoizXUgHFIRgZTAG8sOnqjew3nk4vl3KIGzQFZRxKK8PnvUelijDoTIoK487C8TlcatmosLq27ev6utZs2Zh8eLF2Lt3rxyG/fz8EBUV5fQce/bsweLFi9G2bVsAwH/+8x+8/fbb+PXXX1VhOCQkxOV5yPtYJkHkZZx1uj3OApCz0OSIu1IJ5eyxsh2Yqq5VuZBO2WYtz6b0QT5pIcolPKEM0i5qh5Wt1pxy0ntYolxMZ3+86OUSZbX3MEM23Q6z2YzExERkZmYiNjZWPr5z505UrVoVd999N8aMGYMrV66oHnfvvffi008/xY0bN2CxWJCYmIicnBx07txZNe5///sfqlSpghYtWmDWrFnIzc0tiZfl0zgzTERllk5nLVko6kfbjlqsSTOZtkx51mICaXZYek4t7GeHpZlW1eywtPFFYcolisq2/ZrESau1gq/1EHnWf1gdBcKSKJeQjjkrlzCbNXJY5+wwFbe0tDTV135+fvDz83M49siRI4iNjUV2djaCg4Oxfv16NGrUCADQq1cvDB48GLVr18aZM2fw3//+Fw888AAOHjwon+/TTz/F0KFDUaVKFej1egQGBmL9+vW466675Od49tlncc899yA8PBz79+/Hyy+/jDNnzmDp0qXF9A4QwDBMROWMo37DSp7WDZvyrDXG8nkV4UzZcxiwzg5rDNb6P9UWzQoiz1xQLgFYZ4dvt1zC0bbMUu2wTbkEYG355s7t9B52xV3vYaDgF47C9B4uLqwdLmdMuYDJi2VHJusvhTVr1lQdnjFjBmbOnOnwIfXr18fhw4dx8+ZNrFu3DiNHjsSuXbvQqFEjDB06VB7XpEkTtG7dGrVr18amTZvw8MMPA7CWRaSkpGD79u2IiIjAhg0bMHjwYPz4449o2rQpAOC5556Tz9OsWTOEh4fjX//6lzxbTMWDYZiIKiRHs5hSUHM0OyzVtrpaSKdcPGc7O6yx3YijuGeHJUWYHQZM0MIivz5Xv2CUxmI6zg5TSTl//jxCQ0Plr53NCgPWBXHSArrWrVvjwIEDWLBgAT788EO7sdWqVUPt2rXx559/AgBOnz6NRYsW4ejRo3KNcfPmzfHjjz/ivffewwcffODwOaWOFH/99RfDcDEqsZrhOXPmoE2bNggJCUHVqlUxYMAAnDx5UjVGCIGZM2ciOjoaAQEB6Ny5M44dO6Yak5OTgwkTJiAiIgJBQUHo168fLly4oBqTkpKCuLg4hIWFISwsDHFxcbh586ZqzLlz59C3b18EBQUhIiICEydOtKvLOXLkCDp16oSAgABUr14dr776KoTgX8pkxVml4nO7dcPKOldPal6VVPXDTtqsOew1DLhvtWb756JysFGHo1Zrtr2HbTnrPexuq+bC9B62rQ121nvYVknUDzNkk9QqTbq5CsO2hBDIyclxeN/169dx/vx5VKtWDQBw69YtAIBWq/7/UKfTwWJx/PcJABw6dAgA5PNQ8SixMLxr1y4888wz2Lt3L7Zt2waTyYTu3bsjMzNTHvPGG2/grbfewqJFi3DgwAFERUWhW7duSE9Pl8dMmjQJ69evR2JiIn766SdkZGSgT58+MJsL/oEZPnw4Dh8+jKSkJCQlJeHw4cOIi4uT7zebzejduzcyMzPx008/ITExEevWrcPkyZPlMWlpaejWrRuio6Nx4MABLFy4EPPnz8dbb71VzO8UUcXhjV8YbBd7uVPYgGO7AQfgeCEdAOebcOT3HVaRF895MRDbzirLC+sU/YfzF9M5Cr+OdqZz1HvYEU/fV0ezxp4sprPb8ET5eP7iSRaT92+FMHXqVPz44484e/Ysjhw5gmnTpmHnzp0YMWIEMjIy8MILL2DPnj04e/Ysdu7cib59+yIiIgIDBw4EADRo0AAxMTEYO3Ys9u/fj9OnT+PNN9/Etm3bMGDAAADWbhNvv/02Dh8+jDNnzuCzzz7D2LFj0a9fP9SqVcvb7ygplFiZRFJSkurrFStWoGrVqjh48CDuv/9+CCHwzjvvYNq0aXJ9TUJCAiIjI7FmzRqMHTsWqampWLZsGVatWoUHH3wQALB69WrUrFkT27dvR48ePXDixAkkJSVh7969aNeuHQBgyZIliI2NxcmTJ1G/fn1s3boVx48fx/nz5xEdHQ0AePPNNzFq1CjMmjULoaGh+OSTT5CdnY2VK1fCz88PTZo0walTp/DWW2/h+eefh0bDv5yJSpKyxtOTj/XdkUKbbbs1qVQCgMOFdICiRMJgX/JgVzvsjKclE7ZlEG5qhz1qtQb1YjrA+72Hla3WCruYzpHiKpdg7TB54vLly4iLi8OlS5cQFhaGZs2aISkpCd26dUNWVhaOHDmCjz/+GDdv3kS1atXQpUsXfPrppwgJCQEAGAwGbN68GS+99BL69u2LjIwMxMTEICEhAQ899BAAa4nGp59+ildeeQU5OTmoXbs2xowZgylTppTmS/cJpVYznJqaCgCoXLkyAODMmTNITk5G9+7d5TF+fn7o1KkTdu/ejbFjx+LgwYPIy8tTjYmOjkaTJk2we/du9OjRA3v27EFYWJgchAFrzU1YWBh2796N+vXrY8+ePWjSpIkchAGgR48eyMnJwcGDB9GlSxfs2bMHnTp1Un1k0qNHD7z88ss4e/Ys6tatW2zvDRE5p+wF7IoUcvNMQtVVwrbfsESqEwagCt0WRYAT2SZrza0UgvPMql7DTmuHvbGYzh0HG3Eg1/UMdGEW0yn7Bks87T0sndfTxXSOaoeJStOyZcuc3hcQEIBvv/3W7Tnq1auHdevWOb3/nnvuwd69e4t0fXR7SqXPsBACzz//PO699140adIEgHWbQgCIjIxUjY2MjJTvS05OhtFoRHh4uMsxVatWtXvOqlWrqsbYPk94eDiMRqPLMdLX0hhbOTk5SEtLU92IqOQVpVQCUNcJA/a9cIXcb9jstG5Yut+jUojbrR+WaodttmmWGRX1woraYdtyCSVntcOqpzWoA7E7FtsSFJtfZmxLVJSPMTvoR+xtrB0m8m2lMjM8fvx4/P777/jpp5/s7rMtPxBCuC1JsB3jaLw3xkiL55xdz5w5c/DKK6+4vFYiKjyp37Aj1m2YnZdKSLOXjnoOA45nim17DkvXADhus3Zbs8OezBbblkp4ME7jp3PYaq2gG0bBfa7CoKP2dRaTxun7acvZ7LC8Mx3KRqs1KuPMZsDsxe2YzV5YyEoVRonPDE+YMAEbN27Ejh07UKNGDfm4tPWg7azrlStX5BnZqKgo5ObmIiUlxeWYy5cv2z3v1atXVWNsnyclJQV5eXkux0i7ydjOGEtefvllpKamyrfz58+7eCeIyBVHAU15TJrBvB3Kj/g9WUgnz0zmL6TzeHbYNsi6+9od1e50hZsdVo1xszOdI1oHC+4czQ7blk9I753teyzdx9lhIiotJRaGhRAYP348vvzyS3z//fd2Nbd169ZFVFQUtm3bJh/Lzc3Frl270KFDBwBAq1atYDAYVGMuXbqEo0ePymNiY2ORmpqK/fv3y2P27duH1NRU1ZijR4/i0qVL8pitW7fCz88PrVq1ksf88MMPqnZrW7duRXR0NOrUqePwNfr5+dm1aSEi7/MkBDvaSlhv0DisF7ad4XTYAsxBmzXpa5edJRy1WrM97uhrW846SbgY567VmhSIldy1WlM9lYdt65zNINu+z4DnCyCJiLylxMLwM888g9WrV2PNmjUICQlBcnIykpOTkZWVBcBaejBp0iTMnj0b69evx9GjRzFq1CgEBgZi+PDhAICwsDDEx8dj8uTJ+O6773Do0CE8+uijaNq0qdxdomHDhujZsyfGjBmDvXv3Yu/evRgzZgz69OmD+vXrAwC6d++ORo0aIS4uDocOHcJ3332HF154AWPGjJED7PDhw+Hn54dRo0bh6NGjWL9+PWbPns1OEqTCVeju3c57pJytk2Ytnc0Y2/bGVXJV1+qqI4LdjKWDNmtOew7nmFWlCHat1hwpcss1z2aHnXHUas3ZLxzOeg87wtlh8ppSbq1GFVuJ1QwvXrwYANC5c2fV8RUrVmDUqFEAgClTpiArKwvjxo1DSkoK2rVrh61bt8qtSQDg7bffhl6vx5AhQ5CVlYWuXbti5cqV0OkK/uL/5JNPMHHiRLnrRL9+/bBo0SL5fp1Oh02bNmHcuHHo2LEjAgICMHz4cMyfP18eExYWhm3btuGZZ55B69atER4ejueffx7PP/+8t98aIioiKfR60l3Clm2tsO32zAAAQ0EtKwBVmzXAde0wjCWwI52zWuL847bbNLtrtQaYAJO6FEU+pfVep0HU084Sti3S5Nphg037NdYOE1EJ0QhuqVZs0tLSEBYWhgvxnRFq5M7XFZXtbBJni+3dzoybxcHMrKr7Q/4so/SRuzTDaFGMNeUJVVCTPra33VFNb7AGNUclAgY/i3zM4Gexlhn46wGDFtpAg/1ub/lhWOOns4ZS2w0yHMzmOvxayVHwdbbjXf5xaXZa5JitJRxSOYeitEPkma0z3dkmiDyL9T00F7yXeTla1furfO+lr6X31FEgVs7MS+8xoO5cYb3PfnZfmqGWxijrmotrJpf/D3tfWq4JNZbtRGpqaqFKCKV/R28eeQ2hIf7eu570bFRq+t9CXw9VTKXSWo2IyFOFCTyelkrYfrTvbiEd4LzNGgCHAdOux69djbCDnemkr92FXiU3tcO2G4N40mrNdjGd7S8GSp58f5yVSwDOW62xdpiISgqnK4mo3JBarDnbjc7ZjF5R2oBJ57PdkU6i3IRDLo+wrcvNVWzKIYVSR6URnhzzpL2a7a50RdiIQ8lR0PXWznTSY91txAFYw7ijjTiKa1c6KoOc/ZJ4O+cjyseZYSIqt9x1lbBtA+bp7LAt29IL6Zjt4jm72WFbtq3WXHWXcHRMr3M8E+ym7rgoG3F42moNKL7Z4dLCgE3kWxiGiajYlWQNprM2YI4424RDWSrhqs0aYN2EQ+os4YxdZ4niZFuLXMyt1lSL7GxqsG1JgVj5vtuWQyjrkiWl0VmCiHwHwzARlXmetFhTLsRyxF1Qc8dRmzXbIFao2mFPWq1J42xvjhRhdtjpWAet1twpymyqt2qHiyMQc3aYyHewZpiIyjWdTX2pLeX2v7Y83TRCSfk8Op3IL4coqB12FjIL2pvpHNcIA7ffak3irnbYdptmg85hqzWRV7DpkN2GGyha7bCjVmsAXNYOA9Zg7Kx2mHyA2eT+F8fCno8oH2eGiahC8WTHNE8+xndUKmE3c1mE2WG3G3F4e2GPJ9s0O+FoIw5n/X893abZFXezw6VZR0xEFRfDMNFtYs1iyXBXKiHVtHry+Nuh7G0skbdodrIbHQDX2zQD3pn1UnWf0Du8T2PQqWqHPVlMZ6sw2zQ7CsRFqR22XbwIFH/tMEsliHwDyySIqNzT6YXDWUPVTmce9q2VPsYH7AOWss2a7f06g/W/yplf1a50ilAp8syFb7XmKU9asEmMOo9arbmsxQaQa1PqUNhgqmyRJu9A56BEgnwYW6tRMeLMMBGVCG/P3DmrF1XOWjriaZ2ws77EyjZrXpsd9nQxXWE56SxRlNlhV4HY3eywI+WlswRnh4kqPs4ME1G5odxsw9ExrU54vJDO1eYQ7maH9VAs3PPG7LAjJbWYDvB4Iw6nC9YUreeKc3ZYeX4iIm/hzDARVQiOPkb3pKa1qBy1/fL67HBRP8r1IER7Y5tm+em8sGBRUpjZYflrbt1MRLeBM8NEVGK8vX2utD2z3XEHs7YS5UxyYbcOlo/D+RbNEq/MDntbIWaHVdcJANkF93myTTNgff/dbYVtOwtfmNlhRz9PxbFFs6NPJKiEmSxerhl28Qsr+RzODBNRueIo6Nh2lXDE2W5pzihDsqMg5GqLZre70pX07LBtZ4l8rrZptht7m9s0u2uzJgVmi4MZd0edJVzVDhMRFQbDMBFVGLYf0yvbf9kel3ga0pQc9sI1aRwGMod9h1X3exB2vTEjVohtmgF4dZtm+ZRe6jvsDmdxiagwWCZB5AX8x7dkKT+2lkolbBfSAY6Dk6NFdu7KJQCbhXJ6Ie+SpjwmjdOiYFc6R7OsIs8il07IQVRZvqDX5S96u82/oj1os6Yx6AoCubFgJzrb0A5YZ4cBU/7rs3IXTrV6AT08K5WwLUsp7K50xYmlEqWMrdWoGHFmmIhKVHEHCm8upHO0wMsZZ7PDQjkjnP9n1f055oIw6mojjhKeHXa1mE7J09lhd4vplJQ/I+52pVMdL+Y2a0RUMTEME1GFJQU0W462DvaEsy2abWdHbWuHHfFom2ZvKGxnifzaYdfj1ds0u1OY99pVZwnAcYkKEdHtYBgmonLJk+2ZHT3GG+2/HFGG5KLMDsuKYzGdkoezw+5arSkVtnbYEdsyFUezw6Y8m186SmGLZiKqeFgzTEQlrrhrPG23Z7bWrcIuTEl1oO7af/1/e/ceH1V954//deaahCTDTRIiEfBGuYjtwq6GtovgBfkhutV23WpT7FoXtwW0tO4qdgvrPix0tRbrhXXrDVu76e4i1oeXCC2C9QsoAqlBt9pWENQAXsiNJDNn5nx+f5z5nDnnzJmZM8lMMpN5PR+PPCAzJ5OZA8pr3nmf99vOPmZN/o/UHPzsvcMC9rFqeiBWAH0tMlyOWuvPMo5UvcPmEWvx3mG3izgUvyer3mEAxnnOdqSdPVAb/eKmMWyDsa6ZfcOZWVag5/BciagKoeaufieias4ei4ofwzARFS03G+mA5KBmXOzmchyX5QIvpA7y9sezH2cJwObQG4kN7sV0ciud7T7LxXRIhHchn7N8HYAe8NWIcVtSxR3Oc4fTkQHZae6w/DwKWFZtO4VlIqJssE2CiIYF++zbfIxZk/rbO2zpCx6KUWtOs4dTtUsE3PQaZ9c7DLjf/mfvHXaqPDudb4CtEkSUHVaGiWhIDMY4LMneNiGZx6xlsyktHdfVYftFaoNRHXYh2+owEAXiFd1sqsNuxtnJ4x2rw3Aes+bmMQaCrRJEww/DMBEVNTczh80B1ecXRkAzr/zNxYpmN3OHzY+c9ZpmcyDuT+9wKlmsaXaStBI5TfuJee5wqnOdau4wkBy4U61ojsUU15vyKDecfuqSszcO0SgQzeHq8qj9bRqVMoZhIhq2zBVhczg1c1rC4UamamNSn7LfxYNmqg7nQrre4biUiziQuToMWC8ktFeHc3Wu0y3hoKExmD/tIcol9gwT0ZDJx4+bB3vMWqreYXvos/cO93tNcy5GrWXTO+zSYPQOA85LOID+hWzKLQZhKlasDBNR0Uv341g3Y9bMP2J3O2Yt02QJoICrw6k4jFoDkHV12Kl3OOLQzgCkbkux92jbJ4TYbzd/T6dWCVYtixzXMVMesTJMRCVBLoWw3OZQHZbcTpYA8lMdzvuaZqfqsMN9jtVhv/OWOnN1OJtznUqqhSeplnDI+yyf56FizFDtzOkNKc8VFQOGYSIaUoPRKpFqzJqdm3+41aiwVDIzPX/zVjrXr3Uw1jS74LSm2RyC7Vvp7AHZvpXOcp9PZGxLsdMc3mBI9jcfjq0UnAKRVwy+VKwYholoWHD7D7FTddK+NthtSMt373De1zQPpHfYRXXY8b4sVjQDyWuazczn2szNNjwaPMMhJG/YsAEzZ85EdXU1qqur0dDQgBdeeMG4/7rrroOiKJaP888/3/GxhBBYuHAhFEXB008/nXT/c889h/POOw/l5eUYO3Ysrrzyyny9LIpjzzARDUv2MWtOW+fkmDVzmLL3H7uZh5vtZAkPNChlqR9PqNrgrGm2k9Ml3PQO2+YlZ9s7DLgbs2a8PBdj1uR99rCdj95hzhtOlte+7KiW455hLfMxJhMmTMC6detw5plnAgA2btyIK664Avv378f06dMBAJdeeikee+wx42sCgYDjY61fvx6K4vx3Z9OmTbjhhhvwwx/+EPPnz4cQAq2trVk9V8oewzARDbnBDihOY9bsYdntEg5zSAMSwdc+d1g+P0n0pZ47bJk5HI4N3prmFPfJIG6uVCt+j+ViOnM7h33cmZllgUaW67CB1H9X0l1IRzRQixcvtnx+5513YsOGDdi9e7cRhoPBIGpra9M+zu9//3vcc8892LNnD8aPH2+5LxqN4qabbsJdd92F66+/3rh9ypQpOXoVlArbJIhoWDGHn0xj1tysaAbcjVpL188qmXuHjXaJFISqpe4dzrUs1zQ7tUek6h2WbRHpLp4b6Jg1NxfSETnp7Oy0fITD4YxfE4vF0NTUhJMnT6KhocG4ffv27Rg3bhzOPvts3HDDDTh+/Ljl63p6evDVr34V999/v2No3rdvHz744AN4PB587nOfw/jx47Fw4UK8+eabA3+hlBbDMBEVhMH4kbM5kGXTO5yNdL3DjjNyU/QOm+W9d9gFpzYNxe8xeoeTLp5z6B02v/mQ59p8f7oebXsLRbo3HryQbmikq8IPuEIfjeb+A0B9fT1CoZDxsXbt2pRPobW1FZWVlQgGg7jxxhuxefNmTJs2DQCwcOFCPPnkk9i2bRt+/OMfY8+ePZg/f74lXH/nO9/BnDlzcMUVVzg+/rvvvgsAWLNmDb7//e/j2WefxahRozB37lx8+umnAzt/lBbbJIhoWMtmRbNT77Bsl3AzD9ftj+XluC/zmmaYw6YaS6xpBpJ7h9P1B/e3d9hpM53DmmbEn48CJIV2xe/N2DucKogOdL6z3Ehn/34S1zOTkyNHjqC6utr4PBgMpjx2ypQpaGlpQXt7OzZt2oQlS5Zgx44dmDZtGq6++mrjuBkzZmD27NmYOHEinnvuOVx55ZV45plnsG3bNuzfvz/l42ua/t/T7bffjquuugoA8Nhjj2HChAn4n//5HyxdunSgL5dSYBgmomHH7RION73Dbjld4AU49w4DSApm5jaIpDYE+yIO48Fz2DsMpO8fBpJ6h60Xz+mceoftbzLsK5rNx7i5YNH4mpj1ce3yvZ6ZF9FZFWOPtpwO4UYgEDAuoJs9ezb27NmDe++9Fw899FDSsePHj8fEiRPxxz/+EQCwbds2/PnPf8bIkSMtx1111VX44he/iO3btxs9xLLaDOjh/PTTT8fhw4f78/LIJbZJEFHByFewcLOiOV3vsNs1zUDya0jZ02rvHc7lmuZ0fF53lWMXvcNOzL3DUn9GqknmarHb+c6pZg7nYwEHlS4hRMoe408++QRHjhwxAu6tt96KN954Ay0tLcYHAPzkJz8xJlDMmjULwWAQb7/9tvE4qqri0KFDmDhxYn5fTIljZZiISk6+q8OOj5UivLmupGWzpnkgY9bStUvANmoN6avDit8TbwVJnvaQbsya5en4kyd22D+Xjy+r8PZpFo4j2IqwilnSYjlexxzL7rFWrVqFhQsXor6+Hl1dXWhqasL27dvR3NyM7u5urFmzBldddRXGjx+PQ4cOYdWqVRg7diy+9KUvAQBqa2sdL5o77bTTMHnyZAB6lfrGG2/E6tWrUV9fj4kTJ+Kuu+4CAHzlK18Z4AumdBiGiWhYSvUj7P72DkuZeofdjFozV0i1qGLpHXYatQbYxq0NZO5wjoKyEtT7g80TLyRz77BQIwBMLRMpWhvMt6drlbD3aEv5bIcgOnbsGBobG9HW1oZQKISZM2eiubkZF198MXp7e9Ha2oonnngC7e3tGD9+PObNm4df/epXqKqqyur73HXXXfD5fGhsbERvby/OO+88bNu2DaNGjcrTKyOAYZiICkwuK3bmICsvpJOyrQ67vcDLzGkBhPkxzbz+1I9jLLgA0leH3fYOmwOxU7Utj9Vhy/mwtY/0dwmHk5jpDY/xvX2CF9JRvzzyyCMp7ysvL8eLL76Y9WMKkfz30O/34+6778bdd9+d9eNR/7FnmIhKhlMIytQ7bB7/5bZ3WM4dlga6ptlOhGOJMGoPs7katZaqepyD3mHjoWxj1gbCfo7t9znhxW+5xbYTKlasDBNRwclXddh+m5vq8ECWN2i26qSd05pmIArFn7zG1VwddlzTnE37g2XJRpoKsXFM8qg1Bc4TMGR12Hzhn6wOGyPlvMnnOu3TdegbljKd41TVeSoy0Rz3DOdxJjcVH1aGiajkZdpKl011WDK3VGRaxJEkU3U4EnNexGF83o/qsFOQdhGuU1WH9UUcenVYSreEw/JtMyw7MVfgU5Hn2H4bkNupEqyGEhU/hmEiKki5/BF2uhXN6bbS2X+s7yb4JG1Kc/E6HNc0xwNx4oFdjFobaLUrXSB2GLVmuYgvYNpE57eubJYrms3n3F6tdbORLlPPdrr1zEREqbBNgohKVqrJEsZiCFOoMt/vdiud06g1IPUiDvm9zYz2iEyLOPp7MZ1dusUbsl3CxL6Iw3pfonUCffpt8s2IZeSZQ3jNdMGi01SJdO0QHLFW3PSflOSutSGXj0XFj5VhIioJTtVh4/McV4fTsVcvnX6Un7NFHFJSG0WWQcBeMXZYxCGZq8OW2+PVYclcHbZfSJdNddhNq4TTAg4iIolhmIgK1mAEl1Rb6WQ/q+VY0/3ZTJYA9NdinyAhX5+5p1i2S7iSqnfY+DXNVrq0F8yl6RW2t0tArw479Q7LYGzuHfb4hOXNiNeX3DPs+G0d+oiTWlJsvdnpcBsdEUkMw0RUMtxUhz1ekbPqcKoLvQY6ai0n1eFM7GubXYxaA5DUO2xmrg6nGrNmeegMF9KZuXkDwRFrROSEPcNEVNDy2dMpF3E4jVoDknuHUy3iyLQgQsrlqDVAD8w5W8ThhvG4zos4gOTeYevmPD3AG9voTEs47OuazbfZuVnPbGZfwEHFR0RiEN4c9gw7bE6k0sXKMBGVFDfB2l4ddqpa2j932y4BZB61lhQCU1SHzdIu4pD6M20iZUXY53iMbJcwqsNmDks4zL+3t6UYX5ZmjJ35TYhmO4dOCzjkcUREEsMwERW8wewdtl9MZw+99rFgbn6Ubw9sZim3pqUYtebYLmGqcjmOWkvVO+x2kUG6dolUo9Ykv9faOwykHbNmv5DOfH7t1eD+MFff2TdMRADbJIioBJlHqslWiaRjTKPUAL06HEVyq4MWVVy3S5h/nC8f2xy+U/W0ev3uXpcIx5LbJZKeRBab6jJxGLUG6NVhuYkOsAV0vwcKfBBqxPWYtVTM5zNTqwSQfhsdR6wVuIgG5LBNAhEt8zFUMlgZJqKikM/qcKZFHPYf56e7mM5Nu4T5teTsYrr+VofdylF1WLJXh42HcmhJARLnNdNUiVStEgNZq50JQzRRcWMYJqKS5LZ32Hy8m1FrbjmNWjOzj1qz9wg7yrSmOdPtmTgFYodRa0Dq3mGnFc1Acp92qlaJdNy2Tjj2ZYO9xESlimGYiIpGrsNKrtc0Zzt7GMjuYrp+j1rLVXXYzh6I47e5qQ6nGrOWq5nDqdjfgLBvmIjYM0xEFOc0ag1AyjXN9vvdsK8RNoc/LaoYq5otvcTy+8tRa/DpPbm2aQ1pR63lStp1zQ6j1pDcO6z4vcaKZo8v0bPt8eqj7WR/tpvWhlS9wuY/MynVSmYqfELVIHy56/N19ZMWKhmsDBNRUclndTjlMVmMWstUHZbsm+nswc9eHXbaTOd61BqQXB2OuminSCVdu4T9fjOH6jCgvwmxTJewTZqQrRLmc5qqdcLek51uxJoTtkoQlR6GYSIik2wupjNu8zr/iD+bdgkgPxfT6d8oDwsGXG6kM/cOW26P9w6nu5AuW25bJSQZfNkqQVTaGIaJqOgMVXXYfLy9OlxwF9Mhz9VhwDJFQv/VdDGdQ++wXNFsuaDOVB0GrFV4e282kLni7lY+p0sQUXFhzzARkU26Nc1OK5sB08pfn/XH9KlmD8veYafZwwCM3mEnHp+A6Ev0DkuyX1j+HoF4364a0z/vb++wDLnpLsCzr2o2P6/43GGYQrwMxELV9N9H9TcX5vMoZw7LNxo+KI4TIxzXM8Madu3nk6uZiwt7himfWBkmoqI0WNXhVNVf+6i1gWymM/e5upmR67SZzpF91BqQvjrsxJehZpKuXcJWHTb6hc3VYdNECcB6HjO1SjhtpMu2VYKIiGGYiIrWYIxaMz5PMWrNqV0i24vppHSvJ9uL6RxHrWUKvv0NxqnaJeIUvzcx3cJyu3XMmv1COiC/rRKA8znnRXREpYVtEkREKaQbtWb83vxjfaRul0ilP+0S8nvpgTdzu4Rl1BqQGLcWjcbbGkzj1+yj2OQxUqaWCfPoNfNjAvqFdPHDZNuE05g1r09fhZ1NqwQNc9EYoOawfpePi0qpaLEyTERFrVAupkt1v5vqsJtVzWb2i+lEn7tlGq6qw27Zq8RJK5ozV4dTjVkD4LpVwswckuW5tLedmJnbTzhRgqh0MQwTEdn0ZzOdbJdIt5nOLafpEk6b6SwBLsOotaQ1zdn2Djvx+ZznC9vnEJs/N69ojvcTK8avyf3D5s/N3Ly5cJJqZB0RlS6GYSIqeoPV4+l0kVem2cNmbnpds1nVDGS+mM4eiB1HrQ1UqkDssKZZzh2W0l1IZ/88US1mkCWi3GHPMBENC/bVxgNl7vmVvcNJx3hF8qpmmPqIvYm+Yp9f73VNN2oNQKJ/GEpSH7J9VbP5TYAHGpQy/ffy4jkFMKqvSSx9wra+4P6scHYYqWbcLh/TJDFaLQbF79F/LfNBqBF4vfq5N69njrjsxU61njkd+XcnFlOSLpykwiBUDcLL0WqUHwzDREQu5HL2cDqWC+pgDdWS08IIt7OHjd/7HS6Y608IdmK+iM6BEvRCyC15fv2iOnkhneL3wAMNnph+Tr3xc+exnWM37POGc/lmiYiGD7ZJENGwUSyzh7MZDeZ0EZhT36vr2cNmlg10aXqH0y3bMEt1UZ3Plzx32Nw7DDheSJeqVUI/zwr8PiVlFdip+p7pIjr7sURUGlgZJiJKI127hKwIy8Arg5XPL/rdLgE4j1szPz5Mgdse5lK1S7iuDkv9rRLLdgljI52tSuwzjVczVYeNMWu2jXSeaHKrRCaZWiUs7SVsiygKbJOgfGJlmIiGlXxX9DJNl7BUjVNMl5CyGbdmmT3scDGdebqEuTrstIBDShq15rYCnImsENsvprOH64A3qTJsv5AOSD9VgohooFgZJqJhJ9cX06XitIxD3u4DjB/Jm+/X78t+cYQ95KdaxqFFFXiQWMZhXECnxtwt4jD/Plc9xHa26rDi98T7hbXkC+lsCzjkQg435BuKAP+lI6I0+L8IIqIMZLB2M10C0FsXUk2XkEGuv+0STtvp0k2XMPcOp2yXAFJPljC+WfLtit9v+VyoauITp3YJ47FM1ej4VjrZHmG+kC5Vq4R5G50aFca5TMVyXrJ4kzRYb6qIaGgxDBPRsJTvIJPP6RLm8GufLuH0mtxOlzBaERy/qcMK5Syrw4rfnz4QG7fHl2wAxjIQY7yaacyaR42Y1jM7T5VI92bCiRZVLD3X5ts5Xq1wiWgMIofrmAXXMZMJe4aJaNgayukSsn841XQJOREByO10iVTLOACk3EyXNHkiVe+wi55ie7XYwr6IA7Au4bBtpANgCaZOfdrGQ9sumEtXKXY7UYLcYwWdihnDMBFRP9kriMaFXg7j1lKNY3MKxPYf+5svppNSBWIAxsV0MhADSFrV7Chqv6Cuf9UzSyC2X0wnb7ONWnO6kM68njmxfS75HPeH+XwRUWljGCaiYS3f1eFU0yU8XuskCRmIzfdnU02zT5eQZCCW3EyXAFysaTZXgfsRilMGYoeWC1kdVvwefQmHaeaw01QJO/MbiUg4ywsTXV6MR6Vtw4YNmDlzJqqrq1FdXY2Ghga88MILxv3XXXcdFEWxfJx//vmWxwiHw1i+fDnGjh2LESNG4PLLL8f7779v3L99+/akx5Afe/bsGbTXWooYholo2BuqQCx/n2ncWqrqMADHCjGQaJeQctYuYQ++A+itdAzEgHXUWorqsPk2r9d5RJ3bNxNO4+moyKha7j+yMGHCBKxbtw6vv/46Xn/9dcyfPx9XXHEF3nzzTeOYSy+9FG1tbcbH888/b3mMm2++GZs3b0ZTUxNeeeUVdHd347LLLkMspv83NmfOHMvXt7W14Zvf/CYmTZqE2bNnD/wcUkq8gI6IqB9SXQRnvt08PSLduDXA3XQJIBGIfX4l6fvbx61lnC7hz3BhnHmChHFBXYppEykkX1TnvKpZrmi2XEjn9wB9iWOMarppxJq9pSQQVBAJCwSCiduz6ckmcrJ48WLL53feeSc2bNiA3bt3Y/r06QCAYDCI2tpax6/v6OjAI488gp///Oe46KKLAAC/+MUvUF9fj9/85jdYsGABAoGA5etVVcUzzzyDZcuWQVH4dzifWBkmopKQj2Uclh/hp+kfdmqXMB7D1C6RbmuanQx6ThfUSenaJQCkrw67qAhbQm4aRoXY3i5hqg4DSLqQDki0Snhs5xBI3TccCKY+j9n2Cbv5e8PVzcWrs7PT8hEOhzN+TSwWQ1NTE06ePImGhgbj9u3bt2PcuHE4++yzccMNN+D48ePGfXv37oWqqrjkkkuM2+rq6jBjxgzs3LnT8fs888wz+Pjjj3Hdddf1/wWSK6wME1HJGOpxa/I5yHXNgPO4NXN1WFY+zUE51bpmySmcaV7rMg77qmbHsWv2cWhux6yZx7PFJVWIbYwqtbGi2WNZz+yNygUcqUesAemnSEiZxqtR4RF9MQiRuzcdcqRffX295fbVq1djzZo1jl/T2tqKhoYG9PX1obKyEps3b8a0adMAAAsXLsRXvvIVTJw4EQcPHsS//Mu/YP78+di7dy+CwSCOHj2KQCCAUaNGWR6zpqYGR48edfx+jzzyCBYsWJD0HCn3GIaJiAYgU7uEuR3CvhzDHLzs2+mcArH8PZA8f9jM3i7h9SWWhKRqlxCqlnoRB2DbUJeiVcIelG0tEYrfj7RRU43p1WFVi19IpyW1SgAwWiXk7+0b/WSbBFEmR44cQXV1tfF5MBhMeeyUKVPQ0tKC9vZ2bNq0CUuWLMGOHTswbdo0XH311cZxM2bMwOzZszFx4kQ899xzuPLKK1M+phDCsQXi/fffx4svvoj//u//7ucro2wwDBNRSclH9c8ciNNtp8u2f9hJuoUcluOQ+B+8x2u9TzEt41D8HsdVzfL3hiz6hC0cqsRJ98XvV/xeiLDsG45Xrv1eIL6AwxNLbKMDkNVqZruoqiDAKnBOFHM1XU6HcCMQCODMM88EAMyePRt79uzBvffei4ceeijp2PHjx2PixIn44x//CACora1FJBLBiRMnLNXh48ePY86cOUlf/9hjj2HMmDG4/PLL+/OyKEuD2jP88ssvY/Hixairq4OiKHj66act9wshsGbNGtTV1aG8vBwXXHCB5UpNIPNoEgA4ceIEGhsbEQqFEAqF0NjYiPb2dssxhw8fxuLFizFixAiMHTsWK1asQCQSsRzT2tqKuXPnory8HKeeeiruuOMOCFG8/9ET0eDIdtyaZO8fdpouYec0YcI+f9g+bs08XcI8e9j4fSrmXuL46DW3fcPG1jl7/7D9ftOYNXPrhnmqhNe2zGQosV+4dAkhUvYYf/LJJzhy5AjGjx8PAJg1axb8fj+2bt1qHNPW1oYDBw4khWEhBB577DF8/etfhz/dEhvKmUENwydPnsS5556L+++/3/H+f//3f8c999yD+++/H3v27EFtbS0uvvhidHV1GcdkGk0CANdccw1aWlrQ3NyM5uZmtLS0oLGx0bg/Foth0aJFOHnyJF555RU0NTVh06ZN+O53v2sc09nZiYsvvhh1dXXYs2cP7rvvPtx9992455578nBmiGgw5ftiOmDg49YkN4EYSN8rm27cmuMyDqeL6Vxsn8vIHojlbbJCbB+zJmcOp1kj7XQRnVOLhH0+MxUXEU28ccvJRzS70WqrVq3C7373Oxw6dAitra24/fbbsX37dlx77bXo7u7G9773PezatQuHDh3C9u3bsXjxYowdOxZf+tKXAAChUAjXX389vvvd7+K3v/0t9u/fj6997Ws455xzjOkS0rZt23Dw4EFcf/31OTt/lN6gtkksXLgQCxcudLxPCIH169fj9ttvN/prNm7ciJqaGvzyl7/E0qVLXY0m+b//+z80Nzdj9+7dOO+88wAAP/vZz9DQ0IC3334bU6ZMwZYtW/DWW2/hyJEjqKurAwD8+Mc/xnXXXYc777wT1dXVePLJJ9HX14fHH38cwWAQM2bMwDvvvIN77rkHK1eu5JgToiKX73YJp9ud+odlu4S9fxjIPG5Ntkxk0z9sfn5eP/TqcPxzS3sCAAQdLpaTleF+jFlLfF0s0T8sQ7ZphJscsyafk1OrhJwwEZN/jrYCtblvWJ4fO/OfCeVXsb8ROXbsGBobG9HW1oZQKISZM2eiubkZF198MXp7e9Ha2oonnngC7e3tGD9+PObNm4df/epXqKqqMh7jJz/5CXw+H/72b/8Wvb29uPDCC/H444/D67X+d/bII49gzpw5mDp16mC/zJJVMD3DBw8exNGjRy1jR4LBIObOnYudO3di6dKlGUeTLFiwALt27UIoFDKCMACcf/75CIVC2LlzJ6ZMmYJdu3ZhxowZRhAGgAULFiAcDmPv3r2YN28edu3ahblz51qa6RcsWIDbbrsNhw4dwuTJk/N8Rogo3wq5f1g+P3sgtvcTZxuIzf3DHjUxXQK2i+gUv8e44n5AvcNOPcPmC+vMjxeN6tVhACLghWIeA5fp2zgsKcm3Yg945N4jjzyS8r7y8nK8+OKLGR+jrKwM9913H+677760x/3yl7/M+vnRwBRMGJajRWpqaiy319TU4L333jOOyTSa5OjRoxg3blzS448bN85yjP37jBo1CoFAwHLMpEmTkr6PvM8pDIfDYUv/UGdnZ/oXTURDbqjGrckf78vKpg96aDUv7JDPL1MglswV0FQLOTRThdQ8bg2W/twU0yXs4kFXqKre+mAfveb0e1NlWYGt59hWHUYkBmGaKmEfsebzC2jR/l9ER8VDiyrQPLn7c+YbGTIruKUb9vaDVGNH0h3jdHwujpEXz6V6PmvXrjUu2guFQpwNSFSi3PYPp7qgznx/qoUc5s+jqjDCsRoVKRdyANb+4VhMMfqH5TIOp1XNOWULyIrfn7x8w7SEQ15Ip/i9UMqc6zeplm8A+asUM0wRDR8FE4blCkL78Onjx48bFVnzaJJ0xxw7dizp8T/66CPLMfbvc+LECaiqmvYYuU3GXlWWbrvtNnR0dBgfR44cyfzCiWjIDZcL6pwCsRQzTZeQgVgzB2LTdjpzIAbgfDFdpgvr0i3nsN1nvaDOZ7mQTn/Rid/LXmHzGwXLQ2fY4tefKjKDb2rFPFaNSCqYMDx58mTU1tZaxo5EIhHs2LHDGDviZjRJQ0MDOjo68NprrxnHvPrqq+jo6LAcc+DAAbS1tRnHbNmyBcFgELNmzTKOefnlly3j1rZs2YK6urqk9gkpGAwaMwuzmV1IRENvMAOPORDLqqYMxMaq4XggNlc9ZdBzuhjMiZygIAOgZgvEGcetAZZVzem4HrFmvBhbFdihOqwEvcZ4NTlVwr4pT55Leyjj0o3CwTcTVOgGNQx3d3ejpaUFLS0tAPSL5lpaWnD48GEoioKbb74ZP/zhD7F582YcOHAA1113HSoqKnDNNdcAcDeaZOrUqbj00ktxww03YPfu3di9ezduuOEGXHbZZZgyZQoA4JJLLsG0adPQ2NiI/fv347e//S2+973v4YYbbjAC7DXXXINgMIjrrrsOBw4cwObNm/HDH/6QkySIhrFc/6PtsVV8vSl+nG8OvOZADCQCs71dQgbidNVh+bmb+cNO49bss4ddVYeNOcQu2ivsgdi4XW+HUPzepFYJeR69puq5N0UrSb4w3A2+WEzJ+QeRNKgX0L3++uuYN2+e8fnKlSsBAEuWLMHjjz+Of/qnf0Jvby++9a1v4cSJEzjvvPOwZcuWrEeTPPnkk1ixYoUxdeLyyy+3zDb2er147rnn8K1vfQuf//znUV5ejmuuuQZ33323cUwoFMLWrVvx7W9/G7Nnz8aoUaOwcuVK4zkT0fA0WBMmsrmgLtWECam/F9R5LBVmD/zQ9HXN8XFrQvUkpktEYqlXNccZF9I5cWqbkBfcmUetmY+3XUgHRKHER6whLF+7QCSLcGp/45ENhmCi4UkRXKmWN52dnQiFQnj/+gtQHSiYwR1ElEE++iDtQSoWs4ZUYyGG7YI3c1uDvD9xn7BUgO2cqsiy39brEwiUa5Z2DH+ZplddyxUoZT4oFf5EVTb+ewS88FQGEi0N5vYGny9+QZy9BSJN/7C5khyN6e0W0SjQFwH6whC9EYiuCLSeKESPCq1HhfZpH9SwB+GTXqh9HkR6PVDDHmhRBb29WtK5kNv8fH4FvqBmtKQEyjXjtZv7tlNV8CmZm/9WtKiCzkgUEx7Zjo6OjqxaCOW/o+9dNy+n/452RqKY+PhLWT8fGp6Y0IiIbIZqIYcMvMZyjqgCn19ANiHI++XXyLArx60BSDuDWD5m4n7FcSGHnD8MAKiIV3pNyziEGkuMWnOaO2wfsZaOeeaw/fb4hXRy5jDM/cNhW5XXyw1zhYh/JlQMGIaJiBwM5kIO8+1yIUeqQAzo1WHz16TaUAdYAzGgV0nl12lRxXEhhxpvmTDPH1bURJsE5DIOuYXORF/YEQ/QpjYI60g1OU9Ybp9L3GfMNo7G4lVnU6sEYGyj8/aq0HzWTXROUvURm/uyqX8Gc5IE5wxTPjEMExGlMNiB2PK9bYHYfowMxD5kXsghA7H99zJAykCswgNArwDLhRzp+ocVv62iaw68qarD5kqyz2cN0/HjFQBCXqAnq8MAIBdwyHMRRhJZAR/MTXREVNwYhomI0hiKlc1GCDYFYrmy2R6IgcQFdJkqxGapVjYnNtTFq8NdEXiqAtB6VHgq/NbtdLJdwrZdzlIdzsSyjjlmvd0nt9XF9KkS8WBu3pbn9Zk30QnAYcKbDMipxrARUWljGCYiymCwJ0wYx3gTq4aNCROqta9YPj9zRTjVdImkCrFDIAY88Jdp8e+p36/0RaHAp49Y61ETfcRGu4S9QtyPf1psX2OpDgNGq4R5znCq6rDjw/vNy6UT7BfPUWHSogpibJOgPGEYJiJyIR+B2CxXI9cApGyXABIVYr9PSRmIZQhW+zxAmQb0CnhNF9TJ/uGkdglTL7AATBfZOfUN21ooZI+w/XOfqVUi4I23SniNi+gGwssAnFcMnFQsGIaJiFzKdSC2V4L7E4iBxFQF+wV1qQIxAEs7hT0QR3o9CJRrlsc0Jkz4PYn+YePXeLuEwwVxjhx7ib3WQOzzJqrD5pYJ6NMklPhFdKnWMqfizdAqwbFq7rCSTsMJwzAR0RAaaCA2bvPK3uLEY6W7oA5A2paJxHPSe4flhAlF1Yz+YTluLaldIqqHXYH4xIn0J0B+Q+vt5nAsq8Px9cxOMSyblgkiIjOGYSKiLAzGDOKBBmK3FeJ0F9WZq8Ny5JrmVYwL6sz9w0ntEuYL4eTvXU2asK9lNlWHAaM9QvF7AL/HWBiixkOwxyvg83ssc5flsg25bASwjlVjq0Rx0DTF6J/P1eMRSQzDRERZKoZA7Gbkmpkxh9ghECdGrpkmTABG/7DWA3jg3C4hVDV5fbP8vcf0T5C5QmxsrjP1IpcF9RAd7xs2yybQGlvmTOGYm+dyj/3CVEwYhomI+mG4BGLzhAnjc5g/V2wziOPtEn36d9QAeCr0/mF0RYCqgLVdwrioznaRnfEiTZ/HoqaAnKgeK35/vHc40Sohq8TZtEek6xdmEHaP/cI03DAMExH1U6EEYgBJa5v700MMJE+ZUPv0EGwOxB6fkpgw4fcYlWFU+IFwzNouYZ4sYd5Y59QmYQ7GPtsyDp8P8Pn0DXSAZcSafP1qX+I1mC8QdGqR4Ei14hKLAjEld9XmWDTzMVQ6GIaJiAagEAKxlK6H2M3YNXl/ukCs+RTbyDW9GixUj35B3UlVrxZXBvTg2wegzHYxndMcYnPLhBavEMubfDG9OuzzxsesxVdEl/mA7mjSeZBkvzBgDb72tgpWhXOLLRJUbDyZDyEionTy8Y+/PWB77f2tPmuFU97n8wvjwjJ5v/mx5DY2c2uEJAOyrKpGVT18azE9AEdV/Vf5eSymQKgaRFcEWo8K0aPqF9ZFYhBqzKgMW34FEtVh+wSJxIvXq8QeX7wqLEN0/PeBRCA2B1mvw+uU50B/zdZAzKowEQGsDBMR5UQhVIhz1UNsZ68Qyy11+ucwLqrT74HxKwAo5YHEmmZV1avDTks4jG8WiD8R820xoCwAqPquZSUYv4jOn76eI6dI2KvC5koyq8LZ4RsIGo4YhomIciTfW+qA/gViMzc9xEDiwjr7lAktqljWNpsDsaJqEKqmb6hTPYn+4b5wfBqErBKblmvEooC/LP7kAg4v2Kf3D/u88VYJn2nEmtc43+bzLl+T7BU2V8z1h2RVOF/y1SKhRRVoOewZZisHmTEMExHlUD621MnHlQZzdbN5MUfApz+2ftGeU4U4klQZtvQPR72J6nDUNEJNiwKBCvmC9V/Nad5flqgk2/qGAVhmDUvmXmFz64g8R+aKsMd2boiotDAMExHlWCG2TAwkEAN6D64WVYzXpvZ5oEWTq7IpA7E8QLZIGBVi6NXfaCQRiPUHBQLxf6IiPUAgCPjCmVc9m86XuV9aBmFzVZjVYSICGIaJiPJisFsm0knVMpFtIJaTJlIv5tA5BWIR9Orzh/siequDuTos5wtr5jnDgOL1618bqNDvGzUa3Wd9BlWd3fC88v/g6YlCqDF4whHja4yg601Uhf1lmiUIe73Zt0nEYtY3IqWIbx5ouGIYJiLKE1nJzVWISNcyIe83V4C1WOL38n/2mfqIAeeL6Nysbk4XiIUchwbom+kQH7UWjSUqwHEyBMtgrADAyFNx59u92PyLsbjkgii+e+lcjP7zIeClAwiqGrRoDH1detVYBt9AuWZUiP1lmmUih7nNQqiaw1lJiOVwDfBwls8Wk6iqIIrcPX5U5Z8pJTAMExHlWT76iPsTiPVKsEgZiOWkCSlTKLYHYvtrNAdiwDRhQrY6+P2Jg70+AKYL6GSFOL6Io9On4rW3R+D03x9Hy/gqtE74FBdMPgfKK28Bfg88vqgefKOJICwrwjIIy+dnX9ih+D0pA7FTEI7FlJKtDhMNRwzDRESDYCgDsV0uJk2Yv0afRSzgh4ZIrzVopgzEiC/iQKXeKqH2WfuEpVgUItyFqvZO/PryiXj3/+vC6eUhxJ5+Cb0txwEAvUdj6Pw4AC2qwF+mGefAHIS95Ur8u8a/f5n+fURf6lVkrAgnsEWChjOGYSKiQTIYgRhA1tvq7OxtE0Byldipj1iFJykQa1EF/qiqb6+TzxvQ1yr7fBC9vfHZwz7Arz8jEVP1Z+DxAWWVUMoqIaIRiCNvoX57Kz58qVN/vca51Ns05Lnwl2nwB/UPAPCWK3r4tc8lTtMekSkIszo8uDhajfKJYZiIaBDlOxA73WeZItGPPuJ0Czrss4hVeOCJmo+Ldw2fUOHtiwIoN+5R1BiUqvJ4hdjyxCF8ASjBKsBfhqgWgW/MRGDkqfBdGsS4Eb/Hia0fWV+rVwBB/fcyDHuqEm0XSoUfdvbWCAYkZ27+vvLcUTFjGCYiGmSD3TIhv2e2bRP6femrxOmWc0R6PUbrQiyqwB/TEEAvhKqHVEXV9CoxYA3EPi+ASqDCB03E0Bfrjr8WLzwTpiA4vwxjKvfj5JbDSRVc2RKhVJXBYwvAsldYxJeDpML2CKLSwjBMRDQE8r2cw9wyIe/PdmOdFpP3pV/j7DRpQlaItahAVFUQKJfhU4Nf7QNUDZ6QfpsSjukTJ+TItcoKfRudRw/DmoihN9YJTcSgagrG1UxE8K+AET4vTj5/0HIOPFUBKBV+eCp8ztXgHjXxid8DBT5oXZGk48g9VoWp2DEMExENkVyPXpOP5ebCulR9xEAiFMvjAP26NjehR42KRB+x3wMtJoy2Cf1CO/2CuyD0ACpUDR5VgxL0QvH59ApxWVBfwqFF4fNUwqN44VMC0JQYPEoMPiUAqH0QvRH4T62wTIdQKvzwjAzqW+8ACDVm3Ce6IhB+r6VXWF5AJ18bq8JWhXLhnBZTEMtlzzD/nMmEYZiIaIgN9aQJcx8xkH3bhBpNfu72tgnzxjrJH9X7iIUaA9QYPF0ReMaFIfrCUE4JQ6h9QF8XKqtOQUUwBE+4V7/o7oP/g9j/f9A+7YNv/AggkNhK5xlZDpQFjU11Sl98T3NfBCLgBXriF+mpsaQL6BiEiUoTwzARUQEYzLYJN33EgXhojsIarj1eAS2muArFSX3EprAZiypGH7Ff7YPoisDTE4VX1eAZHQH6wlBOiQAjwxCRHiiBCoiTnwKdnRB/fA+xYyfhrRkBlMUvkvPF/zmrrIBSXqaH4b4IRFkQ6Avryz0Qnylse978Mb8zXjhHpcKT+RAiIhoMWlTJebiwBxqvV1g2sXl8+pIKb3wur9zeJlcX+/z6R9JxvsQSC7nS2e9TLEs7AD0QR1VhvDY17EFvlxd9XV6ET+ofPR0+RLqB2LGTiLZ1I/Z+F7QPOyCOtEEcaQOOtQEfHYF47wjEwfeBsSPh+8I0KLPOgTJ9CpTx46CMPwVKfS2U8acANeP1vuPK8kQw9nmhBOPVYr8Hit8L0Rc1LqSzV4Xz8WdB/afFlJx/ZGPDhg2YOXMmqqurUV1djYaGBrzwwguOxy5duhSKomD9+vVJ9+3atQvz58/HiBEjMHLkSFxwwQXo7e017n/nnXdwxRVXYOzYsaiursbnP/95vPTSS1k9V8oeK8NERAUmH2uc7cHObS+xvM0HAH5hrLE1H6vFFNc9xfIYLaZXhs0X12lRBZ5wBN7jYfhG+eHtCMMT6oVndJceZs+aCPH5heiLdSOi9aI32glVUzC6th6VqAB62oGKkYh4BALecoj39uoVYZ8vUTmOM/cSy/PA8JtQKL3ChWLChAlYt24dzjzzTADAxo0bccUVV2D//v2YPn26cdzTTz+NV199FXV1dUmPsWvXLlx66aW47bbbcN999yEQCOD3v/89PJ5EXXLRokU4++yzsW3bNpSXl2P9+vW47LLL8Oc//xm1tbX5f6ElimGYiKhA5bp1ws6pdUIyB11ZEbaHYqcQnSkY27+P2qcHAfNr9foEPCc1eI92o2xkF5SqAIJfmIzeM6Zj37H38WmfD0d7FfREKwEAZ4d68JmRn2JseQgBTxQn1RMIaiNQOXoiRN87+jeKRiHCMctYNaFq/Q7AXLhRWm8eFi9ebPn8zjvvxIYNG7B7924jDH/wwQdYtmwZXnzxRSxatCjpMb7zne9gxYoVuPXWW43bzjrrLOP3H3/8Mf70pz/h0UcfxcyZMwEA69atw4MPPog333yTYTiP2CZBRFTAcvXjenNbg529dQJAUuuEbJ/wl2nw+AQC5Rp8fv1XT/x2r0/oyy68Ar6g5vjh9HjG84h/vT+ooSIURXm9H/6zRyN4fj2UGdPxbucn2HnMj93HPfhjh4I/deofB7s8+HNnEO2RdkS1CILeEQh4yiHCXfoDR6N6hTiiX6gnVA2IB+FYfLpFKQW7TEqpKtzZ2Wn5CIfDGb8mFouhqakJJ0+eRENDAwBA0zQ0NjbilltusVSKpePHj+PVV1/FuHHjMGfOHNTU1GDu3Ll45ZVXjGPGjBmDqVOn4oknnsDJkycRjUbx0EMPoaamBrNmzcrdi6YkrAwTERWBXLVOpNtYJ1snnL6H0yg2yfiHxFQxNj9n8/e2fF28F9kf1IweZa9XwDfKD0+oAt6x5fCcNgZKfS1EzZl4/Z1j+KhXQacKRFUPolEPoqoHx8siqC334tOwDyN8J1HlH4uApkBEI8aFc4gmqsJCjRkj1TKdKyoMWlSB5nDR5kAeDwDq6+stt69evRpr1qxx/JrW1lY0NDSgr68PlZWV2Lx5M6ZNmwYA+NGPfgSfz4cVK1Y4fu27774LAFizZg3uvvtufPazn8UTTzyBCy+8EAcOHMBZZ50FRVGwdetWXHHFFaiqqoLH40FNTQ2am5sxcuTI3LxwcsQwTERURPIxm9gs3bKOlF9jmUdsCsL+xNeYn698LKM6HNT0rXFl+qIMTyiozwquHQmlvhY4pR4nwh+iPeKF3KPR69Gg+jWgHBgVECj3aaj2x+D3VMLnCQA9JwC1D+iLAN090LojED2qURUWqoZYzNOvivBwbpFw+/dquFTSjxw5gurqauPzYDCY8tgpU6agpaUF7e3t2LRpE5YsWYIdO3agt7cX9957L/bt2wclxSxkTdN/ArJ06VJ84xvfAAB87nOfw29/+1s8+uijWLt2LYQQ+Na3voVx48bhd7/7HcrLy/Hwww/jsssuw549ezB+/PgcvnIyYxgmIipCAwnF6arDktNKZ/l9nYKx7Bc2vt52jP2iPPmY/qAGT1UAnlAQSoUfit8Dz+gyKOPHQDllNHBKPcSoOrSdeA8VPi8imsAIH6BqCiLxDovacmBMWRQj/AoC3nJ4wr0QJz/Rx7B1dUN09UJrD0PrieqBuC9qtEbEMgR9Gt7kdAg3AoGAcQHd7NmzsWfPHtx7772YOnUqjh8/jtNOO804NhaL4bvf/S7Wr1+PQ4cOGUFWVpKlqVOn4vDhwwCAbdu24dlnn8WJEyeM5/Tggw9i69at2Lhxo6XXmHKLYZiIqIj1NxSnamUwM1dAnYJx0vEZnoPRjxwfzab4PVCqyuCJV4OVEX59acbYUfqItKpxUEbXoyNyFCejHlRbtisLY2dGdUDDyEDM6BVG+CMg3A3RdRJo74L2aZ8egntU0+pl68zjpPBeggG51KrCAyWEQDgcRmNjIy666CLLfQsWLEBjY6NRBZ40aRLq6urw9ttvW4575513sHDhQgBAT08PAFimS8jPZWWZ8oNhmIhoGDAHlHwEOXv7hNP3SfcczBfoKX4PlDI/EJ/3q8/99egrlMuC+mxgfxkUfzk0EUNMUzHCp6E6oMF83bffoy+RG1MWxcggUOatTFSF27uA9k5on/bqITgehM0XzcXYIlE0oqpAVOTu3Ecdtiams2rVKixcuBD19fXo6upCU1MTtm/fjubmZowZMwZjxoyxHO/3+1FbW4spU6YAABRFwS233ILVq1fj3HPPxWc/+1ls3LgRf/jDH/C///u/AICGhgaMGjUKS5YswQ9+8AOUl5fjZz/7GQ4ePOg4nYJyh2GYiGiYcarcpQuu2bCHwXTh2HysvE8Pwj5LEIbfa1mpLFcpQ4vCE+7FCP8ojK84Ab8njPaI1/wtMDIQQ3Ughip/Dco0H0R3G9DVAfHRp9A+7tZbI0zTI9SwHqbTBWFWhVMr1arwsWPH0NjYiLa2NoRCIcycORPNzc24+OKLXT/GzTffjL6+PnznO9/Bp59+inPPPRdbt27FGWecAQAYO3Ysmpubcfvtt2P+/PlQVRXTp0/Hr3/9a5x77rn5emkEQBEih2+1yKKzsxOhUAjvX38BqgN830FEpcUcsBS/HkLtQVip8MNT4YNSFYBSHtDXKZ8yGqiuBsqrofjLgcqx6EMf+mLdULU+4zH9njIEPOV6RTiqQnzyHvDJcYgTHdAOHYf2UU88DMcguiKIdCfmGtuZ2yTSjaAbjrIJ//0Nw52RKCY8sh0dHR2ue3SBxL+jO/9qISp9/sxf4FJ3VMWc117I+vnQ8MSERkREOZUyBANJrRGK3wMEvFD83kRFOBoDImEAnRAAlO6PUVYxEmWBWkRi+uragLdcnxbR2w1x8k8Q4W6gvQui7ThEVy9iH3ZD6wgbK5f1C+Y80GLJI+LcBGEaWmpMQEXu/mzUGP+cKYFhmIiIhlYkBqHGoPj0mcCit0+/vK0SADr1ecFqLxR/OQIeH6BFIXoPA+FuoLsnfqFcpx6Cj/XoITjeI6yGPdCiXsSiSsYgnA6rwqXbIkHDH8MwERHljD1cCVWD4vdA9EWN6rBQ9dCr+D3GamTEpzwogL4gIxoFevv0i+l8etVY+HyJbXJ9EYjeXqC7B+iLQOuOQGsPI/ZxD0RXBLFegVjMA7XPYxn5ls1zl4ZrECYiHcMwERHljBZ13mBnUDV9DAQArScKxa/B2KTRFYEIx6AEI0bbhJCtEzIIA3pY7o1AdEX0FoiOMLQe1fhcrwZ7jGpwKqXeHsGqMJGOYZiIiHLKPvvYXB0GAAU+vSIcD8XoUQG/R+8I9SemRRg9xSZypTJUfb2yDMF6JVgB4LFMjTCzL/4wbk83O5lV4YIQVQWiWg5Hq7FnmEwYhomIKC/ModhohwAAxCu8fQDKfPEQHL/QzhaGJfn1Qo3p65RNF8YBsMwOdqoGF3oQdrMVMNffzy1WhWm4YxgmIqK8si/jkMHWXi0GYAnNTnHNHIJjsURYThWEi+GCucFu0SjFlhCidBiGiYho0FiCMUzBV404He74tbGYx1W1MlU1GCisinAhK5SqsBoWiOTwz4Wj1ciMYZiIiAYs5nJiQ3++xh7IzNvjUl0gxyBcON+TqNAxDBMR0YD0JwibZao+pludbGZviQDct0UAQxuEB6MCm20QLpSqMFG+MQwTEdGgcxu0MgVhpwAMZFcNBoZ/RZiIUmMYJiKiQZWrIGyXqgpc6EGYVeHMVFVAzeFoNfYMkxnDMBERFRy3QThdG4SbADjUQXg4f1+iYsEwTEREAyIDpdveYTczdbPp9bU/thuFEoILrQILFOZzIsonhmEiIsoJr1dkFYilgYavbCufpRSEi709gmgwMAwTEVHOZFslBrKfrNDfH/sP9mrlYgvChSwazfE65hw+FhU/hmEiIsq5/oRiKdchbrBDcLFiVZhKFcMwERHlTTatE/n6/oNtqCvCmZ6DEwZhKmUMw0RElFf2QJrvcDyUlWAG4fyIqUDUk7s/15iW+RgqHQzDREQ0qMxhNRfBuFDaIAqhR7cQngNRsWEYJiKiIdPfqnGhBGCpWENoMVSFifKNYZiIiApGoYVcNzIF4UJtjyAiHcMwERFRPxVrEC62irAaFVBz2DOcy9XOVPwYhomIiLJUSFXYQnouRMXIM9RPgIiIqJi4DZ9crEFUHFgZJiIicslN+Cz0FoRCf35OolGBqJLDDXSCbyIogZVhIiIiFwotCPenKlyMQZgo3xiGiYiIMmAQJhq+2CZBRESURqFMjJAYhIlyi2GYiIjIQaGFYKB0L5iLquwZpvxhGCYiIjIptJYIqb9BmFVhovQYhomIiFBYI9PsGISJ8odhmIiISl6hVoMBBmGifGMYJqKi4hQM+I8+9Uc2IZNBeGipqoCaw55hlT3DZMIwTERFI1UwkLcPtwBAuZdtuBzKv1MMwkSDg3OGiWjY8PhEyV5tT+n15+/GcAvCsZiCWIxBuT82bNiAmTNnorq6GtXV1WhoaMALL7zgeOzSpUuhKArWr19v3Pbpp59i+fLlmDJlCioqKnDaaadhxYoV6OjosHztvn37cPHFF2PkyJEYM2YM/uEf/gHd3d35fGkEhmEiKiJuwwlDMQGJvwfZtkPIj6GSryAMAF5vcf53EVOFPl4tRx8xNbvzMGHCBKxbtw6vv/46Xn/9dcyfPx9XXHEF3nzzTctxTz/9NF599VXU1dVZbv/www/x4Ycf4u6770Zraysef/xxNDc34/rrr7ccc9FFF+HMM8/Eq6++iubmZrz55pu47rrr+n3eyB22SRBRUdGiiuuwwPaJ0jOQN0FD/fckX8+92INwIVi8eLHl8zvvvBMbNmzA7t27MX36dADABx98gGXLluHFF1/EokWLLMfPmDEDmzZtMj4/44wzcOedd+JrX/saotEofD4fnn32Wfj9fjzwwAPwePRa5QMPPIDPfe5z+NOf/oQzzzwzz6+ydLEyTERFJ9vQwkrx8NafCrDZUFeCAQbhYhKLxdDU1ISTJ0+ioaEBAKBpGhobG3HLLbcY4TiTjo4OVFdXw+fT65LhcBiBQMAIwgBQXl4OAHjllVdy/CrIjGGYiIpSf8ILA/HwMdAADBRGCAbyG4S9XjEsgnAvougVOfxAFADQ2dlp+QiHwymfQ2trKyorKxEMBnHjjTdi8+bNmDZtGgDgRz/6EXw+H1asWOHq9XzyySf4t3/7NyxdutS4bf78+Th69CjuuusuRCIRnDhxAqtWrQIAtLW19ffUkQtskyCiopVNy4TE1onilYs3M4X05z7Q1+OmIlzsAoEAamtrsfLo/8v5Y1dWVqK+vt5y2+rVq7FmzRrH46dMmYKWlha0t7dj06ZNWLJkCXbs2IHe3l7ce++92LdvHxQl83nv7OzEokWLMG3aNKxevdq4ffr06di4cSNWrlyJ2267DV6vFytWrEBNTQ28Xu+AXiulpwjBYXv50tnZiVAohPevvwDVAb7vIMqXYu4TpYR8Vu4L7c95MIJwIVWEOyNRTHhku9EakI2+vj5EIpGcPychRFJ4DQaDCAaDrr7+oosuwhlnnIGpU6di5cqVlvaGWCwGj8eD+vp6HDp0yLi9q6sLCxYsQEVFBZ599lmUlZU5PvaxY8cwYsQIKIqC6upqNDU14Stf+Ur2L5JcYUIjoqLXnwqx5PGJggtKw9Vgt6kU6p9rqQXhgSorK0sZGoeSEALhcBiNjY246KKLLPctWLAAjY2N+MY3vmHc1tnZiQULFiAYDOKZZ55J+5pqamoAAI8++ijKyspw8cUX5+dFEACGYSIaJgYaiOVjUG4MVX92If8Z5rvNQ/YIU+6tWrUKCxcuRH19Pbq6utDU1ITt27ejubkZY8aMwZgxYyzH+/1+1NbWYsqUKQD0ivAll1yCnp4e/OIXvzB6lAHglFNOMdog7r//fsyZMweVlZXYunUrbrnlFqxbtw4jR44c1NdbahiGXXjwwQdx1113oa2tDdOnT8f69evxxS9+caifFhHZDCQQAwzFAzUYAbhY/2zyGYSHS39wITt27BgaGxvR1taGUCiEmTNnorm52XXFdu/evXj11VcBIGlE2sGDBzFp0iQAwGuvvYbVq1eju7sbn/nMZ/DQQw+hsbExp6+FkrFnOINf/epXaGxsxIMPPojPf/7zeOihh/Dwww/jrbfewmmnnZb2a9kzTDQ0htuFVsUgX0F4OPw5DGYQLtTK8EB6honyjaPVMrjnnntw/fXX45vf/CamTp2K9evXo76+Hhs2bBjqp0ZEKeQiQHE28dAphC1wuZCrv0NO58FptXKhBmGiQsdyZRqRSAR79+7Frbfearn9kksuwc6dO5OOD4fDlhmFcud4VySa3ydKRMkiuatWFnsoGxS5v9i/qHl8IifnJKuKcGzg3y9f5L+D/GE0FSKG4TQ+/vhjxGIx46pOqaamBkePHk06fu3atfjXf/3XpNun/pybY4iIiLq6uhAKhYb6aRBZMAy7YJ9D6DSbEABuu+02rFy50vi8vb0dEydOxOHDh/kf/yDr7OxEfX09jhw5wv60QcTzPjR43ocGz7t7Qgh0dXWhrq5uqJ8KURKG4TTGjh0Lr9ebVAU+fvx4UrUYSD2sOxQK8X+UQ6S6uprnfgjwvA8NnvehwfPuDotCVKh4AV0agUAAs2bNwtatWy23b926FXPmzBmiZ0VEREREucLKcAYrV65EY2MjZs+ejYaGBvznf/4nDh8+jBtvvHGonxoRERERDRDDcAZXX301PvnkE9xxxx1oa2vDjBkz8Pzzz2PixIkZvzYYDGL16tWu95xT7vDcDw2e96HB8z40eN6Jhgcu3SAiIiKiksWeYSIiIiIqWQzDRERERFSyGIaJiIiIqGQxDBMRERFRyWIYzqMHH3wQkydPRllZGWbNmoXf/e53Q/2UCsbLL7+MxYsXo66uDoqi4Omnn7bcL4TAmjVrUFdXh/LyclxwwQV48803LceEw2EsX74cY8eOxYgRI3D55Zfj/ffftxxz4sQJNDY2IhQKIRQKobGxEe3t7ZZjDh8+jMWLF2PEiBEYO3YsVqxYgUgkYjmmtbUVc+fORXl5OU499VTccccdKLZrT9euXYu//Mu/RFVVFcaNG4e/+Zu/wdtvv205huc99zZs2ICZM2caixkaGhrwwgsvGPfznA+OtWvXQlEU3HzzzcZtPPdEBAAQlBdNTU3C7/eLn/3sZ+Ktt94SN910kxgxYoR47733hvqpFYTnn39e3H777WLTpk0CgNi8ebPl/nXr1omqqiqxadMm0draKq6++moxfvx40dnZaRxz4403ilNPPVVs3bpV7Nu3T8ybN0+ce+65IhqNGsdceumlYsaMGWLnzp1i586dYsaMGeKyyy4z7o9Go2LGjBli3rx5Yt++fWLr1q2irq5OLFu2zDimo6ND1NTUiL/7u78Tra2tYtOmTaKqqkrcfffd+TtBebBgwQLx2GOPiQMHDoiWlhaxaNEicdppp4nu7m7jGJ733HvmmWfEc889J95++23x9ttvi1WrVgm/3y8OHDgghOA5HwyvvfaamDRpkpg5c6a46aabjNt57olICCEYhvPkr/7qr8SNN95oue0zn/mMuPXWW4foGRUuexjWNE3U1taKdevWGbf19fWJUCgk/uM//kMIIUR7e7vw+/2iqanJOOaDDz4QHo9HNDc3CyGEeOuttwQAsXv3buOYXbt2CQDiD3/4gxBCD+Uej0d88MEHxjH/9V//JYLBoOjo6BBCCPHggw+KUCgk+vr6jGPWrl0r6urqhKZpOTwTg+v48eMCgNixY4cQgud9MI0aNUo8/PDDPOeDoKurS5x11lli69atYu7cuUYY5rknIoltEnkQiUSwd+9eXHLJJZbbL7nkEuzcuXOInlXxOHjwII4ePWo5f8FgEHPnzjXO3969e6GqquWYuro6zJgxwzhm165dCIVCOO+884xjzj//fIRCIcsxM2bMQF1dnXHMggULEA6HsXfvXuOYuXPnWgbrL1iwAB9++CEOHTqU+xMwSDo6OgAAo0ePBsDzPhhisRiamppw8uRJNDQ08JwPgm9/+9tYtGgRLrroIsvtPPdEJDEM58HHH3+MWCyGmpoay+01NTU4evToED2r4iHPUbrzd/ToUQQCAYwaNSrtMePGjUt6/HHjxlmOsX+fUaNGIRAIpD1Gfl6sf55CCKxcuRJf+MIXMGPGDAA87/nU2tqKyspKBINB3Hjjjdi8eTOmTZvGc55nTU1N2LdvH9auXZt0H889EUlcx5xHiqJYPhdCJN1GqfXn/NmPcTo+F8eI+EUtxfrnuWzZMrzxxht45ZVXku7jec+9KVOmoKWlBe3t7di0aROWLFmCHTt2GPfznOfekSNHcNNNN2HLli0oKytLeRzPPRGxMpwHY8eOhdfrTXo3f/z48aR3/pSstrYWQHI1xHz+amtrEYlEcOLEibTHHDt2LOnxP/roI8sx9u9z4sQJqKqa9pjjx48DSK4qFYPly5fjmWeewUsvvYQJEyYYt/O8508gEMCZZ56J2bNnY+3atTj33HNx77338pzn0d69e3H8+HHMmjULPp8PPp8PO3bswE9/+lP4fL6UVVeee6LSwzCcB4FAALNmzcLWrVstt2/duhVz5swZomdVPCZPnoza2lrL+YtEItixY4dx/mbNmgW/3285pq2tDQcOHDCOaWhoQEdHB1577TXjmFdffRUdHR2WYw4cOIC2tjbjmC1btiAYDGLWrFnGMS+//LJlDNKWLVtQV1eHSZMm5f4E5IkQAsuWLcNTTz2Fbdu2YfLkyZb7ed4HjxAC4XCY5zyPLrzwQrS2tqKlpcX4mD17Nq699lq0tLTg9NNP57knIt3gXatXWuRotUceeUS89dZb4uabbxYjRowQhw4dGuqnVhC6urrE/v37xf79+wUAcc8994j9+/cbo+fWrVsnQqGQeOqpp0Rra6v46le/6jjyaMKECeI3v/mN2Ldvn5g/f77jyKOZM2eKXbt2iV27dolzzjnHceTRhRdeKPbt2yd+85vfiAkTJlhGHrW3t4uamhrx1a9+VbS2toqnnnpKVFdXF93Io3/8x38UoVBIbN++XbS1tRkfPT09xjE877l32223iZdfflkcPHhQvPHGG2LVqlXC4/GILVu2CCF4zgeTeZqEEDz3RKRjGM6jBx54QEycOFEEAgHxF3/xF8YIKxLipZdeEgCSPpYsWSKE0McerV69WtTW1opgMCj++q//WrS2tloeo7e3VyxbtkyMHj1alJeXi8suu0wcPnzYcswnn3wirr32WlFVVSWqqqrEtddeK06cOGE55r333hOLFi0S5eXlYvTo0WLZsmWW8UZCCPHGG2+IL37xiyIYDIra2lqxZs2aoht35HS+AYjHHnvMOIbnPff+/u//3vj/wCmnnCIuvPBCIwgLwXM+mOxhmOeeiIQQQhGC622IiIiIqDSxZ5iIiIiIShbDMBERERGVLIZhIiIiIipZDMNEREREVLIYhomIiIioZDEMExEREVHJYhgmIiIiopLFMExEREREJYthmIiGnUmTJkFRFCiKgvb29gE91gUXXGA8VktLS06eHxERFQ6GYSIqSLFYDHPmzMFVV11lub2jowP19fX4/ve/n/br77jjDrS1tSEUCg3oeTz11FN47bXXBvQYRERUuBiGiaggeb1ebNy4Ec3NzXjyySeN25cvX47Ro0fjBz/4Qdqvr6qqQm1tLRRFGdDzGD16NE455ZQBPQYRERUuhmEiKlhnnXUW1q5di+XLl+PDDz/Er3/9azQ1NWHjxo0IBAJZPdbjjz+OkSNH4tlnn8WUKVNQUVGBL3/5yzh58iQ2btyISZMmYdSoUVi+fDlisVieXhERERUa31A/ASKidJYvX47Nmzfj61//OlpbW/GDH/wAn/3sZ/v1WD09PfjpT3+KpqYmdHV14corr8SVV16JkSNH4vnnn8e7776Lq666Cl/4whdw9dVX5/aFEBFRQWIYJqKCpigKNmzYgKlTp+Kcc87Brbfe2u/HUlUVGzZswBlnnAEA+PKXv4yf//znOHbsGCorKzFt2jTMmzcPL730EsMwEVGJYJsEERW8Rx99FBUVFTh48CDef//9fj9ORUWFEYQBoKamBpMmTUJlZaXltuPHjw/o+RIRUfFgGCaigrZr1y785Cc/wa9//Ws0NDTg+uuvhxCiX4/l9/stnyuK4nibpmn9fr5ERFRcGIaJqGD19vZiyZIlWLp0KS666CI8/PDD2LNnDx566KGhfmpERDRMMAwTUcG69dZboWkafvSjHwEATjvtNPz4xz/GLbfcgkOHDg3tkyMiomGBYZiICtKOHTvwwAMP4PHHH8eIESOM22+44QbMmTNnQO0SREREkiL4rwkRDTOTJk3CzTffjJtvvjknj3fo0CFMnjwZ+/fv7/dYNyIiKkysDBPRsPTP//zPqKysREdHx4AeZ+HChZg+fXqOnhURERUaVoaJaNh57733oKoqAOD000+Hx9P/9/0ffPABent7Aeg9y9luviMiosLGMExEREREJYttEkRERERUshiGiYiIiKhkMQwTERERUcliGCYiIiKiksUwTEREREQli2GYiIiIiEoWwzARERERlSyGYSIiIiIqWQzDRERERFSyGIaJiIiIqGQxDBMRERFRyWIYJiIiIqKSxTBMRERERCWLYZiIiIiIShbDMBERERGVLIZhIiIiIipZDMNEREREVLIYhomIiIioZDEMExEREVHJYhgmIiIiopLFMExEREREJYthmIiIiIhKFsMwEREREZUshmEiIiIiKlkMw0RERERUshiGiYiIiKhkMQwTERERUcliGCYiIiKiksUwTEREREQli2GYiIiIiEoWwzARERERlSyGYSIiIiIqWQzDRERERFSyGIaJiIiIqGQxDBMRERFRyWIYJiIiIqKSxTBMRERERCWLYZiIiIiIShbDMBERERGVLIZhIiIiIipZDMNEREREVLIYhomIiIioZDEMExEREVHJYhgmIiIiopLFMExEREREJYthmIiIiIhKFsMwEREREZUshmEiIiIiKlkMw0RERERUshiGiYiIiKhkMQwTERERUcliGCYiIiKiksUwTEREREQli2GYiIiIiEoWwzARERERlSyGYSIiIiIqWQzDRERERFSyGIaJiIiIqGQxDBMRERFRyWIYJiIiIqKSxTBMRERERCWLYZiIiIiIShbDMBERERGVLIZhIiIiIipZDMNEREREVLIYhomIiIioZDEMExEREVHJYhgmIiIiopLFMExEREREJYthmIiIiIhKFsMwEREREZUshmEiIiIiKlkMw0RERERUshiGiYiIiKhkMQwTERERUcliGCYiIiKiksUwTEREREQli2GYiIiIiEoWwzARERERlaz/H3hmXjX1FgJ3AAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "! seisflows plot2d MODEL_01 vs --savefig m_01_vs.png\n", + "Image(filename='m_01_vs.png') " ] }, { "cell_type": "raw", "metadata": {}, "source": [ - "Have a look at the `working directory documentation page `__ for more detailed explanations of how to navigate the SeisFlows working directory." + "Have a look at the `working directory documentation page `__ for more detailed explanations of how to navigate the SeisFlows working directory. You can also run Example \\#1 with more stations (up to 131), tasks/events (up to 25) and iterations (as many as you want). Note that because this is a serial inversion, the compute time will scale with all of these values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! seisflows examples run 1 --nsta 10 --ntask 5 --niter 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Example \\#2\n", - "Example \\#2 runs a 1 iteration inversion using SPECFEM2D, the Pyaflowa preprocessing module and an L-BFGS optimization algorithm. It successfully completes the line search and is meant to illustrate the output of the Pyaflowa preprocessing module." + "## Example \\#2: Pyaflowa, L-BFGS inversion\n", + "\n", + "Example \\#2 runs a 2 iteration inversion with misfit quantification taken care of by the `Pyaflowa` preprocessing module. Optimization (i.e., model updates) are performed using the `L-BFGS` algorithm. This example is more complex than the default version of Example \\#1, using multiple events, stations and iterations. Example \\#2 also includes smoothing/regularization of the gradient before using it to perturb the starting velocity model." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 12, "metadata": { "scrolled": true }, @@ -282,7 +364,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2 example: ex2_specfem2d_workstation_inversion_w_pyatoa\n", + "No existing SPECFEM2D repo given, default to: /home/bchow/Work/scratch/example_1/specfem2d\n", "\n", " @@@@@@@@@@ \n", " .@@@@. .%&( %@. \n", @@ -307,16 +389,17 @@ " SEISFLOWS EXAMPLE 2 \n", " /////////////////// \n", "This is a [SPECFEM2D] [WORKSTATION] example, which will run an inversion to\n", - "assess misfit between a homogeneous halfspace and checkerboard model using\n", - "Pyatoa for misfit quantification [2 events, 5 stations, 1 iterations]. The tasks\n", - "involved include:\n", + "assess misfit between a starting homogeneous halfspace model and a target\n", + "checkerboard model. This example problem uses the [PYAFLOWA] preprocessing\n", + "module and the [LBFGS] optimization algorithm. [4 events, 32 stations, 2\n", + "iterations]. The tasks involved include:\n", "\n", "1. (optional) Download, configure, compile SPECFEM2D\n", "2. Set up a SPECFEM2D working directory\n", - "3. Generate starting model from Tape2007 example\n", + "3. Generate starting model from 'Tape2007' example\n", "4. Generate target model w/ perturbed starting model\n", "5. Set up a SeisFlows working directory\n", - "6. Run an inversion workflow. The line search is expected to attempt 4 evaluations (i01s04)\n", + "6. Run the inversion workflow\n", "================================================================================\n" ] } @@ -338,7 +421,206 @@ "metadata": {}, "outputs": [], "source": [ - "! seisflows examples run 2 -r path/to/specfem2d" + "! seisflows examples run 2 -r ${PATH_TO_SPECFEM2D}" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Succesful completion of the example problem will end with the following log message\n", + "\n", + ".. code:: bash\n", + "\n", + " LINE SEARCH STEP COUNT 01\n", + " --------------------------------------------------------------------------------\n", + " 2022-08-29 18:07:14 (I) | evaluating objective function for source 001\n", + " 2022-08-29 18:07:14 (D) | running forward simulation with 'Specfem2D'\n", + " 2022-08-29 18:07:20 (D) | quantifying misfit with 'Pyaflowa'\n", + " 2022-08-29 18:07:29 (I) | evaluating objective function for source 002\n", + " 2022-08-29 18:07:29 (D) | running forward simulation with 'Specfem2D'\n", + " 2022-08-29 18:07:35 (D) | quantifying misfit with 'Pyaflowa'\n", + " 2022-08-29 18:07:43 (I) | evaluating objective function for source 003\n", + " 2022-08-29 18:07:43 (D) | running forward simulation with 'Specfem2D'\n", + " 2022-08-29 18:07:49 (D) | quantifying misfit with 'Pyaflowa'\n", + " 2022-08-29 18:07:58 (I) | evaluating objective function for source 004\n", + " 2022-08-29 18:07:58 (D) | running forward simulation with 'Specfem2D'\n", + " 2022-08-29 18:08:04 (D) | quantifying misfit with 'Pyaflowa'\n", + " 2022-08-29 18:08:13 (D) | misfit for trial model (f_try) == 4.73E-03\n", + " 2022-08-29 18:08:13 (D) | step length(s) = 0.00E+00, 1.00E+00\n", + " 2022-08-29 18:08:13 (D) | misfit val(s) = 5.30E-02, 4.73E-03\n", + " 2022-08-29 18:08:13 (I) | pass: misfit decreased, line search successful w/ alpha=1.0\n", + " 2022-08-29 18:08:13 (I) | line search model 'm_try' parameters: \n", + " 2022-08-29 18:08:13 (I) | 5800.00 <= vp <= 5800.00\n", + " 2022-08-29 18:08:13 (I) | 3193.01 <= vs <= 3821.37\n", + " 2022-08-29 18:08:13 (I) | trial step successful. finalizing line search\n", + " 2022-08-29 18:08:13 (I) | \n", + " FINALIZING LINE SEARCH\n", + " --------------------------------------------------------------------------------\n", + " 2022-08-29 18:08:13 (I) | writing optimization stats\n", + " 2022-08-29 18:08:13 (I) | renaming current (new) optimization vectors as previous model (old)\n", + " 2022-08-29 18:08:13 (I) | setting accepted trial model (try) as current model (new)\n", + " 2022-08-29 18:08:13 (I) | misfit of accepted trial model is f=4.727E-03\n", + " 2022-08-29 18:08:13 (I) | resetting line search step count to 0\n", + " 2022-08-29 18:08:13 (I) | \n", + " CLEANING WORKDIR FOR NEXT ITERATION\n", + " --------------------------------------------------------------------------------\n", + " 2022-08-29 18:08:15 (I) | thrifty inversion encountering final iteration, defaulting to inversion workflow\n", + " 2022-08-29 18:08:21 (I) | \n", + " ////////////////////////////////////////////////////////////////////////////////\n", + " COMPLETE ITERATION 02 \n", + " ////////////////////////////////////////////////////////////////////////////////\n", + " 2022-08-29 18:08:21 (I) | setting current iteration to: 3\n", + "\n", + "\n", + "As with Example \\#1, we can look at the output gradients and models to visualize how the inversion performed." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/bchow/Work/scratch/example_2\n", + "logs\tparameters.yaml sflog.txt specfem2d\r\n", + "output\tscratch\t\t sfstate.txt specfem2d_workdir\r\n" + ] + } + ], + "source": [ + "%cd ~/Work/scratch/example_2\n", + "! ls" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " PLOT2D \r\n", + " ////// \r\n", + "Available models/gradients/kernels\r\n", + "\r\n", + "GRADIENT_01\r\n", + "GRADIENT_02\r\n", + "MODEL_01\r\n", + "MODEL_02\r\n", + "MODEL_INIT\r\n", + "MODEL_TRUE\r\n" + ] + } + ], + "source": [ + "! seisflows plot2d # to check what models/gradients/kernels are avilable for plotting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The starting model is a homogeneous halfspace but for Example \\#2 the target model is a checkerboard." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Figure(707.107x707.107)\r\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "! seisflows plot2d MODEL_TRUE vs --savefig m_true_vs.png\n", + "Image(filename='m_true_vs.png') " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the following gradient Vs kernel, we can see how the 5km x 5km smoothing blurs away some of the detail of the raw graident. The blue colors here suggest that the initial model needs to be sped up to best fit waveforms (and vice versa, red colors suggest slowing down the initial model)." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Figure(707.107x707.107)\r\n" + ] + }, + { + "data": { + "image/png": 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Mnjx5IkhX0yZ5eXmsXr16rHv37nyaWJgEF6YQHBzMGjVqxLZt28a2b9/OmjZtygwGg2hdZ8yYwX+/BoOBTZgwQfZ8GGMsIyODValShZUsWZI9fPiQT583bx4DwA4cOCCwnzRpEgMgOKdKlSoxf39/VqhQITZ9+nS2d+9e9uGHH7KAgADWqFEjPkzq5MmTZuEYjDH2ww8/MADM19dXkN6jRw/2xRdfsP3797Ovv/6atWnThgFgEydOlD2nMWPGyN4vN2/eFNwL9erVY9evX5fNUy5M4uHDh6x06dJs3LhxfJpUmES/fv2Yl5eXIPSHIAjboTeSCFH69++PXr16Ydu2bejcuTNyc3Px+eefo3HjxqhQoQJvV7duXSxYsADdu3fH66+/jkaNGiE8PNyqMmvVqoV79+5hwYIFaN++PerXry/Yv337dhgMBrzxxhsC72RkZCRq1qxpNptDaGgoXn75ZdGy2rVrJ/DAcB5E7q3wP/74A7///jvmzJkDAILy2rZti+3bt+P8+fOoUqWKVefKYfrGOJdfu3btzNK3bNmCR48eSXqrODp06CD4bHxupm1qTGxsLI4ePaqo3tHR0bL7Tb3HXl5efLiLcdiLKXL7lNiY7rO1LFNq166N2rVr858bN26MTp06oXr16hg9ejRatWoletyZM2dw+PBhvPXWW/D391dVF+N9c+fOxcWLF7Ft2zbZej579gwA4Ovrix07dqBQoUIAgKZNm/KzNJjWtU+fPmjevDnu3buHH3/8EbNnz0Z6errkTB1ZWVl49dVXceXKFfz444+C67Jnz56YNm0aBg4ciFWrVqFSpUrYsWMH/+KccQjBs2fPkJWVhSlTpmDs2LEA8sOcfH19kZiYiB9++AHNmzdHzZo18dJLL2H27NmoVKkSWrRogbNnz2Lw4MHw8vIyC0v44osvBJ87d+6M9u3bY+bMmRg+fDgf3mBMbm4u1qxZg+eee07yXgkPD8fRo0fx9OlTnDt3Dh9++CGaNm2Kffv2ISoqSvQYOcaOHQsfHx9MnjzZou2KFSuwYsUK1WUQBCEPhUkQorz22msICQnhHysmJyfj1q1b6N+/v8COm+nhypUr6Ny5M4oVK4Z69eph9+7dqsssXrw49u3bh9DQULRq1crsbfxbt26BMYaIiAj4+PgItkOHDuHu3bsCe7mByfitb+C/R69PnjzhywLy3143LYsLtzAtzxrCwsIEn319fWXTlUwvZ+ncpAgKCkKtWrUUbVx9pDBtszVr1vB1y8rKEsxmwHHv3j2z8xY7t3/++ccsPTMzE9nZ2fzxoaGhMBgMorb37t0DYN7G1lK4cGHEx8fj119/lWxjTsCYhkgAytvk6tWrmDx5MqZMmQJfX188ePAADx48QG5uLp49e4YHDx7w5XPXQMOGDXkhDAAFCxZEXFyc6PRykZGRqFOnDlq2bImZM2di2rRpWLBgAU6cOGFm+/TpU3Tq1AkHDx7Etm3bUK9ePcH+8PBwPn67fv36CA0NxbBhwzB37lwA+fe68fkDMBPnXLyxcV2/+uorNGrUCF27dkVoaCiaNm2KV199FbVq1RLkKQX3Q/rYsWOi+5OTk5GWlib6PXF4e3ujTp06aNSoEd588038+OOP+Ouvv8ymbFPCkSNHsGjRInz44YfIysriv9Nnz54hNzcXDx48wNOnT1XnSxCEOsgzTIgSEBCA7t2747PPPsPNmzexcuVKFCpUCF26dDGz7du3L/r27YvMzEzs378fU6ZMQXx8PC5cuCB4eUkJZcqUwb59+9C0aVO0atUKO3fuRMOGDQHkD7AGgwEHDhwwixsEzGMJrfH8cXDe7XHjxknGAhpPzeUOpKSkmE0XJsWlS5ck5+IFYOZhLlOmDID/4mJPnz4tEFBpaWm4e/eu5Hy5HNWrV8fGjRuRlpYmiBvmXvbkjufmwBV7CfT06dMICAiw+sUyMRhjAMSvuezsbKxbtw6xsbGoVauW2X6lbfLXX3/hyZMnePvtt/H222+b5RMaGoq3334b8+bNE42VNq6rkpe76tatCwC4cOGCwBv+9OlTdOzYEXv37sXWrVvRrFkz0eNfeOEFnD17FpcvX+bjl48fPw4AeOmll3i7GjVqiL68yrWpcV2LFSuG5ORk3L59G2lpaYiJiUFAQAAWLVqE1157zeI5ieVpzIoVK+Dr64uEhASLeXGUKFEC0dHRuHDhguJjOM6ePQvGmNlUe0D+/M+hoaH4+OOPkZiYqDpvgiCUQ2KYkKR///5YsmQJZs+ejeTkZPTp0wcFCxaUtA8MDESbNm2QnZ2Njh074syZM6rFMJA/SwIniFu3bo0dO3agUaNGiI+Px8yZM3H9+nV07drVllOzSKVKlVChQgWcOnUK06dPt2tZekHLMIk6deqIprdu3Rr+/v5YvXq1QPitXr0aBoMBHTt2lM33lVdewcSJE7FmzRrB7AOrV69GQEAAWrduzad16tQJ8+bNw7Vr11CyZEkA+TM6fPPNN+jQoYNm81bfv38f27dvR61atURDILZt24a7d+9i2rRposcrbZNatWph7969ZscnJiYiPT0dq1atQokSJQDkPxVp0KABfvrpJ2RkZPDz1j5+/BgpKSmy4TIcXFnGM19wHuEff/wR33zzjWRYiDHcjybGGD766CNER0cLflR37twZy5Ytw44dOwSiOzk5GQBE61qsWDF+Zov58+cjMzMT//vf/yzWZd26dfDx8UFsbKzZvrS0NCQnJ+PVV181e7oixx9//IG///7bLDxJCa1btxb9Tl9//XWUKVMGM2bMELQ/QRD2gcQwIUmdOnVQo0YNzJs3D4wxsxAJABgwYAACAgLQqFEjREVFIS0tDTNmzEBISAheeOEFq8uOiYkRCOLk5GQ0btwYAwcORN++fXHs2DG89NJLCAwMxM2bN3Hw4EFUr14dQ4YMseWUBSxduhRt2rRBq1at0KdPHxQvXhz37t3DuXPn8Msvv+Crr77SrCw9UKhQIUkRqxVhYWGYOHEiJk2ahLCwMH6BialTp+LNN99E1apVedu1a9eiX79+WLlyJXr16gUAeO6559C/f39MmTIFXl5eeOGFF7Br1y4sW7YM//d//ycIfRg1ahTWrVuHdu3aYdq0afDz88PMmTORlZWFqVOnCup19uxZnD17FkC+KHr8+DG+/vprAEDVqlX5evXo0QOlSpVCnTp1EB4ejosXL+Kjjz7CrVu3JGcYWbFiBQICAtCjRw+b2qRw4cJo0qSJ2fGFCxdGbm6u2b45c+bwT1jGjBkDg8GAjz76CHfv3hWs7DZlyhTcunULL730EooXL44HDx5g586d+Oyzz9ClSxeBcHzttdewY8cOTJgwAUWKFBF4dIODgwXf34QJE1C9enVERUXh6tWrWLlyJQ4fPozvvvsOAQEBvF3Lli3Rvn17TJs2Dc+ePUP9+vVx7NgxvPfee4iPj8eLL77I23722WcAgHLlyuHBgwfYsWMHVqxYgenTp+P555/n7WbPno2zZ8+iWbNmKFGiBG7fvo0VK1Zg165dmDp1quh7DWvWrEFubq5kiMSvv/6KESNG4LXXXkPZsmVRoEABnD59Gh9//DGKFCmCUaNGCexTUlJw584dAEBeXh6uXLnCX1NxcXH8rCimM6MAgL+/P4oUKWL2nfbp0wdr1qyx+GSGIAiVOO/dPcIV+OSTTxgAVrVqVdH9a9asYU2bNmURERHM19eXRUdHs65du7Jff/1VVTlSb09fvXqVlStXjgUGBrKUlBTGGGMrV65k9erVY4GBgSwgIICVK1eO9erVix07dow/TmohAm42idmzZ5vtA8CmTJkiSDt16hTr2rUrK1asGPPx8WGRkZHs5ZdfZkuWLOFtbJlNwnTxhlWrVjEA7OjRo4L0KVOmMADszp07fJrUbBKmeXLnvGrVKsX1szeffPIJq1ixIvP19WWlSpViU6ZMEcxcwth/bWFa7+zsbDZlyhRWqlQp5uvryypWrMjmz58vWs4ff/zBOnbsyIKDg1nBggVZs2bN2PHjx83suPYV24yviRkzZrBatWqxkJAQ5uXlxYoWLco6derEjhw5Ilr+1atXWYECBVivXr00aRMxpK51xhg7cOAAi4uLYwULFmQFCxZkL7/8Mvvpp58ENtu2bWPNmzdnERERzNvbmwUFBbG6deuy+fPnm82kItVGAFhcXJzAdsiQIfx3FB4ezjp37izZLzx+/JiNGTOGlSxZknl7e7NSpUqxcePGCRb8YIyxpUuXsipVqrCCBQuyoKAg1rhxY7Zlyxaz/LZt28ZefPFFVrRoUebt7c0KFSrEGjduLJgtxpSKFSuy0qVLSy7yk5aWxt544w1Wrlw5VrBgQebr68vKli3LBg8ezK5evWpmz81oI7ZZ6iuk+sPOnTuzgIAAdv/+fdnjCYJQh4Gxf4OoCIIgCILQLZGRkUhISMDs2bOdXRWCcCtIDBMEQRCEzjlz5gwaNGiAv/76y+rpKwmCEIfEMGFXLK1WVqBAAYcshesoGGMWV3oznnOXIAiCIAjn4j4qhNAlpvPNmm79+vVzdhU1JSUlxeI5c3PuEgRBEAThfMgzTNgVqcntOcLDw93qreiHDx/i/PnzsjZlypRRNXUTQRAEQRD2g8QwQRAEQRAE4bFQmISH06lTJwQEBODBgweSNj179oSPjw+/RLEWlC5dGvHx8Wbpy5cvh5eXFzp06KBo6WFnsG/fPhgMBuzbt8/ZVdE9jx49QmJiIqKjo+Hv749atWph48aNio+/ffs2+vTpg/DwcBQsWBANGjTADz/8IGq7Z88eNGjQAAULFkR4eDj69OmD27dvm9lNnDgR8fHxKF68OAwGA/r06SNZ/ubNm9GoUSOEhYWhcOHCqFu3LtatW2dm9+abb6JatWooXLgwAgICULFiRbz77ruSS3YfPHgQbdu2RWhoKAICAlChQgXB3L9Afvz5/PnzUblyZfj5+SEqKgpDhgzB/fv3zfKbN28eXn31VZQpUwYGg0F0PmIA+Oabb9C9e3eUL18eAQEBKF26NHr27ImLFy+a2W7fvh29evVC9erV4ePjIxnnfvz4cbz11luoXr06ChUqhIiICDRv3hw//vijqL0xb7zxBgwGg2hfYMytW7dQpEgRGAwGfq5ejocPH2L06NFo2bIlihYtCoPBYDaPNIfBYJDcKleuzNtxC55IbcZLLytt04yMDHzwwQdo0qQJIiMjERQUhOrVq2PWrFlmfZ0tbUoQhHpIDHs4/fv3R1ZWFtavXy+6Pz09HUlJSYiPj0dERIRd6zJ79mwMGDAAPXv2xDfffCO6mhfhWrz66qtYs2YNpkyZgh07duCFF15A9+7dJa83Y54+fYpmzZrhhx9+wCeffIKtW7ciIiICrVu3RkpKisA2JSUFbdq0QUREBLZu3YpPPvkEe/bsQbNmzfD06VOB7ccff4x//vkHHTp0gK+vr2T5K1euxGuvvYaoqCh88cUX2LhxI8qVK4devXrh448/FthmZmZi4MCBWL9+Pb777ju8+eabWLZsGeLi4pCdnS2wXb9+PeLi4hASEoK1a9ciOTkZY8aMgelDulGjRmHEiBF45ZVXsH37dowdOxbr169HixYtkJOTI7BdsmQJrly5gpdffhlFixaVPKdZs2bh8ePHmDBhAnbu3In/+7//w4kTJ/D888/jzJkzAtukpCQcOnQIVatWRc2aNSXz3LBhA44cOYJ+/fph69atWL58Ofz8/NCsWTOsXbtW8rjvvvsOW7Zs4VfHk+Ott96S7A/++ecfLFu2jF8mWo7U1FSzbd68eQAgWBK5Xbt2orYtWrQws1XaplevXsW8efPw/PPPY9myZdi2bRtee+01TJ06FfHx8YLv39o2JQjCSpwxuTGhH3Jzc1l0dDSLjY0V3b948WIGgH377bealms6qfy4ceMYADZs2DDJSe/VkpmZqUk+plizyIYn8t133zEAbP369YL0Fi1asOjoaJabmyt7/MKFCxkA9vPPP/NpOTk5rGrVqqxu3boC2xdeeIFVrVpVsEjETz/9xACwRYsWCWzz8vL4/wMDAwULlxjTqFEjFhMTI7B/9uwZq1y5MqtRo4Zs3RljbNGiRQwA++GHH/i0v//+mwUGBrIhQ4bIHvv3338zLy8vNmzYMEH6+vXrGQC2bNkyyXN67rnnzBbA4Lh165ZZ2vXr15mPjw/r37+/ZJ5vvfUWkxouxPLMzc1lNWrUYOXKlRM95sGDB6x48eJs7ty5kgtMcHz99dcsKCiIrVmzRnRRmWfPnvF9xp07d0QXz5GjT58+zGAwsIsXL8raPXr0iAUFBbEXX3xRkK60TR89esQePXpkZjt79mwGgB04cEA2T0ttShCE9ZBn2MPx8vJC7969cfz4cZw+fdps/6pVqxAVFYU2bdrwaYsXL0bNmjURFBSEQoUKoXLlyhg/frxV5T979gxDhgzBjBkzMHnyZMyfP1/wOJYxhkWLFqFWrVoICAhAaGgoXnvtNfz111+CfJo0aYJq1aph//79aNiwIQoWLIh+/frh8uXLMBgMmDNnDubOnYsyZcogKCgIDRo0ECwly3Hs2DF06NABYWFh8Pf3R+3atfHll19adW7GcKEV69evx5gxYxAVFYWgoCC0b98et27dwsOHDzFw4ECEh4cjPDwcffv2xaNHjwR5LFy4EC+99BKKFSuGwMBAVK9eHR9++KHAS3jx4kUEBwejS5cugmN//PFHeHl5YdKkSTafi1KSkpIQFBRkVpe+ffvixo0bOHz4sMXjK1WqhAYNGvBp3t7eeOONN3DkyBFcv34dAHD9+nUcPXoUCQkJ8Pb+b4X5hg0bomLFikhKShLkq3QqPx8fHwQFBQnsDQYDgoODFT214Dy0xnVavnw5MjMzMWbMGNljDx06hLy8PLRt21aQzoUTbN68WZCu9JyKFStmlhYdHY0SJUrg2rVrmuXp5eWF2NhYszw53nnnHURFRWH48OGyed+7dw9vvfUWPvjgA5QqVUrUhgtdsIaHDx/iq6++QlxcHMqXLy9ru2nTJjx69MhsuWalbRoYGIjAwEAz27p16wKAwNaaNiUIwnpIDBPo168fDAYDVq5cKUg/e/Ysjhw5gt69e8PLywsAsHHjRgwdOhRxcXFISkrCli1bMGLECGRmZqouNycnBz179sTSpUvxySef4L333jOzGTRoEBITE9G8eXNs2bIFixYtwpkzZ9CwYUOzGOabN2/ijTfeQI8ePZCcnIyhQ4fy+xYuXIjdu3dj3rx5+OKLL5CZmYm2bdsiPT2dt9m7dy8aNWqEBw8eYMmSJdi6dStq1aqFbt26YfXq1arPT4zx48fj9u3bWL16NT766CPs27cP3bt3R+fOnRESEoINGzZg9OjRWLdundkPjD///BM9evTAunXrsH37dvTv3x+zZ8/GoEGDeJsKFSrgs88+w9dff4358+cDANLS0tCjRw80btxYMpaSgzGG3NxcRZslfvvtN1SpUkUgBgGgRo0a/H5Lx3O2Ysdzj6C5fKRsLZUjxbBhw3Du3Dl88MEHuHPnDu7evYs5c+bg+PHjGDVqlOgxubm5yMzMxE8//YRJkybhxRdfRKNGjfj9+/fvR1hYGH7//XfUqlUL3t7eKFasGAYPHoyMjAzejgut8PPzE+TPxe7++uuvVp2TGH/99ReuXLmC5557TrM8c3NzceDAAdE89+zZg7Vr1/LvB8gxfPhwlClTBv/73/80q5sxGzduRGZmppnAFWPFihWiPzTFUNOmXBywJVupNuV+aFu6twmCkMHZrmlCH8TFxbHw8HCWnZ3Np73zzjsMALtw4QKf9r///Y8VLlzY5vJiYmIYAAaAjR8/XtQmNTWVAWAfffSRIP3atWssICCAjR49WlB/mDySZoyxS5cuMQCsevXqgsfyR44cYQDYhg0b+LTKlSuz2rVrCx61M8ZYfHw8i4qK4h8bWxMmwR3Tvn17QXpiYiIDwIYPHy5I79ixIwsLC5PMLy8vj+Xk5LC1a9cyLy8vdu/ePcH+IUOGMF9fX5aamspefvllVqxYMXbjxg2L9Vy1ahX/vVjaLFGhQgXWqlUrs/QbN24wAGz69Omyx/v4+LBBgwaZpf/888+C8IsvvviCAWCpqalmtgMHDmS+vr6SZciFSTDG2JYtW1hISAh/zgEBAezzzz8XteWuV25r27Yty8jIENhUqlSJ+fv7s0KFCrHp06ezvXv3sg8//JAFBASwRo0a8Y/7T548yQCw999/X3D8Dz/8wADInpNcmIQpOTk5rEmTJiw4OJhdvXpV0k4uTEKMCRMmMABsy5YtgvSHDx+y0qVLs3HjxvFpUmES27dvZz4+Puz06dOMsf/uIdMwCWPUhknUq1ePFS5cmD158kTW7ty5cwyA6PVoitI2ZYyxU6dOsYCAANapUyeL+Uq16b59+5iXlxd77733LOZBEIQ4QpcN4bH0798fvXr1wrZt29C5c2fk5ubi888/R+PGjVGhQgXerm7duliwYAG6d++O119/HY0aNbJ6adBatWrh3r17WLBgAdq3b4/69esL9m/fvh0GgwFvvPGGwBMZGRmJmjVrms3mEBoaipdfflm0rHbt2gm8UJwX8cqVKwCAP/74A7///jvmzJkDQLhyXtu2bbF9+3acP38eVapUsepcOUzfmufya9eunVn6li1b8OjRIwQFBQEATpw4gSlTpuCnn37CvXv3BPYXLlxAvXr1+M8ff/wxDh06hKZNmyI7Oxs7d+5EVFSUxfq1b98eR48etercxJB7fK3k0baa46VsrX2EvnPnTrzxxhvo0qULunbtCm9vb2zbtg19+vRBdnY2+vbtK7CvXr06jh49isePH+PkyZOYOXMmWrRogR9//BEFCxYEkB8WlJWVhSlTpmDs2LEA8kN8fH19kZiYiB9++AHNmzdHzZo18dJLL2H27NmoVKkSWrRogbNnz2Lw4MHw8vLSZNVGxhj69++PAwcOYPPmzShZsqTNeQL5oSAffPAB3nnnHbzyyiuCfWPHjoWPjw8mT54sm0d6ejoGDRqEMWPGoFq1aprUy5QzZ87g8OHDsi/ncaxYsQIALHqQ1bTp5cuXER8fj5IlS2L58uWy+cq1aVxcnKInNQRByOBsNU7og8ePH7OQkBDeQ7N161YGgK1evdrMduXKlaxBgwbMy8uLGQwGVrduXbZr1y5V5XHeoL/++ovFxMSw4OBgwYtSjDH25ptvynomy5Yty9vGxcWxqlWrmpXDeYZnz55ttg9GHqSDBw9a9ITu37+fMWabZ9jUq8V5Yo8ePSpInzJlCgPA7ty5wxhj7MqVKywwMJA9//zzbN26dezAgQPs6NGj/EtmYnXhXsx5/vnnFdfz2bNnLCcnR9Fmifr167MXXnjBLP23335jANjSpUtlj4+MjGRdunQxS9++fTsDwL7//nvGGGM7d+5kANh3331nZvvaa6+xqKgoyTKkPMPPnj1jUVFRrG3btmb7evXqxQIDA0VfhjLm0KFDDACbO3cun1a/fn0GgP3yyy8C2/PnzzMAbNasWXzarVu3WJs2bfjrz9fXl40ZM4bFxsbKvkSlxDP87Nkz1q9fP1agQAG2bt06WVvGlHuGV65cyQoUKMAGDhxo9iLs4cOHmcFgYElJSez+/fv8VrJkSdaqVSt2//59lpWVxZdXunRplpaWxtt9++23DABbs2YNu3//vuiLtmo8wyNGjGAA2IkTJ2TtsrOzWbFixVjNmjVl7dS06eXLl1np0qVZmTJl2LVr12Rt5dqUIAhtoJhhAgAQEBCA7t27Y+fOnbh58yZWrlyJQoUKicbH9e3bFz///DPS09Px3XffgTGG+Ph43suqhjJlymDfvn0ICwtDq1at8PPPP/P7wsPDYTAYcPDgQRw9etRs27JliyAvaz2AXFkAMG7cONGyjh49ilq1almdv61s2bIFmZmZ+Oabb/DGG2/gxRdfRJ06dSSnBvvtt98wefJkvPDCC/jll18wd+5cReWsWbPG4nLS3GaJ6tWr49y5c2ZeK+5FTUsev+rVq4u+1Gl6PPdXytYaz+KtW7dw8+ZN/uUmY1544QVkZmbi8uXLsnnUqVMHBQoUwIULF/g0sbhmAPy0WsYe32LFiiE5ORm3bt3CqVOncPv2bUybNg0XLlzASy+9pPqcjMt68803sWrVKixfvhxvvPGG1XkZs2rVKrz55pvo3bs3lixZYnY/nj17FowxdOrUCaGhofx27do1fP/99wgNDcXixYsB5F+/ly9fRmRkJG/Xvn17AEDv3r0RGhoqiPdXS3Z2NtatW4fY2FiL9/X27dtx+/ZtWa+wmja9cuUKmjRpAsYY9u7dixIlSkjaWmpTgiC0gcIkCJ7+/ftjyZIlmD17NpKTk9GnTx/+8a4YgYGBaNOmDbKzs9GxY0ecOXMGMTExqsstXbo09u3bh6ZNm6J169bYsWMHGjVqhPj4eMycORPXr19H165dbTk1i1SqVAkVKlTAqVOnMH36dLuWZQ3cIGj8QhVjDJ999pmZbWZmJrp06YLSpUtj7969GDt2LMaOHYtGjRoJQinE0DJMolOnTvjss8+wefNmdOvWjU9fs2YNoqOjLdalU6dOGDp0KA4fPszbcuE79erVQ3R0NACgePHiqFu3Lj7//HOMGjWKD4c5dOgQzp8/j8TERNV1Dw0Nhb+/v+iMI6mpqShQoIDFsJOUlBQ8e/ZMMEtB586dsWzZMuzYsQO1a9fm05OTkwHALFQIyBfF3OwC8+fPR2ZmptUvlDHGMGDAAKxatQpLly41C/WwltWrV+PNN9/EG2+8geXLl4uKttatW2Pv3r1m6a+//jrKlCmDGTNm8G01b948s4WATp48iREjRmDq1KmIi4vjw4esYdu2bbh79y6mTZtm0XbFihXw9/dHz549RferadOrV6+iSZMmyMvLw759+2T7SyVtShCENpAYJnjq1KmDGjVqYN68eXzsmykDBgxAQEAAGjVqhKioKKSlpWHGjBkICQnBCy+8YHXZMTExAkGcnJyMxo0bY+DAgejbty+OHTuGl156CYGBgbh58yYOHjyI6tWrY8iQIbacsoClS5eiTZs2aNWqFfr06YPixYvj3r17OHfuHH755Rd89dVXmpWllhYtWsDX1xfdu3fH6NGjkZWVhcWLF4uuRjZ48GBcvXoVR44cQWBgID766COkpqbi9ddfx4kTJ1C4cGHJcooUKYIiRYpoUuc2bdqgRYsWGDJkCDIyMlC+fHls2LABO3fuxOeffy6I4e7fvz/WrFmDP//8kxcI/fr1w8KFC9GlSxfMnDkTxYoVw6JFi3D+/Hns2bNHUNasWbPQokULdOnSBUOHDsXt27cxduxYVKtWzUycpKSk4M6dOwCAvLw8XLlyhV/VLC4uDkWLFoWfnx+GDh2KuXPnolevXujWrRu8vLywZcsWrF+/Hv3790dYWBiAfM/hZ599hg4dOiAmJgY5OTk4duwY5s2bh/Llyws8ii1btkT79u0xbdo0PHv2DPXr18exY8fw3nvvIT4+Hi+++CJvy/3QKVeuHB48eIAdO3ZgxYoVmD59Op5//nnBOR07doz3VGdkZIAxxp/TCy+8wLfp8OHDsWLFCvTr1w/Vq1cXiH0/Pz+BQL9y5Qr/w+jPP/8EAD7P0qVLo06dOgCAr776Cv3790etWrUwaNAgHDlyRFC32rVrw8/PD5GRkYiMjIQp/v7+KFKkiGDVPDlv7XPPPWe2wt6OHTuQmZmJhw8fAsj3QnN1bdu2rdmP+hUrViAgIAA9evSQLAcAbty4gZ07d6Jbt24IDQ0VtVHaprdv30bTpk1x8+ZNrFixArdv3xaskFiiRAneS6y0TQHw/eaUKVNoRgmCsBYnhWcQOuWTTz5hAETjbxljbM2aNaxp06YsIiKC+fr6sujoaNa1a1f266+/qipH6g3yq1evsnLlyrHAwECWkpLCGMuPmatXrx4LDAxkAQEBrFy5cqxXr17s2LFj/HFxcXHsueeeM8tPacwwx6lTp1jXrl1ZsWLFmI+PD4uMjGQvv/wyW7JkCW/jjJhhxhj79ttvWc2aNZm/vz8rXrw4e/fdd9mOHTsEdfnss88YALZq1SpBfn/88QcLDg5mHTt2VFxnLXj48CEbPnw4i4yMZL6+vqxGjRqCGTw4evfuzQCwS5cuCdLT0tJYr169WFhYGPP392f169dnu3fvFi1r165drH79+szf35+FhYWxXr16iS5ewM08IrYZf6d5eXnss88+Y3Xq1GGFCxdmwcHBrHbt2mzBggWCWVfOnTvHXnvtNRYTE8P8/f2Zv78/q1y5Mnv33XfZP//8Y1b+48eP2ZgxY1jJkiWZt7c3K1WqFBs3bhwfL8uxdOlSVqVKFVawYEEWFBTEGjdubDaTgGn7iW3G14LxLC6mW0xMjCBPuZlFjOOs5coW+05NsbToBofcbBJy52Va/tWrV1mBAgVYr169LJb5wQcfMADsxx9/lK2/kjbl6i+1GfdFatqUi6U27qMIglCHgTGTNUAJgiAIgnAJRo8ejQ0bNuDixYu0hD1BWAm9QEcQBEEQLsrevXsxadIkEsIEYQPkGSY0xdJ8lwUKFNBkjlS9wBhDXl6erI2Xlxe9/EIQBEEQOsV9VAmhCyxNx9WvXz9nV1FTUlJSLJ7zmjVrnF1NgiAIgiAkIM8woSnHjh2T3R8eHo7SpUs7pjIO4OHDhzh//rysTZkyZTSboYEgCIIgCG0hMUwQBEEQBEF4LBQmQRAEQRAEQXgsJIY9hE6dOiEgIMBsVSdjevbsCR8fH9y6dUuzckuXLo34+Hiz9OXLl8PLywsdOnRAVlaWZuVpyb59+2AwGLBv3z5nV8Vt2LNnD1q0aIHo6Gj4+fmhWLFiePnll/kV2JTAGMOqVatQt25dBAYGIjg4GM8//zy2bt3K29y8eRMTJ05EgwYNEB4ejuDgYMTGxmLZsmVmLzyePHkS7dq1Q6lSpRAQEICwsDA0aNAAn3/+ucAuLy8Pc+fORevWrVGiRAkULFgQVapUwdixY83uq8zMTLz++uuoVKkSChUqhMDAQDz33HP4v//7P2RmZgps//77byQmJiIuLg6FCxeGwWDA6tWrzc778uXLMBgMklvr1q0F9hcuXEDnzp0RGhqKggULol69eti2bZtZvmfOnMHQoUPRoEEDBAYGyl7zb775JqpVq4bChQsjICAAFStWxLvvvou7d+8K7Pr06SNbV9OV/X755Rc0b94cQUFBKFy4MF599VX89ddfonX49NNPUblyZfj5+aFMmTJ47733kJOTI2q7detWxMXFITg4mP8Oli1bJrB5+vQpZs+ejWrVqiEwMBARERFo06aNYGl4LdmzZw8aNGiAggULIjw8HH369BEsvgHIf9cbN260S70IwpMhMewh9O/fH1lZWVi/fr3o/vT0dCQlJSE+Ph4RERF2rcvs2bMxYMAA9OzZE9988w1NCeRB/PPPP3juuefw8ccfY9euXVi6dCl8fHzQrl07M/EpxZAhQzBkyBA0a9YM27Ztw1dffYUePXrg8ePHvM3x48exdu1aNGvWDGvXrsXmzZsRFxeHIUOGYMCAAYL8Hjx4gJIlS2L69OlITk7G2rVrUbp0aSQkJOD//u//eLsnT55g6tSpiImJwbx585CcnIwBAwZg2bJlaNSoEZ48ecLb5uTkgDGGkSNHYvPmzdi6dSs6d+6MadOm4ZVXXhGU/8cff+CLL76Ar68v2rZtK3neUVFRSE1NNdvGjBkDIP8HL8fly5fRoEEDnD9/HkuWLMFXX32FokWLomPHjti8ebMg32PHjmHLli0ICwtDs2bNZNs+MzMTAwcOxPr16/Hdd9/hzTffxLJlyxAXF4fs7GzebtKkSaJ1DQ8PR/HixQWrVf7+++9o0qQJsrOz8eWXX2LlypW4cOECGjduzK8UyPHBBx/g7bffxquvvorvv/8eQ4cOxfTp0/HWW2+Z1XXmzJl49dVXUa1aNXz55ZfYtm0bhg4dKqgnkL+q5tixY9GxY0d8++23WLhwIe7cuYO4uDizld9sJSUlBW3atEFERAS2bt2KTz75BHv27EGzZs3w9OlTM/thw4aZtWGLFi00rRNBEKAV6DyF3NxcFh0dzWJjY0X3L168mAFg3377rablmq4uNW7cOAaADRs2jD179kyTMjIzMzXJxxRrVpoj1JOdnc2KFy/OGjdubNE2KSmJAWCbNm2Stbt3755glTiOt956iwFgV69etVhWvXr1WMmSJfnPubm57O7du2Z2X331FQPA1q1bZzHP0aNHMwDszz//5NPy8vL4/48ePSq6gqAcTZo0YQULFmTp6el82qBBg5i/vz/7+++/BfWvUqUKK1mypKBM4/+5c1FzzS9atIgBYD/88IOs3b59+xgANnHiREF6ly5dWHh4uKD+ly9fZj4+Pmz06NF82t27d5m/vz8bOHCg4PgPPviAGQwGdubMGT7t2LFjrECBAmzWrFmydcrKymJeXl7sjTfeEKTfuHGDAWDDhw+XPV4tL7zwAqtatSrLycnh03766ScGgC1atIhPk1s5kyAI7SHPsIfg5eWF3r174/jx4zh9+rTZ/lWrViEqKgpt2rTh0xYvXoyaNWsiKCgIhQoVQuXKlTF+/Hiryn/27BmGDBmCGTNmYPLkyZg/f75g7l3GGBYtWoRatWohICAAoaGheO2118welTZp0gTVqlXD/v370bBhQxQsWBD9+vXjHyvOmTMHc+fORZkyZRAUFIQGDRqYPZIF8r1hHTp0QFhYGPz9/VG7dm18+eWXVp2bMVxoxfr16zFmzBhERUUhKCgI7du3x61bt/Dw4UMMHDgQ4eHhCA8PR9++ffHo0SNBHkrbYvfu3XjllVdQokQJ+Pv7o3z58hg0aJDZI+upU6fCYDDgzJkz6N69O0JCQhAREYF+/fohPT3d5nO2FR8fHxQuXBje3t4WbT/55BOULl0aXbt2lbULDQ2Fj4+PWXrdunUB5IcmWCI8PFxQJy8vL9FZQbg8r127ZjHPokWLAoAgX1vm3f7zzz+RkpKCrl27Ijg4mE//6aefULNmTRQvXlxQ/zZt2uDatWsCj6et836LnZMYK1asgMFgEEyvmJubi+3bt6Nz586C+sfExKBp06ZISkri03bu3ImsrCz07dtXkG/fvn3BGMOWLVv4tAULFsDPzw/Dhg2TrRM373lISIggPTg4GAUKFDB7apWWloZBgwahRIkS8PX15cM0LM2vDgDXr1/H0aNHkZCQIGirhg0bomLFioJzJQjCsZAY9iD69esHg8GAlStXCtLPnj2LI0eOoHfv3vDy8gIAbNy4EUOHDkVcXBySkpKwZcsWjBgxwizeUQk5OTno2bMnli5dik8++QTvvfeemc2gQYOQmJiI5s2bY8uWLVi0aBHOnDmDhg0bmsUw37x5E2+88QZ69OiB5ORkDB06lN+3cOFC7N69G/PmzcMXX3yBzMxMtG3bViD69u7di0aNGuHBgwdYsmQJtm7dilq1aqFbt26isZrWMH78eNy+fRurV6/GRx99hH379qF79+7o3LkzQkJCsGHDBowePRrr1q0z+4GhtC3+/PNPNGjQAIsXL8auXbswefJkHD58GC+++KJoDGXnzp1RsWJFbN68GWPHjsX69esxYsQIi+fy7Nkz5ObmWtwsLT4ilueNGzcwZcoUXLhwAe+8847sMbm5uUhNTUXt2rUxd+5cxMTEwMvLC2XLlsWcOXPAFEyM8+OPP8Lb2xsVK1aUrNOdO3ewaNEifP/993wIgqU8AeC5554z28cYQ25uLjIyMrBz50589NFH6N69O0qVKmUxXyWsXLkSjDG8+eabgvTs7Gz4+fmZ2XNpv/76q03l5ubmIjMzEz/99BMmTZqEF198EY0aNZK0T09Px9dff41mzZqhTJkyfPqff/6JJ0+eoEaNGmbH1KhRA3/88Qf/TsFvv/0GAKhevbrALioqCuHh4fx+ANi/fz+qVKmCzZs3o1KlSvDy8kKJEiUwduxYQZiEj48Phg4dijVr1mDLli3IyMjA5cuXMWDAAISEhAhCatLS0lC3bl18//33mDx5Mnbs2IH+/ftjxowZZqE3YnD1kzpX4/pzzJw5E76+vihYsCBefPFF0Zhv7gf41KlTLdaBIAgJnOmWJhxPXFwcCw8PFzxCfueddxgAduHCBT7tf//7HytcuLDN5cXExDAADAAbP368qE1qaioDwD766CNB+rVr11hAQIDgUWlcXJzoI1nusWL16tVZbm4un37kyBEGgG3YsIFPq1y5Mqtdu7bgUSVjjMXHx7OoqCj+sbE1YRLcMe3btxekJyYmij527dixIwsLC7OqLYx59uwZy8nJYVeuXGEA2NatW/l9U6ZMYQDYhx9+KDhm6NChzN/f32K4Cne8pS0mJkY2H2NatWrFHxccHMy++eYbi8fcvHmTty9RogRbs2YN++GHH9jgwYNlry+O77//nhUoUICNGDFCdP+gQYP4Ovn6+goeW0vx999/s4iICFanTh1BuAHHhg0bBG3Ut29fs+vOGDVhErm5uax48eKscuXKZvs6duzIChcuzB4+fChIb9y4MQPApk+fLpqnkjAJ7hrltrZt27KMjAzZunJhWMb3IWP/hQiYpjPG2PTp0xkAduPGDcYYYwMGDGB+fn6i+VesWJG1bNmS/+zn58cKFSrEQkND2YIFC9iPP/7IJkyYwLy8vFiPHj0Exz579oxNnjyZFShQgD+nUqVKsRMnTgjsBg0axIKCgtiVK1cE6XPmzGEABGEaYnzxxRcMAEtNTTXbN3DgQObr68t/vnHjBhswYAD78ssv2YEDB9gXX3zB6tevzwCwzz77THDsvn37mJeXF3vvvfdkyycIQhoSwx7G2rVrGQD29ddfM8YYy8nJYREREWbxmpzd66+/zrZs2cLu3LljVXkxMTGsVq1arFSpUiw4OFh0IJgwYQIzGAzs1q1bLCcnR7DVr1+f1a1bl7eNi4tjoaGhZnlwYnjs2LGC9KysLAaAzZw5kzHG2MWLFxkANmfOHLOyuNjHs2fPMsZsE8NLly4VpC9dupQBYN9//70gnYuh5kSLmra4desWGzRoECtRooRgIDc+X8b+E7O///67oOwlS5YwACwtLU32nK5fv86OHj1qcfv1118Vt9OFCxfYkSNH2NatW1mXLl2Yj48PW79+vcV6cOdneh117NiR+fv7m4k/juPHj7OQkBDWsGFDlpWVJWpz5coVdvToUfbdd9+xwYMHswIFCsjGbP7zzz+sRo0arFixYoIYYGPu3bvHjh49yn788Uf2wQcfsODgYNahQwdR4cyYOjG8fft2ybjSPXv2MIPBwDp16sT+/PNPlpaWxiZOnMi8vLzMrg9jlIjhR48esaNHj7KUlBT2ySefsKioKFavXj3Z2P06deqwIkWKmLU9J4Y3btxodgwnhm/evMkYyxfD/v7+ovlXrFiRtWrViv/s4+MjKrK5H6UXL17k095//31WsGBBNm3aNLZ37162detW1qJFCxYeHs5++eUX3q548eKsffv2ZvflmTNnBDG/ubm5gv3cd82J4UOHDpnVf+DAgZJCnyM7O5vVrl2bFSlSRPYHFUEQ6iEx7GE8fvyYhYSE8C+1bd26lQFgq1evNrNduXIla9CgAfPy8mIGg4HVrVuX7dq1S1V53At0f/31F4uJiWHBwcHs559/Fti8+eabsh7HsmXL8rZxcXGsatWqZuXIvXACgE2ZMoUxxtjBgwctejj379/PGLNNDH/11VeC9FWrVjEA7OjRo4J0TqhyPzaUtkVeXh6rWbMmK1q0KJs/fz7bu3cvO3LkCDt06JDgfMXKMK3TpUuXZM8pLy/PTACIbcYeebW0bt2ahYaGSopExvKvXYPBwIKDg832cT82Dh8+bLbvl19+YWFhYaxOnTrswYMHius0ePBg5u3tzW7fvm227969e+z5559nRYoUYadOnVKc58aNGxkASU+4GjHcqVMn5uPjw27duiW6f/Xq1axIkSL8tVO1alVeYEq97GfNC3TcNTd37lzR/adOnWIA2Ntvv2227/fff2cA2MKFC832jRo1ihkMBvbkyRPGGGNjx45lAERFd3h4OOvevTv/OTIykgFg9+7dE9h9//33DPjvBcyzZ88yg8Fg1m9kZ2ez8uXLsyZNmvBp3t7esvfmtGnTGGP/Pb3itt69ezPGGNu5cycDwL777juz+r/22mssKipKrPkEzJw5U/CDnSAIbbD8xgrhVgQEBKB79+747LPPcPPmTaxcuRKFChVCly5dzGz79u2Lvn37IjMzE/v378eUKVMQHx+PCxcuICYmRlW5ZcqUwb59+9C0aVO0atUKO3fuRMOGDQHkv6hkMBhw4MAB2ThHDuMX79QSHh4OABg3bhxeffVVUZtKlSpZnb+tKG2L3377DadOncLq1avRu3dvfv8ff/yheZ2mTZsmGudtSkxMDC5fvmxVGXXr1sXOnTtx584dyan9AgICUKFCBaSlpZntY//GC5u+DHbixAk0b94cMTEx2LVrl9mLUpbqtGTJEvz111/8S2IAcP/+fTRv3hyXLl3CDz/8IBoDKpcnkD8HsC3cvn0b27dvR4cOHVCsWDFRm969e6Nnz564ePEifHx8UL58ecyYMQMGgwGNGze2qXxj6tSpgwIFCkie04oVKwDALK4ZAMqVK4eAgADRl3pPnz6N8uXL8y+xcbHCp0+fRr169Xi7tLQ03L17F9WqVePTatSooeg6OXXqFBhjgqnegPxY4po1ayIlJYVPCw8PR40aNfDBBx+Inmd0dDQAYOnSpXj48KHgOAB8/U6fPm02hd7p06cF9ZdC6jonCMI2SAx7IP3798eSJUswe/ZsJCcno0+fPihYsKCkfWBgINq0aYPs7Gx07NgRZ86cUS2GgfwFODhB3Lp1a+zYsQONGjVCfHw8Zs6cievXr1ucJcBWKlWqhAoVKuDUqVOYPn26XcuyBqVtwf0gMBXMS5cu1bxOAwcOFF04xRQx8a4ExhhSUlJQuHBh0dkajOncuTNmzJiBn3/+mf8xBQDJyckICgoSvMR28uRJNG/eHCVKlMDu3bsRGhqqql579+5FgQIFULZsWT6NE8J//fUXdu/ejdq1a6vOEwDKly+v6jhT1q5di5ycHPTv31/WztvbG1WqVAGQ/xLbsmXL8Morr1h1/0qRkpKCZ8+eiZ7T06dP8fnnn6Nu3bqiYs/b2xvt27fHN998gw8//BCFChUCAFy9ehV79+4VvODZunVr+Pv7Y/Xq1QIxvHr1ahgMBnTs2JFP69y5M3bt2oUdO3agR48efHpycjIKFCjAi19OwB46dAhxcXGCev/yyy8oUaIEnxYfH4/k5GSUK1dO9lqS+jFdvHhx1K1bF59//jlGjRrFv6x86NAhnD9/HomJiZJ5AvkvIm/atAnh4eE2Xz8EQQghMeyB1KlTBzVq1MC8efPAGBMdUAcMGICAgAA0atQIUVFRSEtLw4wZMxASEmLmRVFDTEyMQBAnJyejcePGGDhwIPr27Ytjx47hpZdeQmBgIG7evImDBw+ievXqGDJkiC2nLGDp0qVo06YNWrVqhT59+qB48eK4d+8ezp07h19++QVfffWVZmWppVGjRoraonLlyihXrhzGjh0LxhjCwsLw7bffYvfu3ZrXKTo6mhcNtvLKK6+gZs2aqFWrFooUKYIbN25g9erVSElJwcKFCwVTTjVr1gwpKSmCaatGjRqFL774Al26dMH777+PEiVK4Ouvv8a2bdswZ84cBAQEAADOnz+P5s2bA8hfqOHixYu4ePEin0+5cuV4b+/AgQMRHByMunXrIiIiAnfv3sVXX32FTZs24d133+Xtnjx5glatWuHEiROYN28ecnNzBdP2FS1aFOXKlQOQf40dOHAALVu2RMmSJZGZmYkDBw7g008/RcOGDc0W3vj6668BgJ8+79ixYwgKCgIAvPbaa2btuGLFCpQsWRKtWrUSbefbt2/jo48+QqNGjVCoUCH8/vvv+PDDD1GgQAEsXLhQYPv48WN+BUDufFJSUnD37l3+hzAAbN++HZ999hk6dOiAmJgY5OTk4NixY5g3bx7Kly8v6vndsmUL7t27J7qP47333sMLL7yA+Ph4jB07FllZWZg8eTLCw8MFM4yEhYVh4sSJmDRpEsLCwtCyZUscPXoUU6dOxZtvvomqVavytn379sXSpUsxdOhQ3L17F1WrVsWePXuwcOFCDB06lP8x8OKLL+KFF17A1KlT8fjxY7z00ktIT0/Hp59+ikuXLmHdunV8ntOmTcPu3bvRsGFDDB8+HJUqVUJWVhYuX76M5ORkLFmyRCCexZg1axZatGiBLl26YOjQobh9+zbGjh2LatWqCaaMGzlyJHJyctCoUSNERkbi2rVr+PTTT3Hy5EmsWrWKF9IA+P50ypQpNKMEQViLE0M0CCfyySef8HGEYqxZs4Y1bdqURUREMF9fXxYdHc26du2q6iUpxswX3eC4evUqK1euHAsMDGQpKSmMsfwY5Xr16rHAwEAWEBDAypUrx3r16sWOHTvGHxcXF8eee+45s/yUxgxznDp1inXt2pUVK1aM+fj4sMjISPbyyy+zJUuW8DbOiBnmUNIWZ8+eZS1atODfmu/SpQu7evWq5jHDWjJr1iz2wgsvsNDQUObl5cWKFCnCWrVqxbZv325my8VemnL16lX2+uuvs9DQUObr68tq1KjBVq5cKbDhzk1qM47JXblyJWvcuDELDw9n3t7erHDhwiwuLs4srpa7xqQ2LjaUsfwXw+Lj41l0dDTz9fVlBQsWZDVr1mTvv/++aMyrXL6mcC+dTZ48WbKd//nnH9ayZUtWtGhR5uPjw0qVKsWGDRsm+iKs3HkZzxBy7tw59tprr7GYmBjm7+/P/P39WeXKldm7777L/vnnH9F6tGjRggUGBlqcbeLYsWOsWbNmrGDBgiw4OJh17NiR/fHHH6K2n3zyCatYsSLz9fVlpUqVYlOmTBFdYOWff/5hgwYNYhEREczHx4dVrFiRzZ492ywu/cGDB2zChAmsSpUqrGDBgqxYsWKsSZMmLDk52SzPO3fusOHDh7MyZcowHx8fFhYWxmJjY9mECRPYo0ePZM+RY9euXax+/frM39+fhYWFsV69epnFfa9YsYLVrVuXhYWFMW9vbxYaGspatWpl9gIuY4x9++23DICg7yIIQh0GxhRMzkkQBEEQhO4YPXo0NmzYgIsXL9LS9gRhJRSFTxAEQRAuyt69ezFp0iQSwgRhA+QZJqzC0vKj3DKn7gJjzOIKa15eXjbNdEEQBEEQhONxH7VCOBQfHx/ZrV+/fs6uoqakpKRYPOc1a9Y4u5oEQRAEQaiEPMOEVRw7dkx2f3h4OEqXLu2YyjiAhw8f4vz587I2ZcqUsTg1GEEQBEEQ+oLEMEEQBEEQBOGxUJgEQRAEQRAE4bHQoht25NmzZ7hx4wYKFSpEL1YRBEEQHgtjDA8fPkR0dLRbvVxNuAckhu3IjRs3ULJkSWdXgyAIgiB0wbVr1yyu1EcQjobEsB0pVKgQAODaX38h+N//s+Erae+LbIfUSwq5ummFPc/REfVXg7O/T4JwBnq7Dy3hzPvUUW1lzTlaWzepsjIePkTJsmX5cZEg9ASJYTvChUYEFyqE4OBgZMMXUtOi53cgzps0Xa5uWmHPc3RE/dXg7O+TIJwJCWLl6NUJobUY5qCQQUKPUOCOg9CzR9gReIpH2BfZHvF9EoQ7oac+xB448vzcvS0J94TEsAPQuxB25c5LT3XXw3dJEHrAFe8FZ/Ulem0rvdaLIOwBiWEnoofORq+P6JRAQpgg9Isr3hPuLIj11F8ShN4gMexA9DY4kBC2HQqLIAhpXPHe0EvfYg/Unpu13587tyHhntALdA5GL4MDCWHb0ct3SRB6xhfZurlnlcLV15H3uCu2k6PIyspCdrb234Wvry/8/elFZ4LEsEdCQth2SAgThHK4+0Uv969SsuHrdoJY7TlZWyet2i4rKwsBAYUBPLU5L1MiIyNx6dIlEsQEiWFPw1WFsB4GURLABGEbruj9JEHsXPI9wk8BNIe2kiUXaWl7kJ2dTWKYIDHsSdi7g3Vnb7CrDBwEoXdcVRADjusHXLGNxNBWdHsD8NEoL4IQQi/QOQhniykSwtbj7O+OINwNV33x1JH9kb3bSM25uOJ3ZS8WLVqEMmXKwN/fH7GxsThw4ICsfUpKCmJjY+Hv74+yZctiyZIlZjabN29G1apV4efnh6pVqyIpKUmwf//+/Wjfvj2io6NhMBiwZcsWszwePXqE//3vfyhRogQCAgJQpUoVLF68WGDz9OlTDBs2DOHh4QgMDESHDh3w999/q28EN4TEsANw9upGriiEHVFvpWTzw5L6jSAIaVxRZDn63nbFNjJGu7YqYIdNHZs2bUJiYiImTJiAEydOoHHjxmjTpg2uXr0qan/p0iW0bdsWjRs3xokTJzB+/HgMHz4cmzdv5m1SU1PRrVs3JCQk4NSpU0hISEDXrl1x+PBh3iYzMxM1a9bEggULJOs2YsQI7Ny5E59//jnOnTuHESNGYNiwYdi6dStvk5iYiKSkJGzcuBEHDx7Eo0ePEB8fj7y8PNVt4W4YGGPM2ZVwVzIyMhASEoL0O3cQHBzs8PJdUQQD+vAGOwNXH/QIwlpc9Z539D1rj3ZSeg62lO2L7PzxsGhRpKenqxoPuXEUaAttwyRyACSrqk+9evXw/PPPCzyuVapUQceOHTFjxgwz+zFjxmDbtm04d+4cnzZ48GCcOnUKqampAIBu3bohIyMDO3bs4G1at26N0NBQbNiwwSxPg8GApKQkdOzYUZBerVo1dOvWDZMmTeLTYmNj0bZtW7z//vtIT09H0aJFsW7dOnTr1g0AcOPGDZQsWRLJyclo1aqVojZwV8gz7KaQEHY9yLNMeCquHDbhTqETlsq2Fnfoy7Kzs3H8+HG0bNlSkN6yZUv8/PPPosekpqaa2bdq1QrHjh1DTk6OrI1UnlK8+OKL2LZtG65fvw7GGPbu3YsLFy7wIvf48ePIyckRlBUdHY1q1aqpLssdoRfo3BASwu6DM+Y7JQhCHa4844QrzSxhDzIyMgSf/fz84OfnZ2Z39+5d5OXlISIiQpAeERGBtLQ00bzT0tJE7XNzc3H37l1ERUVJ2kjlKcX8+fMxYMAAlChRAt7e3ihQoACWL1+OF198ka+Lr68vQkNDbS7LHSHPsJthT0FpL68EeUEtQ+1DeAKuLMo8yUvsHHztsAElS5ZESEgIv4mFOxhjMBgEnxljZmmW7E3T1eYpxvz583Ho0CFs27YNx48fx0cffYShQ4diz549ssdZU5Y7Qp5hN8LeQtgekMhThmcNeoQn46oLdHB4uqdVCr1+n9euXRPEDIt5hQEgPDwcXl5eZl7U27dvm3l2OSIjI0Xtvb29UaRIEVkbqTzFePLkCcaPH4+kpCS0a9cOAFCjRg2cPHkSc+bMQfPmzREZGYns7Gzcv39f4B2+ffs2GjZsqLgsd4U8w26CvToae3of9No56gnP8/4QhOvjSC8x9Q+2ERwcLNikxLCvry9iY2Oxe/duQfru3bslxWSDBg3M7Hft2oU6derAx8dH1kaNQM3JyUFOTg4KFBBKOi8vLzx79gxA/st0Pj4+grJu3ryJ3377jcQwyDPsFthTCNsLEsLy0ABHEK6Pu3mJ3WUxEGsZOXIkEhISUKdOHTRo0ADLli3D1atXMXjwYADAuHHjcP36daxduxZA/swRCxYswMiRIzFgwACkpqZixYoVglki3n77bbz00kuYNWsWXnnlFWzduhV79uzBwYMHeZtHjx7hjz/+4D9funQJJ0+eRFhYGEqVKoXg4GDExcXh3XffRUBAAGJiYpCSkoK1a9di7ty5AICQkBD0798f77zzDooUKYKwsDCMGjUK1atXR/PmzR3RfLqGxLCL42pC2JM7UiW408BJEIRjXoL1DJHqC22nVlMfJ9utWzf8888/mDZtGm7evIlq1aohOTkZMTExAPI9rcZzDpcpUwbJyckYMWIEFi5ciOjoaMyfPx+dO3fmbRo2bIiNGzdi4sSJmDRpEsqVK4dNmzahXr16vM2xY8fQtGlT/vPIkSMBAL1798bq1asBABs3bsS4cePQs2dP3Lt3DzExMfjggw94oQ4AH3/8Mby9vdG1a1c8efIEzZo1w+rVq+Hl5aW6LdwNmmfYjth7nmESwu4DiWCCEOKu/YUe+1c1dbK2nIyMDBQtGmLDPMNdof08w1+qrg/hnpBn2EVx5uTranHXQc1WSAAThOdB0yUShP4gMeyCkBB2XWgAJAgCEPaN1C8owRvaeobpoTjxHySGXQxXEcIkgv+DBjqCIOQgbzFBOBcSwy4ECWHXgQY1grANLV8KM74f9dw/kSgmCOdAYthF0LoDp7AI7aEBjCC0RStBzE1x5ir9k1pR7CrnRRB6hcSwB0LeYO0gAUwQ9kVLQWyaryUbZ0Nxxcb8t4QyQWgNiWEXQMsOmoSw7dCgRBCujdQ9rOeloPVYJ4JwF0gM6xi9h0Z4UudMApggnIfWIQ6WVoZzlRhjgiC0gcSwh0BCWD0kgAlCP9hDEHP5WirX2J4gCPeDxLBO0XNohDsPCiSACUK/2OMlOEteYuOyOXt3Qm2f57wXEX1A8wwT9oLEsA4hIexYSAAThOvgTEHMlW98HEEQrk8BZ1eAsB8khKXxRTa/EQThWujlRWDqQwjCPSDPsM6wxyTztuLqIpgGK4IglGDtohfkLSYI14bEsI4gIWw9JHgJwrOwZ+yqmrAJU5QIYyV567nvdU7csD8APw3zowfjxH+QGNYJehPCeuqISegSnoKa+85UkNB9oi1aLI1s67GO6IdtEf4E4S6QGCbMcLYQpo6ZcGe0ur+cfZ/qAUcIRi1EsbW46wwWBKE3SAzrAL15hZ2BK9edIMRwtIBxpofPlnAAW/EED6rzpjPTE1pPrfZMw7wIV4fEsJPRkxB2RmdLIphwVUicWMbdwjhIEP+H3upDELZAYtiJeLIQdoeBkXBPaIBXj5LH+fYUko4UZs4Om7DHeVLcMOHpkBh2Ep4qhKnDJdQgd23aci2R4LUPlmZSME3Tsj9wtKfSWaKYPLIEoT0khl0YVxLCJIIJS6i9FkkQmKOphy8r67///f21ydMEd/BIOuMc9CKIHVsP3383raDlmIn/IDHsBLToPEgIE+6CHgZ1j8ZY9Cq1USCOlQolLT2szhKJzvASa32u7vDDhCCshWaddjCeJIRpqVJCDm5BbMKJKBHCUsdxmwxq+gB3uBY88d0LPdSBIGyFxLAD8TQhTLgG2bxk8XWYQHUH4aNXFLettUJYLB8FolgJeukjbcGVf+S5ar0JwlYoTMIB6KlztHc9nD0QyaGnlxb1gLPe/NfLveDRWBMaYYppqARnLxFCoXQBCXd5XO+o0Am9xA/bHx9oGzNM8wwT/0Fi2IXQ+9vzehrA7Hm+zpxaSSs8Y/AkJPH3lxe7asWysQC2IIodgZ4EoiP6Cy3P15ofI3pqb4KwBgqTcBH0LLycHRvsjMf8noC95jMl7I/D21ksVEIifEJJf+EO4RKm2Ltv0vJ8ramn3tqbINRAYtgFsLWTcZUOWA0kfK3HldvM/GcPvaRpFyx5daU8x1KiWARHLdWsN1ylP3blfoIg1EJhEjpHr0LYmSKYcE1s+e70KGpsQc27a3aLNrAUKiEFd4zcdGumoRJZWaInIvd43V1ih8WwZ+iEM0Mm7Bsu4fPvphV5GuZFuDokht0Up4tGK+YllcLp5yKCqw7SemxLSzjikboj0WoSB02QE8Ri4lYsncvDUqywSkGs1T2m53hWe4liZ8cQE4SrQWJYx1jbATn1MRyJYEJD3K29dSWE1aBEGBvvl/IIywhiwL5PsvTYn3Do/aVcvdePIGyFxLBOcUSnIzEuSaJaCFuJHgctdxgErGlXZ3qFlJSrx2tFCt0KYSWdgJgAtjQbhUpBDLjOjAv2Qsv7zR4/MJTUz94/bAjCHpAY1iGOmELNOOxPyVgoWScNR3i9dp7uIIRdDWpznSEVDyz2v7GdFYLYnriKIAb0GyaitH7at7UvaJ5hwl6QGHYT1IpgNVgthBUOdnodnNxJkOm1jcXwpOV7LWGNXtT6ujVuZ19ky4dGKPUWOxFXEMSA9l5irc9ZqZfYFdqaIGhqNZ1hTwGmVghLTlulYPlVJYOeXqdGo+m69I89f/zZC6U60N//v00tWk+tZdrOZlMacpU0/cth2ldI/e9gXOX+1rJ/tEe/pqQPd5W2JjwbEsNugKXOSEy7WhpwrRbBXOYy6FUEA+7Zceu1ra1B7trhLk/jzThdDxjfd1KbNSgWOmKNpLCBTM3478K08mpOxMmC2BXud63vX3ucsxJBbHu53vhvejUtNusejC9atAhlypSBv78/YmNjceDAAVn7lJQUxMbGwt/fH2XLlsWSJUvMbDZv3oyqVavCz88PVatWRVJSkmD//v370b59e0RHR8NgMGDLli2yZQ4aNAgGgwHz5s0T3c8YQ5s2bRTl5SmQGNYR1nQW9hA6kkJYCTKDoN5FsCsMjGrRa3vLIVZnS9eO0tWD9SKKtULRdatU8MrsN3XoGmcn8BIb3/9SCl9nX4Ir3Pda95329BK7Yp+jlE2bNiExMRETJkzAiRMn0LhxY7Rp0wZXr14Vtb906RLatm2Lxo0b48SJExg/fjyGDx+OzZs38zapqano1q0bEhIScOrUKSQkJKBr1644fPgwb5OZmYmaNWtiwYIFFuu4ZcsWHD58GNHR0ZI28+bNg8FgUHHm7o+BMcacXQl3JSMjAyEhIbhzJx3BwcGytvYQwnJjjmqPsBIsCGE94goDoS3obVlbNfVR+la6tdpKB+GrVqHJrC6WpkA0+sy1v9R7c4I6SS3IYYqpaNYBeu2jTLFnTLiWmNYzIyMDIUWLIj3d8nhodlxICIA5AAI0rOETAKNU1adevXp4/vnnsXjxYj6tSpUq6NixI2bMmGFmP2bMGGzbtg3nzp3j0wYPHoxTp04hNTUVANCtWzdkZGRgx44dvE3r1q0RGhqKDRs2mOVpMBiQlJSEjh07mu27fv066tWrh++//x7t2rVDYmIiEhMTBTanTp1CfHw8jh49iqioKMm8PA3yDOsAlxfCMo9F9eopcFdPsDF6bHc1KLl2bHEy6sxBKYuipaelvL9KQiJkQie4MuWcvIKQCc5YKTr5IlylT7BH2IS9QidcoQ/KyMgQbE+fPhW1y87OxvHjx9GyZUtBesuWLfHzzz+LHpOammpm36pVKxw7dgw5OTmyNlJ5SvHs2TMkJCTg3XffxXPPPSdq8/jxY3Tv3h0LFixAZGSkqvzdHRLDbogmQliDZ8p67QhdYcCzFU9oey00lN5DJxQLYLFpzSylK91EipT7bJUg1hGu0D+4itAEtAyf8LXDBpQsWRIhISH8JubhBYC7d+8iLy8PERERgvSIiAikpaWJHpOWliZqn5ubi7t378raSOUpxaxZs+Dt7Y3hw4dL2owYMQINGzbEK6+8oipvT4CmVnMyWnuFNZk6TU0mIgOeXjtpVxjktEDr2EJPwPSxv7OxOhRCyuur9Hip+virvK64WAqp6dZ0Ms2aFK4yJZjep19zBa5duyYIk/Dz85O1N421ZYzJxt+K2Zumq83TlOPHj+OTTz7BL7/8Innctm3b8OOPP+LEiROK8/UkyDPsRBwthMXGHquFsERohF47U08Rda6AFt+Fvby5zvQUWwyFUOMFVpJm6gF+8ED6/wcP4JuVAV9kq5/1QsxYx0KYw5XCJrTqd13hfLUmODhYsEmJ4fDwcHh5eZl5bG/fvm3m2eWIjIwUtff29kaRIkVkbaTyFOPAgQO4ffs2SpUqBW9vb3h7e+PKlSt45513ULp0aQDAjz/+iD///BOFCxfmbQCgc+fOaNKkieKy3BXyDDsJZwthrV+UIxGsD/T6Pbgalt4v0xK7eoHlPov9b+yx5f43OXlf7qO/hWtN7E07F8RVPKZaeYld5Xwdja+vL2JjY7F792506tSJT9+9e7dk2EGDBg3w7bffCtJ27dqFOnXqwMfHh7fZvXs3RowYIbBp2LCh4rolJCSgefPmgrRWrVohISEBffv2BQCMHTsWb775psCmevXq+Pjjj9G+fXvFZbkrJIadgLOFsFWZyGRm7QIIThUYbobDptjTAc7w3BqXqdV1q5kIViuKpUSw2H7TSZALF+bTxUSTlo/t9YTSmU2cjbu2fz5aL8ecp/qIkSNHIiEhAXXq1EGDBg2wbNkyXL16FYMHDwYAjBs3DtevX8fatWsB5M8csWDBAowcORIDBgxAamoqVqxYIZgl4u2338ZLL72EWbNm4ZVXXsHWrVuxZ88eHDx4kLd59OgR/vjjD/7zpUuXcPLkSYSFhaFUqVIoUqQI72nm8PHxQWRkJCpVqgQg3wMt9tJcqVKlUKZMGdVt4W6QGHYwehDCNoVGmKBkcHC0eHG3wUCvA7B7D7zS2BruapUItiZNShBbEsNyPHggEMecl1jyGrW0RLOL4QpeUy3uS1c4T2fQrVs3/PPPP5g2bRpu3ryJatWqITk5GTExMQCAmzdvCuYcLlOmDJKTkzFixAgsXLgQ0dHRmD9/Pjp37szbNGzYEBs3bsTEiRMxadIklCtXDps2bUK9evV4m2PHjqFp06b855EjRwIAevfujdWrV9v5rD0DmmfYjpjOM2xtByW34pYczhTCmnirVeJOwkwPA5FcexrXz9GLxehJW6m9ji3ODKEk3VoRbPy/1F+OR4+AoKD8/009w2JL5sk1hJIvzAXDKPRwj8pha3+o9fllZGSgaNEQG+YZXgLt5xkerLo+hHtCnmGdo0chbOvcrySEpdH7AAuY19FTPcRqUS2E7RETbPy/6d9Hj8zTTW/WBw/+C5XgsHRDW/IOK1i+3Ri9XGt6957ael/q/fwIQktIDDsAa99KtusUWRoLYRsczDahl4HRFvQ44Ei1q1RdHSWI9eQVVoPsDBHWpFkjiuXSHj3SZlo2qRtcShCrFMJcml7ue70LRvcSxN4AfDTML0fDvAhXh8SwTrHbzBE2zCFMQlg79DPAaIeeRIpLoLXoNf2s1DPMeYTFbE1FrKUZIuQCqo3TbQy81tO15iov1xEEIY3T5hmeMWMGDAaDYN1sxhimTp2K6OhoBAQEoEmTJjhz5ozguKdPn2LYsGEIDw9HYGAgOnTogL///ltgc//+fSQkJPAryiQkJODBgwcCm6tXr6J9+/YIDAxEeHg4hg8fjuxsYed6+vRpxMXFISAgAMWLF8e0adPgiBBru0+hpgSNhLBpaKFW6GUgVIsrrRxlilb1dtXztxbF0xg6Uwirmb7NeJ/xpgYFHYKl60Rv15Fe+yRb20mv50UQWuIUMXz06FEsW7YMNWrUEKR/+OGHmDt3LhYsWICjR48iMjISLVq0wMOHD3mbxMREJCUlYePGjTh48CAePXqE+Ph45OX9N01Kjx49cPLkSezcuRM7d+7EyZMnkZCQwO/Py8tDu3btkJmZiYMHD2Ljxo3YvHkz3nnnHd4mIyMDLVq0QHR0NI4ePYpPP/0Uc+bMwdy5c+3YMvLYLIQ1DI2wZvzTAleZCJ/DeClSvQ3ecojFBVtznDuj5AeeLoWwHJZs5G58tTNTyOCq15Gr9U9KccdzIghjHB4m8ejRI/Ts2ROfffYZ/u///o9PZ4xh3rx5mDBhAl599VUAwJo1axAREYH169dj0KBBSE9Px4oVK7Bu3Tp+gunPP/8cJUuWxJ49e9CqVSucO3cOO3fuxKFDh/ipST777DM0aNAA58+fR6VKlbBr1y6cPXsW165dQ3R0NADgo48+Qp8+ffDBBx8gODgYX3zxBbKysrB69Wr4+fmhWrVquHDhAubOnYuRI0eqWipRDdYOAhYHZo2FsCZ1UoErdcauOpBLoeX5uFvbaIK1QletraV0QDwsQmkeGoVBqLlG9BQuYYzS0AmxutvjHnGP6da0nmc4V8O8CFfH4Z7ht956C+3atTNbLeXSpUtIS0tDy5Yt+TQ/Pz/ExcXh559/BpC//nZOTo7AJjo6GtWqVeNtUlNTERISIpijr379+ggJCRHYVKtWjRfCQP5qLU+fPsXx48d5m7i4OMHSjK1atcKNGzdw+fJl0XN7+vQpMjIyBJsa7PbCnNKXYFTOGiGHpwlhV/T+WsLa87FXG+jp5TnNvMJqP2tlK4e9YpvshJ7vOeFzIfNN7hg9otd6EYStOFQMb9y4Eb/88gtmzJhhto9bm9t0Pe6IiAh+X1paGnx9fREaGiprU6xYMbP8ixUrJrAxLSc0NBS+vr6yNtxn03XEOWbMmMHHKYeEhKBkyZKidmJYihNW8wK3IiFsepAFIawmNNCThLC7CWCtkJoFwFOwixDWShiLIXfTqrmh1YZlmGDtNeKO15bWolirNtJ7n0wQ1uAwMXzt2jW8/fbb+Pzzz+Ev07mahh8wxiyGJJjaiNlrYcO9PCdVn3HjxiE9PZ3frl27JltvJWg+c4SVQlgpWghhS54TPUAi2DLu2j6WrnGrrlsthbGlvKUQOzEujVuAw47Yer246/VGgpjD3w4bQeTjMDF8/Phx3L59G7GxsfD29oa3tzdSUlIwf/58eHt7S3pdb9++ze+LjIxEdnY27t+/L2tz69Yts/Lv3LkjsDEt5/79+8jJyZG1uX37NgBz7zWHn58fgoODBZsS7NJBiQ2eCuL4jOui9iU5a4WwkkeHesAdQyHsjVZtpqcQCTkUzydsi5dXS6Fsitjqcqb7pLChXJqpRB499ot6rBNBWIvDxHCzZs1w+vRpnDx5kt/q1KmDnj174uTJkyhbtiwiIyOxe/du/pjs7GykpKSgYcOGAIDY2Fj4+PgIbG7evInffvuNt2nQoAHS09Nx5MgR3ubw4cNIT08X2Pz222+4efMmb7Nr1y74+fkhNjaWt9m/f79gurVdu3YhOjoapUuX1r6BRFDjFbYohBVkYiqE1WDNsrR6F78cJIAJm7Bn+IOtvxKUeHzVeoVtDJWwFXe9V7XqK+26mBNBuCgOm02iUKFCqFatmiAtMDAQRYoU4dMTExMxffp0VKhQARUqVMD06dNRsGBB9OjRAwAQEhKC/v3745133kGRIkUQFhaGUaNGoXr16vwLeVWqVEHr1q0xYMAALF26FAAwcOBAxMfHo1KlSgCAli1bomrVqkhISMDs2bNx7949jBo1CgMGDOC9uT169MB7772HPn36YPz48bh48SKmT5+OyZMnazqThN1mj+CQezPcCULYlTpPdx1UXQ2X9gormapMjb0llAb1G9sFBZkvvMGlGx8j9r8G6HX2BD2i1YwOWraP82eZIAjb0dUKdKNHj8aTJ08wdOhQ3L9/H/Xq1cOuXbtQqFAh3ubjjz+Gt7c3unbtiidPnqBZs2ZYvXo1vLy8eJsvvvgCw4cP52ed6NChAxYsWMDv9/LywnfffYehQ4eiUaNGCAgIQI8ePTBnzhzeJiQkBLt378Zbb72FOnXqIDQ0FCNHjsTIkSM1O19rFtcQG4esWmHOwUJYaccr1iaOHNSoUyesQfE16kwvsBxy3l/uJreDILbn/UaC2HE4pk4+0HZqNVqOmfgPA3PEkmoeSkZGBkJCQpB+545Z/LC1q8xZ9cKc6YEuJITV5mENehtYCCF68wxb/EHKoXXsr1JxrHQqRbU/nI3/mv4vZmN6vAiOuPfcURAD2rSd1m0jV6eMjAwULRqC9PR0xe/TcMeFhIQASAIQaHsleTIBdFJdH8I9cdpyzIR6VAthsQN1KISVxORqvfADvQxHaPW036rwCEs44heAkgawUuTqCXe9z7UQslq3i7v+8CDcH12FSXgKWoVHKELiQHsLYS28wVrijoOhJ6ClJpSaOcwhnmdbQyBsraTUiXKNomTVOaX7FeLoe5Irz50Em9KV7uTQOpxEj2EcBGEJEsM6Qs14p2j2CAvzCVuDXeZYVYg1nTZ1ygRgeU0JTQWx3uI6lKBk0Q0X8QRbwh1jiW0Vxa4hiH3+3bTMjyDyITHsYKQ6CE2FsNzk+bDeKyw3FlrTkdpLqJIAJjiU6jdrBbGi695e8wTLeXvVpFsqw5b9OsUdvcSA8HzU9oNatwl5iAlXgmKGHYjm06g5cOYIvQhhuePcNTbQk7FFa6k9Vk0IrSR69wqraRSx+GCNvMR6uU/1Ug97YO1c7jQPMeGJkGfYQdhl9ggb6uDoJZal6qHH/AjXRzcOS0dOj6YUSx5ia8OrdNPo6nBXL7ExasMotJ6HmCD0DolhB5ANX6tWQVc8e4SCgc0ei3s4KzSCxK9noebpvlP1mDVC117iWKngVbBEu9kxlrzELiyKSbjZD9vb1hc0zzBhLyhMwslYNXuEFdOoKT1cKXqKESbcHyWhq2qm+5N6fKzpaop68AJbwt4xwS4mjN091EpNv+3O7UAQppBn2EWQ7MSkZo4wQuvwCPKeEM5AC11ly7Vrc/lKbj6t55KzNj8pb68GscOuILLc2Uus5sU2d24HgjCGxLATUeoVtmW5ZS2FsFbxyoRrIPZ9u/L3qPT6NdWQiu4Fa2KD9eo5tibswcU8wErwhFhigiDyITHsBDQbA1XEWDjzhTnCtZAb/N1RIIvVX/N7wFnCV613WE4IK/EGWwjTcsVrxR29o67pHfaBtjHDejgnQi+QGNYZVnmFLawy5+zQCFccAD0NW75fqWP19L1bEvhq6qqJV1gNzlqa2dL7B262GIccjhCElq5BfQhSgnBPSAw7GKsX15DKSMKTQ3NFeg6m349Nwk5DbKmXPeshZaOkfhaFsBhiNo70FMt5h6WErKV0K8Sznn4cWYM9wibUtIkzV4nTj3eYIOwDiWEHkJUF+Croc6yeQUImA2fGCQOuPwDqGanvRq+Dlq1LxtpSpl3y0VrkWnOs0jAI46nUjD/L2Up9VrrPCHfqB4zPxdrry9ZFh5yxSpzzBTEtx0zYDxLDOkGVELYwoGkVHqFXUUW49nfjCFFsTfuoOkapENbbS3K2LMtnpVfYnYSwKWrFqTu3BUG4MiSGdYps5yozGGnV2Wohtqjjtx/u8Ka7cd21uFYc1ha2CmG9CWRj1IRNeECssFLErl+1sehqy3PGve987zBB2AcSwzrA5jGFZo/gsWoRE8LpWPsSnkMHZi3ErdYCWet5ieU+K0nzIK+wJex97loJU3uKdoJwFUgM6xCr5hWGdZ2vO4VHyDWVmlVnCf2gi2vR0oWl9hg9omRKNTl7EUhguSfO8w5rvRwzXZ/Ef9ByzE5Gdio1azKAbeOwLsSHFSh9qd/VNIolSHDYCe5ikRO7aoWwNRefM6ZVszZUgn5tOhxnhcVZVa67db6EW0GeYT1jhVdYL+ERehZp7uYldof4YaeiRZiDvfY5G/IKEwThAZBn2Ilo5RXW20tzNBA6B2p3BRh7fNU+KnBHIewEr7AtXwFhjst4h93EA7Fo0SKUKVMG/v7+iI2NxYEDB2TtU1JSEBsbC39/f5QtWxZLliwxs9m8eTOqVq0KPz8/VK1aFUlJSYL9+/fvR/v27REdHQ2DwYAtW7aY5cEYw9SpUxEdHY2AgAA0adIEZ86cEdikpaUhISEBkZGRCAwMxPPPP4+vv/5afSO4ISSG9YoVI4SzvcKuJMbcdQCmHyNGaKG6bAmX4PZbi70vUkudgMZeYVuakZDHWfe8Y8v1xn9zDWuxqX8wvmnTJiQmJmLChAk4ceIEGjdujDZt2uDq1aui9pcuXULbtm3RuHFjnDhxAuPHj8fw4cOxefNm3iY1NRXdunVDQkICTp06hYSEBHTt2hWHDx/mbTIzM1GzZk0sWLBAsm4ffvgh5s6diwULFuDo0aOIjIxEixYt8PDhQ94mISEB58+fx7Zt23D69Gm8+uqr6NatG06cOKG6LdwNA2OMObsS7kpGRgZCQkJw7Vo6goODBfssvrhmaWQQ8QrbIobdYXENZ/8Y0CseET7h6CWQbd3vqDLkUOPtFZtjWOJ/sb6A7k3H4KwpMZWUm5GRgZCiRZGebj4eWjwuJATALwAKqa6bNA8BPK+qPvXq1cPzzz+PxYsX82lVqlRBx44dMWPGDDP7MWPGYNu2bTh37hyfNnjwYJw6dQqpqakAgG7duiEjIwM7duzgbVq3bo3Q0FBs2LDBLE+DwYCkpCR07NiRT2OMITo6GomJiRgzZgwA4OnTp4iIiMCsWbMwaNAgAEBQUBAWL16MhIQE/tgiRYrgww8/RP/+/RW1gbtCnmGdYI0QthatBxtbhbAar5Bcp6v0vDxtsHULb7GYl9cez9mVeHr1IIRtxRohrMBWi+uMPMTW4xkeYueQnZ2N48ePo2XLloL0li1b4ueffxY9JjU11cy+VatWOHbsGHJycmRtpPIU49KlS0hLSxPk4+fnh7i4OEE+L774IjZt2oR79+7h2bNn2LhxI54+fYomTZooLstdoRfonIBWXllrvMJ6EIJqBzvT9pFbwcz0/EzLUnL+1q6Kq3dcZsJ8R6shLcSrUhs1dnrDyovc2ugUV7in9IitL9RaO++wy/QvJmRkZAg++/n5wc/Pz8zu7t27yMvLQ0REhCA9IiICaWlponmnpaWJ2ufm5uLu3buIioqStJHKU6oc7jjTfK5cucJ/3rRpE7p164YiRYrA29sbBQsWRFJSEsqVK6e4LHeFPMMOxmIHb2evsCm+//oNrcWZHgEl9fb3F272wlViHnXrwXH021RKy9PKRq2dPXGR1eRc4X7SM9l8767+yZAWDhrt8bXDBpQsWRIhISH8JhbuYIzBYBB8ZoyZpVmyN01Xm6e1dZs4cSLu37+PPXv24NixYxg5ciS6dOmC06dPqy7L3SDPsAOxeYELGx9H6nC847G2beS8xLbWxV09Wrrx4DhD7Wg1e4TWednLzhpo7mC3xLiPtOf9r5v+RSHXrl0TxAyLeYUBIDw8HF5eXmYe29u3b5t5ZDkiIyNF7b29vVGkSBFZG6k8pcoB8j3EUVFRovn8+eefWLBgAX777Tc899xzAICaNWviwIEDWLhwoegsF54EeYbdAGd5UNQIUDlHnGIhLJOJrR5uMaz1JruCR8tpHmJnzKeltkwt4oattXU17CyW9dokvmZ+V/NNzyjxFrvqE0O1BAcHCzYpMezr64vY2Fjs3r1bkL579240bNhQ9JgGDRqY2e/atQt16tSBj4+PrI1UnmKUKVMGkZGRgnyys7ORkpLC5/P48WMAQIECQtnn5eWFZ8+eKS7LXSHPsINQJPjsOJ2aM1+ak6ujovhpsQyM04wyMT5eqw7Z31/9V0MeYiP0GANszXH28ARba28tWoRISNjbOouEFHq5l9TeK9bG3joSe/YB2i8G5PzlmEeOHImEhATUqVMHDRo0wLJly3D16lUMHjwYADBu3Dhcv34da9euBZA/c8SCBQswcuRIDBgwAKmpqVixYoVgloi3334bL730EmbNmoVXXnkFW7duxZ49e3Dw4EHe5tGjR/jjjz/4z5cuXcLJkycRFhaGUqVKwWAwIDExEdOnT0eFChVQoUIFTJ8+HQULFkSPHj0AAJUrV0b58uUxaNAgzJkzB0WKFMGWLVuwe/dubN++3aoWdCdIDDsATTpyjd/YBpwfF2Zz/LSpnUmGWoZQ2BI6oWfsKogd0VhaqS0tyrCXCNeqHFtwshp1tiB29MtojsTeP4r1fv5q6NatG/755x9MmzYNN2/eRLVq1ZCcnIyYmBgAwM2bNwVzDpcpUwbJyckYMWIEFi5ciOjoaMyfPx+dO3fmbRo2bIiNGzdi4sSJmDRpEsqVK4dNmzahXr16vM2xY8fQtGlT/vPIkSMBAL1798bq1asBAKNHj8aTJ08wdOhQ3L9/H/Xq1cOuXbtQqFD+dHQ+Pj5ITk7G2LFj0b59ezx69Ajly5fHmjVr0LZtW7u1matA8wzbEW5+xDt3zOcxVO0VlhDDSsZFm2OVTVDSual9D1ByajlrTlCF54rIxy6Dob1Em1b5OlsEW3usLeUp8QxrOL8woO1l4CxB7A7zsFtC6hy1qntGRgaKFg2xYZ7hC9B+nuGKqutDuCfkGXYC1i67DFi/yIZW2CqErRbBcnOkmXqGLXiKAdcYnAgTtJ5P2NayHC2CbUWJktT5y3PO9hBbC3mICULfkBjWA3YcGLX2CtuCpBAWE8FqxIqUCJYQxYKy/0XvA5W90Xwg1Pqa1pMQtrUuriaiFeIKgo9wZbhllLXMjyDyITHsYFz5l7ctA52xHhX1BouJYKWxFpZEsMTLdsbY+xEhYQN6CYtwpgi2FSl3qiu6WZ2AFi+DucKPBbEfxa5Qb4KwFRLDDsSqjlRHscLWwtVDkQhWIobVimCxkAqx/ERQ0l40ULgAzhTCOvbmOgJrZmORw1VDJQDXEJYULkF4IiSGHYSrdy6WOnBLutXM2Fj8KhHCamKE5TzDYpVSE+QsgtLvVs+DoG6vT0d4Y+0lhPUSKqID5egus7FoIRRdURC7Qp0JwhZIDDuA/E5FYkCyUYipOdRegkeJEPZFtrQAtjZEwtrwCDXeYVUqXx7yMjsYZwlheyk+rV2sWmDiprUkmvR4CoQ4+vMQ+wMI0DC/HA3zIlwdEsPOxAVGBbXizKII58SvEjFsyRusZXiE2rng1NorxNGrPulrsDPB2R5ZV5j/1xI68AqbovZWkjrOWWjlHeby0jN6rx9BaAWJYb1i0vPrtVOyNJAJZowQ20wzEfvfVLQq9QRrIU6UPt/VOPTCGnQtbB2Fmu9czlarfJyJpevMjtehmv5KLyJXDVp5TWlWG4LQBySGnYUbTKdmsxA29Q7LiVwpQSxWESVCWAsbJZ5lsS9Dw9ALXeIscegJQljpjzx7XkuW7kcPwR5hBDQXOkE4BxLDboZexiRZIfzggfgsEpZCIZSGPWjlEbYkWpXEHlt6eU/OVulx7ogrhCg4S/Rbus6tvVY0vMY85YUre8bVukooheOgeYYJ+0Fi2BmofOSux85Q0TtockLYVAw/egQEBclnaMkDZY1HT60AkBLJSgWytQLGFbzJeg0XMEULr7AeztWW794B1w0JYm3wlHYkCGdCYtjRODg8AnBMLKnozBFyQthYBAP5QpjLxPSvGFq+JCUVyqBUMNgqkJXaiKEHkexsYejosAZnn68zUPJDVGalR3cXc1osykEQhPMgMaw3dOTps3l2AjkhzIlgU/z9pcWwWGiF2H4lSHlrLcVCKhXJ9hLIYnZiWCuulWBvMeiJYtOROKGP8SRRbA9BTN5hgrAvJIYdiRO8wo5AMjzC+H9TIWwsKjmvsOnGHW9ckJQgNhbXSkSksb1xeIYWLweJCWElC4AojS9WE4csd5wesWcdLeWttGx3nCxX7TVu5Q9FT3hBjASxvfCBwaBdnC9jFDNM/AeJYUfhpDfA7f3YTlF4hJxHOCgIKFxYKIILF/5vv1h8sWmYhZiNWCUtCVTus3HoBrdf67fnrRHHpvvV2ukdawSmK8X46gF7Xxcq7g13nlaMwiYIwrUgMewIsrIAX+s6eiUDhD3GN6sHJmuFsOlfLi9TuHALYxH84IG0PSDeQMbePSVTuom94KcUU0+iGs+xaf2t9R7L2TobVxSqrugdlvr+pe4PB+PO4lgryDtMEPaBxLBeUDn4OFvXmJYvutyyWGgER3i4UAAb/c8/ZuQEh1i8sXG6JTEsVmHjuGQ5ESz1v7XiWEoIWxs+oSZkQg8v2xljq5h0tlfYlQSxo1+o1KA8dxDH+lvS2HXx9wcMBu3yY8x1bl/C/pAYJrTFuHeRCl0wFsDGIRKFCyMj698Bz//fx4z+/9o/eCAUv9xf06napDAenMUEsGmalAgWQ04YWxJMYl5jrkyp+ovZi+0T2y+GI0N43G30kfPo6wF7imC5+8L0/tIAdxDHWkDeYYLQHhLDesDZbl4bEcQKA5a9wtwLc0aCOBu+gkMEocOc8P37byAtzVwY370rXTnjtuUEq6kANhW/HFa+JCRbF7lwCaXhE2o8w1qFS+hN7DnbK2yKpVhvR6P0O1YaImHtNW8HUczhKuKYvMMEoX9IDDsbHQphNYOKWScv9rKbMaYzRvzrDTbVz/yEEv6+8DWODTYWw3fvCj3Dpt5f0/+NB+agIHMhrLUw5soQQ4kwNq6zVJrpuSrxDLtSPLEYehCbcij5QeKIcrW2F0Ppj0M7imIOVxHHWkDeYYLQFhLDzsSVBIgSLAlhwGwatWz4iq7FwR0e7G+yrPODB8AffwjDJLiYYUDo9TX+3zjNuCBuH1cvJcLYGoyFsVoPsRrvsJwQc6V4YinUCEu9iGY1Lzlak481qHmZTkuUXo8a4AnTuHkSFDNM2BMSw87CASLDYY/mTEMkjP8aY+oV9vcXFcECTL3AJt7hp4zh8b+m3gB8nzzJv6ifPIGXwWAuio0FsanY5WJ/LXmMtRLIXJ2cIYzF9othKd7ZUbjjqOWsHxp6+YHjQcLYHqESevIO66kuBGENJIYJ7ZF7DG8ihh+kyUdU4EFWvvjltj/+AC5fxkPGkAsgC0Duv/beJpsvY/B+8gRepiLWVBBzhWsxIEvF/Rp7hY3nMZYSvmKhHcafLaWZppvuE9svZiOHGoGq5EeEloLXHcWzrVj6bm2JC7b13vEgYawlehKheqoLQaiFxLAz0ItnRmtMBYjpZ+MX2P4NkZAyLVz43xCJtLT8F+cuXwZ++w24fBl3GEMW/hPCuTAXwv5G6bwoNn7GJiWE7SmK5VAbRsHVVUkahzPjieWeGOgBvdZLC5R8f3I2ju6vnCCMXVnE6UGEOrt8grAVEsOOxl2FsCkqY1BN33crXBj/eYMvX/7PI/zkCR7gPyGc+e8xfsgXwJwYhtH/uQAKAvlBYsaeYmsxDZlQYmvpf1u8xcZpYnam6XLHiOEq8cQc7ixs1aD0+9GTEDbFmtAeK3B1Uezq9VeCnx9QoIB2+T17pl1ehOtDYtiROHtgUYDVM0lIDVpZWeZTmv2b7ots+P87n7BpFEUwMv7zCv8rivPu38cDAI+QL4Lz8F+IRB5XLJe90f8A8Bj/eokBgLF8QSyGVJiDcTyx1DFyItkaUfxvO4nWS8pbbHyM0nTTfVI2puglntgYEsLq2l6r70nLOHpL5RijcZmuHkKhBy8xQbgiJIYdhQsIYauwFBph4Vh/f19+PmFeCPtnA5eNvML/hklkIF8IZ+A/EfwEgM+//z/99y/nIeZsOIwv9jxjQSznSbUkmpUIYKVCWKkolvtsnKY03XSflI2UnRhKrwMtQzA8FWva0NbQCb1gr7AeaOttdeQ8wySICUI9JIYdgZMGFadM9m7Jg2lMVhaCC/sjy8g7nO8RfvDfy3L/CuKnT57gEcDHCuf8m0WeUXbeyBfEUkI41+QzVwezuoqJVOO/nFCVO39bvMO2iGKxc7KUbmmflJ0Se0s4W8g6u3y1aBXPbo9yHOUdVlIPY2yskyuKS08ImyAILSExTNiOEo8q5/YVgfMM+2Zl/DdtGhcv/G+YBOcVzoRwBgnuJTkY/c2FcIYJwFwcA8gPlzAY5EMfxNJMwybs5R22RhQbp1mTbrpPbL8prhZTbC1Kzkdrca1lG2oRQ2wJvQhiYzR4Ic8WcenM1edcUchL4e9PMcOE/SAxTNgHY5Em9Qj+X8Hn6//vZ+NFNO7ezU/jtn/JA+D17//Gs0hweEFILuQv8jxOEJvW1VTkiqVJCWIxW1uEsBJRzO2XSrMmXWy/nJ2S49Qcb0/UilZbhaSS8uzRJu4cP2wNNgpjpavc6Wn5ZWcKYncS44R7Q2JY5+i1MxF09kpia40xFYic4DUWw9z2rw3n7fXCfzHCHKZpphe1lCAWpIsJWak6WxLExuctlSZXptz/XD6moth0v1yaknQONeJOjbhwllC2xnPrqNAELXGGCDZGz4KYQ+oeUIGeRK8cJIgJQh4Sw4Q2iHklTfeZYuL1NVtt7l+BnMcYgPyL1Q/5oRLG4tcH5h5iY8TSswG+e1bsHVYqiMWONU3j2kXt/1w+3Gfj+GWpEArjNKXpYvulbKRslRxjz3ws5eduqG0fe4tVDcSmQ3CVetqIM+KIufdWuI0g9AqJYUJblD5mf/BAeAznGX706D8hzIVKQLiQRiDywyWML15v5HuI/WC+AIdVKBXCYoLY+JwsiVw13mAxUWzc3kpCKKxJN20XY2wJl9BKJNsbvXs59SaCTdEgZtchkCjWNX5+gJdpHJwN5OVZtiE8BxLDLoCuHzPJhUiY2ijxEJv+9feHl8EAb8YEgjgX/02lxsEJYYchJYjF9hmnme4HlHmDLYllDrkQClNbe4RLyNlbOtbNhYhmWNNOemhbVxCcriLebcRVRTFB2AMSw4RVWJy2zVRwWfIYc4LYWBgHBeUf5++Pgk+e8CLY3+Qvh+nKc3IeYqu6fzGBa7rPWBCb7tP6f0C9KJay1TJcQsxeyTG2HOcpOFoE2+L5V3KM3r9bV6mnDcj14ySUCU9Bw4lKCHviEvFW/wpXfuAw/SsFN+CIzd/r7w8ULgw/gwH+AIKQL4L98V/ohNhfMREsFTbBxwtL1UtusBfbx52Hqdi35X/jukjtE/vM1YfbTOsuZi+VLmVjyVbuGGuPczTOjjs2vq/seYzxcUqPVWtvjLPbVSnOvPaciK3xvlqOW8aXmVabNSxatAhlypSBv78/YmNjceDAAVn7lJQUxMbGwt/fH2XLlsWSJUvMbDZv3oyqVavCz88PVatWRVJSkupyDQaD6DZ79mwAwL179zBs2DBUqlQJBQsWRKlSpTB8+HCkp6db1xBuBolhQhuMexYpQWypBzL2YBYuLDymcGEUxH9CWE4QmwphUxHsa5RuNWKDoqU0LYSvVFlSgtYUMVEsZ69GhForWrUQyO6MI0SwrQrBOB+1uNp36EnXnhFqRbFLOHBUsmnTJiQmJmLChAk4ceIEGjdujDZt2uDq1aui9pcuXULbtm3RuHFjnDhxAuPHj8fw4cOxefNm3iY1NRXdunVDQkICTp06hYSEBHTt2hWHDx9WVe7NmzcF28qVK2EwGNC5c2cAwI0bN3Djxg3MmTMHp0+fxurVq7Fz507079/fTq3lWhgY+/dVfUJzMjIyEBISgvQ7dxAcHCxpp3SlOGsfWanplNSUwedr6kE1FXTGf00x3m88kwS38Ma/i25waXlPnvDLMRsvrmGKmBDm/pqKYd4zLOfRFvvfkp2xuLeUl+n/1tqJfZZLNw2hUJKPWhtbj3NEGc7MU6uy7G2vBmtFoiuHI7hy3a1EbLyQGm8yMjIQUrQo0tPTZcdD0eNCQlCjRjq8vJQfZ4m8vAz8+muIqvrUq1cPzz//PBYvXsynValSBR07dsSMGTPM7MeMGYNt27bh3LlzfNrgwYNx6tQppKamAgC6deuGjIwM7Nixg7dp3bo1QkNDsWHDBqvKBYCOHTvi4cOH+OGHHyTP56uvvsIbb7yBzMxMeHt7dtQseYadCPcAivvfErr9pS02CMh5hi0NGsZ2QUH5XuJ/N6+AAATD3DsstwEqhLA1yHls1djLeXu18AqrCaEwtlcbLqF16IO1njh7ePAc5QlU+2PBESENarA2f63b15EeXA/2Frvy9GkZGRmC7elT01ez88nOzsbx48fRsmVLQXrLli3x888/ix6TmppqZt+qVSscO3YMOTk5sjZcntaUe+vWLXz33XcWvb7cDwFPF8IAvUDnFKSEr1IPsV4wq6/xy2WmfwHhIGFqw6WZfubWauZs/f3hlZWFgowhF/nzBRsvzQyYX9QWhbAxWooEsdklxDBtI9MX28T2idmZ1l8sTS5d6oU742M4lMaB23KMXHspzdOa/JXkY08xaQ+vuSt5LW1pX0s/qsSwR9tI3WOE1fj5AVpqttx/B4ySJUsK0qdMmYKpU6ea2d+9exd5eXmIiIgQpEdERCAtLU20jLS0NFH73Nxc3L17F1FRUZI2XJ7WlLtmzRoUKlQIr776quh+APjnn3/w/vvvY9CgQZI2ngSJYUKA1dO4GYtYqXRTUWwqlgHh4hUmMcPGeGVlwSsrC95Gotgb0qvNyQphR4kia0Sv3D6x8qXSAPWiGJAOo9BC7Fo6RonotVYYGx9rbZyrs8Mw9C6EpfoEJVhzb1mLNdeymrxJEOuaa9euCcIk/Pz8ZO0NJk4UxphZmiV703Qleaopd+XKlejZsyf8Ja69jIwMtGvXDlWrVsWUKVMk6+5JkBh2MXQ95zAg7g0WE8HG+0wFjdgmNj3YgwfwysoC/p2DOBfyU6aJXuzWxPKKIXXOYlOtiR1j7T4tPMJygtI0fEIuxtgaz5tSIWJPYWytKHam0LFnPLeWOEIQ2yMsQaunCMZ56eH7IMwIDg5WFDMcHh4OLy8vM2/s7du3zby2HJGRkaL23t7eKFKkiKwNl6facg8cOIDz589j06ZNonV6+PAhWrdujaCgICQlJcHHx0fUztOgmGEHo2shqxVyolJsn2kco9H8wsbxwoJ0o31eAQH5C3PAcuywl8FgW5ywrQOamse4auOExfJTk25pHyCMMZaKNZbKU2lMpSU7tXHGSrEm3lNLIaalp1dPwksLIWntfq3QIv7cg2KJ3RFfX1/ExsZi9+7dgvTdu3ejYcOGosc0aNDAzH7Xrl2oU6cOL0KlbLg81Za7YsUKxMbGombNmmb7MjIy0LJlS/j6+mLbtm2SnmNPhDzDHoA9Y5EFeZuGRMiFR3CfxTzD3N/ChfNnkTAWwOHh/x1rvKQz8pdj5vLKM5okRVFssFpPsBo7OeRCItTaSh1rjUdYjWdMTBDLeZDV5G/J26vE66bWM2dve1uwVIY7Dm5i14CzhaUtTxOsOY6Av799YobVMHLkSCQkJKBOnTpo0KABli1bhqtXr2Lw4MEAgHHjxuH69etYu3YtgPyZIxYsWICRI0diwIABSE1NxYoVK/hZIgDg7bffxksvvYRZs2bhlVdewdatW7Fnzx4cPHhQcbkcGRkZ+Oqrr/DRRx+Z1f3hw4do2bIlHj9+jM8//5x/YRAAihYtCi8t17p2QUgMOxAlXmFXe4nODClBzH0G5GOGuf2cmDL1BHPiGMj/zIlik7KMhbFZ/ZSkKdmnFNNQCTWiVq0Alhtsrd1nvJ/DUruoFchK8ldSf7m6qQ2jsCZu1Zkixx5POrQSnbaESxjnoTdIFHsU3bp1wz///INp06bh5s2bqFatGpKTkxETEwMgf65f47l/y5Qpg+TkZIwYMQILFy5EdHQ05s+fz8/9CwANGzbExo0bMXHiREyaNAnlypXDpk2bUK9ePcXlcmzcuBGMMXTv3t2s7sePH+fnLi5fvrxg36VLl1C6dGmb28eVoXmG7YjpPMNKQyTsMeewveYaFs1b6tG8VJrx3wcPhI8juXmHuX3c/3fvij9+lwstMEWJMFbqQbZ0nKkQVFOOpbKk0uTSlexXO1BbM7Bb8iBbyteW+qupr71s1RznrPMBtPXEWpOXo0S7LdgibD1AFNs6z3Djxunw9tZunuHc3AwcOKBunmHCfSHPMGGG2pf0RKdYAyw/3jT2DEu9CGYcS2zsHQ4P/08UG2OtB8oaYWwL1oY8cPtM66M2RELJfrXeYKWeeGOUvKDnCG+3knPTY9gEh71De7Tw7FqTl5rzcrYgtuXJgLOfKhCEh0NimNAE0fAOsQFKaaiElK3xX9P/jZEKv5AqQ2m60v2myM0qoQQlg6W9haFacSx2jKXj5KZ1UxLjbM25cza2tK+1eSpFCw++rXXRWhAD1l1TcnnqQRBzdXHksR5AYCCg5cQH/655QRAASAx7DE6LRZYb9KRii02PDQ/P9wBznznvMLcvKytfRJmGSXCoHWTUhCFIoVRcqY0HVhIfrFVsrT08wUrrAChbBERLUazmvPXsJRZDS1GupeDUul2k+htHQ15ignApSAw7CE+YUk1WcMuFSRjvl8I4RAIQ/i9lz4ljMQEpVje5vLRAzDusxUta1ghDNfs57OEJVlKGEm+xWlGspE622qgp056QsHIOal/YFDtWwXFcv2s8xrj0S9gE4QRIDLso9l58w9r8LXqgxTydlgaNrCzh7BIclgSxWD5qByUtvMTGWAqXUBI7LBcfbM/YWWsfaauNzZWzlfIWa90mcseqtVFqq9X1qWa/NeghHMESequjtU8ILFwTXB9t2le7/KxEIvj7axsm4eEziREmkBgmNIfrmBV3xnKP/sUGA2MBbMtgLzVYaiVIpAZAS1OtieWjhTdYa4+nNV52NYJari5iotiZXmJX8BBrid7Ephh6rKO1T1vc4ZohCB3jsBXoFi9ejBo1avBLHzZo0AA7duzg9zPGMHXqVERHRyMgIABNmjTBmTNnBHk8ffoUw4YNQ3h4OAIDA9GhQwf8/fffApv79+8jISEBISEhCAkJQUJCAh6YeA+vXr2K9u3bIzAwEOHh4Rg+fDiys4XC7fTp04iLi0NAQACKFy+OadOmwdVnoXN0qIbF8qS8rsbp3GduE1uFTszG1M54k8rbdL+luilFbECWW7lNLu5ZLF3KXu0xYjZqxYTxcUqOVWIrt1+sHeXOW23+lo5Vk4fSvAjPQem9IrHfF9n8ZoonhOYRhFY4TAyXKFECM2fOxLFjx3Ds2DG8/PLLeOWVV3jB++GHH2Lu3LlYsGABjh49isjISLRo0QIPHz7k80hMTERSUhI2btyIgwcP4tGjR4iPj0deXh5v06NHD5w8eRI7d+7Ezp07cfLkSSQkJPD78/Ly0K5dO2RmZuLgwYPYuHEjNm/ejHfeeYe3ycjIQIsWLRAdHY2jR4/i008/xZw5czB37lwHtJR7kS3orn3NO2hjgWn8V81mKoi5z8aCWCp/JVgrgo1RO9hpJXCtFZlidtaIOC2FsdQ+sWWhrW0PS/WzhK1C15WEsit4K12hjhxKrn+VkCAmCGU4ddGNsLAwzJ49G/369UN0dDQSExMxZswYAPle4IiICMyaNQuDBg1Ceno6ihYtinXr1qFbt24AgBs3bqBkyZJITk5Gq1atcO7cOVStWhWHDh3iV285dOgQGjRogN9//x2VKlXCjh07EB8fj2vXriE6OhpA/qotffr0we3btxEcHIzFixdj3LhxuHXrFvz8/AAAM2fOxKeffoq///4bBrHlfUUwXnTDPzhccbs4dIEMjfNXg+RiHcYDgqmnkVt0w3j2CEsb8J9YMh5QtBIeagZcU1vjx/ym+6yNB7U2ztla4WDNcWp+iKjZJxaPrbY9tGgrW/JQU19nxAwb4wri3RXqKIXCa0Gun9ZL7LCti25065YOX1/tFsfIzs7Apk206AaRj8M8w8bk5eVh48aNyMzMRIMGDXDp0iWkpaWhZcuWvI2fnx/i4uLw888/A8hfSjAnJ0dgEx0djWrVqvE2qampCAkJESxjWL9+fYSEhAhsqlWrxgthAGjVqhWePn2K48eP8zZxcXG8EOZsbty4gcuXL0ue19OnT/n1vo3X/SbEkezAxcIZTPfJhUGIbaahE8Z52SIC7Sk2rH00r9Yjaik/pcepDRVQE66hdJ/SsAlbPeZahE2oaWu9CjpX8Ly6Qh2lELuOVF4L5B0mCMs4VAyfPn0aQUFB8PPzw+DBg5GUlISqVasiLS0NABARESGwj4iI4PelpaXB19cXoaGhsjbFihUzK7dYsWICG9NyQkND4evrK2vDfeZsxJgxYwYfqxwSEoKSJUvKN4gE9uy89NYxCuoj5hmVC51QK4gB4awUluKHxbBVQKtFy0fzjopptYcwVvP4WAtBrMV+pTZaHEPI48qCGLAoiPXi/SUIV8Whs0lUqlQJJ0+exIMHD7B582b07t0bKSkp/H7T8APGmMWQBFMbMXstbLhoErn6jBs3DiNHjuQ/Z2RkWC2IPQnB7BP+/vkdvfFfe/HokTB/0wFH67LlQiTksNQOauqptE25ttAqVlppPpbspfabnpfSOZ0ttYeS+lg6NzkbW69xe98jSuDuVb3jCvU0rp/Ka1Wr6TbF8iCxTbg7DhXDvr6+KF++PACgTp06OHr0KD755BM+TjgtLQ1RUVG8/e3bt3mPbGRkJLKzs3H//n2Bd/j27dto2LAhb3Pr1i2zcu/cuSPI5/Dhw4L99+/fR05OjsDG1AN8+/ZtAObea2P8/PwEoRWOQK/zDduEqSDm0uyJqTAG7BtTbMvSzNaUZy1yg7M1+Wgpih0liC3VR8m5qRWt1tbT1nLdHa4t9CSKlTzxMK632P8aIdXX62HeYn9/wFfDoaiAU4JECb3i1MuBMYanT5+iTJkyiIyMxO7du/l92dnZSElJ4YVubGwsfHx8BDY3b97Eb7/9xts0aNAA6enpOHLkCG9z+PBhpKenC2x+++033Lx5k7fZtWsX/Pz8EBsby9vs379fMN3arl27EB0djdKlS2vfEA5Gb6ESHJIhE1LhE7aETIiFT3CbXDlKETvGUhlyecmVIfVZ6jysxZrYYKk8tLBXEvqgJmTC1rAHa8MqtAhLsWW/Fria4HZ0uJOWSHyftvbrlo7X67hBEFrgMM/w+PHj0aZNG5QsWRIPHz7Exo0bsW/fPuzcuRMGgwGJiYmYPn06KlSogAoVKmD69OkoWLAgevToAQAICQlB//798c4776BIkSIICwvDqFGjUL16dTRv3hwAUKVKFbRu3RoDBgzA0qVLAQADBw5EfHw8KlWqBABo2bIlqlatioSEBMyePRv37t3DqFGjMGDAAP6N0h49euC9995Dnz59MH78eFy8eBHTp0/H5MmTFc8kQViHwAMh5QHRwlsp5VXJyhKKVVMxZU15Smc4EENOCKv5bA9s+R7UHqs0PEIsTc0y2LZ6ee3tibU2f/IQSyMXKmVv1MTV6+D704OHmCDsgcPE8K1bt5CQkICbN28iJCQENWrUwM6dO9GiRQsAwOjRo/HkyRMMHToU9+/fR7169bBr1y4UKlSIz+Pjjz+Gt7c3unbtiidPnqBZs2ZYvXo1vIzWVfziiy8wfPhwftaJDh06YMGCBfx+Ly8vfPfddxg6dCgaNWqEgIAA9OjRA3PmzOFtQkJCsHv3brz11luoU6cOQkNDMXLkSEE8sJ5wSiiDHTHrcOXih219/Cz12JEbpOSEsSVMBZjawcwaIeysAVMLYaxUFNtbECupk6WwCbVi2dawCFtjl7VAjyEIanGmMHYRSBAT7ohT5xl2d6ydZ5jDnvMNOyJ/KZTqJr5+3AHGj7JNH9mbphvPQ2xqb5qnaaWk/jfFVBxLxQFLhS5Ygx6FsBS21MfSsUrb1DTNWi+9tWEsao5Rc51YU6YaG1txJyFpz3NRk7dMSJSl/tlSX2/v8QOwfZ7hQYO0n2d46VKaZ5jIx6Ev0BGE1AxBsuOzqWdYzSNzuXSpCioJzwDkX4KzR9iCKwlhwDZPpLUv0Jkeo9RDLFeWJRt7PbWwJg+l5QD29xK7iyC2p8dbaTvJ3Ovu9FSQIJwFiWE3wRVCJSy9e2Q6NpvFD8uFOFhbIeN8LYVMSIkvDlu9fXINpFQIW5O3PbFVeFkSoWL7pH7EcGmcR19N2IQlG63SlaLVDz9jtBbH1tZDr9hL4KvJl2vTf/+a9vmO+J3DQeEShDtBYthB6FGs6q0zsyiIjZHz3toaO6nUO6z0MbjSkUlOwCnJ21J9nCk6tBDFjvQSy9VVqycT1sY/W5tuCUcqKWMs/cjUE864j0wEsJgQ1nOTEYQrQGKYUISjxLzYeMwLYiVeXKlMxOYRNj7WGu+wrZ5hNZ5gsXS1gtvZghiwTXBZ+2KacXlKvcRK6uqosAZr0CJEhcMZ4tjZ16kc9gibsNTGEkJYqgpqvn5b+nVHOlT8/PI3raCJoQhjSAzrGLUdjR69z9Zi2pmrFsRqC5Dbr0RsmyIX1iCWpjZu0NWxVhQrCZsQ2y8mii15iW2tq2kejvQOc/sA268bsWvT3tei3gUx4Lg6qhTCjkZvTxgJwhpIDLsiGnqf9NyRSZ6mnCDm9otlkJUl/9KbJRFizUtUUvZSNlIjnBJx7YrYIoot/ZjhkAqVUCOIpcrUSrgqwdp87eGtdrb3WC+o/UFrbd4yQthZ0S0E4U6QGHYgmnpunfA41tb62+pIkXyhTk4EywliMUEtlS4XO2xctqU0S3iqx01pLLbUMXLHqbk+LAliqfy1Dpew55MOe2DN92cJvV6rUij9caY2HwVCmPtfbdNrNR45wqni709hEoT9IDHsZlgjWF3JOyz7Qp1cvK8aD7FcJbRww9jTm+QO2BpCYYyUV9ha7CFy1eRpTdy0NeXYgj2Esatjhx/G1HUQhHYUcHYFCCvwsAHGtNPnxb5JLJ3gr9z/3MYJYkv5SOWhBNM8xLw+Wnyf7jgyanFOavNQ6xV2NbKyHHutcOVZU6Y7XtNKMekT7BEnrPX7Je7yvgrhmZBnWOfIem1dMFTCWlTFDyvxECttO7VxmtZ6NS3FDIvZWoOrCQx7vrymJ7R8ac6W69qeUJyxZSREMCAdHqEn9PyUkSDkIDHsYBwhJvUcKmFLGKDx+C0bP2waR2xJEHNhE9z0a2pFtekJ2oIlUayHx+DOwF5vCRnnp8QrrJUX397xu0oFMeC8a8ZSWIun/OiTOE+1fbia5nJFL66fn/t2b4TzITHsqjjx5RK9TOEmKuClhLAlr7CpIDbdpzQWWSusjXWVEuemcZyuJBaMkYtHlWonuTAYwPbQCGeM0FrGB+vpR5RWAl2P17iKc9JD/2ot5B0mXBESwy6AxVXYRNCzd9gWJF+oMx38lIZPSAkBUy+xaeFavFSndLC2h4dM7jG7q2BNXdUKYUeKRK1npbDmR5ReRLEWOEsQ29iGUkssqylSrgqOENquMJYQhDH0Ap0T0KyTcPTLMEbYcg62jreSp2zJA2j6EpycTVBQ/mZqK/e/NXHC1hxnT/RWH1sx/V6N/zcWwsbfhZrvxZltpeTeV9s/OLFPMauHFjji+1F7zYiQDV9+M0bJb1m93q6u7N0mPA/yDLsyenwU6AQUxQ9LeYjlEAudEItJ5tKNPxvnoQRLscJaoXTkdFR9tEJNeARgLoQdUR9rscd0bvYu093RoG1sFYtSVRBL5/pIRwtULT3E7vY7ndAX5Bl2Y6zphNR0ls58DCY53RqgzkOs5C/nJTbdZ+qekRJkevE0WpO3q44+ct+p8Xdpy/mpUSOmaPUjQ2k+1k5t5io/hhyFRt5fS/2stU3v7PAIPZVLEGogz7CTUPsr3Zq4YT3jMKe2Gk+xpSmrpLzExjamn8XqI7ff2EYvQkTvTyAsBUzKhUqIfbZ0rnL3nBYxQPa6p63NW+6lRXuiF++0DXVQKwT1fJvZAgliQu+QZ9jNcWfvsCmi3mHj/8X+SsWJStmaeonlvMOWPMKWvExaPhe0NR89iBIxlApTYy+/pTwsfWfW1MWeqFFQtqotV/QWW/u9WHn/KfX+cnBNamuz6vUWdTcWLVqEMmXKwN/fH7GxsThw4ICsfUpKCmJjY+Hv74+yZctiyZIlZjabN29G1apV4efnh6pVqyIpKUl1uVOnTkXlypURGBiI0NBQNG/eHIcPHzbLJzU1FS+//DICAwNRuHBhNGnSBE+ePFHZCu4HiWFH4GqDh4OwR+etWhDL7RMLiZAKnbAUMuFMUay3UVIrQSUVliL2F1AmhMX2KwlzsXcba9mHaJGXq4lipd+PmpAmE5whgF0JW73D3DzDWm1+furrsGnTJiQmJmLChAk4ceIEGjdujDZt2uDq1aui9pcuXULbtm3RuHFjnDhxAuPHj8fw4cOxefNm3iY1NRXdunVDQkICTp06hYSEBHTt2lUgZJWUW7FiRSxYsACnT5/GwYMHUbp0abRs2RJ37twRlNW6dWu0bNkSR44cwdGjR/G///0PBQo4Twrm5eVh69at6NChg9PqAAAGxhhzag3cmIyMDISEhCD92jUEFysmauMoL6w1HZHS8mzp5GwZDKTGK7N6GxfC/S+XpnYfkB8+IZYu9tnadKX7ObQUaFqLMa3FvaXQCM6rL7XfFmzxPGqRbo86ODoPR+Zvh7APJX2gXt6NdRYZGRkoWjQE6enpCA4OVnVcSEgI5sxJR0CA8uMs8eRJBkaNUlefevXq4fnnn8fixYv5tCpVqqBjx46YMWOGmf2YMWOwbds2nDt3jk8bPHgwTp06hdTUVABAt27dkJGRgR07dvA2rVu3RmhoKDZs2GBVucB/7bZnzx40a9YMAFC/fn20aNEC77//vqLztSfnz5/HypUrsXbtWty5cwdNmzbF7t27nVYf8gy7EHqNu3LmNGtimLWTlBdYjSdYbh8gPk2X1GexeilJN83PWk+zWvTkvlJyXnLeYaV52Bt7Cjy1x+k9dELrvG3w/ppij5fhPM1j7CpkZ2fj+PHjaNmypSC9ZcuW+Pnnn0WPSU1NNbNv1aoVjh07hpycHFkbLk9rys3OzsayZcsQEhKCmjVrAgBu376Nw4cPo1ixYmjYsCEiIiIQFxeHgwcPKmwB28nMzMSqVavw4osvokqVKvj6668xdOhQXLp0yalCGCAx7Dic3LvZO3bYFqwdk+SaVJEgNv7fFpEsFjphuk/ss1SaXLqUnYYDvAB7XLfW1NGS+Lclb1twVHmOCMPQQhTbCx2qQ61EsHHIhOmDJ08Mp3A0GRkZgu3p06eidnfv3kVeXh4iIiIE6REREUhLSxM9Ji0tTdQ+NzcXd+/elbXh8lRT7vbt2xEUFAR/f398/PHH2L17N8LDwwEAf/31F4D82OIBAwZg586deP7559GsWTNcvHhRsn204NChQxgwYACio6MxbNgwlCtXDnv37sWff/6JSZMmoWTJknYtXwk0m4ST0WxWCReHG+u17PTN2srfXzgfsOn/xpUwTpPbZ5zG/c8JYi50wvTkxE5WqgHs0TBK0cMIrCY0QO5Hjq1LLisp3x2Qmi/bUcdbylsHba5VSITa28vS6eukeeyG1r/1uQBRUyE2ZcoUTJ06VfI4g8Fgkg8zS7Nkb5quJE8lNk2bNsXJkydx9+5dfPbZZ3zscbFixfDs2TMAwKBBg9C3b18AQO3atfHDDz9g5cqVkuEWWtCoUSMULlwYH374IXr27Ikge/THNkKeYQ9Cz95hDjUdnhI7ix5isUfpaj3CUp5JZ3iKtcKVhbAa9HCeWqG3l+zsgZPrpYUQtsXTq9evxZW5du0a0tPT+W3cuHGiduHh4fDy8jLzxt6+fdvMa8sRGRkpau/t7Y0iRYrI2nB5qik3MDAQ5cuXR/369bFixQp4e3tjxYoVAICoqCgAQNWqVQXHVKlSRfIFQK1o1aoVMjIy8MEHH2DmzJn4888/7VqeNZAYdiTUkylGSVisUszi+sREqWm6rWmmoliu8mrjie0pivXyTNYWIay2fbRe3cCWY/Xk2tOranPC9al0pgit3n21Ng893LquRnBwsGDzk5hmwtfXF7GxsWaxrbt370bDhg1Fj2nQoIGZ/a5du1CnTh34+PjI2nB5WlMuB2OMD/soXbo0oqOjcf78eYHNhQsXEBMTI5uPrSQnJ+PSpUsYNGgQNmzYgIoVK6JJkyZYs2aNbqZ1IzGsA9R6bG3x1rqCd9gUrcJirYojtkUI2yue2NI+a9HDSKr2nJWESpgidp56+RGgR/QoiB2EGhHsCCGsBDdodlECA//rRrXYAgPV12HkyJFYvnw5Vq5ciXPnzmHEiBG4evUqBg8eDAAYN24cevXqxdsPHjwYV65cwciRI3Hu3DmsXLkSK1aswKhRo3ibt99+G7t27cKsWbPw+++/Y9asWdizZw8SExMVl5uZmYnx/9/em4dJUd1t//cAs7FMswwzzegoqIiQ0RhHxcEoKAoaEeMSiCQTfSWIUdAReFyjgm8EQYPGYHjcElyDb1yiRkTGnwbjxSIZ5QFcyGNEkcg4oEMP6GyO9ftjqKa65pxT51SdWrr7+7muvrr71NnqdFfVXXd969RNN2HdunX49NNP8c477+CXv/wlduzYgZ/85CcAOsMs/uu//gv33XcfnnnmGXz00Ue45ZZb8OGHH2Lq1KkufhE1Dj74YNx8883497//jddeew3l5eX41a9+hXg8jmnTpiVn1wgLihnOQtw8oz5TYpUd44iB1Lhgax7ZOGNWveZnVjyxU+ywKG44zJhinTgJexn3XMfJgUzgpV+usB/t6cJtQKpfgaw+B8iq7B/9iA+Wqc8pfhiIxl8nk5g8eTK+/PJL3H777di5cycqKiqwYsWKpLO6c+fOlJCDIUOGYMWKFbj22mtx//33o6ysDPfddx8uvPDCZJ5Ro0Zh+fLl+PWvf41bbrkFhx9+OJ5++mmMHDlSut3u3bvjww8/xKOPPordu3djwIABOOGEE/CPf/wD3/ve95L11NTUoKWlBddeey2++uorfP/730dtbS0OP/xwv4cuhdNOOw2nnXYalixZgqeeegoPP/wwHnnkkWRccxjQPMM+kjLPsHUeQ8YeSlWcehWmuucdDts9dkPK+ojmBpaZf9jNZ+vcxE59EKWpLHdTzs8jq1vhqRoq4RSqoqNPTsiUUwmZcduGW4I+CQihTt0iWCWfG9z+pcLA6zzDf/pTAj176ptn+JtvmvB//o96fwh17rjjDrS1tWHevHkAgJUrV2Lp0qUoLy/Hb37zG/Tt2xcbN27EscceG1ofKUwiIgQZKuGmPbNNVrvpKIQBxtPqZMImWGmi5aLPbkMnRHiNJbHiNnRAJtRBpo9uhbAMQQdeBqVIMixWV4jm/vghhP0mDFeaIFT585//jMrKSgDAl19+iQsvvBADBw7E2rVrcdVVVwFAqEIYoDCJrMZNuAQQbfGrevVUy/RrouVOn4FOQWx1iVmhE6LvPKwD4faI6MV68sNJjIrN5Qe61o03PV+Y+BHWoKk+P0VwVIRoJky7pvs2iRCvyGcdn3zySTJc4+WXX8aRRx6Jhx9+GO+++y7Gjx8fcu86IWc4y8mEOGCvKN9YZ/3sxRlWdYl5fZTByY1VvTyv04Fm1ctb5jYtTGT7w1NOXhVVVBRZxJC9OQ5wd5EkajfM0d+ACIvCwkK07P8Dvvbaa8mn6Q0YMAD77OGCIUFiOAw4e6WgQyXctpsOmAcv60uEJ0Fs/Swrjln5AXFsq47wAC/lzLJ+iU0d9UZNCKtCgjgQ/A6JCHq4SRATUeaUU07BjTfeiAcffBDPPPMMfvzjHwMAPvroo0g8fQ7ghEls2rRJuaIRI0agRw+KuiDCx0lP8PQSM2TCLCj7WTZswprH3pbTjBM6YNXJa0clVEI1fEOWKDnAKtecoyTOM+FauQZU3GBZoiAyaQYJIqrcc889uPjii3HddddhxowZqKqqAgA0NzfjpptuCrl3nTDV67HHHoucnBzITjTRrVs3/Otf/8Jhhx2mtXOEM7qmPHMbP5yOiDSBOQbcMfUaU2x+tneENw2bXRC7jR/mrYu1bWuaUxm3ywlneH9QHWLWax1pLqgzVQhbkZl2TfYndDq2BHnMyM/X+9fr6NBXF5HKnj17sHTp0uTT/A499FCsWbOmS75zzjkn6K5x4Vq569evx8CBAx0rMAwDFRUVWjuVzWSTKPUDWW3odEBIOclwcnRVxLGoHms+3s11rJVUOUI4Ob8y9pKOG/NUkTmC85bbQ0+ciILYyzSbLwLroVsIR00EqyCzOcmYLNY8dNwiTL766ivMnz+f+2jrKMIUw6NHj8YRRxyBvn37SlVy6qmnorCwUGe/CAXIHU7FF0Fsr9jrZ3sHWCJaFMbg1hF2ErKqcck8B5PQg/1PmubObNTJlKnKwvibZMrxg8hOmGL4jTfeUKpkxYoVWjpDhE+27dC0CmJ7hTLOMC+f9Z3lDtvbd4OuuFfeVF7poBrckI7xwmESgXHQtU/z4y/t9cKOKK/bcAm389ATRLpCd7xFkEwWpOZO1u/1U9GJ2gSxaJlKuIRIEHsJkbCusBNON6yx+sATxSrtukU1RIKXPwLCrQvkBgeC098zbCFsz6/j6hcPXVcb7X3xQkEBxQwT/uEohg3DwDPPPIM33ngDDQ0NXZ4d/dxzz/nWOUIenTsv3WKc1a+oxZp5FsRmJTLL7A06OcMsQWztuB3VUAgeqnHDTo6wV3EcBVEYRvvWNmVFvOxv4IY0m00jCvsXP9ARuh/UJpWpF4mIzMFRDF9zzTV48MEHcdppp6G0tBQ5OTlB9IvIIvxywlWjCDwJYvt3kQi2f5cRwlZBbGIXxixUY3pZ+XnuKm/qN9mBV/2BeKIwKFc4ikI4nRxuIgWdP5HXiCk/iWq/CMKKoxh+4okn8Nxzz+FHP/pREP0h9uNGIEbZHQ6rPd2C2LEBFRFsL28Vvk6dBNRnSbCKVxmc6mdN/WbilyBmlef1TSV/ugnhdBC76dDHDEL1vNeah1XWj1AJIjsoLCzEqaeeGnY3lHAUw7FYjOYPJtIanYKYeYBgNeDVGbbWaxfJbm5Qs7rKTo+/VBHZToJYpp9OP5CKIyzqu1tx5qacFyEoEr4kMAkFgvq78IwMna6w7nmGv/1WX11EKoMGDcLLL78cdjeUcBTDc+fOxbx58/DHP/6Rpk9zi13E+Eg6u8PpAlcQA/Ki2EzjhUjYl7OQEZss8S0SxSwxqRLrKzoxEJV1Kmf/bl/mJOBl459VlunGjQjWETjqB0EFotLJgRbIHSb8pHv37lIPcbPfkxYkjmL4Jz/5Cf785z+jpKQEgwcPRm5ubsryd955x7fOZRQudtpREKNB9sHPtnyPH7Y2ZFbglC4KkVAVxSrXQ2XDLEQCzNqefeo3UX9EAl5WlFo/y7jYQYtdlTqd4oBVXOEgA0ezUIhGOS7XT9wK4mwcK4LN888/n/L9iSeewIsvvojf/e53GDRoUEi9SsVRDF966aWoq6vDz3/+c7qBLk2gs3k2ug9mwnHmOXYyMcOsz06wVk7GeRbVp9IeSxCL2nDTB1URzKpDdpnMcq/oFMLWfEGokCwTwtmAcniYZVkQFBRQmES6MnHixOTnJ554As8//zyOPPJILF26FKtXr0YsFguxd504iuGXX34Zr776Kn74wx8G0R8ignh1bKMkzgMVxNZGTXgxwyrTsonaYa0ca1YKUX63wov36GjrOji1xToiq4hglb77gdv2ZUWw25CTTEWjQy0zbNk0tHZY+7qwr1wS6cVTTz2F//N//g/uuusuXHHFFTjzzDNxzjnnoLa2NvQw3G5OGcrLy1FUVBREXwgGbkWk7p2UVzEbpZ2mbh3UhjzmS6pxVjw5K481n8yLl1emLRVYsbu8uGMZa4fVd3u9Tuut2me3eax5dbXP+m149YvaJefWN3QNbRRFtVOfpPZxBMFg+fLluOSSS3DnnXeipqYGBQUF+Nvf/oZ9+/bhggsuwLchW/WOYvi3v/0trrvuOnzyyScBdIfQiR+C2IsojtLOU+aA5vVgxT1wyAgYVZHLW8aqV9QWD9FyVjpPFPP6yOqHWYddBLtF93VWXfXJ/k72ZW7SdBA1oe2woarss8K+sBAFoijSifTmL3/5C6qrqzF//nzMnj07mR6LxbBq1Sp89NFH+PnPfx5iDyXCJH7+85/jm2++weGHH46ePXt2uYHuq6++8q1zRCdRuJHOipf+mOV4B6gg1zXoS57Cm+6cQhfMZbw00eVipwd78HASWKxYaHu6XRCzZrBgucu8ZaJ1FKFbvbitT2Z9eELYTZu6/+SZrAL3IztkQe8/RP2woqNPUbw/Uve5LMUMB8fPf/5z/OY3v8F//dd/dVlWUlKC2tpanHzyySH07ACOYvjee+8NoBuEX/gVr6sjjtheXxg4HdB0HxRSfg+Zo6msILKLUZ7Atgthq0gW1S/TplM6wHaL9+3rms5ySWX7ki7IOsE62wmKKKopBVQEMRANUWyiSxy72SWw+hGlsSHCYd68ebj++uuxb98+vP/+++jWrRtGjBiBnj17AgAGDx6MV199NdQ+5hgyk78RrmhqakIsFkNi1y7Pcdde3VK/xGaUHGsveJlYgVefqFzy9zAbVjny8jplrcNaL++zU7uiFZCZ21ilDK/NMAWVSliIm/pEYljFlRelhU2Qv5+gLbf7KdUhVc2vM8pGBrd/Ea/9bGnpPB6Wl8eQSCSUjofmcbSuLoHevfXdv7RvXxMqK9X7Q7jjlltuwd13343W1lYAQEFBAa699lrccccdIfesE6Yz3NTUpPTn2Lt3L/r06aOtU0RXojqjg1lnuotir5c8nbSJ48FENmTBKa7XbJjl/MqEVMgisp9kHGPV+nlpURSALGSEsBNuTkDCJkscYmt+wN15XxC43c+53YytZdqiMaEQEQJLlizBAw88gIcffhiHHnoofvSjH6G2thaXXXYZYrEYrrvuurC7yBbD/fr1w86dO1FSUiJVyUEHHYSNGzfSY5sjjp9TnEUtrtkNvAMF73KhykHFrgmSv4UoXtjBJeWGmohCIXQegUV99TJoKu5w0IGbbpWAX3WnA0EJYp/acfMXi/JP6TV8wWv4hFt0P465vV1fXYSYP/zhD7j77rvxs5/9DB9//DEMw8DIkSPxu9/9DtOnT4+uGDYMAw8//DB6S05s307/qkDQIThJELvHq+biHqvtgpi1fD+i8U25OZEniHUgI3RlxbEOghLEqn2XCbPQMR5RdYWthOwQe903RTn+1e3Q6roaFmXhT0SDjz/+mPmsiiOOOAI7d+4MoUddYYrhQw45BA899JB0JfF4vMssE0R0IUGcJjg4wQD7gNTFddYhjL26wE7xrixUwyr8VCqyYRu6kP2doqLOWP1g/R/8Vk4+txH0hQi/0SHy0zwShgiAvn37oqmpqUv6m2++iWHDhoXQo64wxTDNKRxdSGz6i98HO+uBo8vMEhysv7fTvWld6na6Cc7pKKbTBZYNndB1g5oIr1OxBdFHJ/z6o/L66OZuMhLEgRB1Qep1jnoivTnuuOOwZs0aHHvssQA6owmmTZuGJ598Eo8//ni4nduP49RqRGZC7jCbIA5ydkEsk9+pHnveggJ5sa2ELnHs9Y4cr8j2RWdIg0ostIigRbB9Oc8F5gXch/H7MtrVuV/KNEGsI2SC17au40xBAcUMpys33XQTtm3bBgDIz8/Hcccdh+bmZrz66qs45ZRTQu5dJzS1mo/onFrNjq6dup9n6+koiIM+wMlMkOA2qsD+nflbq8xQoDKdl2x61BSFFXMAZUIkVJ1m2bALN+ElblBVGSr/haBDTBza0blfitrf1+uwelkfVtvWfU5TUxNiAwe6nlrto48S6NNH33F0794mHHEETa1GdELOcACYj+ON4mUicogPEMaBzYu+dDJnWaETXSgQPxEwpXKWMHMbIuHkFocJzw12mtVCV5tWonhjoFkmKr+XiCxziKMULhHF4x0RDqtXrxYuHz16dEA94UNiOEB0Cs90E5rZDutKvKqp6pSPp1VF9fGEckq8sX16Nl5j9nRWft53mc6q4DYWmlWe5+q67auucImwsf9evN80bAIQxGYzUSDMcAk/0T21Gs17HBynn346DMNATk5OMs0alPDdd9+F0a0USAwT5A7DvwOAjPOr4g5b0RGaywv/7DJNm5lZxR1mdYYlit0IYj+cWFVHWDVUQlRXWOgYR9UbIHWPgexMFrZ03fumKAnJSAriqAwOETiNjY0p37/++mvU1dXhlltuwcKFC0PqVSpcMTx27FhcddVVuOCCC5jLd+/ejRNPPBEff/yxb53LRKIaLkHox2nf70YUywhd2UkbWNjDK7iiWLUz1nS7KFYRxLqElKojLBPG4HSDmUo/0gkZa9SP9ZSNd+edkMEfQezUtaCIlCCOwoAQoWGPyS4qKsKECRNQWFiIG264AePGjQupZwfgiuE33ngDq1evxs0334x58+Z1Wd7R0YFPP/3U185lKroEsc4deTa7w37sp1WEMO+zUzknAaxq2JllWJqVOTOF2xkk7IqbJ4h59djR6RLLxgjb80QtNEAWnUGmQa63ykbLuqLhoyAGoiOKw4ohJsOHkOGwww7D5s2bw+4GAKCbaOHSpUvxu9/9Dueffz727dsXVJ+ygiiLQz+I6s4x7IOVFa8TM4jymstEL1Yd1mXmjaBJCgoOvOywljl9FtXDq9vtzV/28k6fWS97nW76kU6IxkL0W8jm8xOHyzB+7Z/CWl0rukLbw0bm76f6csMf/vAHDBkyBAUFBaisrMQ//vEPYf7Vq1ejsrISBQUFOOyww/Df//3fXfI8++yzGDFiBPLz8zFixAg8//zzyu0ahoG5c+eirKwMhYWFGDNmDN57772UPK2trZg5cyaKi4vRq1cvTJw4ETt27HAxCnqIxWJ49dVX0dHREVofTIRi+LzzzsPatWvx/vvvo6qqikIiIkhURSbBR9YJtgpRu2AVlefl9dIv6/cuohiQE8asz9Y89uUy9Xs9AsoIYh6iupyIktLQeUaoKpCDxGE985L/bP0PiAjzPADIHEEcNk8//TRqampw8803491338Upp5yCs88+G9u3b2fm37ZtG370ox/hlFNOwbvvvoubbroJV199NZ599tlknrVr12Ly5Mmorq7G//zP/6C6uhqTJk3C+vXrldpdtGgRFi9ejCVLlmDDhg2Ix+M488wzsXfv3mSempoaPP/881i+fDneeust7Nu3DxMmTPBdjJ5++uk47bTTurwuvPBCzJ07F927d/e1fRm48wx369YN9fX1KCkpQSKRwMUXX4z169fj6aefxhlnnIEvvvgCZWVlkVD0UcWcH3HXLv48hjp2ujpdZr/FdZQc8SBvmuMtdxMuIdIXqmVk8vIMXSvc/41IVVuVPi+/U+dU4Qltp892VPqcTrj4E6ls08pzXYtwU0729xWgax8W9N/Gy6bj5t5Q+2/d1NCAWHm563mGRcdRNzQ1NWHgQLV5hkeOHInjjjsOS5cuTaYNHz4cP/7xj7FgwYIu+a+//nq8+OKL+OCDD5JpV1xxBf7nf/4Ha9euBQBMnjwZTU1NeOWVV5J5zjrrLPTr1w9//vOfpdo1DANlZWWoqanB9ddfD6DTBS4tLcXChQsxffp0JBIJDBw4EI8//jgmT54MAPj8889RXl6OFStWYPz48bJDp8ysWbNSvre3t2PTpk3YtGkTfvGLX+B3v/udb23LIjWbRCwWw8svv4wbb7wRP/rRj7Bw4UJMmTLF775lBdl2Q11U4oejpl9U44ZZBzZRfKDKPV2iEEteCK/1N035P7MKiuKFnRpy6rwsbhS/PT+vz+mMTIy2BdVtOeWGTBPrWAaJy4Bas+9e92NBr7af8cPp7CA3NTWlfM/Pz0d+fn6XfG1tbairq8MNN9yQkj5u3DisWbOGWffatWu73Bw2fvx4PPLII2hvb0dubi7Wrl2La6+9tkuee++9V7rdbdu2ob6+PqWt/Px8jB49GmvWrMH06dNRV1eH9vb2lDxlZWWoqKjAmjVrfBXDixcvZqb/3//7fyMTgssVw9b54Mzvd955J37wgx9g6tSpeP31133vHJGZhC2IM0Gz6EBF97AEsomUMLYKRtZnO6ohCjKddqrHlpf1HxWKuLBEnR9IjB1rfFROujzDOyOUyaehUzpEcbqcR2npp8cx1x2+YtZVXl6ekn7bbbdh7ty5XfLv3r0bHR0dKC0tTUkvLS1FfX09s436+npm/m+//Ra7d+/GoEGDuHnMOmXaNd9ZecyJDurr65GXl4d+/fpJ999vpkyZghNPPDES06txxTDvKc2TJ0/GsGHD8OMf/9ivPmUdUXKHg+pLWILY74OOl9g8WaNR5uq9ynGHpXucJoNg5WUt6+IE8lZU9UApIzxlhbaECLYucxTEJrr/bKKB9wPB72IfI6fYdyAg91DW1deILqc4CLy6w7IXbtKJzz77LCVMguUKW7EbhfaHScjkt6fL1Kkrjx2ZPH6xZs0a5OVFY7sRTq3Wv39/5rJjjz0WdXV1ePnll33rGJH5hO0QRx2W3pM9+Kge9FRCL2RmVLMvY85XzGpA5SirOVRC9r/oKIhZ9fshXP2u36zXYZwj7WoGqNa8iOJ0u6ggM6xRMnlEFBUVScUMFxcXo3v37l1c1IaGhi6OrEk8Hmfm79GjBwYMGCDMY9Yp0248HgfQ6f4OGjSIm6etrQ2NjY0p7nBDQwNGjRrluP5eOP/881O+G4aBnTt34p///CduvfVWX9uWhTubxOjRo9GjBz+keMCAAfjFL37hS6eyES+iMB12ODyC7HtUDjYq92vJ3JjPQ3V9RVO4OS1j5WGlO07PxpqNQPXFg5OPOTuGA9zZNJzadotMuIfXNnzG/j/Qhpv/go/4NSOFLoLcD2aS2ZGXl4fKykrU1tampNfW1nLFZFVVVZf8q1atwvHHH4/c3FxhHrNOmXaHDBmCeDyekqetrQ2rV69O5qmsrERubm5Knp07d2LLli2+i+F+/fqlvIqLizF27FisWrUKt912m69ty0KPYyZCJwiHOIgDgI42dDtEOm+akXGpnSIGzDRRXLHb/0KX+GQHeO3Ihp0ynS8nt5b3A3uJnxbl9+GPbx03bdXL/vE9/rZWohYOFqQ77OfNdHa07dtbWgCdl9RdDPasWbNQXV2N448/HlVVVXjwwQexfft2XHHFFQCAG2+8Ef/5z3/w2GOPAeicOWLJkiWYNWsWpk2bhrVr1+KRRx5JzhIBANdccw1OPfVULFy4EOeddx5eeOEFvPbaa3jrrbek283JyUFNTQ3mz5+PoUOHYujQoZg/fz569uyZnOwgFoth6tSpmD17NgYMGID+/ftjzpw5OProo3HGGWe4HkYZ/vjHP/pavw5IDEeIdLms5Ad+CuKoOMI8/L4PS+XAp5rXRCSMnaIgvIorlsBWRXY6PFZMNHeb5QV/q8S/6AwF8TK4jH6oVmetgjlmHuKC3Pz2zJktfCCq4WBBCmIgM1ziyZMn48svv8Ttt9+OnTt3oqKiAitWrMChhx4KoNNptc79O2TIEKxYsQLXXnst7r//fpSVleG+++7DhRdemMwzatQoLF++HL/+9a9xyy234PDDD8fTTz+NkSNHSrcLANdddx2am5tx5ZVXorGxESNHjsSqVavQp0+fZJ577rkHPXr0wKRJk9Dc3IyxY8di2bJlgc3z+9prr+Gdd95Bt27dcNxxx+H0008PpF0ZuPMME95xMz+i2x1zOs01LELnegQ9bZHX/LJpblA56GkOxVWq24vAkm3DbVsy9Qu3HVYciZuGeCJbBZW2bSElbpt2FMMukJ3RQtQXYX/cBu0zkNm3hXHirvp/druPdjOvr1kuFoshYbvRzStNTU2u5j0m1Pn666/xox/9CGvXrkU8Hsfnn3+OPn364Hvf+x5WrFgRifEnZzhiRMEdDsoxYRFVF0U3PBNMJs0NfrnDvPImohvsZMqLsI5NEPeS2evnhU6YMEMoWIPjZLGruMYqg6eQz8m9d1oFnaiEt/CQ+o+rhrM4ENV9m+oqpdPsGUQ0uPnmm7F371589NFH6OjowDHHHIOGhgZMmjQJc+bMwYMPPhh2F0kME3zCEuY6DhpRdoVV0SH03AoUlfZktBmvH7KuOC/kwo0Y83vdHOdbFlXOSJM6SVUZYInyMiEsLMPb6Tdws29REcFaDN0gg3jDa9I1Kvtp0U240rS26h2c1lZ9dRFCnn32WTzyyCM45JBD8PHHHwMAcnNzceutt+Kss84iMUzowatwFJ3pi+r2Uyi7FcTpciBxi44r5H7VrXqDnUwdouXW/vrtDHu5eRDgzLfsAO//7/gwEBYKalA0h7DOsZUVxKL9gGzkCe8KQtATTmSKO2zitD6Zvj8m5Ni1axeGDRvWJb2oqAgtEfmTkBiOIH46sqJ6VXfUwkvChCu83FTvZZ/CCRH1hMysDG7ipFkiOMjL9DLt8Zbbty+nS868sZByoSVxirt187+SFVe8fZ3TfshtfD2vX9x9rv0PFuGp66JERPQNERHi8Tj+85//pNzwBwAPPPAATjjhhJB6lQqJ4YiiUxAHIVTDjDM2CWMH7JdD6/YGJdlpwWSWB+GwummPJYKDihl2Ey4hWu50QxpPg8m40Co4Oay8kBRW+9YyMk6san/9vNGUi0YR7GQ6pFOoBBCg293SAuyfm1dbfUQgnHrqqXjllVeS8xm3tLRg6NChSCQSeO2110LuXSckhtMcp51Q0OI0CjcAZgJeBLGf+Xm4CUlVuZmOJwaDdobdhIKIhKwpEEViWLTczWxkMrG/svHB1jSZcfdisKr0iQgP+j0IOwsWLMAXX3wBAOjbty/mzJmDww8/HBdddBH69u0bbuf2Q2I4wngVlmGJUl0useoNGoQ+VO/M15XXjUPqZsYKN8iGpqj0R+bGIpHY9eKMy7rBLHhj4XTS4pTu1EfV5SrQibx/tLTQ/WrZzEEHHYSDDjoIANC/f38sWLAg5B51hcRwhhKFnTodXLzh9+VSrw6qm5AHr+EMMuLPL2dYNgRFdb1U8svMzsbql6yr6uQOOznxKicton7J4Me2Qfss/ZBRQcybN086b1iPZyYxHHGC3Dn7Efflpf9RvOs6kxDFgcqWdUpzk0e1/ag5w7y8vDIqISa88BBVN9ptf3jur6pjr2sea1Vk+hmF+x/SLW44EFpb9cYMk1UdGC+88IJUPsMwQhPD3YJqaMGCBTjhhBPQp08flJSU4Mc//jG2bt2akscwDMydOxdlZWUoLCzEmDFj8N5776XkaW1txcyZM1FcXIxevXph4sSJ2LFjR0qexsZGVFdXIxaLIRaLobq6Gnv27EnJs337dpx77rno1asXiouLcfXVV6OtLXXnt3nzZowePRqFhYU46KCDcPvtt4Me2KdOporaTDlYBSWERfWpvJzK+4Vsf5zWye068z6r1mcvYy/PqpO1bvY00RjIjI/sOAZFG/K6vIIi6H0LTZJB+Mk777wj9Xr33XdD62NgzvDq1atx1VVX4YQTTsC3336Lm2++GePGjcP777+PXr16AQAWLVqExYsXY9myZTjyyCPxm9/8BmeeeSa2bt2afL52TU0NXnrpJSxfvhwDBgzA7NmzMWHCBNTV1SWfrz1lyhTs2LEDK1euBABcfvnlqK6uxksvvQQA6OjowDnnnIOBAwfirbfewpdffolLLrkEhmHg97//PYDORzWeeeaZOO2007Bhwwb861//wqWXXopevXph9uzZQQ0bkeUE7RCFLYzdtBmUM6yzPZVxszvCIkdfdWYQWVeY50Db88i2rztkQhaRYy07FZwVN+5x1MwBEsIEAeQYIVmdu3btQklJCVavXo1TTz0VhmGgrKwMNTU1uP766wF0usClpaVYuHAhpk+fjkQigYEDB+Lxxx/H5MmTAQCff/45ysvLsWLFCowfPx4ffPABRowYgXXr1mHkyJEAgHXr1qGqqgoffvghhg0bhldeeQUTJkzAZ599hrKyMgDA8uXLcemll6KhoQFFRUVYunQpbrzxRnzxxRfIz88HANx55534/e9/jx07diAnJ8dxHc1nqu/a5f3Z57ydrqsJ+CXr0Y1Kv3RMt+Q3XtuULe+326mjrI4+OtUR1kwZutpTEZ5OD6XzMi+0lxMYe1tup+1zm08HMm156Y/KvpqHU6gKCzd9dioju88WPanQ/L53bxOOOCKGRELteGgeRxN1dSjq3Vu6nGO9+/YhVlmp3B8iMwktZjiRSADovLMQALZt24b6+nqMGzcumSc/Px+jR4/GmjVrMH36dNTV1aG9vT0lT1lZGSoqKrBmzRqMHz8ea9euRSwWSwphADjppJMQi8WwZs0aDBs2DGvXrkVFRUVSCAPA+PHj0drairq6Opx22mlYu3YtRo8enRTCZp4bb7wRn3zyCYYMGeLb2Lgl6jd+6HjiFOEdWZfQTV1hCPcg50kWte325MZJnIoeMuL1yoGbKwEqT3GT/c1UxThrfGSRdbnd1u/n/ktGJOsU+5G6obClBeihUbKE4aAQkSUUMWwYBmbNmoUf/vCHqKioAADU19cDAEpLS1PylpaW4tNPP03mycvLQ79+/brkMcvX19ejpKSkS5slJSUpeezt9OvXD3l5eSl5Bg8e3KUdcxlLDLe2tqLVEpTf1NQkGAU96NhRBSk+nXauJIT147TPj1JohKieMEUvr323bauU44VI2OvxMk2ZyrizBLFM+07tqcxTLOqPah9Uxi0IB9vryarTeLgR9yrHGbr5j0hHQhHDM2bMwKZNm/DWW291WWYPPzAMwzEkwZ6HlV9HHjOihNefBQsWKE0hokKkztB9wKsI5k0xpZonm/Aa5hB0vLDuMIog29chRFnOsDWPjpMaXj63QpWH2zmbndrW9TAPpxOvqMfZyoTaBAWJYyIdCFwMz5w5Ey+++CLefPNNHHzwwcn0eDwOoNN1HTRoUDK9oaEh6cjG43G0tbWhsbExxR1uaGhIPuYvHo8nn3RiZdeuXSn1rF+/PmV5Y2Mj2tvbU/KYLrG1HaCre21y4403YtasWcnvTU1NKC8vFw1H6IThxPrZpuwlwijsnMPshxshrDO8wk07gHvB6QduL/W7cYXN9nSIYDeohkfI1GfHSSDLCnIdgs+p3aBFcdj7q0w3YwgisKnVDMPAjBkz8Nxzz+H111/vEmYwZMgQxONx1NbWJtPa2tqwevXqpNCtrKxEbm5uSp6dO3diy5YtyTxVVVVIJBJ4++23k3nWr1+PRCKRkmfLli3YuXNnMs+qVauQn5+PysrKZJ4333wzZbq1VatWoaysrEv4hEl+fj6KiopSXjqhEAJ9RN3ZCYuWFv6Ll0+1HqeX2/7JlHe7/jrXUaUOVl5WWZ3rKnvSo9K+bB7Z8Q4SJ1c/bKEaFF6ml9Oyv/WyU/FjZ0FkFIE5w1dddRWeeuopvPDCC+jTp0/SdY3FYigsLEROTg5qamowf/58DB06FEOHDsX8+fPRs2dPTJkyJZl36tSpmD17NgYMGID+/ftjzpw5OProo3HGGWcAAIYPH46zzjoL06ZNwwMPPACgc2q1CRMmYNiwYQCAcePGYcSIEaiursZdd92Fr776CnPmzMG0adOSAnbKlCmYN28eLr30Utx000343//9X8yfPx+33nqr1EwS6QCJ6/RAt4NsF1Ruyrkpr8q+ffxlopvK/ehTWMdNlgMZhDvMc0Z5zqsOQey0jva6RKEMuk92nZxg+7rpbt/tg3H8OOmXcYlZ/bXci04QkSMwMbx06VIAwJgxY1LS//SnP+HSSy8FAFx33XVobm7GlVdeicbGRowcORKrVq1KzjEMAPfccw969OiBSZMmobm5GWPHjsWyZcuScwwDwJNPPomrr746OevExIkTsWTJkuTy7t274+WXX8aVV16Jk08+GYWFhZgyZQruvvvuZJ5YLIba2lpcddVVOP7449GvXz/MmjUrJQwinSEhTNiRFbs6hZdI8HotJxLMbttVbUcXInFqxQ/xYxdVTsLQSzuselXT3bTJQlWEe6k33bAL4jy00TGFSGtCm2c4G9A5z7CJrrgt2nF5E3V+ObV+tMe6vO30WeY7oFdUsuCtdyYIChVEgtuPseDVqTrPsBU3AlH2JjC3/ZLZroK8EU3kAIvS/fi9ZLAej1jHFGufO++h8TDP8P/3/+mfZ3jsWJpnmAAQ4jzDRHiQEE4/dIZKuL3kasUPEew1ZINHFG6Y9CpC7ONt1QR+uMOyYRIy4Qwq7TlN48ZL85Og+8D7v4rSo4rWba+1Ve88w5ZpUAmCxHCWQUI4eoQp1uxz1wbZj6Da8ktk89A9bRgLFXGsgszMCbLC1U0/RDHSTqI8aFHoR7iITFx12Cd2VtzGDxNE1CAxTESeMA46fpX1gtuDilM5VSfKqyvstA7pfuCUDe3wup7W+qy/iZcrySKXWUaA6tpWZdxYHQJYdptyumHPrEsXov9KVOdLp7hhIp0hMUxEGlZMa5QvC7pF9qCsSxCrCmCvpIMA9qMPKiEFqvDqE7nGbtoQubR+OrJhuL0inPqj+v9RWTeWAI7SDBMmPEFcUAB4fiBrSwtguVHeM1HY6RCRgcRwFpFOZ+1+7qfS0RW2okuw8kIkWPVb03r3lnOHdd4YKHP5WFdbKjjF6/KW64i1lZ3hQdbJ54lmmTAFXv9kT4Jkp0lzcmhlbiQT/beDRPQf4K0LL1QlCvslE5EgJoioQmI4AOjJPWpEacceJCoHZTcH8KDKeBXBXmJNveBXHbKhBV7a4okiFQHi5CjzBJqMCJURxW7Ekqwod+qTjnAJL6he9WJd6bHW49SW38KUQiaIdIPEcEDQziEaRN0VVhXEgPcbpty4wzy8LA9ifHW2IetWy96A5rZ+UZ1eQjNMcWy/OY8VImH9zHM03QpiN7HCupzroBH12zRVzOMIbxv12o41j0qdduiYR6QTJIYDhHYOzkQ1PCJIVA/SfjvKLGRDJUy8iuAo/HZeHHL7iYab+pzqt6Y5hV6oOsYygpi1PAhk+gPoE8ey6+c2htisn9dv63HEy/bsFHKiA63HvNZWvTHDNLUaYYHEcMCQIPaG24OsTuERBG4EMRCuaOS1rZrutEwHQTjEPOdWRpR6jRmWjUmW3Z7sgtgNTlcbVLZtmdhhN46yiiDWjegKTUHBgWnM7McPr4JYJg/F+xKZDonhECBB7A7aITvjx6Vft+ERYYvgsK8yyDi3KvXIhECw8jq5xW4EMcuNdSuadPxnecI3ikLO6eSJJYStgphXNgpXT1jQ8Y5IB0gMhwTtILri18483VxhE7cHuCgcGHW6xF7y6iZIEezUtowzLBtX7FUQs/qqIkJ13DwnEsRmG7KOu+7/mJv/jV0IR1HY083hRKZAYjhLSHfhnS3hEVGENQbWeGH7cl0uscxyFcI42eLFDbuFJW6t6TKhATpFlUiwuQl/cNu+9bu1TZbLGiRu/t8iISxahyicBPPQIppbWoBu3bzXY62PIPZDYjhEyB12xsvBKxOEsO4DnGxdKoJWlCesUIkgfjuZdeCJVztOjjFvOW9mB1YZ0bbkRSg6CTZdv6dTfLWTKNYZry2DHyeAIkEchf0VQaQrJIbTDJlnwacrOh2lTBDCQWBdT1kBzHOFvTjETstEhPFb6RD99nxOYtUKz/nkLbO34UX8iqZcUxGdunEKT1EJJdHVD5llTic4onCPIG6cjVpoBkH4gcZrDoQbMlXYeqGggISwiY7YUl66ioBzSnOqu6VF3J6s68x6ycIrr1qfrOgX1c9qS6Vf9jT7Z9EyUb9V2LePfWLEaj9ot1803rz+RUEI2/vHevdjbIMgm451jY2NqK6uRiwWQywWQ3V1Nfbs2SMsYxgG5s6di7KyMhQWFmLMmDF47733UvK0trZi5syZKC4uRq9evTBx4kTs2LFDqe0vv/wSZ511FsrKypCfn4/y8nLMmDEDTbbnZRuGgbvvvhtHHnlkMt/8+fM9jUuUIWeYICxE4cDipQ863VWRK6wqhGXbdJNHd1mRwyiqX8UFVikjmi7N7vaKlql8VsF+U53ZB5F76TUkw20ZHesrakdlmdNvz3KG/ep7WvD118B33+mrr7lZX102pkyZgh07dmDlypUAgMsvvxzV1dV46aWXuGUWLVqExYsXY9myZTjyyCPxm9/8BmeeeSa2bt2KPn36AABqamrw0ksvYfny5RgwYABmz56NCRMmoK6uDt33z8Hs1Ha3bt1w3nnn4Te/+Q0GDhyIjz76CFdddRW++uorPPXUU8n+XHPNNVi1ahXuvvtuHH300UgkEti9e7cv4xUFcgzDMMLuRKbS1NSEWCyGxK5dKCoq4uZTjRt2c4adTbHJOgVhkPjpaIuEm9Nn810mPEJG6MmIALcE9Rs6raeuftgFj/W7aBkvr2yaCLvItWMNn1Cp2+vsE/YxVx0Pt7g9qVM5ETI/2/sdhCAWtaFyLGpqakJs4EAkEgnh8ZBZLhZDYskSFBUWSpdzrLe5GbEZM5T748QHH3yAESNGYN26dRg5ciQAYN26daiqqsKHH36IYcOGdSljGAbKyspQU1OD66+/HkCnC1xaWoqFCxdi+vTpSCQSGDhwIB5//HFMnjwZAPD555+jvLwcK1aswPjx4121DQD33Xcf7rrrLnz22WfJdTjmmGOwZcsWbplMg8IkIoCquM0mYatKuglhXZdo3QphXrpdCDvlE322ponadjMOolAC2TJuLr9bv4uWWzHDCmReMu05XTJ3+j15aU6/kf27Pd26DiouuJvfQfQbqo6HG3QKYdb68NZBV/+JTrFtfbV6fDLd2rVrEYvFkmIUAE466STEYjGsWbOGWWbbtm2or6/HuHHjkmn5+fkYPXp0skxdXR3a29tT8pSVlaGioiKZx03bn3/+OZ577jmMHj06mfbSSy/hsMMOw9/+9jcMGTIEgwcPxi9/+Ut89dVXLkYkPSAxTGQ9QR9QdAlga32idkR5ZQ/UrEcvqwph2T6K8CpiZcS46MXrN6tunsDltc0qa80j27aMAGSJKpHQlRV9dlEs6o+ojiC3STdtqYyJrj6EIXwjFYohu5GqvACUl5cn42tjsRgWLFjgqZv19fUoKSnpkl5SUoL6+npuGQAoLS1NSS8tLU0uq6+vR15eHvr16yfMI9v2xRdfjJ49e+Kggw5CUVERHn744eSyjz/+GJ9++in+8pe/4LHHHsOyZctQV1eHiy66yGn10xYSwxEhm24uiBJBH3R1tuck7pzS3AhYliMlqkfWtRPhViD5cdLBcu3sAlAkflUEtlkfb7nT78kTvyIhrGO8vApia74wBKAIv/okIzr9ngouW/nss8+QSCSSrxtvvJGZb+7cucjJyRG+/vnPfwIAcnJyupQ3DIOZbsW+XKaMPY9s2/fccw/eeecd/PWvf8W///1vzJo1K7nsu+++Q2trKx577DGccsopGDNmDB555BG88cYb2Lp1q7A/6QrdQJemZPIUa0ERdffJTT2qopb32UnQyKAi1N3k0VlOpS5WupPz66ZdU/xYb1Azl8vOKczLa6az0tz02dqmvV1ev2VdR/s6hYXf+wvRwzScUBlPr/3JNIqKiqRihmfMmIGf/vSnwjyDBw/Gpk2b8MUXX3RZtmvXri7Or0k8HgfQ6ewOGjQomd7Q0JAsE4/H0dbWhsbGxhR3uKGhAaNGjUrmkW07Ho8jHo/jqKOOwoABA3DKKafglltuwaBBgzBo0CD06NEDRx55ZDL/8OHDAQDbt2/PyDhicoazAIox7kq6CWEnR0rkwIryOTl2bsMj3LjBYTnAgiuowjZELrCoTzLtmfmsbfGWWetkfef95jyX2+1Y8tqRnZdatX6duNm23Nbp14kxOcT+UlxcjKOOOkr4KigoQFVVFRKJBN5+++1k2fXr1yORSCRFq50hQ4YgHo+jtrY2mdbW1obVq1cny1RWViI3Nzclz86dO7Fly5ZkHjdtA53OMYBkvPTJJ5+Mb7/9Fv/+97+Tef71r38BAA499FC5AUszyBmOEKpPpEtHd9i6w9btPETtYKDzcrNqHllh7PRZNs1NeZllIoIoZ8/r1QF2GgfRNsFyiAG1xxGL4D3YwZ5HJ25cX7/2IXa3PJvxwxX2bMq0tgIOIQPK9fnA8OHDcdZZZ2HatGl44IEHAHRObzZhwoQUR/Woo47CggULcP755yMnJwc1NTWYP38+hg4diqFDh2L+/Pno2bMnpkyZAgCIxWKYOnUqZs+ejQEDBqB///6YM2cOjj76aJxxxhnSba9YsQJffPEFTjjhBPTu3Rvvv/8+rrvuOpx88skYPHgwAOCMM87Acccdh8suuwz33nsvvvvuO1x11VU488wzU9ziTILEcJqTToI4mw4yfgs1XSLY/l3kRqr0T7aMG3fQDV7bEcUA62zfLojt33lPfwPkRbH9u4xIttfLW87qs9lv61zErDJO9bJwOoFwMyex36gI7jBCFbIlPMJPnnzySVx99dXJmR8mTpyIJUuWpOTZunUrEolE8vt1112H5uZmXHnllWhsbMTIkSOxatWq5BzDQGecb48ePTBp0iQ0Nzdj7NixWLZsWXKOYZm2CwsL8dBDD+Haa69Fa2srysvLccEFF+CGG25I5unWrRteeuklzJw5E6eeeip69eqFs88+G7/97W/1DlSEoHmGfUR2nmErbs+eeYI4KiESQbpMuvL53Q/VvKL8btxc+3eeEOZdYneqQ7W/InSLcNkyOk4IVPOztguZOX1Vy/PmAFaZy1ilPt78wzK4Ebwq8zD7gdPvrnISouP3kUG1vIoRs7upBQMHxtzPM3zHHSjS+KM1tbQgdvPN2ucZJtITcoYjhmqohIlZxm15Gew7Ptl2yBFWzyOT360AZqWpCGFZdAjhsEQw4Byn6xci55TluALyTrE1neUOiz7z6pGF96Q6GUT5ndzhqOImFEX1ASmq/SGIbIXEcIYRpBPsRXjL7nj9FPe68MuRlhWTOkMmvDxlTveJQJghFKLY4CDgCTw3otie354uEsK8MAtWH+35Wf3nxT2z+snCrfCNqtDzMqVa0P1gIRum17kP93g2qftsNJtcGsIREsOEFGHEJZttRlUQ6xZ2qrG1XtJY6ToFoEo/3OZzm99N2TCOmyLhxxO6LFFsz28t4ySEWc6wrCDmpdkFsUw/RfVlMn6vZ7aMI0E4QWI4gkRN/IUphKOIThHsRiTLOMMqbrHMtF0qn70QBSEctitsRSacgJXHvg48ccybj5jlDMvMZWxvg9dvkSAW9ZNXtxuB7Leo9jozhd8xziSECeIAJIaJwHHaCUdRCOuMXXXrErsRxaJ0mZvDghK5QYpgUXldM2joRiZWVyScrevFiy/mOcMyrrBTiAQrjdcn3roFJd6cfu8w3Nqwb5TzQpSMHYLgQWKYEBJFYcrDqxPDQmccqh8usJt2ZKcJk3WIVdsPOr/u8mHj5BY7CWdefDHPGZZ1hZ1EsKxYt/eNVZfMd1FZa7oqMm69ico+SXZWDK+krSOse15gn+YZJtITEsMRJWqhEjpI252wIiruqqyb6yRE3V7yd9O+bB06ynrNr7t8lNDlFjvN/+sUO6wSYuHUJ17f7P1xg253WVYUewnnkHGJndJV8+gk045hROZCYpjgossVVnFHouRE6wiNcOPcysblysT/OtWv2jfZPLoJQgTzxs4pNla1HacybgSLkzAWLWc9EEMmBELVFWYtF/XZ3jcWQYZP6OiDVyHshbDHiSCiDInhCBN1d1ilbzI74kwXwioiVFYQe308sFNeP51b2bxehLBO4e72srjoZFD2ZElFyMiGUahMY6byWbSc166sWLfXKdt/1bJhIuqf276Hsc5RPnYRhB0Sw0QkiJIQVsVJ0Ki4wzIi2OvDILyIUBWBrwO39YcZDhFU7LqsSy0TR8t7XLIfrrCMi+0kiFXacuNIq6JLZLsVwlEX+FpoaQF0PjCXYoYJCySGicgTtMPg1d2UdXV56awyvIdhyPTHDekaW+u232FMp6Yr9MOLKNYtiFVdYVG6aL10iU+Z3yCM2SNU8qjELBMEwYbEcMQJK1QiSKdWZ1t+uHI8VMIiVGOJnURwGKJXR+iISl1+hmj4TRj/Q5WwBx5+CmJRX1np9rIqcxOr3rDnVJ+X8iL8FrJhCWEKkSDSDRLDhCt07eyCDo9wEihuQwhk3GAV8WyKYFVB7RdBC810FsKy6BbMMmJXJo7WiyBm1SkKVXDqi2id3IRHeBGHOt1qXTM/+Cl2Ixdf3dqqN0yiLX1D8wj9dAu7A9lAG/Iy6kw5k9ZFlZYW70LYWof52eoE79vHzsPKHwRe3N0gDqbpKIT9wq3jbk+TuSLB+izzn1ftC69fTuV4dXvZfnT814ISwl6ccC/rmc3HByJ9IWc4QNqQlxY3igXVx6iNhdcwAFVxYEXkBPsRHhFl/A7dsBNEvHDQYRNhOsS6HVsvDqWTs2xFtg0v/UkHR9gLIiGcDfsuIn0hMRww5s5CRQhGfYo1N0RNCMsg4wiriFkn941VntcXHlE9aOogXYSwCL9Eskycqx+CmFUPSxDb+6YSHiHqr5fwCOvvEEZ8cNB5WLD2cZm8DyEIExLDIZEuLrGdTBPlJiJB4iRGReKVlcYSwX45wqJyXuMn052whXAU0C2I3QpmXh7WMjcP4pA5OXCqw2/CFsJe8d0VbmkBvvtOQ0X7oZhhwgKJ4RBJV0HslXRaZ6c4RJ4QFolgXaEVMjg5gzL5oojXg2uUhLB9+jGdBBkywSqrKohV+65ar4oodtM3tyJa1/aX7Se4BOEWuoEuZKLmtKaTUFWFt7OXTXcrhFVuiuOl2UUxbxlrHXTmY42DG8IU32EJYZkbnsIaF97VDzdhPW7aES1nIfPgGZn/uxNREojpdsJKEOkEieEIEDVBzENHP6MmtlUFsrlMRgibItieznuxlvPSeH1SySeCly9K4sANYTvCYVzmlv3NZLYFp4e/OF3dkOmP2ysjqoJYZrkoT9S2hbBcYafZkqI2TgTBgsIkIkK2hkxEEdHBW+SIWcUjSwSr1OXUDzuiS8JOy2Uuo3shyJkURHgRwqwYVaf6eOstMx5+hk6I4P0nWlrYMbtmunU5q4x9udN3GVixw7x+sNZJdnkQuFn/IIhUn1paKGaY8A0Sw0RghCn2ZV0jGSHs9G6GRLCWOaWJPouw51MRBDrFQFSErw5knnbmFtlxYs3CEARO4lYkRGWFnRsB6EU0RlVw6iCqscKZsi8gMh8Kk4gQ6RIuwUI2Bi8q+CGErbHBZppsWITos7UPMqEQvPKidNb6+w3vAB62YOndW04Iy+QTxQGrxgib+VXKeYlDdtpGWDeE2vO5DXOQLcdy5522C93LZPEasuHH9plO+2yC8BNyhiMGhUt0JYh5lt0KYRk3WDVUQtQvp+WyjrBqulei7hh7cYF793YfMuG0zKlOv1F1eb2GS7hBNlwiKLy610Bws1gQBNEJieEIEkVBnM43SIiErug7TxA7hUW4FcOiNFlEIlflIBr2wdVJILoRkH6GPZh1i0SxkyAGorktiQSsKH6YJ4JF9al+Vum3U3oQeO27zjIy/zUdwl4bra16Y4bb2/XVRaQ9FCYRUax36KZz+IQu8vaPiFd0CmH7lGmyL1ZdvOUydcmsp71Np7yidCdUwh9EB9p0dLq8hE3ILA8L0XYjOgHQ/d/iEfYsIXactk1ZIapaN0EQ7iAxHHGiLoSdxKWJLqdbp2PuVQhb0728rG3JHihlyqiI3zAPvG7FXxRFI+A9llhmeRjICGLWNiRTXuazqC5rH5zyidKDRGZb96v/UVh/gogSJIaJtENFEMseZO0HcZGj68YNlnF/rZiuM+sls65O6VETxDyiJghVkBXFTsujNAYyJ7+sbUmmvKowzBRB5+Yqj468fhKVfhCELBQzTGQUbnbCvIM3yxW2C2HrclE5Vt/cXto1y7GEVkuLP+JJV71u5t714+azIHGKJ5aJF/ZzijV7fTLzTrNift3ED7O+89LCxE1fVNfB/B1k45x59Tu1G/XthUtLC9DRoa8+ihkmLJAzTDgSxVANGXdY5TKskxBWjQ/es6drmorDK3MJlVeHFydY14HSTSyw2/hh3e6pfQoz3ksVJ6dYtl4vfZDBzeV768kiLy/rhNGpXnuam20iTPHn9uRcx9UcXdt32opnglCAxDCR8XgVwtb8qi+r+JUtY++7SJzwhLUfgljnQVG3IJZZ7kQQ8/4CcqLYTR/cIprGS/SbOwliJxEru12KSAeh5raPOk5gVU4kCCKboTAJwjVR3rnKHnxlHGHrd554tX/nPZBAdsx4lz95y4KipUXtgQ+i9dUdMmEuN/sp0z+dsC5jixCFT7iZas1tKIWsCyxzSd78boZMWJebn631sZbz+qDb/fcjLw/rGKuGTsiERziNXZD41t7XXwM9NEqWb7/VVxeR9pAzTGQMqk6KDiHc0tIZEmGGRTi5wKz6eOui4tB6cYdl21CtA3Dv5orcTtVQAl1hDqrItiVyit32VaWcbD7ef1J01cK+XNYBllmmm6BOMp2uBvHy29NY+fwiysYHQeiAxDChlSjc9MI6kMoIYPPdFLVOItj6sopgVryw6MAns8xP/K7fy4wJbsRyFJEVxV7K88rpxkmIWT+rCmJdJ2G6CEMgi/KIvvPSdEGCmMhkKEyCyHicDrg8IWymicQtkCqc7fXLHkDsl8VV7+j3A6dLr24uNYvGQxQ2wSure5YFmXXy0o7TOOiYeYJVxim/TB4rrP+p9T9h/SwKmeDVbQ2hYOWVrcveRy95gkK0D7Cvq9PY+NE3Ud0kmIl0hZxhQoiOmST8eLS0vV+yzq+9jJMjLBLC+/YBu3c7O8EyDrHq93TFyeF0collyjqFJ3gNndARcuFUzutDO1j5/UD0P5V1iGVcYZ3oCh8J4mRUJj3oK0qiq1i+4mYnq7IT1khjYyOqq6sRi8UQi8VQXV2NPXv2CMsYhoG5c+eirKwMhYWFGDNmDN57772UPK2trZg5cyaKi4vRq1cvTJw4ETt27FBqe9myZcjJyWG+GhoaAAB///vfcd5552HQoEHo1asXjj32WDz55JNaxiaqkBgmsgqee2sXxTwhvGdPpwDevZud11qGJ5JZ7dr7xPruJ0GLbT9FMSuv37HCbtsQ5Vd5kl2Y7qcfgphXZ6acFMqiIojDEsU+68q0ZMqUKdi4cSNWrlyJlStXYuPGjaiurhaWWbRoERYvXowlS5Zgw4YNiMfjOPPMM7F3795knpqaGjz//PNYvnw53nrrLezbtw8TJkxAh2X+Zae2J0+ejJ07d6a8xo8fj9GjR6OkpAQAsGbNGhxzzDF49tlnsWnTJlx22WX4xS9+gZdeeknzSEWHHMMwjLA7kak0NTUhFoth164EioqKwu6OFHYXV+QMs3aArAOu386wrCvMerd+trrCdiFrvUGOdRBwc7C2jpXqZ9Z3lnBi/R5u4nN56BJYTuMmM65RPiDr6L/sQ1q8jKWXMRT9P62fzf+pmeb2nZdm3w6cthuVbUR2uW5Uts0gtlc3NDU1obw8hkRC7XhoHkcTJ5yAIo2zSTR9+y1iGzYo98eJDz74ACNGjMC6deswcuRIAMC6detQVVWFDz/8EMOGDetSxjAMlJWVoaamBtdffz2AThe4tLQUCxcuxPTp05FIJDBw4EA8/vjjmDx5MgDg888/R3l5OVasWIHx48e7anvXrl046KCD8MgjjwgF+znnnIPS0lL88Y9/9DxGUYScYSLtkAndkBXE5mfRk+XsQtg6ewTPFZa5MifzOVtQCW2QqcOPl47185JHximWqSdqDrHKtsr6zkvLJGQdYjNNlD+MsfLzqoxXmpqaUl6tra2e6lu7di1isVhSjALASSedhFgshjVr1jDLbNu2DfX19Rg3blwyLT8/H6NHj06WqaurQ3t7e0qesrIyVFRUJPO4afuxxx5Dz549cdFFFwnXK5FIoH///g5rn77QDXREWuMkcp3eWWELLIFrD4vgOcMizINBSwv7M9GJdWxEy0V5/ID1O6m277RuMnmcbrKz1iMaQ96VHS9jav8/O/3XzTSZ7UDHtqLDFQ4L3vqbvxdrbFnporr8QFs7ra165wbeH1pQXl6eknzbbbdh7ty5rqutr69PhhtYKSkpQX19PbcMAJSWlqakl5aW4tNPP03mycvLQ79+/brkMcu7afuPf/wjpkyZgsLCQu46PfPMM9iwYQMeeOABbp50h8QwwUU1RCJs3AhioOvsEXZBbAphlvMLAIYhfsZ9Tk5uSpu8g7/KAcptiERY8PoiIwpFeWXW0c//qkwfReX8FsVexa0bZASxOcMEr6ysSGYt59WtizC3KyeBy1omSlfZflTXO0r7HxGfffZZSphEfn4+M9/cuXMxb948YV0bNmwAAOTk5HRZZhgGM92KfblMGXselbbXrl2L999/H4899hi3/r///e+49NJL8dBDD+F73/uesC/pDIlhwlf8iBc2cRK/Tq4tKzzCLnp5Qri52RTAYiEMANao/JycXOUDfth46Z9q7KVXceymD06oiElV11qXKHYjiP1yhwGxIDaxT7km2h6C2maivB1acSuKVQSx/T+gIorTZRwBoKioSCpmeMaMGfjpT38qzDN48GBs2rQJX3zxRZdlu3bt6uL8msTjcQCdzu6gQYOS6Q0NDcky8XgcbW1taGxsTHGHGxoaMGrUqGQelbYffvhhHHvssaisrGT2a/Xq1Tj33HOxePFi/OIXv+CtdkZAMcOENoLYAfLcapYg5n1m7eRZgtgqhK2vTiHcDqAJQPP+V5Pl1Wx5YX/eTtHs5CLrQuWGm6D74FRGNk7X7zhfN31ildOVV5THKZ44jP+D6CTUhPfYcmuaX8622/9nlLCe9POW2feBPJGro023daYDxcXFOOqoo4SvgoICVFVVIZFI4O23306WXb9+PRKJRFK02hkyZAji8Thqa2uTaW1tbVi9enWyTGVlJXJzc1Py7Ny5E1u2bEnmUWl73759+H//7/9h6tSpzD79/e9/xznnnIM777wTl19+ueJopR/kDBORow15TEfZ6cY5uxvME8VO5a2OsVUEdwrZdnSKXFPUyjrfqfFYOh0uPy8Ju0WnEGXhlyus4ubKlpFxf1XqFtUncolVnGA/witY4RK8PG7CiKJ+hcVPWL+ffbnusRHV6cvv0NICdO+urz7LdGQ6GT58OM466yxMmzYtGWN7+eWXY8KECSmzORx11FFYsGABzj//fOTk5KCmpgbz58/H0KFDMXToUMyfPx89e/bElClTAACxWAxTp07F7NmzMWDAAPTv3x9z5szB0UcfjTPOOEOpbQB4+umn8e233+JnP/tZl3UwhfA111yDCy+8MBlvnJeXl7E30ZEYJtICp4dssJaxvtvLsd6t7vABIfwNDojgNsiJ4Lz9+XP3f7d+VsN6cFENPfCK6gEvCEHih8jl1Ssrdp3yqohimbp5gtWNIPYLUbiE+ZkXLmHNI0rTJfQySUhbf2P7/07nembzCYiIJ598EldffXVy5oeJEydiyZIlKXm2bt2KRCKR/H7dddehubkZV155JRobGzFy5EisWrUKffr0Sea555570KNHD0yaNAnNzc0YO3Ysli1bhu6WkwSZtgHgkUcewQUXXNDlhjyg88Ec33zzDRYsWIAFCxYk00ePHo2///3v7gYl4tA8wz6SzvMMy7iwdlg7RTcxwyxnWCSG7S9rOq/f5rs9FKK+vjOtvr7zdUAIN+GACDYFcde17STX8t2aVphclpOTmxwv6+Vvp3f7Z5Ub59yKaJ1iWPeBOKy6ZK806GpPVJZXlyiOWOYkUlS3KvbfnfV/7t2b/b+XTbPXZa9f1AfWd5n10IEfIpWF7D7FipurGby0pqYmDBzoYZ7hI49EkUZnuKmjA7F//Uv7PMNEekLOMJHEz5vd/EAmJpGXT+SQWZ3hA0J4H4Cv9+ewimH7SYPdDbbj3h2Wwe0B1e+wBp1tuK1PxrWVyWvPz8sr+o/x2pNxj+31+ukQ63KSWesnEyZhLefkGGcjKlc8eI661/HL9t+AyAxIDBNM8tAmdbNaGLBuCGE5w/a8QNeDKe9gfyBGuAlAIzqFsDU8whoz7PQQEOeHhMjih9h1s0zWDXKq300+Fm4dLF5ZlXAJp1AGmf45uZROIRJuBDGrDzoca9nf0S7KZMIlWOVEdenGD+HnV8iPvYxMaIns/yoUWluBbhrv+f/uO311EWkPzSZBANDjCuvaaXrtiyie2IR1OdXqCHeWtQph0xk2X6Ywtn8XzRbRLFimjtNjZ53S3aJSn5PQtr689knm5aUfMvWJ0r3Aqlf2Mj9PFMr2U6bvvBNUVj57Xus7a3YJ3mdVdO2fwhSHTm3zzAFrWdUTM4LIBsgZJiIPy6F2OtDKuH0s18M67/CBcAhTCJuZrWJdxfWVC48Qxfap4lRWV1iDH06xal47bh1fN66wjCMs6xKLcOvc8RxiHc6fTGw0y+mWcXWd3GFW+SAIIzRARgiL0r3sCwgi0yExTKQtTg6I+dnuqvEcta4HA9PpbUenEHZyfgGdIREquDmQ+Rke4UYIB+VgO4lfGREtGw7BEpsyYRV2ZAWwaJlsyASvz7wTUBl4JwUiQWxv1/7ZXoe1rSCEHWudwkLld5Dpq5eTJF79nq8+trRQmAThGySGibS7cU4WkdDo2/dAnn37Or+3tADFxZ2zSBzAGg7BGie3LnHXPqk4NzLxkG6Fpx9C2ItL7FVoqIpfVXHs5P46iVcZ7PXyRKJTeyxBrMMdVsFNLLD9s9MyoGsMsp+E4RRb2xali8bIxGl/4PX/kYc2BPgXIwhlSAwTGYHdIXYSH+a7KYLtDnLnzXOmELZOq2ZiOsS99n/O27/cDIWwh0SYU6vpR6f4DEoIh+kQ6xbHMgJYR4iEvV63DrFqO34gEvXmWFnFrLncXtb8LiP4ZPqkIywnbJcYYF8dk8E6tWbQJ0kEESYkhomMw74Dtx8M+vY9IHxbWlIFMR9eiITMbBJWusYNq4pFJ3SHKAQhhJ3W1+vMAFY3VKc4Fglenkhm1clCNfRCVrgE4Q7LOuE8Z9jMa48fttbhRhTrEs4iwnSJzfajRKZeeSQyCxLDWU467KhE07wB/HhhoOsUS6YINsMkrOESu3cfWJ6Tk4sDj6MxH57BEsOsfuXZPqfmsT5ww449nZXPjTDUFRbBS5dJUxHBsusoE9Jgr48lBEV18MSvdZmTKyxTp9N488I1dIVLqOImhtjp5IEniHn5rfWyxi/IUIl0J/LHgtZWICdHX330vDHCAolhgouTCI0aLCFsxRS65isPbSiI5yXLHnww8MknnQK5b1+gsbE3UoVsLsQ30NnHSjx7hPXgrCt8QUfIhB/iWLZfKtOAqeSz/jfsbaiIY7eusJOglUE2RIJXxo5dEHt1h1XL8pxdUcywPb+JSCir9imqolnHby6C9eRPr0ReYBPEfkgME1qJysFE5HqZQhgtLchDC+LxA4/ijMcPiOLGxiIA/ZB685wpeK3fcwEU2L7bRfSBRzHb+yKTZsJ6zKxqXbpcYp0OscpjpZ2WywhOt+LYSRg7ubMqYRIsnEIkvIZQ6Chnx+n3cHKGzc/W+GFRHdY0N6ESUdmHySK68uBmXUwDxOn+C15fCCIdITFMZBxSl39bWjqfrgEgry8Qjxcl3eH6+k5RXF/fE83NvdB5k5z9yXMmpvC1i2Br+gHMEAmWK8wTjTIHGBnB6Xe6G3EuK4JVDrI8AcTLY18mEsciYaziCoval0VFrPJEuNtwCVZcvui7qDzPGeYJYlY9IkHrJlQiqoJY9QTFvp9xWic3VwJ5dZIrTKQTgT6B7s0338S5556LsrIy5OTk4K9//WvKcsMwMHfuXJSVlaGwsBBjxozBe++9l5KntbUVM2fORHFxMXr16oWJEydix44dKXkaGxtRXV2NWCyGWCyG6upq7NkvfEy2b9+Oc889F7169UJxcTGuvvpqtLWlbrybN2/G6NGjUVhYiIMOOgi33347DIozUiLMMAvzpjjrgzSSN8oxjgzx+IHXwQd3vgPFAErQ6RAXARiATnHcj5HWG6ki2BTIna6wPVZYJIhZQpjnCsuKUFY+nemiPtjz9O7NfoKeTN28PonyipY7lTf76tRf3kkO77toHUR9cjpZ8CLidAhAN+ESdhfS+m79bN+WRXVZ67ELftF9BlHHzcmi6v8jkmNi34nreBHEfgIVw19//TW+//3vY8mSJczlixYtwuLFi7FkyRJs2LAB8XgcZ555Jvbu3ZvMU1NTg+effx7Lly/HW2+9hX379mHChAno6OhI5pkyZQo2btyIlStXYuXKldi4cSOqq6uTyzs6OnDOOefg66+/xltvvYXly5fj2WefxezZs5N5mpqacOaZZ6KsrAwbNmzA73//e9x9991YvHixDyNDOKHrcdG8fWAe2lBQ0BkrbArhwYOBfv164oDwLd3/PgCdItgMo+iNTjFsffGFsFXg2D9b3624FcIqAlNHupNABNyLYDfC1434FS1nCWORCOaNh6ro1JXfD7fTSVjK6hG7AGYJYqc6ef0SpcmsV9SQ+d/a/2ui/QZvXSlEgsgWAg2TOPvss3H22WczlxmGgXvvvRc333wzLrjgAgDAo48+itLSUjz11FOYPn06EokEHnnkETz++OM444wzAABPPPEEysvL8dprr2H8+PH44IMPsHLlSqxbtw4jR44EADz00EOoqqrC1q1bMWzYMKxatQrvv/8+PvvsM5SVlQEAfvvb3+LSSy/FHXfcgaKiIjz55JNoaWnBsmXLkJ+fj4qKCvzrX//C4sWLMWvWLOTovKs1wqTDTXQFBXKXZa07686bRSwLW1qQV1CAvn2L0LdvpxDes6czZKKlZQCamwGgEQce0WyGS/BCI9wJYft6Ae6EsIp75DWvbB0sEeylL7I4HfhVlluXmevDe1qaNb/9u6heFext8kI3eGXM9fA6swQL2XVi9V+0XgD/RktR+AUrb7YIOFlBLLMvZdVLEOlOoM6wiG3btqG+vh7jxo1LpuXn52P06NFYs2YNAKCurg7t7e0pecrKylBRUZHMs3btWsRisaQQBoCTTjoJsVgsJU9FRUVSCAPA+PHj0drairq6umSe0aNHIz8/PyXP559/jk8++UT/AISEzhixsOH1i3V5dc8eHJhw2LLAnEmib99Od/iIIzrfc3IGACjDgZCJvvtfVjfYdIhN1zg3JUZYxhG2p8kIYZEzyVpuTfPDNZZ1g0VlnPon+2LhxRl2Wjc3v4Vovex5VD47tacDnius6hBbl1vLs5bzQp+cnGBT8Ks4xlF3h2V+R5WTZl3rS/HCRLoRmRvo6vc/A7e0tDQlvbS0FJ9++mkyT15eHvr169clj1m+vr4eJSUlXeovKSlJyWNvp1+/fsjLy0vJM3jw4C7tmMuGDBnSpY3W1la0trYmvzc1NYlXmnCFyNEwl7FiBK1CuKAAaGrJQ5HtEXTmAaa4OFUUd5briZaWnjCMInQ+lQ7odIitT5frnDFCxg22rgvLuTFFlorwEX1XSdOVLiOCZepxK954jqAoD285z3UE1Fxie5siAWIva69XxvkV5TWxusOq7qAdmXAFXhl726xxlK3TWiaTXWDZ9eJtC6Lyov+CaF/kCy0tNM8w4RuREcMm9vADwzAcQxLseVj5deQxb57j9WfBggWYN2+esK/ZADskwbtTwAvZsAph0UHSerBP3k/ZNw8FBXnI29/hlpbUJ9SZMcRAZ5k9e4CWllzs2TMAgPnY5k7xa9bNe2cJXnsaTzyquDtuBaeudLczRLgRwTJi16mMbFgEK50limUfEiHbHzOPSASLRKRKHhm8OKvm9sIiJyfXUeibqIgv+0mydWYJ6zJd4RRRFN0y25odtydE5AoT6UhkxHC889Z91NfXY9CgQcn0hoaGpCMbj8fR1taGxsbGFHe4oaEBo0aNSub54osvutS/a9eulHrWr1+fsryxsRHt7e0peUyX2NoO0NW9Nrnxxhsxa9as5PempiaUl5dLrH20iVLcsL0vdiHMc4ZN9u2zCGFLHQUFeck6TOfYdIVT89kPoLkpy+31it6tn2XnD46yMxyECJY5iPPyyDhc9nxOApYl3lgusWy9TstYfVeJGVYVwKr5eaESIhFsz2OKYtmTXCeywR1mofMKi6iebBpTInOJjBgeMmQI4vE4amtr8YMf/AAA0NbWhtWrV2PhwoUAgMrKSuTm5qK2thaTJk0CAOzcuRNbtmzBokWLAABVVVVIJBJ4++23ceKJJwIA1q9fj0QikRTMVVVVuOOOO7Bz586k8F61ahXy8/NRWVmZzHPTTTehra0NeXl5yTxlZWVdwidM8vPzU2KMCX9hCWGrIBGJD6sgNkWu6Rib5Q4+uGsdpvPHEjis/jl9lo0J5tXr5jsvzU26yhPj/HKHZZF1kWUFsD2N5RIDYlEsas9c5hQi4UUQ27cZlRvpRP99vhB2EsW5yfz2E02n30qEV3fYT9y0pzoWOtcpLPHbAaBDY2hDh3MWIosIVAzv27cPH330UfL7tm3bsHHjRvTv3x+HHHIIampqMH/+fAwdOhRDhw7F/Pnz0bNnT0yZMgUAEIvFMHXqVMyePRsDBgxA//79MWfOHBx99NHJ2SWGDx+Os846C9OmTcMDDzwAALj88ssxYcIEDBs2DAAwbtw4jBgxAtXV1bjrrrvw1VdfYc6cOZg2bRqKijqfRjZlyhTMmzcPl156KW666Sb87//+L+bPn49bb701a2aSEOHk1vh9UOEd9HkHet6lXdMFtrq+VoqLD4ROmHXu2ZMqHFQu57pxgHV8V0kTpQPqj0x2K4Kd+qEDkStsX+4UAywKnQC6imJRe6K6VWOGvTqrToi2MWch3Gz5XJiSxzC6hk7w2mbB+k38RqUdL33y0o7Odu3fKUSCSFcCFcP//Oc/cdpppyW/myEFl1xyCZYtW4brrrsOzc3NuPLKK9HY2IiRI0di1apV6NOnT7LMPffcgx49emDSpElobm7G2LFjsWzZMnTv3j2Z58knn8TVV1+dnHVi4sSJKXMbd+/eHS+//DKuvPJKnHzyySgsLMSUKVNw9913J/PEYjHU1tbiqquuwvHHH49+/fph1qxZKWEQmYJMTK/XUAmdz703+8LbMTsJHF4MJ8+5M4WwdZkpkEVCgNU3t991leGlidJ54tdNXV776LRMBtmTFyehbTlaQgAANeZJREFUqvrdOo4sYexUl5eYYdF3mfpUYJezCuFmVgawhLFpBFpdYhG8bdraL6f/TxDucBDi3MtJqNMygsg0cgx6pJpvNDU1IRaLYdeuRNJxjioyQtUuhlUFoE7XwOxLS0vXlxnqsGcP370tKOgUJ1YBzRImLS3A7t1d67aLYFnh4EUMBpEGuBO/omVehbHMMh3IXvbnpal+N2GFJvBib1mfRe3ILjM/W/vC+4/Lvh9whXlCmBcyYRW+B2ZpYU1TaL769j2wPVvTrNu1dRnQdbYWlSszLHTl8YLq9qPTJXbavzc1NSE2cCASCbXjoXkc/QqdE1bqoglAf0C5P0RmEpmYYSL98PvyqwieO2x1dZxcN3t+HlZxaJ2JzVqnm3HwUzyGKX5V++WlHZ24CV+wpsl+t9djD6Mw88rGB6u4vk6OsK4HcLBvmDOFsH2ZKaLybMtz95c54BKzHGL7OpmwfiszXfd/Kiwh7KVdnUI4CL7d/9JZH0GYkBgmfMN+0NEZKgGkhm7YBbA97EEk+Mw8LMwDrdVFsrrQVkHCqot3CZpHEAIzSPHrtNxvESyqw024hBdR7JRmD6Pg/Zd5gthev4wgFqWpwD8pNIWtXQiz9gPO+wa7IHZaT1Y/01EIuynv17bFKkuxwkS6Q2KYABBM3LBfsJwhlivMc71YAoNVP2sOYJYQDuMSq2iZ6g1vXtvzu26v+UXlWIJQJICt6W5FME8Y8x7gwRPEsp/t66ZDBLOREcLqIsoUxKJ15fXNOtaiWSVk0S2E/T4B9NoGCWEiUyExTHjC68HUK7xwib59u84nLOMQm/ms9bEOAKxL2364TqI+8PrkpnzUl3vN76ZekaMv4wyz0lQEtfU/plsQ+7ndskMk7EK4jbFMpY1Uh5gV/+x0VSCIS/1BuMZBtBNGWARBBAmJYSLtsYdLWA+G9rAGp4Omm1AE3mVuHciId3t/vObRJVzTRQDLtOcmhpiVpiKo7S6xnw6xdRmrPfewZo4whTAvdhjojB8Wi2SWGC4o4I+V9XMUHNigQif8bCdIV7gNbq4jiOsjCBMSw0QS3TG9Xtowxa1Kf3Rf6uRdRueFUcjUKSsugjhQ6nST/LoUHJRrBuiPIbamq6S5EcRmHSoimCWoeesu879NzcOaX5jlBvO2b6ftvhCG0Z4SLsF6N/vF+iwLK7+oDr9EcJAnjzJlKTyCyCRIDBOeER0svbow1hhl0c6XFy7hBt7BT7QuIhfRqW6/0S1ogxCzQblmojIiB9W+XJdYFok41tMP3bjFbh1fb06xNTzCLoTVwyRMx9kaLmFdN+sTKEX/BTNuWIWghLCO/3BQZQki3SExTCih4yY6tw60UzkVQSwr0mVCKERlzLaigJ/OUlii18/6nE5wnASwNd1NeATrM+vGOhVBbG2X56bq+7/aQyRYN845OcTOWN1hcx14s3GkltO7TegQwukggMkRJjIREsNECkGESviJXRCzboZz64q5Ra/A0EtQl179dp2CrN8PYSzjDOsQxLL/RX1xw3bXtx2pQtjLzBKdJ+WGkZviDvNCJQD1cAkdeXSHTYRx1cQkzGMDzTNM+AmJYUILQQk+lVhiJxfH3l+ny6o6HJioimIr6XSpVac4dxvPLYoXd3ODnUyIA2umCZZwFtXh1hUW5WPPJMHaVq1C2GlmiXakPpXOWm8ugAOxw8ABQWydNs1EJnSCB6tMUEI4TAFsks4mCUE4QWKY6IJud5jlwvjpQLNml9B9+dFNfV7ip3WIaF1CNQqxhX5cTnYrkEWusYwANtN47qXI3ZWZaUL1RFXkQLvHGissShftE0wBbQb55sLuDlu3MWuohBNO26Z9WRAiOAoC2ISEMJHpdAu7A0T6wdsxBrnzdYpbzkNbsg6zX9Z3+4uVbkVlmWz9MmVU2lCpT4Suepzq9FKHm3JuUO23zO/H6xfruz2dlWZ9OiKrrJt3e99Zn3n5xZji1z5RVhuAJgAtnFeTpey+/e/tlrT2pCNsvkx0X43hrbPMf0R2udftg4RwuDQ2NqK6uhqxWAyxWAzV1dXYY5/43oZhGJg7dy7KyspQWFiIMWPG4L333kvJ09raipkzZ6K4uBi9evXCxIkTsWPHjpQ8d9xxB0aNGoWePXuib9++zLa2b9+Oc889F7169UJxcTGuvvpqtLWl/s6vvvoqTjrpJPTp0wcDBw7EhRdeiG3btimPRbpAYpgIBNYBSceNeE6wBLHosxthKoMbsapDAPspmr2sm5v1c9sv3cj2y+l3FX0XfWaJY6sg1iGE7fXal7O+A0BODiucQYQpaFtwQCB/vf9l/2zmsYpp89XMCdHoxEkQO50EOKU5/c+8imRRGb/+50C0hHAHDsQN63h1+NjXKVOmYOPGjVi5ciVWrlyJjRs3orq6Wlhm0aJFWLx4MZYsWYINGzYgHo/jzDPPxN69e5N5ampq8Pzzz2P58uV46623sG/fPkyYMAEdHQfWpq2tDT/5yU/wq1/9itlOR0cHzjnnHHz99dd46623sHz5cjz77LOYPXt2Ms/HH3+M8847D6effjo2btyIV199Fbt378YFF1zgcWSiS45hGEbYnchUmpqaEIvFsGtXAkVFRWF3Rxk37qzqtGJeHWCZOqz18OKERWkyBzpVvLhVQcYd+3WQ9ZOw+yz6fWT+bzxX0/zslGaGNtiXid5Fy6z12fOy0jpFaTs6Z5OwClfz89e276bIBbreVGclD52hEUX7P/cC0G9/Wm8AA1BYmIu+fYF4vPMplL17d74XFBx4t7969xafaPBOTljf7ejcdwT9v9YthJuamhAbOBCJhNrx0DyO/htAH4392QvgcEC5P0588MEHGDFiBNatW4eRI0cCANatW4eqqip8+OGHGDZsWJcyhmGgrKwMNTU1uP766wF0usClpaVYuHAhpk+fjkQigYEDB+Lxxx/H5MmTAQCff/45ysvLsWLFCowfPz6lzmXLlqGmpqaLI/3KK69gwoQJ+Oyzz1BWVgYAWL58OS699FI0NDSgqKgIzzzzDC6++GK0traiW7dOz/Sll17Ceeedh9bWVuTmqp70Rh9yhgmtiHbYfok4VYfYfsDjOXWyjq0bvDihOl1g3W5smESlz6J+sJaJvss6w9Z89rAJFSdYVB+vv7y0rvDihoHUsAf762ukCuom27J2Sx2d2EMldMDaR/DyybrFMm2G8b+OkiPsN01NTSmv1tZWT/WtXbsWsVgsKYQB4KSTTkIsFsOaNWuYZbZt24b6+nqMGzcumZafn4/Ro0cny9TV1aG9vT0lT1lZGSoqKrj18vpXUVGRFMIAMH78eLS2tqKurg4AcPzxx6N79+7405/+hI6ODiQSCTz++OMYN25cRgphgG6gC4Rs2rGoIjt3sNd6zLrMvMCBA4yXO8ytuC3PO2jL1hfWDBUq6+tnH/0UCva6VW9E45Xj/fd4M0mYy+zpvGnE7DfWid7tdbPaYPWRl8bH/ohluytsPsOcdUOd1Um27g9Mt9gfZNxhp3Q3+cI6qYvyscqvqdXKy8tT0m+77TbMnTvXdb319fUoKSnpkl5SUoL6+npuGQAoLS1NSS8tLcWnn36azJOXl4d+/fp1ycOrl9eWvZ1+/fohLy8vWc/gwYOxatUq/OQnP8H06dPR0dGBqqoqrFixQrqddIPEMMEl3eYclu2vXWBbD/iq6Dho8epwO9VXFPEiKmXr1IFbN082JpUlimVmlbAu4wlYe5pXQWxNA1JnlpBdbz72xzW32d5N7NOumcI3DweEdK9kPl0nXSqCV6e4TYdtOdP47LPPUsIk8vPzmfnmzp2LefPmCevasGEDACAnJ6fLMsMwmOlW7MtlysjkcWrHXk99fT1++ctf4pJLLsHFF1+MvXv34tZbb8VFF12E2tpa5fbSARLDhGt4rq1IXLqZXkzlqXey8xCzBLG9nzyCOmCJ2gnLDdYFa93CGnPdJzQy6yFyV51cYllx7FUQ29szwyVY65e6/rkK8w3b5xq2zz9sF8bW/UAuUsMrmgH0ZLThDZY7zPrOK6fSRpikk/Ghk6KiIqmY4RkzZuCnP/2pMM/gwYOxadMmfPHFF12W7dq1q4sjaxKPxwF0itBBgwYl0xsaGpJl4vE42tra0NjYmOIONzQ0YNSoUY79t7a1fv36lLTGxka0t7cn27r//vtRVFSERYsWJfM88cQTKC8vx/r163HSSSdJt5cukBgOgDbkIQL7OlcE4Q7Lhjh4nX2CVafZvh0dBye3j5yWQaZ/6SaYgxYEfrUnI4xZJ4yyLrHos05BzPrMWkf2euai6+OY7ZiFWDNEmCLYvAnPrK9wf3oeut6cx59Nwo17KyrjNVTCbX6/yFYhrEJxcTGKi4sd81VVVSGRSODtt9/GiSeeCABYv349EokEV7QOGTIE8XgctbW1+MEPfgCgc1aI1atXY+HChQCAyspK5Obmora2FpMmTQIA7Ny5E1u2bEkRrTL9u+OOO7Bz586k8F61ahXy8/NRWVkJAPjmm2/QvXv3lHLm9++++066rXSCxDDhCTfucBCoiHiRKHaDlwMLr6ybvgV9oE0n8R20uy9yU3W7xLJXX1QEsbUvvPXpiilcZbcHa5hEs+27NS44F503zvVCasxxKgUFB16yyIhct6ESbvKysO4jdOyz0kUIp8vjmIcPH46zzjoL06ZNwwMPPAAAuPzyyzFhwoSUmSSOOuooLFiwAOeffz5ycnJQU1OD+fPnY+jQoRg6dCjmz5+Pnj17YsqUKQCAWCyGqVOnYvbs2RgwYAD69++POXPm4Oijj8YZZ5yRrHf79u346quvsH37dnR0dGDjxo0AgCOOOAK9e/fGuHHjMGLECFRXV+Ouu+7CV199hTlz5mDatGlJh/ycc87BPffcg9tvvz0ZJnHTTTfh0EMPTYr1TIPEMOEbugWxqjus8uhmaz4vBxg/n6pnR7dT7hU9saT+EpYTp1MUyzjDrDS7S2zFSRCzvotIDZXIw4EQB7t7y3Jzm3FglghryIRZ3u4id0Ukar0IVydRLFuPKrz9ildhnC5CON148skncfXVVydnfpg4cSKWLFmSkmfr1q1IJBLJ79dddx2am5tx5ZVXorGxESNHjsSqVavQp8+BCeXuuece9OjRA5MmTUJzczPGjh2LZcuWpbi4t956Kx599NHkd1O8vvHGGxgzZgy6d++Ol19+GVdeeSVOPvlkFBYWYsqUKbj77ruTZU4//XQ89dRTWLRoERYtWoSePXuiqqoKK1euRGFhITIRmmfYR9J9nmErop2maCesMlOCynzBqqju9NP1wBI1gQyEL4yjcinaiiim3imN992abk+zv4vmIhZ9tqbt29c13Xzt2WOdb9icCo31II0my/fG/e/2/CZ5+199ARSjc47hEgCDAJQC6IecnDgGD+6cZ7i4WDzHMND5bs4zzFvOihnWedOcCLf7FF3zs+vE6zzD70H/PMPfg/55hon0hJxhwjMix1bFHfYzdlg19lnFJY6CCDZxe9LiJ0E4xlEUvCJ424WX0AmRSywDLz8r3XSZWXkP9Ml0h80QB9PJzUNXV1e0DX2Hzinx7e5yLg4I5M7vLPHqNr5XtZzO/6DXfUqU9kkEkQ6QGCak0H0jnZtZJUy8CGKzvEpb1rLpTBDuuBPpJlr9xM/QCTc311lxu33ayzQ3W2N9rULW/JyHTgfYekNcLlJnjjA/m0+dK9r/3tvyuTO9b99OF7i4ONURdouTsNb9fyYRyyddYoaJ9ITEMKGFIN1hp/Zk2jDrkMUuirPhoJUOccqZgG5RLIr79Zu+fTtDJEw63WHzmzUG2PxcgANOsen0wvIOy/fc/S9T/A5AZ6hEPwDFyUcxm+ER1tAIgB0vbH0UsxN+CuFs2J8QRJQhMUxI48Ud9mN2Ca/TrXkRxdlKJjnlUUOHKFa5wY4FyyVWxVq+Uxybgtgqds3P7Tjw0Awzxth+g47pLttdYVMUF6OwsCfi8c5YYbsQlhGxrHhhXn4v45Pt+w+CiCokhgklRIJYVZzyDsxBP/nO2mc6WMlBotg/RDHWTqLY6bO1XlbohEofeSe3dnf4QPywVeRaZ4UowAFBbDq/5gM1rPMMF6FTBPeD1RW2CmH7TXPW8VHBLoqd4o/t0H6EINILEsOEMm7FKusA6lUQ634Yh2xddLDrxI+HoWQKOsaG5xazBLMbUey2T05Xefr2PTCrxIHPPWEYuZwSZkzw1wD2WdJNwdxr//J+6Jw9ogTAoRg0KBeDB3fmPPhgYPDgA0LWbJfnBMugMl60T/CXDuiN8+3QWBeR/pAYJrTiJADSQRDLkE2xw06QS9wVc0x0jY2KW6xbFJs32Tn1b8+eA0J09+7O9K6iOBfNzeY0VtY4YDNcwpxaDUDyuZ0tOBAe0TmdWmHhABx11AEBbPYhHld3ce3rwQuLsKfTtk8QmQOJYcIVQc0uEWVBDJAotkKimI8fTxYMWhSbddpDNMy5hU3hC3SGK7S0dIroVHcYAExBbH8ohymIe1nSrfHC/ZCTMwCDB3cKYFMIm2LYfjOc0/zM1qnhWGERLGFsQts7QWQWJIYJ1/CEqh+hC1EWxECERLHsXYo+TjHgxxhkqsD2IpJ5ws+e7vRTe5lGTfbvZn+AR0FB7v4b6+yxwdaQCFMMFyInpzMuePBg4IgjkIwRNj/b+2SNWZZxtWXW1ST0bZwgCO2QGCYCh3cQ1RHTGHYMa2REcYZhH89MFccmqtPaOQljkVssi3W7tYZOWN1h1stcbs5UsWePNT13/6tnl/ass0LE46lO8MEHH1gej3cV28CBdkRhHqyQCNHsEbRdhwfNM0z4CYlhwhNu3WFVQawiMsMWxEDERXGQE8/6RJRDMvz6zWVPCEQ315lpXsImeKLYukwkiu15rP201mPOGWy6wFYnOB4HSvq27Ve8QFFBAdC3AE0teaivd+4/K80pTCKS2zJBEFogMUx4JkpPpzOJilgKXBTz5tDKUKzjGvZvHTQy4lh2KjY3sEQxL57YLorN9u2OrRnHa4phU/gefDCSYRIlfduA+nrgE4uatt45h9R1ZrnC1vhi1hRqdkgIE0RmQ2KYiCS65iCOgksMhOgUZ7AQthOFE6AwRZPoxMBpKjb7d97fhnVFhyeKRcLYbMecC9javunImg/QMEMiUoQwo0Ab8pKhEWbMsF2gs9bH2qb9M0BCOCpQmAThJySGCS24cYedbsDJNEEMRDx8IkMIK744Sr+paAxEN9i5CZVgpbFEsUxohPVlFcJm7HAXe9lSwBTC9hev/yqfCYLIbEgME9rw48lxmSiIgeCfspfNBCGOo/5b8lxjr8LYHgdsTQNSRbHVHba2Ya3L+rIK4b59gaKCNqB+T9fOFXTGCpuOsCmKZWaR4DnDJlH/XXnY/+Ppuh4EERQkhgmtWEWezGVrlemZRG3JEIXL6FbIJQ4HXeI49N9NxvZkwFt/Nw4xq2m78LXPJiHqthnLazrDKa4w0PWxcmZ4RD1SnGGrK213e3nzC9s/h/77KiL6H9O+hiDEkBgmIo/owOzGYSWXmLCSNmOvMqmvFUlxLOMYq4pklmMs80Q7K8mQh4I8FBTkoaBv0QGHuQUpjrA9VIK1PuZ3Xowwd/0ifDOq7P4snfc1FDNM+AmJYUI79h2u22nWrGS6ICYIIW4vn/DKMjYmp1AKL6LYLeYjns1+WNtnvczHQLNW2TpThb0++1Rq6SQYaT9GEN4hMUz4gqpA9SqI3RClsIl0dmwIn/EihGXrtG1YLGHsRQjbwydkt3fed7sIZs1gwesH62UlZTv0Y+w14mbfFca+pg15kdjPEgQPEsOEbwQpiL3s4KPiEpMgJlIIUogJxLFdGOuILXZq3prGE8W8m+Sc+sQTxF3ihCMuhNOBKOxXCUIGEsOEr5AgVoMEMREJ7JMP78d+NcWtW8wKueDNMmH9bBW/LCeYN12beWOeeTMeLzyCW5GZOUJEYX/Fw4++Ucww4SckhgnfMQVeEDvvTBHEQHrFLRKaiYorybkLzUkU86Zpc0rnucGmCHaal9zaF7sQtr9SZqmAw/YWMSEcVaKw/yQIN5AYJgJBZSep44Y6wJ2YjFocMUCimIgIDLeYJYqdbrRjOcG8bd7uBFvf7dhnhGBN1cZyglPCI6JyEpJhyMRzE0SYdAu7A0T2oBou4YTTztWLoI2SADVvPrG/3NTB+04Q0jCUjXV7YU1TJprBwYo9v3VOYFb99nTrSySEmXHCLudtTjf82Lfx9ickgol0gZxhIjBUxZfVXeLhFKvoNWzCrCOKuL2T3CRKgp+wIXN5JGJYw4ys3WfNJAHw44bt7+a8xCK32S6eeWER9mWO20CGCWE/4O2HdP992wC0aq6PIEzIGSYij9PxyE+HGCDRSBBMGBuerENsT2M5yNb33r3FQrd3b6C4mO0CW59ix7xZjrMumSiEde/LghLCBOE35AwTaYFXo8zrLA1Rd4lVIYGfBqSDO8ywaWUcYmtRGYfYXCYKm+DNEmEXwea7MDwiDYRw2Df8khAmMgkSw0TaELYgBjJDFJMQTiNkYoUiiKogVoUVJiEjiq39S3bAqfIMQed2H4YQ7tj/0lkfQZiQGCZCw42zwRPEnGlRu6BrHt9MEMV+ozI2JNAdsP6x00QYywpioOt3uztsptkRhVfYP0v9x9JMCMvuQ9NdCBOE35AYJkLDKkxVp14D+KI4KEEMhH+pMkp4GQdeWRLJDOx/8DBViIJ4lLmpjpXfqQmneONMFcImvH2oX7NGsJCZCrNV591vBKEZEsNEqJjCVKdLrNKuDtLJJfZLXPq17jT7hQQiEeeHUFYQjfbt2skhdtsNnhDu8p/J4OnT/N4+yBEmMhkSw0Ra4+UYpvvRx7pc4nQKYwzyBICEsQsi+MdxCn1wKitKY8YFm2SwEPYb3hzCshQUAG0eN1l6HDPhJySGichgPXilg8vKwqsg5h1g3IoHP4nCnewkiqMPa5vghUCzbowTYV8uLYBlKifIDSayBhLDROiwHNqgYnF1u8OA+77LHGC8XFYG9K1vVE5WyC1OD0TbhMw84qI8zN/daWMiIewICWEim6CHbhCRgLXjNWOJw2g7DNLl+ByV8bJDj5iONtaHiatgD32wv5KYz/4lIewZ2o680djYiOrqasRiMcRiMVRXV2PPnj3CMoZhYO7cuSgrK0NhYSHGjBmD9957LyVPa2srZs6cieLiYvTq1QsTJ07Ejh07UvLccccdGDVqFHr27Im+5mMXOXz55Zc4+OCDkZOTw+3fRx99hD59+jjWle6QGCYiTzoKYrd9Fh2nuU/PUsSPWR+iBIni6MMSteY2w1vmeT+gawPKcETbjhtXWPjkPwW+9eHlF1OmTMHGjRuxcuVKrFy5Ehs3bkR1dbWwzKJFi7B48WIsWbIEGzZsQDwex5lnnom9e/cm89TU1OD555/H8uXL8dZbb2Hfvn2YMGECOjoOzJrc1taGn/zkJ/jVr37l2M+pU6fimGOO4S5vb2/HxRdfjFNOOUVirdMbCpMgIoPoEn4QYRN+hEy4IYjjtUrMbboKS4orTj9c/VbkBGvBDxGcjXzwwQdYuXIl1q1bh5EjRwIAHnroIVRVVWHr1q0YNmxYlzKGYeDee+/FzTffjAsuuAAA8Oijj6K0tBRPPfUUpk+fjkQigUceeQSPP/44zjjjDADAE088gfLycrz22msYP348AGDevHkAgGXLlgn7uXTpUuzZswe33norXnnlFWaeX//61zjqqKMwduxYrFmzxtV4pAvkDBNpg1aXiEO2uYp8Dy4vY8Yik9ZFNzK/f+T+G9ZwCKcb5LJVkSmiUwizhj2okLcosHbtWsRisaQQBoCTTjoJsViMKyi3bduG+vp6jBs3LpmWn5+P0aNHJ8vU1dWhvb09JU9ZWRkqKiqUher777+P22+/HY899hi6dWPLwNdffx1/+ctfcP/99yvVna6QM0xEChV31s/5faPiEhN6yRa3OCihGsSDHpLIqjISwNI4/U/cCGEr6bCdNTU1pXzPz89Hfn6+6/rq6+tRUlLSJb2kpAT19fXcMgBQWlqakl5aWopPP/00mScvLw/9+vXrkodXL4vW1lZcfPHFuOuuu3DIIYfg448/7pLnyy+/xKWXXoonnngCRUVF0nWnM+QME2mPX66DFweMXMhoEwmH0wORdGzh85UVa/Cp6EVIoVMI89xg1xUy+BZAu8aXGTNcXl6evNEtFothwYIFzPbnzp2LnJwc4euf//wnACAnJ6dLecMwmOlW7MtlysjksXLjjTdi+PDh+PnPf87NM23aNEyZMgWnnnqqdL3pDjnDAeD2CWvZiltX1m+nmNcekf4E6nAqko77jWxx4NMVXUKYde6hWwT7zWeffZbifvJc4RkzZuCnP/2psK7Bgwdj06ZN+OKLL7os27VrVxfn1yQejwPodH8HDRqUTG9oaEiWicfjaGtrQ2NjY4o73NDQgFGjRgn7ZeX111/H5s2b8cwzzwDoFNMAUFxcjJtvvhnz5s3D66+/jhdffBF33313Ms93332HHj164MEHH8Rll10m3V66QGI4QEgQB0NQj0em3zJz4f22foo7+j8RQaDrf+YohCMugk2KioqkQgGKi4tRXFzsmK+qqgqJRAJvv/02TjzxRADA+vXrkUgkuKJ1yJAhiMfjqK2txQ9+8AMAnbNCrF69GgsXLgQAVFZWIjc3F7W1tZg0aRIAYOfOndiyZQsWLVokta4A8Oyzz6K5uTn5fcOGDbjsssvwj3/8A4cffjiAzrhn6wwVL7zwAhYuXIg1a9bgoIMOkm4rnSAxTEQSHTG7QYliInug/5IaFHuvFy9XqGT/uzIaVjkkoqUFaG2Vaj/dGT58OM466yxMmzYNDzzwAADg8ssvx4QJE1JmkjjqqKOwYMECnH/++cjJyUFNTQ3mz5+PoUOHYujQoZg/fz569uyJKVOmAABisRimTp2K2bNnY8CAAejfvz/mzJmDo48+Ojm7BABs374dX331FbZv346Ojg5s3LgRAHDEEUegd+/eScFrsnv37mS/zbmEhw8fnpLnn//8J7p164aKigqtYxUlSAwHDLnDwUNjThBEusPbhzmdcKjs+3wTwhro2P/Shc667Dz55JO4+uqrkzM/TJw4EUuWLEnJs3XrViQSieT36667Ds3NzbjyyivR2NiIkSNHYtWqVejTp08yzz333IMePXpg0qRJaG5uxtixY7Fs2TJ07949mefWW2/Fo48+mvxuOs1vvPEGxowZ48fqZgQ5hhkwQminqakJsVgMiV27ulyGIXEmh05XicacIIKHnGHvOO277GPsZl/H06zSj8J2EMFNe/cidsQRSCQSSjMUmMfRpwD0lC7lzDcApgDK/SEyE5pNgog0OgUsHZQJgkg3ZPaBZp6gZhNhPgrbSprECxOECYVJEJGH7kwniPSEtlnvyIZ5eRHBdu0q7QazCvskhHU/QtnPxzET6QeJYSJt0CGKKX6YIIKBhLA+dIRB8DC1q9MUzczfkxxgIkMgMRwA5qUrOjjowXogyIQxZa0DCXYiXcmEbTLq6Dypl3lOSVhuMEEEBYnhACGBo59MEMaZsA4EkQ3/Xdl9eCaNhRYhXFAAtGXOmBCZB4nhiOHkEpqOgMzONtvEdyasbyasA5FdZJLwM/G6Hfp9JTCom+RSYIleXrBxS4v2R2NTzDDhJySG0wD7Tkl2J0sPnSDSBbdXWTUfbwlJMk0A+/UIdz/Gye/9uXRssGijpQ2TSDNIDAdASwuQt3//FcY+Qrco9vNmDiJ70BFm6FQHHZP1kGniF8iu/Zbrm+RURTALs1EKkyAiDInhgJHdKfmB15sueAdEcqCJqMI6bpNAloMEcPTQ/TQ5K56EMGujog2NSCNIDIcETxT7HWvmRRDzbvRK9wMMkV2QQGaTieIXCG//pHs8ZdbDbeSCViHMacjr7/AtgHZPNXStjyBMSAwHQGsrX/yy7jOIsiA2yUQBrDLmmbj+2Yz1OJ9NwjhTBTCQOSIY8C6EeXieKUJCCB/oO02/RkQXEsMBwxLFYQhiwhs0N7B3CgqiOT1pmKFMQZHJ+5Ywt8MwhLDszGZ2/BbCtD8k0gkSwyFhP+CyDsB+PoaYnsSmH4qdVieqghjITFFMItg/whhbX4Swm7tSLWl+/Q7NEa+PSG9IDAdASwvQpw9/mZNL7AdhHzgyGRLFakRZEAOZKYozibC3szDmE/ayvaT016sbbEsT9ddtn/Py8hCPx3FNfb27CgTE43Hk5dF+miAxHBiimEQnQUwhE+kJue/y2K+QRJF0F8WZtg+JwrYVdSFs/696EsIODbD6q2N7LigowLZt29Dmw9RseXl5KEjXDZrQConhEODFDVvTSBATRDQJ6uoNwSYKIhiIbliEiXYhLIgRtv8muk9qCwoKSLQSvtIt7A5kM06z19iX6zoIROVgkg3QCYwaBQXpITSj7GBnMlHZd7nars1YAYmYAd0uK1cIu41fCCBGmCCChMSwBH/4wx8wZMgQFBQUoLKyEv/4xz+01c3aF6ncy0AQmQgJYv2ks2hpQ15k+q8shHmCU+EP5Pahb9qwVhhAaARBBA2JYQeefvpp1NTU4Oabb8a7776LU045BWeffTa2b9+utR3Zq1ZROSBkIn6NLbnD7iBBrJ90239ESQS7wsUfRHfIgdAVtuPhTjcvN8kRRNiQGHZg8eLFmDp1Kn75y19i+PDhuPfee1FeXo6lS5dqb4u3I6EdDJGtpIMgTjfSRVxGsZ/SJ7ZRVIYiISzqr4QrHLVVJQhV6AY6AW1tbairq8MNN9yQkj5u3DisWbOmS/7W1la0trYmvycSCQDAvn1N0m3u3Xtgf7N3L5Cf3/m5qSl1n+TFbYziQSYqhHF3OOFMlA+29m0zXYjqFYuoPrEsD21yPVL5s9pmSOhc9wPlWVVZDjEADhwjWNWm9NmszF4Bq5F2y4OPrX1ss04bKe6nnb17O4+DhmE4ZyaIgCExLGD37t3o6OhAaWlpSnppaSnqGXMeLliwAPPmzeuSftJJ5b71kSAIgiDShb179yIWi4XdDYJIgcSwBDk5OSnfDcPokgYAN954I2bNmpX8vmfPHhx66KHYvn07bfwB09TUhPLycnz22WcoKioKuztZA417ONC4hwONuzyGYWDv3r0oKysLuysE0QUSwwKKi4vRvXv3Li5wQ0NDF7cYAPLz85HPuGYVi8VoRxkSRUVFNPYhQOMeDjTu4UDjLgeZQkRUoRvoBOTl5aGyshK1tbUp6bW1tRg1alRIvSIIgiAIgiB0Qc6wA7NmzUJ1dTWOP/54VFVV4cEHH8T27dtxxRVXhN01giAIgiAIwiMkhh2YPHkyvvzyS9x+++3YuXMnKioqsGLFChx66KGOZfPz83HbbbcxQycIf6GxDwca93CgcQ8HGneCyAxyDJrnhCAIgiAIgshSKGaYIAiCIAiCyFpIDBMEQRAEQRBZC4lhgiAIgiAIImshMUwQBEEQBEFkLSSGfeQPf/gDhgwZgoKCAlRWVuIf//hH2F2KDG+++SbOPfdclJWVIScnB3/9619TlhuGgblz56KsrAyFhYUYM2YM3nvvvZQ8ra2tmDlzJoqLi9GrVy9MnDgRO3bsSMnT2NiI6upqxGIxxGIxVFdXY8+ePSl5tm/fjnPPPRe9evVCcXExrr76arS1taXk2bx5M0aPHo3CwkIcdNBBuP3225Fu954uWLAAJ5xwAvr06YOSkhL8+Mc/xtatW1Py0LjrZ+nSpTjmmGOSD2aoqqrCK6+8klxOYx4MCxYsQE5ODmpqapJpNPYEQQAADMIXli9fbuTm5hoPPfSQ8f777xvXXHON0atXL+PTTz8Nu2uRYMWKFcbNN99sPPvsswYA4/nnn09Zfueddxp9+vQxnn32WWPz5s3G5MmTjUGDBhlNTU3JPFdccYVx0EEHGbW1tcY777xjnHbaacb3v/9949tvv03mOeuss4yKigpjzZo1xpo1a4yKigpjwoQJyeXffvutUVFRYZx22mnGO++8Y9TW1hplZWXGjBkzknkSiYRRWlpq/PSnPzU2b95sPPvss0afPn2Mu+++278B8oHx48cbf/rTn4wtW7YYGzduNM455xzjkEMOMfbt25fMQ+OunxdffNF4+eWXja1btxpbt241brrpJiM3N9fYsmWLYRg05kHw9ttvG4MHDzaOOeYY45prrkmm09gTBGEYhkFi2CdOPPFE44orrkhJO+qoo4wbbrghpB5FF7sY/u6774x4PG7ceeedybSWlhYjFosZ//3f/20YhmHs2bPHyM3NNZYvX57M85///Mfo1q2bsXLlSsMwDOP99983ABjr1q1L5lm7dq0BwPjwww8Nw+gU5d26dTP+85//JPP8+c9/NvLz841EImEYhmH84Q9/MGKxmNHS0pLMs2DBAqOsrMz47rvvNI5EsDQ0NBgAjNWrVxuGQeMeJP369TMefvhhGvMA2Lt3rzF06FCjtrbWGD16dFIM09gTBGFCYRI+0NbWhrq6OowbNy4lfdy4cVizZk1IvUoftm3bhvr6+pTxy8/Px+jRo5PjV1dXh/b29pQ8ZWVlqKioSOZZu3YtYrEYRo4cmcxz0kknIRaLpeSpqKhAWVlZMs/48ePR2tqKurq6ZJ7Ro0enTKw/fvx4fP755/jkk0/0D0BAJBIJAED//v0B0LgHQUdHB5YvX46vv/4aVVVVNOYBcNVVV+Gcc87BGWeckZJOY08QhAmJYR/YvXs3Ojo6UFpampJeWlqK+vr6kHqVPphjJBq/+vp65OXloV+/fsI8JSUlXeovKSlJyWNvp1+/fsjLyxPmMb+n6+9pGAZmzZqFH/7wh6ioqABA4+4nmzdvRu/evZGfn48rrrgCzz//PEaMGEFj7jPLly/HO++8gwULFnRZRmNPEIQJPY7ZR3JyclK+G4bRJY3g42b87HlY+XXkMfbf1JKuv+eMGTOwadMmvPXWW12W0bjrZ9iwYdi4cSP27NmDZ599FpdccglWr16dXE5jrp/PPvsM11xzDVatWoWCggJuPhp7giDIGfaB4uJidO/evcvZfENDQ5czf6Ir8XgcQFc3xDp+8XgcbW1taGxsFOb54osvutS/a9eulDz2dhobG9He3i7M09DQAKCrq5QOzJw5Ey+++CLeeOMNHHzwwcl0Gnf/yMvLwxFHHIHjjz8eCxYswPe//3387ne/ozH3kbq6OjQ0NKCyshI9evRAjx49sHr1atx3333o0aMH13WlsSeI7IPEsA/k5eWhsrIStbW1Kem1tbUYNWpUSL1KH4YMGYJ4PJ4yfm1tbVi9enVy/CorK5Gbm5uSZ+fOndiyZUsyT1VVFRKJBN5+++1knvXr1yORSKTk2bJlC3bu3JnMs2rVKuTn56OysjKZ580330yZBmnVqlUoKyvD4MGD9Q+ATxiGgRkzZuC5557D66+/jiFDhqQsp3EPDsMw0NraSmPuI2PHjsXmzZuxcePG5Ov444/Hz372M2zcuBGHHXYYjT1BEJ0Ed69edmFOrfbII48Y77//vlFTU2P06tXL+OSTT8LuWiTYu3ev8e677xrvvvuuAcBYvHix8e677yannrvzzjuNWCxmPPfcc8bmzZuNiy++mDnl0cEHH2y89tprxjvvvGOcfvrpzCmPjjnmGGPt2rXG2rVrjaOPPpo55dHYsWONd955x3jttdeMgw8+OGXKoz179hilpaXGxRdfbGzevNl47rnnjKKiorSb8uhXv/qVEYvFjL///e/Gzp07k69vvvkmmYfGXT833nij8eabbxrbtm0zNm3aZNx0001Gt27djFWrVhmGQWMeJNbZJAyDxp4giE5IDPvI/fffbxx66KFGXl6ecdxxxyWnsCIM44033jAAdHldcsklhmF0Tnt02223GfF43MjPzzdOPfVUY/PmzSl1NDc3GzNmzDD69+9vFBYWGhMmTDC2b9+ekufLL780fvaznxl9+vQx+vTpY/zsZz8zGhsbU/J8+umnxjnnnGMUFhYa/fv3N2bMmJEyvZFhGMamTZuMU045xcjPzzfi8bgxd+7ctJvuiDXeAIw//elPyTw07vq57LLLkvuBgQMHGmPHjk0KYcOgMQ8SuximsScIwjAMI8cw6PE2BEEQBEEQRHZCMcMEQRAEQRBE1kJimCAIgiAIgshaSAwTBEEQBEEQWQuJYYIgCIIgCCJrITFMEARBEARBZC0khgmCIAiCIIishcQwQRAEQRAEkbWQGCYIgiAIgiCyFhLDBEFkHIMHD0ZOTg5ycnKwZ88eT3WNGTMmWdfGjRu19I8gCIKIDiSGCYKIJB0dHRg1ahQuvPDClPREIoHy8nL8+te/Fpa//fbbsXPnTsRiMU/9eO655/D22297qoMgCIKILiSGCYKIJN27d8ejjz6KlStX4sknn0ymz5w5E/3798ett94qLN+nTx/E43Hk5OR46kf//v0xcOBAT3UQBEEQ0YXEMEEQkWXo0KFYsGABZs6cic8//xwvvPACli9fjkcffRR5eXlKdS1btgx9+/bF3/72NwwbNgw9e/bERRddhK+//hqPPvooBg8ejH79+mHmzJno6OjwaY0IgiCIqNEj7A4QBEGImDlzJp5//nn84he/wObNm3Hrrbfi2GOPdVXXN998g/vuuw/Lly/H3r17ccEFF+CCCy5A3759sWLFCnz88ce48MIL8cMf/hCTJ0/WuyIEQRBEJCExTBBEpMnJycHSpUsxfPhwHH300bjhhhtc19Xe3o6lS5fi8MMPBwBcdNFFePzxx/HFF1+gd+/eGDFiBE477TS88cYbJIYJgiCyBAqTIAgi8vzxj39Ez549sW3bNuzYscN1PT179kwKYQAoLS3F4MGD0bt375S0hoYGT/0lCIIg0gcSwwRBRJq1a9finnvuwQsvvICqqipMnToVhmG4qis3Nzfle05ODjPtu+++c91fgiAIIr0gMUwQRGRpbm7GJZdcgunTp+OMM87Aww8/jA0bNuCBBx4Iu2sEQRBEhkBimCCIyHLDDTfgu+++w8KFCwEAhxxyCH7729/iv/7rv/DJJ5+E2zmCIAgiIyAxTBBEJFm9ejXuv/9+LFu2DL169UqmT5s2DaNGjfIULkEQBEEQJjkGHU0IgsgwBg8ejJqaGtTU1Gip75NPPsGQIUPw7rvvup7WjSAIgogm5AwTBJGRXH/99ejduzcSiYSnes4++2x873vf09QrgiAIImqQM0wQRMbx6aefor29HQBw2GGHoVs39+f9//nPf9Dc3AygM2ZZ9cl3BEEQRLQhMUwQBEEQBEFkLRQmQRAEQRAEQWQtJIYJgiAIgiCIrIXEMEEQBEEQBJG1kBgmCIIgCIIgshYSwwRBEARBEETWQmKYIAiCIAiCyFpIDBMEQRAEQRBZC4lhgiAIgiAIImshMUwQBEEQBEFkLSSGCYIgCIIgiKyFxDBBEARBEASRtZAYJgiCIAiCILIWEsMEQRAEQRBE1kJimCAIgiAIgshaSAwTBEEQBEEQWQuJYYIgCIIgCCJrITFMEARBEARBZC0khgmCIAiCIIishcQwQRAEQRAEkbWQGCYIgiAIgiCyFhLDBEEQBEEQRNZCYpggCIIgCILIWkgMEwRBEARBEFkLiWGCIAiCIAgiayExTBAEQRAEQWQtJIYJgiAIgiCIrIXEMEEQBEEQBJG1kBgmCIIgCIIgshYSwwRBEARBEETWQmKYIAiCIAiCyFpIDBMEQRAEQRBZC4lhgiAIgiAIImshMUwQBEEQBEFkLSSGCYIgCIIgiKyFxDBBEARBEASRtZAYJgiCIAiCILIWEsMEQRAEQRBE1kJimCAIgiAIgshaSAwTBEEQBEEQWQuJYYIgCIIgCCJrITFMEARBEARBZC0khgmCIAiCIIishcQwQRAEQRAEkbWQGCYIgiAIgiCyFhLDBEEQBEEQRNZCYpggCIIgCILIWkgMEwRBEARBEFkLiWGCIAiCIAgiayExTBAEQRAEQWQtJIYJgiAIgiCIrIXEMEEQBEEQBJG1kBgmCIIgCIIgshYSwwRBEARBEETWQmKYIAiCIAiCyFpIDBMEQRAEQRBZC4lhgiAIgiAIImshMUwQBEEQBEFkLSSGCYIgCIIgiKyFxDBBEARBEASRtZAYJgiCIAiCILIWEsMEQRAEQRBE1kJimCAIgiAIgshaSAwTBEEQBEEQWQuJYYIgCIIgCCJrITFMEARBEARBZC0khgmCIAiCIIishcQwQRAEQRAEkbWQGCYIgiAIgiCyFhLDBEEQBEEQRNZCYpggCIIgCILIWkgMEwRBEARBEFkLiWGCIAiCIAgiayExTBAEQRAEQWQtJIYJgiAIgiCIrIXEMEEQBEEQBJG1kBgmCIIgCIIgshYSwwRBEARBEETWQmKYIAiCIAiCyFpIDBMEQRAEQRBZy/8Pvt8kdYdZg3QAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "! seisflows plot2d GRADIENT_01 vs_kernel --savefig g_01_vs.png\n", + "Image(filename='g_01_vs.png') " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Figure(707.107x707.107)\r\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "! seisflows plot2d MODEL_02 vs --savefig m_02_vs.png\n", + "Image(filename='m_02_vs.png') " ] } ], diff --git a/docs/specfem2d_example.rst b/docs/specfem2d_example.rst index 384fd59e..1a4bbc4e 100644 --- a/docs/specfem2d_example.rst +++ b/docs/specfem2d_example.rst @@ -1,12 +1,10 @@ Specfem2D Workstation Example ============================= -To demonstrate the inversion capabilities of SeisFlows, we will run a -**Specfem2D synthetic-synthetic example** on a **local machine** (tested -on a Linux workstation running CentOS 7, and an Apple Laptop running -macOS 10.14.6). Many of the setup steps here may be unique to our OS and -workstation, but hopefully they may serve as templates for new Users -wanting to explore SeisFlows. +SeisFlows comes with some **Specfem2D synthetic examples** to showcase +the package. These examples are meant to be run on a **local machine** +(tested on a Linux workstation running CentOS 7, and an Apple Laptop +running macOS 10.14.6). The numerical solver we will use is: `SPECFEM2D `__. We’ll @@ -17,25 +15,28 @@ activate the required Conda environment. -------------- -Option 1: Automated run ------------------------ +.. warning:: + If you do not have a compiled version of SPECFEM2D, then each example will attempt to automatically download and compile SPECFEM2D. This step may fail if you do not have software required by SPECFEM2D, if there are issues with the SPECFEM2D repository itself, or if the configuration and compiling steps fail. If you run any issues, it is recommended that you manually install and compile SPECFEM2D, and directly provide its path to this example problem using the -r or --specfem2d_repo flags (shown below). + +.. code:: ipython3 -We have set up this example to run using a single command line argument. -The following command will run an example script which will (1) download -and compile SPECFEM2D, (2) setup a SPECFEM2D working directory to -generate initial and target models, and (3) Run a SeisFlows inversion. + from IPython.display import Image # To display .png files in the notebook -.. warning:: - If you do not have a compiled version of SPECFEM2D, then this example will attempt to automatically download and compile SPECFEM2D. This step may fail if you do not have software required by SPECFEM2D, if there are issues with the SPECFEM2D repository itself, or if the configuration and compiling steps fail. If you run any issues, it is recommended that you manually install and compile SPECFEM2D, and directly provide its path to this example problem using the -r or --specfem2d_repo flags (shown below). +Example #1: Simple, default inversion +------------------------------------- -Example #1 -~~~~~~~~~~ +Example #1 runs a 1-iteration synthetic inversion with 1 event and 1 +station, used to illustrate misfit kernels in adjoint tomography. -Example #1 runs a 2 iteration inversion using SPECFEM2D, the default -preprocessing module and a gradient descent optimization algorithm. +The starting model (MODEL_INIT) and target model (MODEL_TRUE) are used +to generate synthetics and data, respectively. Both models are +homogeneous halfspace models with slightly varying P- and S-wave +velocity values. Only Vp and Vs are updated during the example. -.. note:: - Example number 1 is meant to **FAIL** during the line search of Iteration #2, after exceeding the maximum allowable line search step count. This is meant to illustrate line search behavior and allow the User to explore a working directory mid-workflow. +Misfit during Example #1 is defined by a ‘traveltime’ misfit using the +default preprocessing module. It also uses a gradient-descent +optimization algorithm paired with a bracketing line search. No +smoothing/regularization is applied to the gradient. .. code:: ipython3 @@ -44,7 +45,7 @@ preprocessing module and a gradient descent optimization algorithm. .. parsed-literal:: - 1 example: ex1_specfem2d_workstation_inversion + No existing SPECFEM2D repo given, default to: /home/bchow/REPOSITORIES/seisflows/docs/notebooks/specfem2d @@@@@@@@@@ .@@@@. .%&( %@. @@ -70,90 +71,97 @@ preprocessing module and a gradient descent optimization algorithm. /////////////////// This is a [SPECFEM2D] [WORKSTATION] example, which will run an inversion to assess misfit between two homogeneous halfspace models with slightly different - velocities. [3 events, 1 station, 2 iterations]. The inversion is expected to - fail after the 5th line search step count of the 2nd iteration. The tasks - involved include: + velocities. [1 events, 1 station, 1 iterations]. The tasks involved include: 1. (optional) Download, configure, compile SPECFEM2D 2. Set up a SPECFEM2D working directory - 3. Generate starting model from Tape2007 example + 3. Generate starting model from 'Tape2007' example 4. Generate target model w/ perturbed starting model 5. Set up a SeisFlows working directory - 6. Run an inversion workflow + 6. Run the inversion workflow ================================================================================ +Running the example +~~~~~~~~~~~~~~~~~~~ + You can either setup and run the example in separate tasks using the -``examples setup`` and ``submit`` commands. or directly run the example -after setup using the ``examples run`` command. Use the ``-r`` or -``--specfem2d_repo`` flag to point SeisFlows at an existing SPECFEM2D/ -repository (with compiled binaries). If not given, SeisFlows will -automatically download, configure and compile SPECFEM2D in your current -working directory. +``seisflows examples setup`` and ``seisflows submit`` commands, or by +directly running the example after setup using the ``examples run`` +command (illustrated below). + +Use the ``-r`` or ``--specfem2d_repo`` flag to point SeisFlows at an +existing SPECFEM2D/ repository (with compiled binaries) if available. If +not given, SeisFlows will automatically download, configure and compile +SPECFEM2D in your current working directory. .. code:: ipython3 - ! seisflows examples setup 1 -r path/to/specfem2d + ! seisflows examples setup 1 -r ${PATH_TO_SPECFEM2D} ! seisflows submit + # The above commands are the same as the below - ! seisflows examples run 1 --specfem2d_repo path/to/specfem2d + ! seisflows examples run 1 --specfem2d_repo ${PATH_TO_SPECFEM2D} A successfully completed example problem will end with the following log messages: .. code:: bash - ... - 2022-08-25 17:29:16 (I) | 5800.00 <= vp <= 5800.00 - 2022-08-25 17:29:16 (I) | 3236.17 <= vs <= 3802.01 - 2022-08-25 17:29:16 (I) | trial step unsuccessful. re-attempting line search - 2022-08-25 17:29:16 (I) | - LINE SEARCH STEP COUNT 06 + LINE SEARCH STEP COUNT 02 -------------------------------------------------------------------------------- - 2022-08-25 17:29:16 (I) | evaluating objective function for source 001 - 2022-08-25 17:29:16 (D) | running forward simulation with 'Specfem2D' - 2022-08-25 17:29:20 (D) | quantifying misfit with 'Default' - 2022-08-25 17:29:20 (I) | evaluating objective function for source 002 - 2022-08-25 17:29:20 (D) | running forward simulation with 'Specfem2D' - 2022-08-25 17:29:24 (D) | quantifying misfit with 'Default' - 2022-08-25 17:29:24 (I) | evaluating objective function for source 003 - 2022-08-25 17:29:24 (D) | running forward simulation with 'Specfem2D' - 2022-08-25 17:29:28 (D) | quantifying misfit with 'Default' - 2022-08-25 17:29:28 (D) | misfit for trial model (f_try) == 7.53E-03 - 2022-08-25 17:29:28 (D) | step length(s) = 0.00E+00, 1.47E+08, 2.95E+08, 5.89E+08, 1.18E+09, 2.36E+09, 4.72E+09 - 2022-08-25 17:29:28 (D) | misfit val(s) = 8.65E-04, 7.53E-03, 6.28E-03, 5.02E-03, 3.77E-03, 2.51E-03, 1.26E-03 - 2022-08-25 17:29:28 (I) | fail: bracketing line search has failed to reduce the misfit before exceeding `step_count_max`=5 - 2022-08-25 17:29:28 (D) | checking gradient/search direction angle, theta: 0.000 - 2022-08-25 17:29:28 (C) | - ================================================================================ - LINE SEARCH FAILED - ////////////////// - Line search has failed to reduce the misfit and has run out of fallback options. - Aborting inversion. - ================================================================================ - EXAMPLE COMPLETED SUCCESFULLY + 2022-08-29 15:50:58 (I) | evaluating objective function for source 001 + 2022-08-29 15:50:58 (D) | running forward simulation with 'Specfem2D' + 2022-08-29 15:51:03 (D) | quantifying misfit with 'Default' + 2022-08-29 15:51:03 (D) | misfit for trial model (f_try) == 4.61E-01 + 2022-08-29 15:51:03 (D) | step length(s) = 0.00E+00, 4.78E+09, 7.73E+09 + 2022-08-29 15:51:03 (D) | misfit val(s) = 1.04E+00, 2.30E-01, 4.61E-01 + 2022-08-29 15:51:03 (I) | pass: bracket acceptable and step length reasonable. returning minimum line search misfit. + 2022-08-29 15:51:03 (I) | line search model 'm_try' parameters: + 2022-08-29 15:51:03 (I) | 5800.00 <= vp <= 5800.00 + 2022-08-29 15:51:03 (I) | 3431.53 <= vs <= 3790.00 + 2022-08-29 15:51:03 (I) | trial step successful. finalizing line search + 2022-08-29 15:51:03 (I) | + FINALIZING LINE SEARCH + -------------------------------------------------------------------------------- + 2022-08-29 15:51:03 (I) | writing optimization stats + 2022-08-29 15:51:03 (I) | renaming current (new) optimization vectors as previous model (old) + 2022-08-29 15:51:03 (I) | setting accepted trial model (try) as current model (new) + 2022-08-29 15:51:03 (I) | misfit of accepted trial model is f=2.304E-01 + 2022-08-29 15:51:03 (I) | resetting line search step count to 0 + 2022-08-29 15:51:03 (I) | + CLEANING WORKDIR FOR NEXT ITERATION + -------------------------------------------------------------------------------- + 2022-08-29 15:51:05 (I) | thrifty inversion encountering first iteration, defaulting to standard inversion workflow + 2022-08-29 15:51:06 (I) | + //////////////////////////////////////////////////////////////////////////////// + COMPLETE ITERATION 01 + //////////////////////////////////////////////////////////////////////////////// + 2022-08-29 15:51:06 (I) | setting current iteration to: 2 + -Using the `working directory documentation page `__ you can figure out how to navigate around and look at the results of our small inversion problem. We will have a look at a few of the files and directories here. I've run the example problem in a scratch directory but your output directory should look the same. +Using the `working directory documentation page `__ you can figure out how to navigate around and look at the results of this small inversion problem. + +We will have a look at a few of the files and directories here. I've run the example problem in a scratch directory but your output directory should look the same. .. code:: ipython3 - %cd ~/Work/scratch + %cd ~/Work/scratch/example_1 ! ls .. parsed-literal:: - /home/bchow/Work/scratch + /home/bchow/Work/scratch/example_1 logs parameters.yaml sflog.txt specfem2d output scratch sfstate.txt specfem2d_workdir -In the ``output/`` directory, we can see the updated model from our -first iteration (MODEL_01) and the gradient that was used to create it -(GRADIENT_01). The 2nd iteration produced a gradient (GRADIENT_02), but -was unable to succesfully reduce the misfit during the line search, -which is why we don’t have a MODEL_02. +In the ``output/`` directory, we can see our starting model +(MODEL_INIT), our target model (MODEL_TRUE) and the updated model from +our first iteration (MODEL_01) alongside the gradient that was used to +create it (GRADIENT_01). .. code:: ipython3 @@ -164,7 +172,7 @@ which is why we don’t have a MODEL_02. .. parsed-literal:: - GRADIENT_01 GRADIENT_02 MODEL_01 MODEL_INIT MODEL_TRUE + GRADIENT_01 MODEL_01 MODEL_INIT MODEL_TRUE proc000000_vp.bin proc000000_vs.bin @@ -172,11 +180,35 @@ which is why we don’t have a MODEL_02. Because we’re working with SPECFEM2D, we can plot the models and gradients that were created during our workflow using the ``seisflows plot2d`` command. If we use the ``--savefig`` option we can -also save the output .png files to disk. +also save the output .png files to disk. Because this docs page was made +in a Jupyter Notebook, we need to use the IPython Image class to open +the resulting .png file. + +This figure shows the starting homogeneous halfspace model in Vs. + +.. code:: ipython3 + + ! seisflows plot2d MODEL_INIT vs --savefig m_init_vs.png + Image(filename='m_init_vs.png') + + +.. parsed-literal:: + + Figure(707.107x707.107) + + + + +.. image:: images/specfem2d_example_files/specfem2d_example_13_1.png + + + +Here we see the gradient created during the adjoint simulation. .. code:: ipython3 - ! seisflows plot2d GRADIENT_01 vs_kernel --savefig i02_gradient_vs_kernel.png + ! seisflows plot2d GRADIENT_01 vs_kernel --savefig g_01_vs.png + Image(filename='g_01_vs.png') .. parsed-literal:: @@ -184,28 +216,48 @@ also save the output .png files to disk. Figure(707.107x707.107) + + +.. image:: images/specfem2d_example_files/specfem2d_example_15_1.png + + + +Finally we see the updated model, which is the sum of the initial model, +and a scaled gradient. + .. code:: ipython3 - # Because this docs page was made in a Jupyter Notebook, we need to use IPython to open the resulting .png - from IPython.display import Image - Image(filename='i02_gradient_vs_kernel.png') + ! seisflows plot2d MODEL_01 vs --savefig m_01_vs.png + Image(filename='m_01_vs.png') +.. parsed-literal:: + Figure(707.107x707.107) -.. image:: images/specfem2d_example_files/specfem2d_example_15_0.png -Have a look at the `working directory documentation page `__ for more detailed explanations of how to navigate the SeisFlows working directory. +.. image:: images/specfem2d_example_files/specfem2d_example_17_1.png -Example #2 -~~~~~~~~~~ -Example #2 runs a 1 iteration inversion using SPECFEM2D, the Pyaflowa -preprocessing module and an L-BFGS optimization algorithm. It -successfully completes the line search and is meant to illustrate the -output of the Pyaflowa preprocessing module. + +Have a look at the `working directory documentation page `__ for more detailed explanations of how to navigate the SeisFlows working directory. You can also run Example \#1 with more stations (up to 131), tasks/events (up to 25) and iterations (as many as you want). Note that because this is a serial inversion, the compute time will scale with all of these values. + +.. code:: ipython3 + + ! seisflows examples run 1 --nsta 10 --ntask 5 --niter 2 + +Example #2: Pyaflowa, L-BFGS inversion +-------------------------------------- + +Example #2 runs a 2 iteration inversion with misfit quantification taken +care of by the ``Pyaflowa`` preprocessing module. Optimization (i.e., +model updates) are performed using the ``L-BFGS`` algorithm. This +example is more complex than the default version of Example #1, using +multiple events, stations and iterations. Example #2 also includes +smoothing/regularization of the gradient before using it to perturb the +starting velocity model. .. code:: ipython3 @@ -214,7 +266,7 @@ output of the Pyaflowa preprocessing module. .. parsed-literal:: - 2 example: ex2_specfem2d_workstation_inversion_w_pyatoa + No existing SPECFEM2D repo given, default to: /home/bchow/Work/scratch/example_1/specfem2d @@@@@@@@@@ .@@@@. .%&( %@. @@ -239,16 +291,17 @@ output of the Pyaflowa preprocessing module. SEISFLOWS EXAMPLE 2 /////////////////// This is a [SPECFEM2D] [WORKSTATION] example, which will run an inversion to - assess misfit between a homogeneous halfspace and checkerboard model using - Pyatoa for misfit quantification [2 events, 5 stations, 1 iterations]. The tasks - involved include: + assess misfit between a starting homogeneous halfspace model and a target + checkerboard model. This example problem uses the [PYAFLOWA] preprocessing + module and the [LBFGS] optimization algorithm. [4 events, 32 stations, 2 + iterations]. The tasks involved include: 1. (optional) Download, configure, compile SPECFEM2D 2. Set up a SPECFEM2D working directory - 3. Generate starting model from Tape2007 example + 3. Generate starting model from 'Tape2007' example 4. Generate target model w/ perturbed starting model 5. Set up a SeisFlows working directory - 6. Run an inversion workflow. The line search is expected to attempt 4 evaluations (i01s04) + 6. Run the inversion workflow ================================================================================ @@ -256,4 +309,143 @@ You can run the example with the same command as shown for Example 1: .. code:: ipython3 - ! seisflows examples run 2 -r path/to/specfem2d + ! seisflows examples run 2 -r ${PATH_TO_SPECFEM2D} + +Succesful completion of the example problem will end with the following log message + +.. code:: bash + + LINE SEARCH STEP COUNT 01 + -------------------------------------------------------------------------------- + 2022-08-29 18:07:14 (I) | evaluating objective function for source 001 + 2022-08-29 18:07:14 (D) | running forward simulation with 'Specfem2D' + 2022-08-29 18:07:20 (D) | quantifying misfit with 'Pyaflowa' + 2022-08-29 18:07:29 (I) | evaluating objective function for source 002 + 2022-08-29 18:07:29 (D) | running forward simulation with 'Specfem2D' + 2022-08-29 18:07:35 (D) | quantifying misfit with 'Pyaflowa' + 2022-08-29 18:07:43 (I) | evaluating objective function for source 003 + 2022-08-29 18:07:43 (D) | running forward simulation with 'Specfem2D' + 2022-08-29 18:07:49 (D) | quantifying misfit with 'Pyaflowa' + 2022-08-29 18:07:58 (I) | evaluating objective function for source 004 + 2022-08-29 18:07:58 (D) | running forward simulation with 'Specfem2D' + 2022-08-29 18:08:04 (D) | quantifying misfit with 'Pyaflowa' + 2022-08-29 18:08:13 (D) | misfit for trial model (f_try) == 4.73E-03 + 2022-08-29 18:08:13 (D) | step length(s) = 0.00E+00, 1.00E+00 + 2022-08-29 18:08:13 (D) | misfit val(s) = 5.30E-02, 4.73E-03 + 2022-08-29 18:08:13 (I) | pass: misfit decreased, line search successful w/ alpha=1.0 + 2022-08-29 18:08:13 (I) | line search model 'm_try' parameters: + 2022-08-29 18:08:13 (I) | 5800.00 <= vp <= 5800.00 + 2022-08-29 18:08:13 (I) | 3193.01 <= vs <= 3821.37 + 2022-08-29 18:08:13 (I) | trial step successful. finalizing line search + 2022-08-29 18:08:13 (I) | + FINALIZING LINE SEARCH + -------------------------------------------------------------------------------- + 2022-08-29 18:08:13 (I) | writing optimization stats + 2022-08-29 18:08:13 (I) | renaming current (new) optimization vectors as previous model (old) + 2022-08-29 18:08:13 (I) | setting accepted trial model (try) as current model (new) + 2022-08-29 18:08:13 (I) | misfit of accepted trial model is f=4.727E-03 + 2022-08-29 18:08:13 (I) | resetting line search step count to 0 + 2022-08-29 18:08:13 (I) | + CLEANING WORKDIR FOR NEXT ITERATION + -------------------------------------------------------------------------------- + 2022-08-29 18:08:15 (I) | thrifty inversion encountering final iteration, defaulting to inversion workflow + 2022-08-29 18:08:21 (I) | + //////////////////////////////////////////////////////////////////////////////// + COMPLETE ITERATION 02 + //////////////////////////////////////////////////////////////////////////////// + 2022-08-29 18:08:21 (I) | setting current iteration to: 3 + + +As with Example \#1, we can look at the output gradients and models to visualize how the inversion performed. + +.. code:: ipython3 + + %cd ~/Work/scratch/example_2 + ! ls + + +.. parsed-literal:: + + /home/bchow/Work/scratch/example_2 + logs parameters.yaml sflog.txt specfem2d + output scratch sfstate.txt specfem2d_workdir + + +.. code:: ipython3 + + ! seisflows plot2d # to check what models/gradients/kernels are avilable for plotting + + +.. parsed-literal:: + + PLOT2D + ////// + Available models/gradients/kernels + + GRADIENT_01 + GRADIENT_02 + MODEL_01 + MODEL_02 + MODEL_INIT + MODEL_TRUE + + +The starting model is a homogeneous halfspace but for Example #2 the +target model is a checkerboard. + +.. code:: ipython3 + + ! seisflows plot2d MODEL_TRUE vs --savefig m_true_vs.png + Image(filename='m_true_vs.png') + + +.. parsed-literal:: + + Figure(707.107x707.107) + + + + +.. image:: images/specfem2d_example_files/specfem2d_example_28_1.png + + + +In the following gradient Vs kernel, we can see how the 5km x 5km +smoothing blurs away some of the detail of the raw graident. The blue +colors here suggest that the initial model needs to be sped up to best +fit waveforms (and vice versa, red colors suggest slowing down the +initial model). + +.. code:: ipython3 + + ! seisflows plot2d GRADIENT_01 vs_kernel --savefig g_01_vs.png + Image(filename='g_01_vs.png') + + +.. parsed-literal:: + + Figure(707.107x707.107) + + + + +.. image:: images/specfem2d_example_files/specfem2d_example_30_1.png + + + +.. code:: ipython3 + + ! seisflows plot2d MODEL_02 vs --savefig m_02_vs.png + Image(filename='m_02_vs.png') + + +.. parsed-literal:: + + Figure(707.107x707.107) + + + + +.. image:: images/specfem2d_example_files/specfem2d_example_31_1.png + + From 06c947cce6c328cfd100fa76c3616ece91cba08e Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Tue, 30 Aug 2022 10:57:57 -0800 Subject: [PATCH 154/195] bugfix forward workflow failing check with no 'data' even though preprocessing module was not set' --- seisflows/workflow/forward.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index e0f94790..4e46ac5e 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -173,7 +173,10 @@ def check(self): f"'{self.__class__.__name__}' workflow may be " f"skipped") - if self.data_case is not None: + # If we are using the preprocessing module, we must have either + # 1) real data located in `path.data`, or 2) a target model to generate + # synthetic data, locaed in `path.model_true` + if self.data_case is not None and self._modules.preprocess: assert(self.data_case.lower() in self._acceptable_data_cases), \ f"`data_case` must be in {self._acceptable_data_cases}" if self.data_case.lower() == "data": From 7d483b5a28e81775056f335da5b5ffea94d2cfb9 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Tue, 30 Aug 2022 10:58:26 -0800 Subject: [PATCH 155/195] updating docstrings --- seisflows/examples/ex3_fwd_solver.py | 6 ++++-- seisflows/examples/sfexample2d.py | 8 ++++++-- seisflows/seisflows.py | 2 +- seisflows/solver/specfem.py | 6 ++++-- 4 files changed, 15 insertions(+), 7 deletions(-) diff --git a/seisflows/examples/ex3_fwd_solver.py b/seisflows/examples/ex3_fwd_solver.py index 6324a6f0..8e4d7167 100644 --- a/seisflows/examples/ex3_fwd_solver.py +++ b/seisflows/examples/ex3_fwd_solver.py @@ -30,11 +30,13 @@ def __init__(self, ntask=None, nsta=None, method="run", specfem2d_repo=None, :type ntask: int :param ntask: number of events to use in inversion, between 1 and 25. defaults to 3 - :type niter: int - :param niter: number of iterations to run. defaults to 2 :type nsta: int :param nsta: number of stations to include in inversion, between 1 and 131 + :type method: str + :param method: method for running the example problem, can be: + * 'run': setup and run the example problem + * 'setup': only setup the example problem, do not `submit` job :type specfem2d_repo: str :param specfem2d_repo: path to the SPECFEM2D directory which should contain binary executables. If not given, SPECFEM2D will be diff --git a/seisflows/examples/sfexample2d.py b/seisflows/examples/sfexample2d.py index 3ad3f660..03802c36 100644 --- a/seisflows/examples/sfexample2d.py +++ b/seisflows/examples/sfexample2d.py @@ -58,6 +58,10 @@ def __init__(self, ntask=None, niter=None, nsta=None, method="run", :type nsta: int :param nsta: number of stations to include in inversion, between 1 and 131 + :type method: str + :param method: method for running the example problem, can be: + * 'run': setup and run the example problem + * 'setup': only setup the example problem, do not `submit` job :type specfem2d_repo: str :param specfem2d_repo: path to the SPECFEM2D directory which should contain binary executables. If not given, SPECFEM2D will be @@ -342,7 +346,7 @@ def finalize_specfem2d_par_file(self): Need to tell them to read models from .bin files, and to use existing station files rather than create them from the Par_file """ - print("> Finalizing SPECFEM2D Par_file for SeisFlows inversion") + print("> Finalizing SPECFEM2D Par_file for SeisFlows example") cd(self.workdir_paths.data) self.sf.sempar("model", "gll") # GLL so SPECFEM reads .bin files @@ -354,7 +358,7 @@ def finalize_specfem2d_par_file(self): with open("STATIONS_checker", "r") as f: lines = f.readlines() - print(f"> Using {self.nsta} stations in this inversion workflow") + print(f"> Using {self.nsta} stations in this SeisFlows example") with open("STATIONS", "w") as f: f.writelines(lines[:self.nsta]) diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 7b4c38ea..23cdd49f 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -924,7 +924,7 @@ def examples(self, method=None, choice=None, specfem2d_repo=None, self._print_examples() sys.exit(0) # e.g., $ seisflows examples 1 - elif method and choice is None: + elif method and (choice is None): try: choice = int(method) except ValueError: diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index d32199dd..31ef8f16 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -54,10 +54,12 @@ class Specfem: attenution (Q_mu, Q_kappa) model :type smooth_h: float :param smooth_h: Gaussian half-width for horizontal smoothing in units - of meters. If 0., no smoothing applied + of meters. If 0., no smoothing applied. Only applicable for workflows: + ['migration', 'inversion'], ignored for 'forward' workflow. :type smooth_h: float :param smooth_v: Gaussian half-width for vertical smoothing in units - of meters. + of meters. Only applicable for workflows: ['migration', 'inversion'], + ignored for 'forward' workflow. :type components: str :param components: components to consider and tag data with. Should be string of letters such as 'RTZ' From d744eeb484ac8787c7c1421eb4267977230e7834 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Tue, 30 Aug 2022 11:18:23 -0800 Subject: [PATCH 156/195] bugfix forward workflow w/o preprocessing module kept hitting errors looking for true model and preparing data for solver. added logic statements to sidestep all data preparation if forward workflow does not include preprocessing module --- seisflows/solver/specfem.py | 35 +++++++++++++++++++++++---------- seisflows/solver/specfem2d.py | 7 +------ seisflows/system/workstation.py | 5 +++-- seisflows/workflow/forward.py | 18 ++++++++++------- 4 files changed, 40 insertions(+), 25 deletions(-) diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 31ef8f16..7d08cb65 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -405,16 +405,7 @@ def setup(self): Exports INIT/STARTING and TRUE/TARGET models to disk (output/ dir.) """ self._initialize_working_directories() - - # Export the initial and target models to the SeisFlows output directory - for name, model in zip(["MODEL_INIT", "MODEL_TRUE"], - [self.path.model_init, self.path.model_true]): - dst = os.path.join(self.path.output, name, "") - if not os.path.exists(dst): - unix.mkdir(dst) - for par in self._parameters: - src = glob(os.path.join(model, f"*{par}{self._ext}")) - unix.cp(src, dst) + self._export_starting_models() def forward_simulation(self, executables=None, save_traces=False, export_traces=False, **kwargs): @@ -823,3 +814,27 @@ def _initialize_working_directory(self, cwd=None): logger.debug(f"linking source '{source_name}' as 'mainsolver'") unix.ln(cwd, self.path.mainsolver) + def _export_starting_models(self, parameters=None): + """ + Export the initial and target models to the SeisFlows output/ directory. + + :type parameters: list + :param parameters: list of parameters to export. If None, will default + to `self._parameters` + """ + if parameters is None: + parameters = self._parameters + + # Export the initial and target models to the SeisFlows output directory + for name, model in zip(["MODEL_INIT", "MODEL_TRUE"], + [self.path.model_init, self.path.model_true]): + # Skip over if user has not provided model path (e.g., real data + # inversion will not have `model_true`) + if not model: + continue + dst = os.path.join(self.path.output, name, "") + if not os.path.exists(dst): + unix.mkdir(dst) + for par in parameters: + src = glob(os.path.join(model, f"*{par}{self._ext}")) + unix.cp(src, dst) diff --git a/seisflows/solver/specfem2d.py b/seisflows/solver/specfem2d.py index 8bb4e009..996ecc91 100644 --- a/seisflows/solver/specfem2d.py +++ b/seisflows/solver/specfem2d.py @@ -66,12 +66,7 @@ def setup(self): super().setup() # Copy in coordinate files to the Model definition so we can plot - for name, model in zip(["MODEL_INIT", "MODEL_TRUE"], - [self.path.model_init, self.path.model_true]): - dst = os.path.join(self.path.output, name) - for par in ["x", "z"]: - src = glob(os.path.join(model, f"*{par}{self._ext}")) - unix.cp(src, dst) + self._export_starting_models(parameters=["x", "z"]) def smooth(self, input_path, output_path, parameters=None, span_h=None, span_v=None, use_gpu=False): diff --git a/seisflows/system/workstation.py b/seisflows/system/workstation.py index c0af8830..f7cd980e 100644 --- a/seisflows/system/workstation.py +++ b/seisflows/system/workstation.py @@ -1,7 +1,8 @@ #!/usr/bin/env python3 """ -This is a subclass seisflows.system.workstation -Provides utilities for submitting jobs in serial on a single machine +The `workstation` class is the foundational `System` module in SeisFlows, +it provides utilities for submitting jobs in SERIAL on a small-scale machine, +e.g., a workstation or a laptop. All other `System` classes build on this class. """ import os from contextlib import redirect_stdout diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index 4e46ac5e..7f91b4a5 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -320,13 +320,17 @@ def evaluate_initial_misfit(self): _model = Model(os.path.join(self.path.model_true)) _model.check() - self.system.run( - [self.prepare_data_for_solver, - self.run_forward_simulations, - self.evaluate_objective_function], - path_model=self.path.model_init, - save_residuals=os.path.join(self.path.eval_grad, "residuals.txt") - ) + # Define the tasks that will be run through `system.run` + run_list = [self.run_forward_simulations, + self.evaluate_objective_function] + # Only need to prepare data for solver if we are preprocessing + if self.preprocess: + run_list = [self.prepare_data_for_solver] + run_list + + self.system.run(run_list, path_model=self.path.model_init, + save_residuals=os.path.join(self.path.eval_grad, + "residuals.txt") + ) def prepare_data_for_solver(self, **kwargs): """ From 70635db68eda7c2a6d0aeda7da2a7228676396e7 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Tue, 30 Aug 2022 12:08:34 -0800 Subject: [PATCH 157/195] feature: added a very simple stream plotter to look at output waveforms --- seisflows/seisflows.py | 59 +++++++++++++++++++++++++++++++++--------- 1 file changed, 47 insertions(+), 12 deletions(-) diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 23cdd49f..066a6382 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -21,8 +21,10 @@ import traceback from glob import glob from IPython import embed +from obspy import Stream, read from seisflows import logger, ROOT_DIR, NAMES +from seisflows.preprocess.default import Default from seisflows.tools import unix, msg from seisflows.tools.config import load_yaml, custom_import, import_seisflows from seisflows.tools.specfem import (getpar, setpar, getpar_vel_model, @@ -121,18 +123,6 @@ def _format_action(self, action): swap.add_argument("module", nargs="?", help="Module name to swap") swap.add_argument("classname", nargs="?", help="Classname to swap to") # ========================================================================= - init = subparser.add_parser( - "init", help="Initiate working environment", - description="""Establish a SeisFlows working environment but don't - submit the workflow to the system and do not perform variable error - checking. Saves the initial state as pickle files to allow for active - environment inspection prior to running 'submit'. Useful for debugging, - development and code exploration.""" - ) - # init.add_argument("-c", "--check", action="store_true", - # help="Perform parameter and path checking to ensure that " - # "user-defined parameters are accepatable") - # ========================================================================= submit = subparser.add_parser( "submit", help="Submit initial workflow to system", description="""The main SeisFlows execution command. Submit a SeisFlows @@ -245,6 +235,23 @@ def _format_action(self, action): plot2d.add_argument("-s", "--savefig", type=str, nargs="?", default=None, help="optional name and path to save figure") # ========================================================================= + plotst = subparser.add_parser( + "plotst", formatter_class=argparse.RawDescriptionHelpFormatter, + description="""Plots waveforms output by the solver. Uses ObsPy's +Stream.plot() function under the hood. Example call would be +`seisflows plotst scratch/solver/mainsolver/traces/syn/*` + """) + + plotst.add_argument("fids", type=str, nargs="*", + help="File IDs to be passed to plotting. Wildcards " + "acceptable") + plotst.add_argument("--data_format", type=str, nargs="?", default="ASCII", + help="Data format of the files. Must match file type " + "that SeisFlows can read. These include:" + "['SU', 'ASCII']. Defaults to 'ASCII'. See " + "SeisFlows.preprocess.default.read() for " + "all options.") + # ========================================================================= print_ = subparser.add_parser( "print", formatter_class=argparse.RawDescriptionHelpFormatter, description=""" @@ -1000,6 +1007,34 @@ def print(self, choice=None, **kwargs): acceptable_args[choice](*self._args.args, **kwargs) + @staticmethod + def plotst(self, fids, data_format="ASCII", **kwargs): + """ + Simple stream/waveform plotter to visualize synthetic waveforms created + by the solver. Uses ObsPy under the hood to generate a large stream + and then plots all waveforms together. + + .. note:: + Very simple function to look at waveforms. If you want more + sophisticated plotting tools, look at Python packages `Pyatoa` + or `PySEP` + + :type fids: list + :param fids: list of file ID's to plot + :type data_format: str + :param data_format: + """ + # Take advantage of the Default Preprocessing module's read() function + plotter = Default(data_format=data_format) + assert(data_format.upper() in plotter._acceptable_data_formats), \ + f"data format must be in {plotter._acceptable_data_formats}" # NOQA + + st = Stream() + for fid in fids: + st += plotter.read(fid) + + st.plot(**kwargs) + def plot2d(self, name=None, parameter=None, cmap=None, savefig=None, **kwargs): """ From 259f567f80b22d930eaede534f1f0a77cf60d3e8 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Tue, 30 Aug 2022 12:19:20 -0800 Subject: [PATCH 158/195] forward workflow example working, fixed up the stream plotter, fixing some example print dialogues --- seisflows/examples/ex3_fwd_solver.py | 24 ++-- seisflows/preprocess/default.py | 104 +++++++------- seisflows/seisflows.py | 11 +- seisflows/solver/specfem.py | 8 +- seisflows/system/multicore.py | 196 +++++++++++++++++++++++++++ seisflows/workflow/forward.py | 17 +-- 6 files changed, 283 insertions(+), 77 deletions(-) create mode 100644 seisflows/system/multicore.py diff --git a/seisflows/examples/ex3_fwd_solver.py b/seisflows/examples/ex3_fwd_solver.py index 8e4d7167..9ed7b2b6 100644 --- a/seisflows/examples/ex3_fwd_solver.py +++ b/seisflows/examples/ex3_fwd_solver.py @@ -43,7 +43,7 @@ def __init__(self, ntask=None, nsta=None, method="run", specfem2d_repo=None, downloaded configured and compiled automatically. """ # Setting default values for ntask, niter, nsta here vvv - super().__init__(ntask=ntask or 25, niter=1, nsta=nsta or 131, + super().__init__(ntask=ntask or 10, niter=1, nsta=nsta or 25, method=method, specfem2d_repo=specfem2d_repo) def print_dialogue(self): @@ -53,19 +53,18 @@ def print_dialogue(self): """ print(msg.ascii_logo_small) print(msg.cli( - f"This is a [SPECFEM2D] [WORKSTATION] example, which will " - f"run forward simulations generate synthetic seismograms through " - f"a given starting model. This example uses no preprocessing or " - f"optimization modules" - f"[{self.ntask} events, {self.nsta} stations, {self.niter} " - f"iterations]. " + f"This is a [SPECFEM2D] [WORKSTATION] example, which will run " + f"forward simulations to generate synthetic seismograms through " + f"a homogeneous halfspace starting model. This example uses no " + f"preprocessing or optimization modules. " + f"[{self.ntask} events, {self.nsta} stations] " f"The tasks involved include: ", items=["1. (optional) Download, configure, compile SPECFEM2D", - "2. Set up a SPECFEM2D working directory", - "3. Generate starting model from 'Tape2007' example", - "4. Set up a SeisFlows working directory", - "5. Run the forward simulation workflow"], - header="seisflows example 2", + "2. [Setup] a SPECFEM2D working directory", + "3. [Setup] starting model from 'Tape2007' example", + "4. [Setup] a SeisFlows working directory", + "5. [Run] the forward simulation workflow"], + header="seisflows example 3", border="=") ) @@ -90,6 +89,7 @@ def setup_seisflows_working_directory(self): self.sf.par("data_case", "synthetic") # synthetic-synthetic inversion self.sf.par("attenuation", False) self.sf.par("components", "Y") + self.sf.par("export_traces", True) # copy waveforms to disk self.sf.par("path_specfem_bin", self.workdir_paths.bin) self.sf.par("path_specfem_data", self.workdir_paths.data) diff --git a/seisflows/preprocess/default.py b/seisflows/preprocess/default.py index 38147999..5fc71741 100644 --- a/seisflows/preprocess/default.py +++ b/seisflows/preprocess/default.py @@ -243,9 +243,9 @@ def read(self, fid): """ st = None if self.data_format.upper() == "SU": - st = obspy_read(os.path.join(fid), format="SU", byteorder="<") + st = obspy_read(fid, format="SU", byteorder="<") elif self.data_format.upper() == "ASCII": - st = self._read_ascii(fid) + st = read_ascii(fid) return st def write(self, st, fid): @@ -550,54 +550,54 @@ def _apply_normalize(self, st): return st_out - @staticmethod - def _read_ascii(fid, origintime=None): - """ - Read waveforms in two-column ASCII format. This is copied directly from - pyatoa.utils.read.read_sem() - """ - try: - times = np.loadtxt(fname=fid, usecols=0) - data = np.loadtxt(fname=fid, usecols=1) - - # At some point in 2018, the Specfem developers changed how the ascii files - # were formatted from two columns to comma separated values, and repeat - # values represented as 2*value_float where value_float represents the data - # value as a float - except ValueError: - times, data = [], [] - with open(fid, 'r') as f: - lines = f.readlines() - for line in lines: - try: - time_, data_ = line.strip().split(',') - except ValueError: - if "*" in line: - time_ = data_ = line.split('*')[-1] - else: - raise ValueError - times.append(float(time_)) - data.append(float(data_)) - - times = np.array(times) - data = np.array(data) - - if origintime is None: - origintime = UTCDateTime("1970-01-01T00:00:00") - - # We assume that dt is constant after 'precision' decimal points - delta = round(times[1] - times[0], 4) - - # Honor that Specfem doesn't start exactly on 0 - origintime += times[0] - - # Write out the header information - net, sta, cha, fmt = os.path.basename(fid).split('.') - stats = {"network": net, "station": sta, "location": "", - "channel": cha, "starttime": origintime, "npts": len(data), - "delta": delta, "mseed": {"dataquality": 'D'}, - "time_offset": times[0], "format": fmt - } - st = Stream([Trace(data=data, header=stats)]) - return st +def read_ascii(fid, origintime=None): + """ + Read waveforms in two-column ASCII format. This is copied directly from + pyatoa.utils.read.read_sem() + """ + try: + times = np.loadtxt(fname=fid, usecols=0) + data = np.loadtxt(fname=fid, usecols=1) + + # At some point in 2018, the Specfem developers changed how the ascii files + # were formatted from two columns to comma separated values, and repeat + # values represented as 2*value_float where value_float represents the data + # value as a float + except ValueError: + times, data = [], [] + with open(fid, 'r') as f: + lines = f.readlines() + for line in lines: + try: + time_, data_ = line.strip().split(',') + except ValueError: + if "*" in line: + time_ = data_ = line.split('*')[-1] + else: + raise ValueError + times.append(float(time_)) + data.append(float(data_)) + + times = np.array(times) + data = np.array(data) + + if origintime is None: + origintime = UTCDateTime("1970-01-01T00:00:00") + + # We assume that dt is constant after 'precision' decimal points + delta = round(times[1] - times[0], 4) + + # Honor that Specfem doesn't start exactly on 0 + origintime += times[0] + + # Write out the header information + net, sta, cha, fmt = os.path.basename(fid).split('.') + stats = {"network": net, "station": sta, "location": "", + "channel": cha, "starttime": origintime, "npts": len(data), + "delta": delta, "mseed": {"dataquality": 'D'}, + "time_offset": times[0], "format": fmt + } + st = Stream([Trace(data=data, header=stats)]) + + return st diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 066a6382..e762c699 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -251,6 +251,8 @@ def _format_action(self, action): "['SU', 'ASCII']. Defaults to 'ASCII'. See " "SeisFlows.preprocess.default.read() for " "all options.") + plotst.add_argument("-s", "--savefig", type=str, nargs="?", default=None, + help="optional name and path to save figure") # ========================================================================= print_ = subparser.add_parser( "print", formatter_class=argparse.RawDescriptionHelpFormatter, @@ -1008,7 +1010,7 @@ def print(self, choice=None, **kwargs): acceptable_args[choice](*self._args.args, **kwargs) @staticmethod - def plotst(self, fids, data_format="ASCII", **kwargs): + def plotst(self, fids, data_format="ASCII", savefig=None, **kwargs): """ Simple stream/waveform plotter to visualize synthetic waveforms created by the solver. Uses ObsPy under the hood to generate a large stream @@ -1022,7 +1024,10 @@ def plotst(self, fids, data_format="ASCII", **kwargs): :type fids: list :param fids: list of file ID's to plot :type data_format: str - :param data_format: + :param data_format: data format used to determine how to read data files + :type savefig: str or None + :param savefig: full path and filename to save the output figure. If + NoneType, will not save the figure """ # Take advantage of the Default Preprocessing module's read() function plotter = Default(data_format=data_format) @@ -1033,7 +1038,7 @@ def plotst(self, fids, data_format="ASCII", **kwargs): for fid in fids: st += plotter.read(fid) - st.plot(**kwargs) + st.plot(outfile=savefig, **kwargs) def plot2d(self, name=None, parameter=None, cmap=None, savefig=None, **kwargs): diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 7d08cb65..524103d7 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -453,14 +453,18 @@ def forward_simulation(self, executables=None, save_traces=False, for tag in ["d", "v", "a", "p"]: unix.rename(old=f"single_{tag}.su", new="single.su", names=glob(os.path.join("OUTPUT_FILES", "*.su"))) - + # Exporting traces to disk (output/) for more permanent storage if export_traces: + if not os.path.exists(export_traces): + unix.mkdir(export_traces) unix.cp( src=glob(os.path.join("OUTPUT_FILES", self.data_wildcard())), dst=export_traces ) - + # Save traces somewhere else in the scratch/ directory for easier access if save_traces: + if not os.path.exists(save_traces): + unix.mkdir(save_traces) unix.mv( src=glob(os.path.join("OUTPUT_FILES", self.data_wildcard())), dst=save_traces diff --git a/seisflows/system/multicore.py b/seisflows/system/multicore.py new file mode 100644 index 00000000..f7cd980e --- /dev/null +++ b/seisflows/system/multicore.py @@ -0,0 +1,196 @@ +#!/usr/bin/env python3 +""" +The `workstation` class is the foundational `System` module in SeisFlows, +it provides utilities for submitting jobs in SERIAL on a small-scale machine, +e.g., a workstation or a laptop. All other `System` classes build on this class. +""" +import os +from contextlib import redirect_stdout + +from seisflows import logger +from seisflows.tools import unix +from seisflows.tools.config import Dict, import_seisflows +from seisflows.tools.config import number_fid, set_task_id + + +class Workstation: + """ + Workstation System + ------------------ + Defines foundational structure for System module. When used standalone, + runs tasks in serial on a local machine. + + Parameters + ---------- + :type ntask: int + :param ntask: number of individual tasks/events to run during workflow. + Must be <= the number of source files in `path_specfem_data` + :type nproc: int + :param nproc: number of processors to use for each simulation + :type log_level: str + :param log_level: logger level to pass to logging module. + Available: 'debug', 'info', 'warning', 'critical' + :type verbose: bool + :param verbose: if True, formats the log messages to include the file + name, line number and message type. Useful for debugging but + also very verbose. + + Paths + ----- + :type path_output_log: str + :param path_output_log: path to a text file used to store the outputs of + the package wide logger, which are also written to stdout + :type path_par_file: str + :param path_par_file: path to parameter file which is used to instantiate + the package + :type path_log_files: str + :param path_log_files: path to a directory where individual log files are + saved whenever a number of parallel tasks are run on the system. + *** + """ + def __init__(self, ntask=1, nproc=1, log_level="DEBUG", verbose=False, + workdir=os.getcwd(), path_output=None, path_system=None, + path_par_file=None, path_output_log=None, path_log_files=None, + **kwargs): + """ + Workstation System Class Parameters + + .. note:: + Paths listed here are shared with `workflow.forward` and so are not + included in the class docstring. + + :type workdir: str + :param workdir: working directory in which to look for data and store + results. Defaults to current working directory + :type path_output: str + :param path_output: path to directory used for permanent storage on disk. + Results and exported scratch files are saved here. + :type path_system: str + :param path_system: scratch path to save any system related files + """ + self.ntask = ntask + self.nproc = nproc + self.log_level = log_level.upper() + self.verbose = verbose + + # Define internal path system + self.path = Dict( + workdir=workdir or os.getcwd(), + scratch=path_system or os.path.join(workdir, "scratch", "system"), + par_file=path_par_file or os.path.join(workdir, "parameters.yaml"), + output=path_output or os.path.join(workdir, "output"), + log_files=path_log_files or os.path.join(workdir, "logs"), + output_log=path_output_log or os.path.join(workdir, "sflog.txt"), + ) + self._acceptable_log_levels = ["CRITICAL", "WARNING", "INFO", "DEBUG"] + + def check(self): + """ + Checks parameters and paths + """ + assert(os.path.exists(self.path.par_file)), \ + f"parameter file does not exist but should" + + assert(self.ntask > 0), f"number of events/tasks `ntask` cannot be neg'" + assert(self.nproc == 1), f"system.workstation rqeuires `nproc`==1" + assert(self.log_level in self._acceptable_log_levels), \ + f"`system.log_level` must be in {self._acceptable_log_levels}" + + def setup(self): + """ + Create the SeisFlows directory structure in preparation for a + SeisFlows workflow. Ensure that if any config information is left over + from a previous workflow, that these files are not overwritten by + the new workflow. Should be called by submit() + + .. note:: + This function is expected to create dirs: SCRATCH, SYSTEM, OUTPUT + and the following log files: output, error + + .. note:: + Logger is configured here as all workflows, independent of system, + will be calling setup() + + :rtype: tuple of str + :return: (path to output log, path to error log) + """ + for path in [self.path.scratch, self.path.output, self.path.log_files]: + unix.mkdir(path) + + # If resuming, move old log files to keep them out of the way. Number + # in ascending order, so we don't end up overwriting things + for src in [self.path.output_log, self.path.par_file]: + i = 1 + if os.path.exists(src): + dst = os.path.join(self.path.log_files, number_fid(src, i)) + while os.path.exists(dst): + i += 1 + dst = os.path.join(self.path.log_files, number_fid(src, i)) + logger.debug(f"copying par/log file to: {dst}") + unix.cp(src=src, dst=dst) + + def submit(self, workdir=None, parameter_file="parameters.yaml"): + """ + Submits the main workflow job as a serial job submitted directly to + the system that is running the master job + + :type workdir: str + :param workdir: path to the current working directory + :type parameter_file: str + :param parameter_file: paramter file file name used to instantiate + the SeisFlows package + """ + workflow = import_seisflows(workdir=workdir or self.path.workdir, + parameter_file=parameter_file) + workflow.check() + workflow.setup() + workflow.run() + + def run(self, funcs, single=False, **kwargs): + """ + Executes task multiple times in serial. + + .. note:: + kwargs will be passed to the underlying `method` that is called + + :type funcs: list of methods + :param funcs: a list of functions that should be run in order. All + kwargs passed to run() will be passed into the functions. + :type single: bool + :param single: run a single-process, non-parallel task, such as + smoothing the gradient, which only needs to be run by once. + This will change how the job array and the number of tasks is + defined, such that the job is submitted as a single-core job to + the system. + """ + if single: + ntasks = 1 + else: + ntasks = self.ntask + + for task_id in range(ntasks): + # Set Task ID for currently running process + set_task_id(task_id) + log_file = self._get_log_file(task_id) + + # Redirect output to a log file to mimic cluster runs where 'run' + # task output logs are sent to different files + with open(log_file, "w") as f: + with redirect_stdout(f): + for func in funcs: + func(**kwargs) + + def _get_log_file(self, task_id): + """ + To mimic clusters which assign job numbers to spawned processes, our + on-system runs will also assign job numbers simply be incrementing the + number on the log files on system. + """ + idx = 1 + while True: + log_file = os.path.join(self.path.log_files, + f"{idx:0>4}_{task_id:0>2}.log") + if os.path.exists(log_file): + idx += 1 + else: + return log_file diff --git a/seisflows/workflow/forward.py b/seisflows/workflow/forward.py index 7f91b4a5..1cb8936e 100644 --- a/seisflows/workflow/forward.py +++ b/seisflows/workflow/forward.py @@ -320,12 +320,14 @@ def evaluate_initial_misfit(self): _model = Model(os.path.join(self.path.model_true)) _model.check() - # Define the tasks that will be run through `system.run` - run_list = [self.run_forward_simulations, - self.evaluate_objective_function] - # Only need to prepare data for solver if we are preprocessing + # If no preprocessing module, than all of the additional functions for + # working with `data` are unncessary. if self.preprocess: - run_list = [self.prepare_data_for_solver] + run_list + run_list = [self.prepare_data_for_solver, + self.run_forward_simulations, + self.evaluate_objective_function] + else: + run_list = [self.run_forward_simulations] self.system.run(run_list, path_model=self.path.model_init, save_residuals=os.path.join(self.path.eval_grad, @@ -387,8 +389,9 @@ def run_forward_simulations(self, path_model, **kwargs): f"'{self.solver.__class__.__name__}'") # Figure out where to export waveform files to, if requested + # path will look like: 'output/solver/001/syn/NN.SSS.BXY.semd' if self.export_traces: - export_traces = os.path.join(self.path.output, + export_traces = os.path.join(self.path.output, "solver", self.solver.source_name, "syn") else: export_traces = False @@ -417,8 +420,6 @@ def evaluate_objective_function(self, save_residuals=False, **kwargs): logger.debug(f"quantifying misfit with " f"'{self.preprocess.__class__.__name__}'") self.preprocess.quantify_misfit( - # observed=self.solver.data_filenames(choice="obs"), - # synthetic=self.solver.data_filenames(choice="syn"), source_name=self.solver.source_name, save_adjsrcs=os.path.join(self.solver.cwd, "traces", "adj"), save_residuals=save_residuals From a8c87fac62afa3535e3986ac7b37a5705b7b47f6 Mon Sep 17 00:00:00 2001 From: bch0w Date: Thu, 1 Sep 2022 17:55:41 -0800 Subject: [PATCH 159/195] reworking example problems to be more dynamic and modular workstation system now allows for multicore runs and accepts mpiexec parameter to do so bugfix two arguments called 'setup' for seisflows --- docs/notebooks/parameter_file.ipynb | 4 +- docs/parameter_file.rst | 368 ++++++++++++------------ seisflows/examples/ex2_hh_w_pyatoa.py | 77 ++--- seisflows/examples/ex3_fwd_solver.py | 73 ++--- seisflows/examples/ex4_multicore_fwd.py | 102 +++++++ seisflows/examples/sfexample2d.py | 99 ++++--- seisflows/seisflows.py | 48 ++-- seisflows/solver/specfem.py | 6 +- seisflows/system/multicore.py | 196 ------------- seisflows/system/workstation.py | 36 ++- 10 files changed, 483 insertions(+), 526 deletions(-) create mode 100644 seisflows/examples/ex4_multicore_fwd.py delete mode 100644 seisflows/system/multicore.py diff --git a/docs/notebooks/parameter_file.ipynb b/docs/notebooks/parameter_file.ipynb index fe727007..bc1567b5 100644 --- a/docs/notebooks/parameter_file.ipynb +++ b/docs/notebooks/parameter_file.ipynb @@ -116,7 +116,7 @@ "cell_type": "raw", "metadata": {}, "source": [ - "* For an explanation of base classes and Python inheritance, see the `inheritance page.`__ " + "* For an explanation of base classes and Python inheritance, see the `inheritance page `__ " ] }, { @@ -183,7 +183,7 @@ "cell_type": "raw", "metadata": {}, "source": [ - "This is also covered in the `command line tool page.`__" + "This is also covered in the `command line tool page `__" ] }, { diff --git a/docs/parameter_file.rst b/docs/parameter_file.rst index 148abdf5..d49e6ff2 100644 --- a/docs/parameter_file.rst +++ b/docs/parameter_file.rst @@ -22,17 +22,17 @@ will create the template file. .. parsed-literal:: - usage: seisflows setup [-h] [-f] - - In the specified working directory, copy template parameter file containing - only module choices, and symlink source code for both the base and super - repositories for easy edit access. If a parameter file matching the provided - name exists in the working directory, a prompt will appear asking the user if - they want to overwrite. - - optional arguments: - -h, --help show this help message and exit - -f, --force automatically overwrites existing parameter file + usage: seisflows setup [-h] [-f] + + In the specified working directory, copy template parameter file containing + only module choices, and symlink source code for both the base and super + repositories for easy edit access. If a parameter file matching the provided + name exists in the working directory, a prompt will appear asking the user if + they want to overwrite. + + optional arguments: + -h, --help show this help message and exit + -f, --force automatically overwrites existing parameter file .. code:: ipython3 @@ -42,7 +42,7 @@ will create the template file. .. parsed-literal:: - creating parameter file: parameters.yaml + creating parameter file: parameters.yaml .. code:: ipython3 @@ -52,36 +52,36 @@ will create the template file. .. parsed-literal:: - # ////////////////////////////////////////////////////////////////////////////// - # - # SeisFlows YAML Parameter File - # - # ////////////////////////////////////////////////////////////////////////////// - # - # Modules correspond to the structure of the source code, and determine - # SeisFlows' behavior at runtime. Each module requires its own sub-parameters. - # - # .. rubric:: - # - To determine available options for modules listed below, run: - # > seisflows print modules - # - To auto-fill with docstrings and default values (recommended), run: - # > seisflows configure - # - To set values as NoneType, use: null - # - To set values as infinity, use: inf - # - # MODULES - # /////// - # workflow (str): The types and order of functions for running SeisFlows - # system (str): Computer architecture of the system being used - # solver (str): External numerical solver to use for waveform simulations - # preprocess (str): Preprocessing schema for waveform data - # optimize (str): Optimization algorithm for the inverse problem - # ============================================================================== - workflow: forward - system: workstation - solver: specfem2d - preprocess: default - optimize: gradient + # ////////////////////////////////////////////////////////////////////////////// + # + # SeisFlows YAML Parameter File + # + # ////////////////////////////////////////////////////////////////////////////// + # + # Modules correspond to the structure of the source code, and determine + # SeisFlows' behavior at runtime. Each module requires its own sub-parameters. + # + # .. rubric:: + # - To determine available options for modules listed below, run: + # > seisflows print modules + # - To auto-fill with docstrings and default values (recommended), run: + # > seisflows configure + # - To set values as NoneType, use: null + # - To set values as infinity, use: inf + # + # MODULES + # /////// + # workflow (str): The types and order of functions for running SeisFlows + # system (str): Computer architecture of the system being used + # solver (str): External numerical solver to use for waveform simulations + # preprocess (str): Preprocessing schema for waveform data + # optimize (str): Optimization algorithm for the inverse problem + # ============================================================================== + workflow: forward + system: workstation + solver: specfem2d + preprocess: default + optimize: gradient How do I choose modules? @@ -90,7 +90,7 @@ How do I choose modules? As seen above, each of the modules comes with a default value which represents the base class\* for this module. -* For an explanation of base classes and Python inheritance, see the `inheritance page.`__ +* For an explanation of base classes and Python inheritance, see the `inheritance page `__ These default values are likely not suitable for all, e.g., if you want to run an inversion and not a forward workflow, or use SPECFEM3D not @@ -104,34 +104,34 @@ SPECFEM2D. To see all available module options, use the .. parsed-literal:: - SEISFLOWS MODULES - ///////////////// - '-': module, '*': class - - - workflow - * forward - * inversion - * migration - - system - * chinook - * cluster - * frontera - * lsf - * maui - * slurm - * workstation - - solver - * specfem - * specfem2d - * specfem3d - * specfem3d_globe - - preprocess - * default - * pyaflowa - - optimize - * LBFGS - * NLCG - * gradient + SEISFLOWS MODULES + ///////////////// + '-': module, '*': class + + - workflow + * forward + * inversion + * migration + - system + * chinook + * cluster + * frontera + * lsf + * maui + * slurm + * workstation + - solver + * specfem + * specfem2d + * specfem3d + * specfem3d_globe + - preprocess + * default + * pyaflowa + - optimize + * LBFGS + * NLCG + * gradient How do I change modules? @@ -141,7 +141,7 @@ Feel free to use any text editor, or use the ``seisflows par`` command to make changes directly from the command line. For example, say we want to use SPECFEM3D as our solver module. -This is also covered in the `command line tool page.`__ +This is also covered in the `command line tool page `__ .. code:: ipython3 @@ -151,7 +151,7 @@ This is also covered in the `command line tool page.`__ .. parsed-literal:: - solver: specfem2d -> specfem3d + solver: specfem2d -> specfem3d .. code:: ipython3 @@ -162,7 +162,7 @@ This is also covered in the `command line tool page.`__ .. parsed-literal:: - solver: specfem3d + solver: specfem3d How do I create a full parameter file? @@ -181,19 +181,19 @@ file. .. parsed-literal:: - usage: seisflows configure [-h] [-a] - - SeisFlows parameter files will vary depending on chosen modules and their - respective required parameters. This function will dynamically traverse the - source code and generate a template parameter file based on module choices. - The resulting file incldues docstrings and type hints for each parameter. - Optional parameters will be set with default values and required parameters - and paths will be marked appropriately. Required parameters must be set before - a workflow can be submitted. - - optional arguments: - -h, --help show this help message and exit - -a, --absolute_paths Set default paths relative to cwd + usage: seisflows configure [-h] [-a] + + SeisFlows parameter files will vary depending on chosen modules and their + respective required parameters. This function will dynamically traverse the + source code and generate a template parameter file based on module choices. + The resulting file incldues docstrings and type hints for each parameter. + Optional parameters will be set with default values and required parameters + and paths will be marked appropriately. Required parameters must be set before + a workflow can be submitted. + + optional arguments: + -h, --help show this help message and exit + -a, --absolute_paths Set default paths relative to cwd .. code:: ipython3 @@ -233,86 +233,86 @@ required by SeisFlows. Section headers will look something: .. parsed-literal:: - # ////////////////////////////////////////////////////////////////////////////// - # - # SeisFlows YAML Parameter File - # - # ////////////////////////////////////////////////////////////////////////////// - # - # Modules correspond to the structure of the source code, and determine - # SeisFlows' behavior at runtime. Each module requires its own sub-parameters. - # - # .. rubric:: - # - To determine available options for modules listed below, run: - # > seisflows print modules - # - To auto-fill with docstrings and default values (recommended), run: - # > seisflows configure - # - To set values as NoneType, use: null - # - To set values as infinity, use: inf - # - # MODULES - # /////// - # workflow (str): The types and order of functions for running SeisFlows - # system (str): Computer architecture of the system being used - # solver (str): External numerical solver to use for waveform simulations - # preprocess (str): Preprocessing schema for waveform data - # optimize (str): Optimization algorithm for the inverse problem - # ============================================================================== - workflow: forward - system: workstation - solver: specfem3d - preprocess: default - optimize: gradient - # ============================================================================= - # - # Forward Workflow - # ---------------- - # Run forward solver in parallel and (optionally) calculate - # data-synthetic misfit and adjoint sources. - # - # Parameters - # ---------- - # :type modules: list of module - # :param modules: instantiated SeisFlows modules which should have been - # generated by the function `seisflows.config.import_seisflows` with a - # parameter file generated by seisflows.configure - # :type data_case: str - # :param data_case: How to address 'data' in the workflow, available options: - # 'data': real data will be provided by the user in - # `path_data/{source_name}` in the same format that the solver will - # produce synthetics (controlled by `solver.format`) OR - # synthetic': 'data' will be generated as synthetic seismograms using - # a target model provided in `path_model_true`. If None, workflow will - # not attempt to generate data. - # :type export_traces: bool - # :param export_traces: export all waveforms that are generated by the - # external solver to `path_output`. If False, solver traces stored in - # scratch may be discarded at any time in the workflow - # :type export_residuals: bool - # :param export_residuals: export all residuals (data-synthetic misfit) that - # are generated by the external solver to `path_output`. If False, - # residuals stored in scratch may be discarded at any time in the workflow - # - # - # ============================================================================= - data_case: data - export_traces: False - export_residuals: False - # ============================================================================= - # - # Workstation System - # ------------------ - # Runs tasks in serial on a local machine. - # - # Parameters - # ---------- - # :type ntask: int - # :param ntask: number of individual tasks/events to run during workflow. - # Must be <= the number of source files in `path_specfem_data` - # :type nproc: int - # :param nproc: number of processors to use for each simulation - # :type log_level: str - # :param log_level: logger level to pass to logging module. + # ////////////////////////////////////////////////////////////////////////////// + # + # SeisFlows YAML Parameter File + # + # ////////////////////////////////////////////////////////////////////////////// + # + # Modules correspond to the structure of the source code, and determine + # SeisFlows' behavior at runtime. Each module requires its own sub-parameters. + # + # .. rubric:: + # - To determine available options for modules listed below, run: + # > seisflows print modules + # - To auto-fill with docstrings and default values (recommended), run: + # > seisflows configure + # - To set values as NoneType, use: null + # - To set values as infinity, use: inf + # + # MODULES + # /////// + # workflow (str): The types and order of functions for running SeisFlows + # system (str): Computer architecture of the system being used + # solver (str): External numerical solver to use for waveform simulations + # preprocess (str): Preprocessing schema for waveform data + # optimize (str): Optimization algorithm for the inverse problem + # ============================================================================== + workflow: forward + system: workstation + solver: specfem3d + preprocess: default + optimize: gradient + # ============================================================================= + # + # Forward Workflow + # ---------------- + # Run forward solver in parallel and (optionally) calculate + # data-synthetic misfit and adjoint sources. + # + # Parameters + # ---------- + # :type modules: list of module + # :param modules: instantiated SeisFlows modules which should have been + # generated by the function `seisflows.config.import_seisflows` with a + # parameter file generated by seisflows.configure + # :type data_case: str + # :param data_case: How to address 'data' in the workflow, available options: + # 'data': real data will be provided by the user in + # `path_data/{source_name}` in the same format that the solver will + # produce synthetics (controlled by `solver.format`) OR + # synthetic': 'data' will be generated as synthetic seismograms using + # a target model provided in `path_model_true`. If None, workflow will + # not attempt to generate data. + # :type export_traces: bool + # :param export_traces: export all waveforms that are generated by the + # external solver to `path_output`. If False, solver traces stored in + # scratch may be discarded at any time in the workflow + # :type export_residuals: bool + # :param export_residuals: export all residuals (data-synthetic misfit) that + # are generated by the external solver to `path_output`. If False, + # residuals stored in scratch may be discarded at any time in the workflow + # + # + # ============================================================================= + data_case: data + export_traces: False + export_residuals: False + # ============================================================================= + # + # Workstation System + # ------------------ + # Runs tasks in serial on a local machine. + # + # Parameters + # ---------- + # :type ntask: int + # :param ntask: number of individual tasks/events to run during workflow. + # Must be <= the number of source files in `path_specfem_data` + # :type nproc: int + # :param nproc: number of processors to use for each simulation + # :type log_level: str + # :param log_level: logger level to pass to logging module. .. code:: ipython3 @@ -322,16 +322,16 @@ required by SeisFlows. Section headers will look something: .. parsed-literal:: - path_model_true: null - path_state_file: /Users/Chow/Repositories/seisflows/docs/notebooks/sfstate.txt - path_data: null - path_par_file: /Users/Chow/Repositories/seisflows/docs/notebooks/parameters.yaml - path_log_files: /Users/Chow/Repositories/seisflows/docs/notebooks/logs - path_output_log: /Users/Chow/Repositories/seisflows/docs/notebooks/sflog.txt - path_specfem_bin: null - path_specfem_data: null - path_solver: /Users/Chow/Repositories/seisflows/docs/notebooks/scratch/solver - path_preconditioner: null + path_model_true: null + path_state_file: /Users/Chow/Repositories/seisflows/docs/notebooks/sfstate.txt + path_data: null + path_par_file: /Users/Chow/Repositories/seisflows/docs/notebooks/parameters.yaml + path_log_files: /Users/Chow/Repositories/seisflows/docs/notebooks/logs + path_output_log: /Users/Chow/Repositories/seisflows/docs/notebooks/sflog.txt + path_specfem_bin: null + path_specfem_data: null + path_solver: /Users/Chow/Repositories/seisflows/docs/notebooks/scratch/solver + path_preconditioner: null How do I know how parameters need to be set? @@ -349,13 +349,13 @@ verifies that parameters are set correctly. .. parsed-literal:: - - ================================================================================ - PARAMETER ERRROR - //////////////// - `path_specfem_bin` must exist and must point to directory containing SPECFEM - executables - ================================================================================ + + ================================================================================ + PARAMETER ERRROR + //////////////// + `path_specfem_bin` must exist and must point to directory containing SPECFEM + executables + ================================================================================ .. code:: ipython3 diff --git a/seisflows/examples/ex2_hh_w_pyatoa.py b/seisflows/examples/ex2_hh_w_pyatoa.py index 2faaa5c4..3e662639 100644 --- a/seisflows/examples/ex2_hh_w_pyatoa.py +++ b/seisflows/examples/ex2_hh_w_pyatoa.py @@ -26,8 +26,8 @@ class SFPyatoaEx2D(SFExample2D): advantage of the default SPECFEM2D stuff, onyl changes the generation of MODEL TRUE, the number of stations, and the setup of the parameter file. """ - def __init__(self, ntask=None, niter=None, nsta=None, method="run", - specfem2d_repo=None): + def __init__(self, ntask=None, niter=None, nsta=None, nproc=None, + method="run", specfem2d_repo=None): """ Overload init and attempt to import Pyatoa before running example. @@ -44,7 +44,8 @@ def __init__(self, ntask=None, niter=None, nsta=None, method="run", contain binary executables. If not given, SPECFEM2D will be downloaded configured and compiled automatically. """ - # Setting default values for ntask, niter, nsta here vvv + # We set defaults here because `seisflows examples` may input these + # values as NoneType which would override __init__ defaults. super().__init__(ntask=ntask or 4, niter=niter or 2, nsta=nsta or 32, method=method, specfem2d_repo=specfem2d_repo) @@ -59,6 +60,25 @@ def __init__(self, ntask=None, niter=None, nsta=None, method="run", ) sys.exit(-1) + # Define the main SeisFlows modules, which will be different to the + # base example + self._modules = { + "workflow": "inversion", + "preprocess": "pyaflowa", + "optimize": "LBFGS", + } + + # Adjust the existing parameter list + self._parameters["smooth_h"] = 5000. + self._parameters["smooth_v"] = 5000. + + # Pyaflowa preprocessing parameters + self._parameters["unit_output"] = "DISP" + self._parameters["min_period"] = 10. # filter bounds define windows + self._parameters["max_period"] = 200. + # self.sf.par("pyflex_preset", "") # To turn off windowing completely + + def print_dialogue(self): """ Print help/system dialogue message that explains the setup of th @@ -75,11 +95,11 @@ def print_dialogue(self): f"iterations]. " f"The tasks involved include: ", items=["1. (optional) Download, configure, compile SPECFEM2D", - "2. Set up a SPECFEM2D working directory", - "3. Generate starting model from 'Tape2007' example", - "4. Generate target model w/ perturbed starting model", - "5. Set up a SeisFlows working directory", - "6. Run the inversion workflow"], + "2. [Setup] a SPECFEM2D working directory", + "3. [Setup] starting model from 'Tape2007' example", + "4. [Setup] target model w/ perturbed starting model", + "5. [Setup] a SeisFlows working directory", + "6. [Run] the inversion workflow"], header="seisflows example 2", border="=") ) @@ -95,46 +115,7 @@ def setup_specfem2d_for_model_true(self): cd(self.workdir_paths.data) assert(os.path.exists("Par_file")), f"I cannot find the Par_file!" - print("> EX: Updating SPECFEM2D to set checkerboard model as " - "MODEL_TRUE") + print("> Updating SPECFEM2D to set checkerboard model as current model") self.sf.sempar("model", "legacy") # read model_velocity.dat_checker rm("proc000000_model_velocity.dat_input") ln("model_velocity.dat_checker", "proc000000_model_velocity.dat_input") - - def setup_seisflows_working_directory(self): - """ - Create and set the SeisFlows parameter file, making sure all required - parameters are set correctly for this example problem - """ - cd(self.cwd) - - print("> EX2: Setting SeisFlows parameters for Pyatoa preprocessing") - self.sf.setup(force=True) # Force will delete existing parameter file - self.sf.par("workflow", "inversion") - self.sf.par("preprocess", "pyaflowa") - self.sf.par("optimize", "LBFGS") - self.sf.configure() - - self.sf.par("end", 1) # only 1 iteration - self.sf.par("ntask", self.ntask) # 3 sources for this example - self.sf.par("materials", "elastic") # how velocity model parameterized - self.sf.par("density", False) # update density or keep constant - self.sf.par("data_format", "ascii") # output synthetic seismograms - self.sf.par("data_case", "synthetic") # synthetic-synthetic inversion - self.sf.par("attenuation", False) - self.sf.par("components", "Y") - self.sf.par("smooth_h", 5000.) - self.sf.par("smooth_v", 5000.) - - # PYATOA preprocessing parameters - self.sf.par("unit_output", "DISP") - self.sf.par("min_period", 10) # filter bounds define window selection - self.sf.par("max_period", 200) - # self.sf.par("pyflex_preset", "") # To turn off windowing completely - - self.sf.par("path_specfem_bin", self.workdir_paths.bin) - self.sf.par("path_specfem_data", self.workdir_paths.data) - self.sf.par("path_model_init", self.workdir_paths.model_init) - self.sf.par("path_model_true", self.workdir_paths.model_true) - - diff --git a/seisflows/examples/ex3_fwd_solver.py b/seisflows/examples/ex3_fwd_solver.py index 9ed7b2b6..d4364130 100644 --- a/seisflows/examples/ex3_fwd_solver.py +++ b/seisflows/examples/ex3_fwd_solver.py @@ -11,19 +11,20 @@ .. rubric:: $ seisflows examples run 3 """ +import os from seisflows.tools import msg -from seisflows.tools.unix import cd +from seisflows.tools.unix import cd, ln, rm from seisflows.examples.sfexample2d import SFExample2D class SFFwdEx2D(SFExample2D): """ A class for running SeisFlows examples. Overloads Example 1 to take - advantage of the default SPECFEM2D stuff, onyl changes the generation of - MODEL TRUE, the number of stations, and the setup of the parameter file. + advantage of the default SPECFEM2D stuff, only removes the generation of + MODEL TRUE and makes the parameter file more bare bones. """ - def __init__(self, ntask=None, nsta=None, method="run", specfem2d_repo=None, - **kwargs): + def __init__(self, ntask=None, nsta=None, nproc=None, method="run", + specfem2d_repo=None, **kwargs): """ Overloads init of the base problem @@ -42,9 +43,19 @@ def __init__(self, ntask=None, nsta=None, method="run", specfem2d_repo=None, contain binary executables. If not given, SPECFEM2D will be downloaded configured and compiled automatically. """ - # Setting default values for ntask, niter, nsta here vvv - super().__init__(ntask=ntask or 10, niter=1, nsta=nsta or 25, - method=method, specfem2d_repo=specfem2d_repo) + # We set defaults here because `seisflows examples` may input these + # values as NoneType which would override __init__ defaults. + super().__init__(ntask=ntask or 10, nsta=nsta or 25, nproc=nproc or 1, + niter=1, method=method, specfem2d_repo=specfem2d_repo) + + self._modules = { + "workflow": "forward", + "preprocess": "null", + "optimize": "null" + } + + self._parameters["export_traces"] = True + self._parameters["path_model_true"] = "null" # overload default par. def print_dialogue(self): """ @@ -68,47 +79,43 @@ def print_dialogue(self): border="=") ) - def setup_seisflows_working_directory(self): + def setup_specfem2d_for_model_true(self): """ - Create and set the SeisFlows parameter file, making sure all required - parameters are set correctly for this example problem - """ - cd(self.cwd) + Overwrites MODEL TRUE creation from EX1. The same as in Example 2 - print("> EX2: Setting SeisFlows parameters for Pyatao preprocessing") - self.sf.setup(force=True) # Force will delete existing parameter file - self.sf.par("workflow", "forward") - self.sf.par("preprocess", "null") - self.sf.par("optimize", "null") - self.sf.configure() + Make some adjustments to the parameter file to create the final velocity + model. This function assumes it is running from inside the + SPECFEM2D/DATA directory + """ + cd(self.workdir_paths.data) + assert(os.path.exists("Par_file")), f"I cannot find the Par_file!" - self.sf.par("ntask", self.ntask) # 3 sources for this example - self.sf.par("materials", "elastic") # how velocity model parameterized - self.sf.par("density", False) # update density or keep constant - self.sf.par("data_format", "ascii") # output synthetic seismograms - self.sf.par("data_case", "synthetic") # synthetic-synthetic inversion - self.sf.par("attenuation", False) - self.sf.par("components", "Y") - self.sf.par("export_traces", True) # copy waveforms to disk + print("> Updating SPECFEM2D to set checkerboard model as current model") + self.sf.sempar("model", "legacy") # read model_velocity.dat_checker + rm("proc000000_model_velocity.dat_input") + for i in range(self.nproc): + ln("model_velocity.dat_checker", + f"proc00000{i}_model_velocity.dat_input") - self.sf.par("path_specfem_bin", self.workdir_paths.bin) - self.sf.par("path_specfem_data", self.workdir_paths.data) - self.sf.par("path_model_init", self.workdir_paths.model_init) def main(self): """ Setup the example and then optionally run the actual seisflows workflow + Mostly the same as Example 1 main() except it does not generate + MODEL_TRUE, and instead sets MODEL_TRUE as the starting model. """ print(msg.cli("EXAMPLE SETUP", border="=")) # Step 1: Download and configure SPECFEM2D, make binaries. Optional self.download_specfem2d() - self.configure_specfem2d_and_make_binaries() + self.configure_specfem2d() + self.make_specfem2d_executables() # Step 2: Create a working directory and generate initial/final models self.create_specfem2d_working_directory() # Step 2a: Generate MODEL_INIT, rearrange consequent directory structure print(msg.cli("GENERATING INITIAL MODEL", border="=")) - self.setup_specfem2d_for_model_init() + self.setup_specfem2d_for_model_init() # setup SPECFEM run directory + self.setup_specfem2d_for_model_true() # Use checkerboard model self.run_xspecfem2d_binaries() self.cleanup_xspecfem2d_run(choice="INIT") # Step 3: Prepare Par_file and directory for MODEL_TRUE generation @@ -117,6 +124,4 @@ def main(self): print(msg.cli("COMPLETE EXAMPLE SETUP", border="=")) # Step 4: Run the workflwo if self.run_example: - print(msg.cli("RUNNING SEISFLOWS FORWARD WORKFLOW", border="=")) self.run_sf_example() - print(msg.cli("EXAMPLE COMPLETED SUCCESFULLY", border="=")) diff --git a/seisflows/examples/ex4_multicore_fwd.py b/seisflows/examples/ex4_multicore_fwd.py new file mode 100644 index 00000000..c67fd099 --- /dev/null +++ b/seisflows/examples/ex4_multicore_fwd.py @@ -0,0 +1,102 @@ +#!/usr/bin/env python3 +""" + SEISFLOWS SPECFEM2D WORKSTATION EXAMPLE 4 + +This example mimics Example 3 (mass forward simulations) but uses the +parallelized version of SPECFEM2D to test out multi-core functionality. + +.. note:: + See Example 1 docstring for more information + +.. rubric:: + $ seisflows examples run 3 +""" +import os +import subprocess +from seisflows.tools import msg +from seisflows.tools.unix import cd, rm, ln +from seisflows.examples.ex3_fwd_solver import SFFwdEx2D + + +class SFMultiCoreEx2D(SFFwdEx2D): + """ + A class for running SeisFlows examples. Overloads Example 1 to take + advantage of the default SPECFEM2D stuff, onyl changes the generation of + MODEL TRUE, the number of stations, and the setup of the parameter file. + """ + def __init__(self, ntask=None, nsta=None, nproc=None, + method="run", specfem2d_repo=None, **kwargs): + """ + Overloads init of the base problem + + :type ntask: int + :param ntask: number of events to use in inversion, between 1 and 25. + defaults to 3 + :type nsta: int + :param nsta: number of stations to include in inversion, between 1 and + 131 + :type nproc: int + :param nproc: number of processors to be sent to MPI executable + :type method: str + :param method: method for running the example problem, can be: + * 'run': setup and run the example problem + * 'setup': only setup the example problem, do not `submit` job + :type specfem2d_repo: str + :param specfem2d_repo: path to the SPECFEM2D directory which should + contain binary executables. If not given, SPECFEM2D will be + downloaded configured and compiled automatically. + """ + # We set defaults here because `seisflows examples` may input these + # values as NoneType which would override __init__ defaults. + super().__init__(ntask=ntask or 10, nsta=nsta or 25, nproc=nproc or 4, + niter=1, method=method, specfem2d_repo=specfem2d_repo) + + self.mpiexec = "mpirun" + self._parameters["nproc"] = self.nproc + self._parameters["mpiexec"] = self.mpiexec + + # Overwrite configure cmd to get MPI + self._configure_cmd = \ + "./configure FC=gfortran CC=gcc MPIF90=mpif90 --with-mpi" + + + def print_dialogue(self): + """ + Print help/system dialogue message that explains the setup of th + this workflow + """ + print(msg.ascii_logo_small) + print(msg.cli( + f"This is a [SPECFEM2D] [WORKSTATION] example, which will run " + f"forward simulations to generate synthetic seismograms through " + f"a homogeneous halfspace starting model. This example uses no " + f"preprocessing or optimization modules. This example uses MPI to " + f"run the external solver, SPECFEM2D." + f"[{self.ntask} events, {self.nsta} stations] " + f"The tasks involved include: ", + items=["1. (optional) Download, configure, compile " + "SPECFEM2D w/ MPI", + "2. [Setup] a SPECFEM2D working directory", + "3. [Setup] starting model from 'Tape2007' example", + "4. [Setup] a SeisFlows working directory", + "5. [Run] the forward simulation workflow"], + header="seisflows example 4", + border="=") + ) + + def run_xspecfem2d_binaries(self): + """ + Runs the xmeshfem2d and then xspecfem2d binaries using subprocess and then + do some cleanup to get files in the correct locations. Assumes that we + can run the binaries directly with './' + """ + cd(self.workdir_paths.workdir) + + mpicmd = f"{self.mpiexec} -n {self.nproc}" + cmd_mesh = f"{mpicmd} bin/xmeshfem2D > OUTPUT_FILES/mesher.log.txt" + cmd_spec = f"{mpicmd} bin/xspecfem2D > OUTPUT_FILES/solver.log.txt" + + for cmd in [cmd_mesh, cmd_spec]: + print(f"Running SPECFEM2D with command: {cmd}") + subprocess.run(cmd, shell=True, check=True, + stdout=subprocess.DEVNULL) diff --git a/seisflows/examples/sfexample2d.py b/seisflows/examples/sfexample2d.py index 03802c36..9bb1ae94 100644 --- a/seisflows/examples/sfexample2d.py +++ b/seisflows/examples/sfexample2d.py @@ -44,8 +44,8 @@ class SFExample2D: A class for running SeisFlows examples. Simplifies calls structure so that multiple example runs can benefit from the code written here """ - def __init__(self, ntask=None, niter=None, nsta=None, method="run", - specfem2d_repo=None): + def __init__(self, ntask=None, niter=None, nsta=None, nproc=None, + method="run", specfem2d_repo=None): """ Set path structure which is used to navigate around SPECFEM repositories and the example working directory @@ -71,6 +71,8 @@ def __init__(self, ntask=None, niter=None, nsta=None, method="run", self.sem2d_paths, self.workdir_paths = self.define_dir_structures( cwd=self.cwd, specfem2d_repo=specfem2d_repo ) + # We set defaults here because `seisflows examples` may input these + # values as NoneType which would override __init__ defaults. self.ntask = ntask or 1 assert(1 <= self.ntask <= 25), \ f"number of tasks/events must be between 1 and 25, not {self.ntask}" @@ -79,8 +81,9 @@ def __init__(self, ntask=None, niter=None, nsta=None, method="run", f"number of iterations must be between 1 and inf, not {self.niter}" self.nsta = nsta or 1 # -1 because it represents index but we need to talk in terms of count - assert(1 <= self.nsta <= 131), \ + assert(1 <= self.nsta <= 132), \ f"number of stations must be between 1 and 131, not {self.nsta}" + self.nproc = nproc or 1 # must be 1 for Examples 1-3 # This bool information is provided by the User running 'setup' or 'run' self.run_example = bool(method == "run") @@ -90,6 +93,35 @@ def __init__(self, ntask=None, niter=None, nsta=None, method="run", sys.argv = [sys.argv[0]] self.sf = SeisFlows() + # Set the main SeisFlows modules prior to running the configure() cmd. + self._modules = { + "workflow": "inversion", + } + + # SeisFlows parameters are set as an attribute so that other examples + # can overwrite or override + self._parameters = { + "ntask": self.ntask, # default 3 sources for this example + "materials": "elastic", # how velocity model parameterized + "density": False, # update density or keep constant + "data_format": "ascii", # how to output synthetic seismograms + "start": 1, # first iteration + "end": self.niter, # final iteration -- we will run 2 + "step_count_max": 5, # will cause iteration 2 to fail + "data_case": "synthetic", # synthetic-synthetic inversion + "components": "Y", # only Y component seismograms avail. + "attenuation": False, + "misfit": "traveltime", # cross-correlation phase measure + "adjoint": "traveltime", # cross-correlation phase measure + "path_specfem_bin": self.workdir_paths.bin, + "path_specfem_data": self.workdir_paths.data, + "path_model_init": self.workdir_paths.model_init, + "path_model_true": self.workdir_paths.model_true, + } + + # Used to configure SPECFEM2D binaries + self._configure_cmd = "./configure" + def print_dialogue(self): """ Print help/system dialogue message that explains the setup of th @@ -103,11 +135,11 @@ def print_dialogue(self): f"[{self.ntask} events, 1 station, {self.niter} iterations]. " f"The tasks involved include: ", items=["1. (optional) Download, configure, compile SPECFEM2D", - "2. Set up a SPECFEM2D working directory", - "3. Generate starting model from 'Tape2007' example", - "4. Generate target model w/ perturbed starting model", - "5. Set up a SeisFlows working directory", - "6. Run the inversion workflow"], + "2. [Setup] a SPECFEM2D working directory", + "3. [Setup] starting model from 'Tape2007' example", + "4. [Setup] target model w/ perturbed starting model", + "5. [Setup] a SeisFlows working directory", + "6. [Run] the inversion workflow"], header="seisflows example 1", border="=") ) @@ -167,7 +199,7 @@ def download_specfem2d(self): f"does not exist, please check your path and try again." ) - def configure_specfem2d_and_make_binaries(self): + def configure_specfem2d(self): """ Run ./configure within the SPECFEM2D repo directory. This function assumes it is being run from inside the repo. Should guess @@ -177,10 +209,10 @@ def configure_specfem2d_and_make_binaries(self): cd(self.sem2d_paths.repo) try: if not os.path.exists("./config.log"): - cmd = "./configure" - print(f"Configuring SPECFEM2D with command: {cmd}") + print(f"Configuring SPECFEM2D with command: " + f"{self._configure_cmd}") # Ignore the configure outputs from SPECFEM - subprocess.run(cmd, shell=True, check=True, + subprocess.run(self._configure_cmd, shell=True, check=True, stdout=subprocess.DEVNULL) else: print("SPECFEM2D already configured, skipping 'configure'") @@ -190,6 +222,10 @@ def configure_specfem2d_and_make_binaries(self): f"to configure SPECFEM2D manually.\n{e}") sys.exit(-1) + def make_specfem2d_executables(self): + """ + Run `$ make all` in SPECFEM2D to create binary executables + """ try: if not glob.glob("./bin/x*"): cmd = "make all" @@ -248,6 +284,7 @@ def setup_specfem2d_for_model_init(self): print("> Setting the SPECFEM2D Par_file for SeisFlows compatiblility") + self.sf.sempar("nproc", self.nproc) self.sf.sempar("setup_with_binary_database", 1) # create .bin files self.sf.sempar("save_model", "binary") # output model in .bin format self.sf.sempar("save_ASCII_kernels", ".false.") # kernels also .bin @@ -318,26 +355,14 @@ def setup_seisflows_working_directory(self): cd(self.cwd) self.sf.setup(force=True) # Force will delete existing parameter file - self.sf.par("workflow", "inversion") + for key, val in self._modules.items(): + self.sf.par(key, val) + self.sf.configure() - self.sf.par("ntask", self.ntask) # default 3 sources for this example - self.sf.par("materials", "elastic") # how velocity model parameterized - self.sf.par("density", False) # update density or keep constant - self.sf.par("data_format", "ascii") # how to output synthetic seismograms - self.sf.par("start", 1) # first iteration - self.sf.par("end", self.niter) # final iteration -- we will run 2 - self.sf.par("step_count_max", 5) # will cause iteration 2 to fail - self.sf.par("data_case", "synthetic") # synthetic-synthetic inversion - self.sf.par("components", "Y") # only Y component seismograms avail. - self.sf.par("attenuation", False) - self.sf.par("misfit", "traveltime") # cross-correlation phase measure - self.sf.par("adjoint", "traveltime") # cross-correlation phase measure - - self.sf.par("path_specfem_bin", self.workdir_paths.bin) - self.sf.par("path_specfem_data", self.workdir_paths.data) - self.sf.par("path_model_init", self.workdir_paths.model_init) - self.sf.par("path_model_true", self.workdir_paths.model_true) + # Adjust the parameters.yaml file + for key, val in self._parameters.items(): + self.sf.par(key, val) def finalize_specfem2d_par_file(self): """ @@ -351,6 +376,7 @@ def finalize_specfem2d_par_file(self): cd(self.workdir_paths.data) self.sf.sempar("model", "gll") # GLL so SPECFEM reads .bin files self.sf.sempar("use_existing_stations", ".true.") # Use STATIONS file + self.sf.sempar("nproc", self.nproc) # Assign STATIONS_checker file which has 132 stations rm("STATIONS") @@ -366,8 +392,14 @@ def run_sf_example(self): """ Use subprocess to run the SeisFlows example we just set up """ + print(msg.cli("RUNNING SEISFLOWS EXAMPLE WORKFLOW", border="=")) cd(self.cwd) - subprocess.run("seisflows submit", check=False, shell=True) + try: + subprocess.run("seisflows submit", check=True, shell=True) + except subprocess.CalledProcessError as e: + print(msg.cli("EXAMPLE FAILED", items=[str(e)], border="=")) + sys.exit(-1) + print(msg.cli("EXAMPLE COMPLETED SUCCESFULLY", border="=")) def main(self): """ @@ -377,7 +409,8 @@ def main(self): # Step 1: Download and configure SPECFEM2D, make binaries. Optional self.download_specfem2d() - self.configure_specfem2d_and_make_binaries() + self.configure_specfem2d() + self.make_specfem2d_executables() # Step 2: Create a working directory and generate initial/final models self.create_specfem2d_working_directory() # Step 2a: Generate MODEL_INIT, rearrange consequent directory structure @@ -396,7 +429,5 @@ def main(self): print(msg.cli("COMPLETE EXAMPLE SETUP", border="=")) # Step 4: Run the workflwo if self.run_example: - print(msg.cli("RUNNING SEISFLOWS INVERSION WORKFLOW", border="=")) self.run_sf_example() - print(msg.cli("EXAMPLE COMPLETED SUCCESFULLY", border="=")) diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index e762c699..8e4db1d1 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -203,21 +203,14 @@ def _format_action(self, action): # ========================================================================= check = subparser.add_parser( "check", formatter_class=argparse.RawDescriptionHelpFormatter, - description=""" -Check parameters, state, or values of an active environment - - model check the min/max values of currently active models tracked by - optimize. 'seisflows check model [name]' to check specific model. - iter Check current interation and step count of workflow - src List source names and respective internal indices - isrc Check source name for corresponding index - """, - help="Check state of an active environment") - - # check.add_argument("choice", type=str, nargs="?", - # help="Parameter, state, or value to check") - # check.add_argument("args", type=str, nargs="*", - # help="Generic arguments passed to check functions") + description="Run check functions to ensure that the provided parameter " + "file has been set correctly" + ) + # ========================================================================= + init = subparser.add_parser( + "init", formatter_class=argparse.RawDescriptionHelpFormatter, + description="Run check and setup functions to generate a SeisFlows " + "working directory") # ========================================================================= plot2d = subparser.add_parser( "plot2d", formatter_class=argparse.RawDescriptionHelpFormatter, @@ -660,6 +653,24 @@ def check(self, **kwargs): except AssertionError as e: print(msg.cli(str(e), border="=", header="parameter errror")) + def init(self, **kwargs): + """ + Run check() + setup() functions for a given parameter file and each of + the SeisFlows modules, ensuring that parameters are acceptable for the + given set of user-defined parameters and running setup procedure + which may create directories and perform some file management. + """ + unix.mkdir(self._args.workdir) + unix.cd(self._args.workdir) + + workflow = import_seisflows(workdir=self._args.workdir, + parameter_file=self._args.parameter_file) + try: + workflow.check() + workflow.setup() + except AssertionError as e: + print(msg.cli(str(e), border="=", header="parameter errror")) + def submit(self, **kwargs): """ Main SeisFlows execution command. Submit the SeisFlows workflow to @@ -817,7 +828,7 @@ def sempar(self, parameter, value=None, skip_print=False, delim="=") except KeyError: print(msg.cli(f"'{parameter}' not found in {par_file}")) - sys.exit(-1) + return # Option 1: Simply print out the value of the given parameter if value is None: @@ -888,7 +899,7 @@ def par(self, parameter, value=None, skip_print=False, **kwargs): except KeyError: print(msg.cli(f"'{parameter}' not found in " f"{self._args.parameter_file}")) - sys.exit(-1) + return # Option 1: Simply print out the value of the given parameter if value is None: @@ -950,6 +961,9 @@ def examples(self, method=None, choice=None, specfem2d_repo=None, import SFPyatoaEx2D as Example elif choice == 3: from seisflows.examples.ex3_fwd_solver import SFFwdEx2D as Example + elif choice == 4: + from seisflows.examples.ex4_multicore_fwd \ + import SFMultiCoreEx2D as Example else: print(f"no SeisFlows example matching given number: {choice}") sys.exit(0) diff --git a/seisflows/solver/specfem.py b/seisflows/solver/specfem.py index 524103d7..345355bc 100644 --- a/seisflows/solver/specfem.py +++ b/seisflows/solver/specfem.py @@ -839,6 +839,6 @@ def _export_starting_models(self, parameters=None): dst = os.path.join(self.path.output, name, "") if not os.path.exists(dst): unix.mkdir(dst) - for par in parameters: - src = glob(os.path.join(model, f"*{par}{self._ext}")) - unix.cp(src, dst) + for par in parameters: + src = glob(os.path.join(model, f"*{par}{self._ext}")) + unix.cp(src, dst) diff --git a/seisflows/system/multicore.py b/seisflows/system/multicore.py deleted file mode 100644 index f7cd980e..00000000 --- a/seisflows/system/multicore.py +++ /dev/null @@ -1,196 +0,0 @@ -#!/usr/bin/env python3 -""" -The `workstation` class is the foundational `System` module in SeisFlows, -it provides utilities for submitting jobs in SERIAL on a small-scale machine, -e.g., a workstation or a laptop. All other `System` classes build on this class. -""" -import os -from contextlib import redirect_stdout - -from seisflows import logger -from seisflows.tools import unix -from seisflows.tools.config import Dict, import_seisflows -from seisflows.tools.config import number_fid, set_task_id - - -class Workstation: - """ - Workstation System - ------------------ - Defines foundational structure for System module. When used standalone, - runs tasks in serial on a local machine. - - Parameters - ---------- - :type ntask: int - :param ntask: number of individual tasks/events to run during workflow. - Must be <= the number of source files in `path_specfem_data` - :type nproc: int - :param nproc: number of processors to use for each simulation - :type log_level: str - :param log_level: logger level to pass to logging module. - Available: 'debug', 'info', 'warning', 'critical' - :type verbose: bool - :param verbose: if True, formats the log messages to include the file - name, line number and message type. Useful for debugging but - also very verbose. - - Paths - ----- - :type path_output_log: str - :param path_output_log: path to a text file used to store the outputs of - the package wide logger, which are also written to stdout - :type path_par_file: str - :param path_par_file: path to parameter file which is used to instantiate - the package - :type path_log_files: str - :param path_log_files: path to a directory where individual log files are - saved whenever a number of parallel tasks are run on the system. - *** - """ - def __init__(self, ntask=1, nproc=1, log_level="DEBUG", verbose=False, - workdir=os.getcwd(), path_output=None, path_system=None, - path_par_file=None, path_output_log=None, path_log_files=None, - **kwargs): - """ - Workstation System Class Parameters - - .. note:: - Paths listed here are shared with `workflow.forward` and so are not - included in the class docstring. - - :type workdir: str - :param workdir: working directory in which to look for data and store - results. Defaults to current working directory - :type path_output: str - :param path_output: path to directory used for permanent storage on disk. - Results and exported scratch files are saved here. - :type path_system: str - :param path_system: scratch path to save any system related files - """ - self.ntask = ntask - self.nproc = nproc - self.log_level = log_level.upper() - self.verbose = verbose - - # Define internal path system - self.path = Dict( - workdir=workdir or os.getcwd(), - scratch=path_system or os.path.join(workdir, "scratch", "system"), - par_file=path_par_file or os.path.join(workdir, "parameters.yaml"), - output=path_output or os.path.join(workdir, "output"), - log_files=path_log_files or os.path.join(workdir, "logs"), - output_log=path_output_log or os.path.join(workdir, "sflog.txt"), - ) - self._acceptable_log_levels = ["CRITICAL", "WARNING", "INFO", "DEBUG"] - - def check(self): - """ - Checks parameters and paths - """ - assert(os.path.exists(self.path.par_file)), \ - f"parameter file does not exist but should" - - assert(self.ntask > 0), f"number of events/tasks `ntask` cannot be neg'" - assert(self.nproc == 1), f"system.workstation rqeuires `nproc`==1" - assert(self.log_level in self._acceptable_log_levels), \ - f"`system.log_level` must be in {self._acceptable_log_levels}" - - def setup(self): - """ - Create the SeisFlows directory structure in preparation for a - SeisFlows workflow. Ensure that if any config information is left over - from a previous workflow, that these files are not overwritten by - the new workflow. Should be called by submit() - - .. note:: - This function is expected to create dirs: SCRATCH, SYSTEM, OUTPUT - and the following log files: output, error - - .. note:: - Logger is configured here as all workflows, independent of system, - will be calling setup() - - :rtype: tuple of str - :return: (path to output log, path to error log) - """ - for path in [self.path.scratch, self.path.output, self.path.log_files]: - unix.mkdir(path) - - # If resuming, move old log files to keep them out of the way. Number - # in ascending order, so we don't end up overwriting things - for src in [self.path.output_log, self.path.par_file]: - i = 1 - if os.path.exists(src): - dst = os.path.join(self.path.log_files, number_fid(src, i)) - while os.path.exists(dst): - i += 1 - dst = os.path.join(self.path.log_files, number_fid(src, i)) - logger.debug(f"copying par/log file to: {dst}") - unix.cp(src=src, dst=dst) - - def submit(self, workdir=None, parameter_file="parameters.yaml"): - """ - Submits the main workflow job as a serial job submitted directly to - the system that is running the master job - - :type workdir: str - :param workdir: path to the current working directory - :type parameter_file: str - :param parameter_file: paramter file file name used to instantiate - the SeisFlows package - """ - workflow = import_seisflows(workdir=workdir or self.path.workdir, - parameter_file=parameter_file) - workflow.check() - workflow.setup() - workflow.run() - - def run(self, funcs, single=False, **kwargs): - """ - Executes task multiple times in serial. - - .. note:: - kwargs will be passed to the underlying `method` that is called - - :type funcs: list of methods - :param funcs: a list of functions that should be run in order. All - kwargs passed to run() will be passed into the functions. - :type single: bool - :param single: run a single-process, non-parallel task, such as - smoothing the gradient, which only needs to be run by once. - This will change how the job array and the number of tasks is - defined, such that the job is submitted as a single-core job to - the system. - """ - if single: - ntasks = 1 - else: - ntasks = self.ntask - - for task_id in range(ntasks): - # Set Task ID for currently running process - set_task_id(task_id) - log_file = self._get_log_file(task_id) - - # Redirect output to a log file to mimic cluster runs where 'run' - # task output logs are sent to different files - with open(log_file, "w") as f: - with redirect_stdout(f): - for func in funcs: - func(**kwargs) - - def _get_log_file(self, task_id): - """ - To mimic clusters which assign job numbers to spawned processes, our - on-system runs will also assign job numbers simply be incrementing the - number on the log files on system. - """ - idx = 1 - while True: - log_file = os.path.join(self.path.log_files, - f"{idx:0>4}_{task_id:0>2}.log") - if os.path.exists(log_file): - idx += 1 - else: - return log_file diff --git a/seisflows/system/workstation.py b/seisflows/system/workstation.py index f7cd980e..ca61ad0c 100644 --- a/seisflows/system/workstation.py +++ b/seisflows/system/workstation.py @@ -5,6 +5,7 @@ e.g., a workstation or a laptop. All other `System` classes build on this class. """ import os +import subprocess from contextlib import redirect_stdout from seisflows import logger @@ -17,8 +18,9 @@ class Workstation: """ Workstation System ------------------ - Defines foundational structure for System module. When used standalone, - runs tasks in serial on a local machine. + Defines foundational structure for System module. When used standalone, + runs solver tasks either in serial (if `nproc`==1; i.e., without MPI) or in + parallel (if `nproc`>1; i.e., with MPI). All other tasks are run in serial. Parameters ---------- @@ -26,7 +28,10 @@ class Workstation: :param ntask: number of individual tasks/events to run during workflow. Must be <= the number of source files in `path_specfem_data` :type nproc: int - :param nproc: number of processors to use for each simulation + :param nproc: number of processors to use for each simulation. Choose 1 for + serial simulations, and `nproc`>1 for parallel simulations. + :type mpiexec: str + :param mpiexec: MPI executable on system. Defaults to 'mpirun -n ${NPROC}' :type log_level: str :param log_level: logger level to pass to logging module. Available: 'debug', 'info', 'warning', 'critical' @@ -48,10 +53,10 @@ class Workstation: saved whenever a number of parallel tasks are run on the system. *** """ - def __init__(self, ntask=1, nproc=1, log_level="DEBUG", verbose=False, - workdir=os.getcwd(), path_output=None, path_system=None, - path_par_file=None, path_output_log=None, path_log_files=None, - **kwargs): + def __init__(self, ntask=1, nproc=1, mpiexec=None, log_level="DEBUG", + verbose=False, workdir=os.getcwd(), path_output=None, + path_system=None, path_par_file=None, path_output_log=None, + path_log_files=None, **kwargs): """ Workstation System Class Parameters @@ -70,6 +75,7 @@ def __init__(self, ntask=1, nproc=1, log_level="DEBUG", verbose=False, """ self.ntask = ntask self.nproc = nproc + self.mpiexec = mpiexec self.log_level = log_level.upper() self.verbose = verbose @@ -92,10 +98,24 @@ def check(self): f"parameter file does not exist but should" assert(self.ntask > 0), f"number of events/tasks `ntask` cannot be neg'" - assert(self.nproc == 1), f"system.workstation rqeuires `nproc`==1" assert(self.log_level in self._acceptable_log_levels), \ f"`system.log_level` must be in {self._acceptable_log_levels}" + if self.nproc > 1: + assert(self.mpiexec is not None), ( + f"Multi-core workflows (`nproc`>1) require an MPI executable " + f"`mpiexec`" + ) + # Make user that `mpiexec` exists on system + stdout = subprocess.run(f"which {self.mpiexec}", shell=True, + text=True, stdout=subprocess.PIPE).stdout + assert(stdout.strip()), ( + f"MPI executable {self.mpiexec} was not found on system with " + f"cmd: `which {self.mpiexec}. Please check that your MPI " + f"module is loaded and accessible from the command line" + ) + logger.debug(f"MPI executable is located at: {stdout.strip()}") + def setup(self): """ Create the SeisFlows directory structure in preparation for a From 6a5c0c8a62c945fc5ea2de5c6dfd70e50d900629 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Fri, 2 Sep 2022 16:44:55 -0800 Subject: [PATCH 160/195] bugfix specfem model not able to get min or max val from multi processor model, used np.hstack to merge model values --- seisflows/tools/specfem.py | 21 +++++++++++---------- 1 file changed, 11 insertions(+), 10 deletions(-) diff --git a/seisflows/tools/specfem.py b/seisflows/tools/specfem.py index dfe70801..cbc1d1a8 100644 --- a/seisflows/tools/specfem.py +++ b/seisflows/tools/specfem.py @@ -331,22 +331,23 @@ def check(self, min_pr=-1., max_pr=0.5): if pr.min() > max_pr: logger.warning(f"minimum poisson's ratio out of bounds: " f"{pr.min():.2f} < {min_pr}") - - if "vs" in self.model and self.model.vs.min() < 0: + + if "vs" in self.model and np.hstack(self.model.vs).min() < 0: logger.warning(f"Vs minimum is negative {self.model.vs.min()}") - if "vp" in self.model and self.model.vp.min() < 0: + if "vp" in self.model and np.hstack(self.model.vp).min() < 0: logger.warning(f"Vp minimum is negative {self.model.vp.min()}") # Tell the User min and max values of the updated model for key, vals in self.model.items(): + min_val = np.hstack(vals).min() + max_val = np.hstack(vals).max() # Choose formatter based on the magnitude of the value - if vals.min() < 1 or (vals.max() > 1E4): - parts = "{minval:.2E} <= {key} <= {maxval:.2E}" + if min_val < 1 or max_val> 1E4: + parts = f"{min_val:.2E} <= {key} <= {max_val:.2E}" else: - parts = "{minval:.2f} <= {key} <= {maxval:.2f}" - logger.info(parts.format(minval=vals.min(), key=key, - maxval=vals.max())) + parts = f"{min_val:.2f} <= {key} <= {max_val:.2f}" + logger.info(parts) def save(self, path): """ @@ -530,8 +531,8 @@ def _read_model_fortran_binary(self, parameter): ) for fid in sorted(fids): # make sure were going in numerical order array.append(read_fortran_binary(fid)) - - array = np.array(array) + + array = np.array(array, dtype="object") return array From e4e6236ab14f498e6adb98ba1c58a55eae9aa976 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Fri, 2 Sep 2022 17:32:32 -0800 Subject: [PATCH 161/195] feature allow tape2007 example problems to set event id (with '--event id' so that we can recreate panels from Figure9 of 2007 paper, which show misfit kernels for single statios --- seisflows/examples/ex2_hh_w_pyatoa.py | 10 ++++-- seisflows/examples/ex4_multicore_fwd.py | 40 +++++++++++++++++++++-- seisflows/examples/sfexample2d.py | 42 ++++++++++++++++++++++--- seisflows/seisflows.py | 10 ++++-- 4 files changed, 92 insertions(+), 10 deletions(-) diff --git a/seisflows/examples/ex2_hh_w_pyatoa.py b/seisflows/examples/ex2_hh_w_pyatoa.py index 3e662639..9f446af4 100644 --- a/seisflows/examples/ex2_hh_w_pyatoa.py +++ b/seisflows/examples/ex2_hh_w_pyatoa.py @@ -27,7 +27,7 @@ class SFPyatoaEx2D(SFExample2D): MODEL TRUE, the number of stations, and the setup of the parameter file. """ def __init__(self, ntask=None, niter=None, nsta=None, nproc=None, - method="run", specfem2d_repo=None): + event_id=None, method="run", specfem2d_repo=None): """ Overload init and attempt to import Pyatoa before running example. @@ -39,6 +39,10 @@ def __init__(self, ntask=None, niter=None, nsta=None, nproc=None, :type nsta: int :param nsta: number of stations to include in inversion, between 1 and 131 + :type event_id: str + :param event_id: allow user to choose a specific event ID from the + example problem. Must match source files in SPECFEM2D example DATA/ + directory. Overwrites `ntask` to be 1 :type specfem2d_repo: str :param specfem2d_repo: path to the SPECFEM2D directory which should contain binary executables. If not given, SPECFEM2D will be @@ -47,7 +51,8 @@ def __init__(self, ntask=None, niter=None, nsta=None, nproc=None, # We set defaults here because `seisflows examples` may input these # values as NoneType which would override __init__ defaults. super().__init__(ntask=ntask or 4, niter=niter or 2, nsta=nsta or 32, - method=method, specfem2d_repo=specfem2d_repo) + event_id=event_id, method=method, + specfem2d_repo=specfem2d_repo) # Make sure that Pyatoa has been installed before running try: @@ -119,3 +124,4 @@ def setup_specfem2d_for_model_true(self): self.sf.sempar("model", "legacy") # read model_velocity.dat_checker rm("proc000000_model_velocity.dat_input") ln("model_velocity.dat_checker", "proc000000_model_velocity.dat_input") + diff --git a/seisflows/examples/ex4_multicore_fwd.py b/seisflows/examples/ex4_multicore_fwd.py index c67fd099..8539d3f1 100644 --- a/seisflows/examples/ex4_multicore_fwd.py +++ b/seisflows/examples/ex4_multicore_fwd.py @@ -15,10 +15,10 @@ import subprocess from seisflows.tools import msg from seisflows.tools.unix import cd, rm, ln -from seisflows.examples.ex3_fwd_solver import SFFwdEx2D +from seisflows.examples.sfexample2d import SFExample2D -class SFMultiCoreEx2D(SFFwdEx2D): +class SFMultiCoreEx2D(SFExample2D): """ A class for running SeisFlows examples. Overloads Example 1 to take advantage of the default SPECFEM2D stuff, onyl changes the generation of @@ -55,6 +55,15 @@ def __init__(self, ntask=None, nsta=None, nproc=None, self._parameters["nproc"] = self.nproc self._parameters["mpiexec"] = self.mpiexec + self._modules = { + "workflow": "forward", + "preprocess": "null", + "optimize": "null", + } + + self._parameters["export_traces"] = True + self._parameters["path_model_true"] = "null" # overload default par. + # Overwrite configure cmd to get MPI self._configure_cmd = \ "./configure FC=gfortran CC=gcc MPIF90=mpif90 --with-mpi" @@ -100,3 +109,30 @@ def run_xspecfem2d_binaries(self): print(f"Running SPECFEM2D with command: {cmd}") subprocess.run(cmd, shell=True, check=True, stdout=subprocess.DEVNULL) + + def main(self): + """ + Setup the example and then optionally run the actual seisflows workflow + Mostly the same as Example 1 main() except it does not generate + MODEL_TRUE, and instead sets MODEL_TRUE as the starting model. + """ + print(msg.cli("EXAMPLE SETUP", border="=")) + + # Step 1: Download and configure SPECFEM2D, make binaries. Optional + self.download_specfem2d() + self.configure_specfem2d() + self.make_specfem2d_executables() + # Step 2: Create a working directory and generate initial/final models + self.create_specfem2d_working_directory() + # Step 2a: Generate MODEL_INIT, rearrange consequent directory structure + print(msg.cli("GENERATING INITIAL MODEL", border="=")) + self.setup_specfem2d_for_model_init() # setup SPECFEM run directory + self.run_xspecfem2d_binaries() + self.cleanup_xspecfem2d_run(choice="INIT") + # Step 3: Prepare Par_file and directory for MODEL_TRUE generation + self.setup_seisflows_working_directory() + self.finalize_specfem2d_par_file() + print(msg.cli("COMPLETE EXAMPLE SETUP", border="=")) + # Step 4: Run the workflwo + if self.run_example: + self.run_sf_example() diff --git a/seisflows/examples/sfexample2d.py b/seisflows/examples/sfexample2d.py index 9bb1ae94..5c201f4e 100644 --- a/seisflows/examples/sfexample2d.py +++ b/seisflows/examples/sfexample2d.py @@ -44,8 +44,8 @@ class SFExample2D: A class for running SeisFlows examples. Simplifies calls structure so that multiple example runs can benefit from the code written here """ - def __init__(self, ntask=None, niter=None, nsta=None, nproc=None, - method="run", specfem2d_repo=None): + def __init__(self, ntask=None, event_id=None, niter=None, nsta=None, + nproc=None, method="run", specfem2d_repo=None): """ Set path structure which is used to navigate around SPECFEM repositories and the example working directory @@ -53,6 +53,10 @@ def __init__(self, ntask=None, niter=None, nsta=None, nproc=None, :type ntask: int :param ntask: number of events to use in inversion, between 1 and 25. defaults to 3 + :type event_id: str + :param event_id: allow user to choose a specific event ID from the + example problem. Must match source files in SPECFEM2D example DATA/ + directory. Overwrites `ntask` to be 1 :type niter: int :param niter: number of iterations to run. defaults to 2 :type nsta: int @@ -71,6 +75,15 @@ def __init__(self, ntask=None, niter=None, nsta=None, nproc=None, self.sem2d_paths, self.workdir_paths = self.define_dir_structures( cwd=self.cwd, specfem2d_repo=specfem2d_repo ) + + if event_id is not None: + assert(1 <= event_id <= 25), \ + f"event id must be between 1 and 25, not {event_id}" + self.event_id = f"SOURCE_{event_id:0>3}" + ntask = 1 # hard set 1 event if we choose a specific event + else: + self.event_id = None + # We set defaults here because `seisflows examples` may input these # values as NoneType which would override __init__ defaults. self.ntask = ntask or 1 @@ -268,11 +281,20 @@ def create_specfem2d_working_directory(self): # Make sure that SPECFEM2D can find the expected files in the DATA/ dir cd(self.workdir_paths.data) - rm("SOURCE") - ln("SOURCE_001", "SOURCE") rm("Par_file") ln("Par_file_Tape2007_onerec", "Par_file") + rm("SOURCE") + # Check if user-chosen source file exists + if self.event_id is not None: + assert(os.path.exists(self.event_id)), \ + f"{self.event_id} chosen but does not exist. Please check DATA" + rm("SOURCE") + ln(self.event_id, "SOURCE") + print(f"> Setting {self.event_id} as SOURCE") + else: + ln("SOURCE_001", "SOURCE") + def setup_specfem2d_for_model_init(self): """ Make some adjustments to the original parameter file to. @@ -388,6 +410,18 @@ def finalize_specfem2d_par_file(self): with open("STATIONS", "w") as f: f.writelines(lines[:self.nsta]) + # Assign unique event id if necessary, move all the other sources + # out of the DATA directory so that SeisFlows does not see them + if self.event_id is not None: + print(f"> Assigning {self.event_id} as only source for workflow") + rm("SOURCE") + mkdir("SOURCES") + for event_id in glob.glob("SOURCE_???"): + if event_id == self.event_id: + continue + mv(event_id, f"SOURCES/{event_id}") + ln(self.event_id, "SOURCE") + def run_sf_example(self): """ Use subprocess to run the SeisFlows example we just set up diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 8e4db1d1..969e4967 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -333,6 +333,11 @@ def _format_action(self, action): help="User-defined number of iterations to run for " "the example problem (1 <= NITER <= inf). If " "not given, each example has its own default.") + examples.add_argument("--event_id", type=int, nargs="?", default=None, + help="Allow User to choose a specific event ID from " + "the Tape 2007 example (1 <= EVENT_ID <= 25). " + "If not used, example will default to choosing " + "sequential from 1 to NTASK") # ========================================================================= # Defines all arguments/functions that expect a sub-argument subparser_dict = {"check": check, "par": par, "inspect": inspect, @@ -913,7 +918,7 @@ def par(self, parameter, value=None, skip_print=False, **kwargs): print(msg.cli(f"{key}: {cur_val} -> {value}")) def examples(self, method=None, choice=None, specfem2d_repo=None, - nsta=None, nevent=None, niter=None, **kwargs): + nsta=None, nevent=None, niter=None, event_id=None, **kwargs): """ List or run a SeisFlows example problems @@ -970,7 +975,8 @@ def examples(self, method=None, choice=None, specfem2d_repo=None, # Run or setup example, or just print system dialogue example = Example(specfem2d_repo=specfem2d_repo, method=method, - nsta=nsta, ntask=nevent, niter=niter) + nsta=nsta, ntask=nevent, niter=niter, + event_id=event_id) example.print_dialogue() # e.g., $ seisflows examples run 1 From 01a59145250fe312fc2250d14e28a854ac51bf7e Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Wed, 7 Sep 2022 14:01:22 -0800 Subject: [PATCH 162/195] small docstring change specfem model tool --- seisflows/tools/specfem.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/seisflows/tools/specfem.py b/seisflows/tools/specfem.py index cbc1d1a8..704f7a3e 100644 --- a/seisflows/tools/specfem.py +++ b/seisflows/tools/specfem.py @@ -531,8 +531,11 @@ def _read_model_fortran_binary(self, parameter): ) for fid in sorted(fids): # make sure were going in numerical order array.append(read_fortran_binary(fid)) - - array = np.array(array, dtype="object") + + # !!! Causes a visible deprecation warning from NumPy but setting + # !!! array type as 'object' causes problems with pickling and + # !!! merging arrays + array = np.array(array) return array From 1d4b8695785723cf16be9315459680b890391366 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Fri, 9 Sep 2022 11:21:33 -0800 Subject: [PATCH 163/195] bugfix pyaflowa preprocessing failing when not set as it was trying to add NoneType windows to an integer --- docs/notebooks/specfem2d_example.ipynb | 634 +++++++++++++++++++++---- seisflows/preprocess/pyaflowa.py | 4 +- seisflows/seisflows.py | 1 - seisflows/workflow/inversion.py | 5 +- 4 files changed, 542 insertions(+), 102 deletions(-) diff --git a/docs/notebooks/specfem2d_example.ipynb b/docs/notebooks/specfem2d_example.ipynb index 110f695d..341114f5 100644 --- a/docs/notebooks/specfem2d_example.ipynb +++ b/docs/notebooks/specfem2d_example.ipynb @@ -23,11 +23,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "from IPython.display import Image # To display .png files in the notebook" + "from IPython.display import Image # Used to display .png files in the notebook/docs" ] }, { @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -80,11 +80,11 @@ "velocities. [1 events, 1 station, 1 iterations]. The tasks involved include:\r\n", "\r\n", "1. (optional) Download, configure, compile SPECFEM2D\r\n", - "2. Set up a SPECFEM2D working directory\r\n", - "3. Generate starting model from 'Tape2007' example\r\n", - "4. Generate target model w/ perturbed starting model\r\n", - "5. Set up a SeisFlows working directory\r\n", - "6. Run the inversion workflow\r\n", + "2. [Setup] a SPECFEM2D working directory\r\n", + "3. [Setup] starting model from 'Tape2007' example\r\n", + "4. [Setup] target model w/ perturbed starting model\r\n", + "5. [Setup] a SeisFlows working directory\r\n", + "6. [Run] the inversion workflow\r\n", "================================================================================\r\n" ] } @@ -110,10 +110,11 @@ "metadata": {}, "outputs": [], "source": [ + "# Run command with open variable to set SPECFEM2D path\n", "! seisflows examples setup 1 -r ${PATH_TO_SPECFEM2D}\n", "! seisflows submit\n", "\n", - "# The above commands are the same as the below\n", + "# The following command is the same as above\n", "! seisflows examples run 1 --specfem2d_repo ${PATH_TO_SPECFEM2D}" ] }, @@ -130,28 +131,6 @@ "source": [ ".. code:: bash\n", "\n", - " LINE SEARCH STEP COUNT 02\n", - " --------------------------------------------------------------------------------\n", - " 2022-08-29 15:50:58 (I) | evaluating objective function for source 001\n", - " 2022-08-29 15:50:58 (D) | running forward simulation with 'Specfem2D'\n", - " 2022-08-29 15:51:03 (D) | quantifying misfit with 'Default'\n", - " 2022-08-29 15:51:03 (D) | misfit for trial model (f_try) == 4.61E-01\n", - " 2022-08-29 15:51:03 (D) | step length(s) = 0.00E+00, 4.78E+09, 7.73E+09\n", - " 2022-08-29 15:51:03 (D) | misfit val(s) = 1.04E+00, 2.30E-01, 4.61E-01\n", - " 2022-08-29 15:51:03 (I) | pass: bracket acceptable and step length reasonable. returning minimum line search misfit.\n", - " 2022-08-29 15:51:03 (I) | line search model 'm_try' parameters: \n", - " 2022-08-29 15:51:03 (I) | 5800.00 <= vp <= 5800.00\n", - " 2022-08-29 15:51:03 (I) | 3431.53 <= vs <= 3790.00\n", - " 2022-08-29 15:51:03 (I) | trial step successful. finalizing line search\n", - " 2022-08-29 15:51:03 (I) | \n", - " FINALIZING LINE SEARCH\n", - " --------------------------------------------------------------------------------\n", - " 2022-08-29 15:51:03 (I) | writing optimization stats\n", - " 2022-08-29 15:51:03 (I) | renaming current (new) optimization vectors as previous model (old)\n", - " 2022-08-29 15:51:03 (I) | setting accepted trial model (try) as current model (new)\n", - " 2022-08-29 15:51:03 (I) | misfit of accepted trial model is f=2.304E-01\n", - " 2022-08-29 15:51:03 (I) | resetting line search step count to 0\n", - " 2022-08-29 15:51:03 (I) | \n", " CLEANING WORKDIR FOR NEXT ITERATION\n", " --------------------------------------------------------------------------------\n", " 2022-08-29 15:51:05 (I) | thrifty inversion encountering first iteration, defaulting to standard inversion workflow\n", @@ -160,7 +139,10 @@ " COMPLETE ITERATION 01 \n", " ////////////////////////////////////////////////////////////////////////////////\n", " 2022-08-29 15:51:06 (I) | setting current iteration to: 2\n", - "\n", + " \n", + " ================================================================================\n", + " EXAMPLE COMPLETED SUCCESFULLY\n", + " ================================================================================\n", "\n", " \n", "Using the `working directory documentation page `__ you can figure out how to navigate around and look at the results of this small inversion problem. \n", @@ -170,21 +152,21 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "/home/bchow/Work/scratch/example_1\n", + "/home/bchow/Work/work/seisflows_example/example_1\n", "logs\tparameters.yaml sflog.txt specfem2d\r\n", "output\tscratch\t\t sfstate.txt specfem2d_workdir\r\n" ] } ], "source": [ - "%cd ~/Work/scratch/example_1\n", + "%cd ~/sfexamples/example_1\n", "! ls" ] }, @@ -192,27 +174,45 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In the `output/` directory, we can see our starting model (MODEL_INIT), our target model (MODEL_TRUE) and the updated model from our first iteration (MODEL_01) alongside the gradient that was used to create it (GRADIENT_01). " + "### Understanding example outputs\n", + "\n", + "In the `output/` directory, we can see our starting/initial model (*MODEL_INIT*), our true/target model (*MODEL_TRUE*) and the updated model from the first iteration (*MODEL_01*). In addition, we have saved the gradient generated during the first iteration (*GRADIENT_01*) because we set the parameter `export_gradient` to True." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "GRADIENT_01 MODEL_01 MODEL_INIT MODEL_TRUE\n", - "\n", - "proc000000_vp.bin proc000000_vs.bin\n" + "GRADIENT_01 MODEL_01 MODEL_INIT MODEL_TRUE\r\n" ] } ], "source": [ - "! ls output\n", - "! echo\n", + "# The output directory contains important files exported during a workflow\n", + "! ls output" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "proc000000_vp.bin proc000000_vs.bin\r\n" + ] + } + ], + "source": [ + "# A MODEL output directory contains model files in the chosen solver format. \n", + "# In this case, Fortran Binary from SPECFEM2D\n", "! ls output/MODEL_01" ] }, @@ -220,9 +220,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Because we're working with SPECFEM2D, we can plot the models and gradients that were created during our workflow using the `seisflows plot2d` command. If we use the `--savefig` option we can also save the output .png files to disk. Because this docs page was made in a Jupyter Notebook, we need to use the IPython Image class to open the resulting .png file.\n", + "### Plotting results (only available w/ SPECFEM2D)\n", + "\n", + "We can plot the model and gradient files created during our workflow using the `seisflows plot2d` command. The `--savefig` flag allows us to save output .png files to disk. The following figure shows the starting/initial homogeneous halfspace model in Vs.\n", "\n", - "This figure shows the starting homogeneous halfspace model in Vs." + ">__NOTE:__ Because this docs page was made in a Jupyter Notebook, we need to use the IPython Image class to open the resulting .png file from inside the notebook. Users following along will need to open the figure using the GUI or command line tool." ] }, { @@ -250,6 +252,7 @@ } ], "source": [ + "# Plot and open the initial homogeneous halfspace model\n", "! seisflows plot2d MODEL_INIT vs --savefig m_init_vs.png\n", "Image(filename='m_init_vs.png') " ] @@ -258,7 +261,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Here we see the gradient created during the adjoint simulation." + "We can also plot the gradient that was created during the adjoint simulation. In this example we only have one source and one receiver, so the gradient shows a \"banana-doughnut\" style kernel, representing volumetric sensitivity of the measurement (waveform misfit) to changes in model values." ] }, { @@ -296,7 +299,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Finally we see the updated model, which is the sum of the initial model, and a scaled gradient." + "Finally we can plot the updated model (*MODEL_01*), which is the sum of the initial model and a scaled gradient. The gradient was scaled during the line search, where we used a steepest-descent algorithm to reduce the misfit between data and synthetics. Since we only have one source-receiver pair in this workflow, the updated model shown below almost exactly mimics the Vs kernel shown above." ] }, { @@ -328,11 +331,20 @@ "Image(filename='m_01_vs.png') " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Closing thoughts" + ] + }, { "cell_type": "raw", "metadata": {}, "source": [ - "Have a look at the `working directory documentation page `__ for more detailed explanations of how to navigate the SeisFlows working directory. You can also run Example \\#1 with more stations (up to 131), tasks/events (up to 25) and iterations (as many as you want). Note that because this is a serial inversion, the compute time will scale with all of these values." + "Have a look at the `working directory documentation page `__ for more detailed explanations of how to navigate the SeisFlows working directory that was created during this example.\n", + "\n", + "You can also run Example \\#1 with more stations (up to 131), tasks/events (up to 25) and iterations (as many as you want!). Note that because this is a serial inversion, the compute time will scale with all of these values." ] }, { @@ -341,6 +353,7 @@ "metadata": {}, "outputs": [], "source": [ + "# An example call for running Example 1 with variable number of stations, events and iterations\n", "! seisflows examples run 1 --nsta 10 --ntask 5 --niter 2" ] }, @@ -348,14 +361,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Example \\#2: Pyaflowa, L-BFGS inversion\n", + "## Example \\#2: Checkerboard inversion using Pyaflowa \\& L-BFGS \n", + "\n", + "Building on the foundation of the previous example, Example \\#2 runs a 2 iteration inversion with misfit quantification taken care of by the `Pyaflowa` preprocessing module, which uses the misfit quantification package [Pyatoa](https://github.com/adjtomo/pyatoa) under the hood. Model updates are performed using an [`L-BFGS` nonlinear optimization algorithm](https://en.wikipedia.org/wiki/Limited-memory_BFGS). Example \\#2 also includes smoothing/regularization of the gradient. This example more closely mimics a research-grade inversion problem.\n", "\n", - "Example \\#2 runs a 2 iteration inversion with misfit quantification taken care of by the `Pyaflowa` preprocessing module. Optimization (i.e., model updates) are performed using the `L-BFGS` algorithm. This example is more complex than the default version of Example \\#1, using multiple events, stations and iterations. Example \\#2 also includes smoothing/regularization of the gradient before using it to perturb the starting velocity model." + ">__NOTE:__ This example is computationally more intense than the default version of Example \\#1 as it uses multiple events and stations, and runs multiple iterations. " ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": { "scrolled": true }, @@ -364,7 +379,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "No existing SPECFEM2D repo given, default to: /home/bchow/Work/scratch/example_1/specfem2d\n", + "No existing SPECFEM2D repo given, default to: /home/bchow/Work/work/seisflows_example/example_1/specfem2d\n", "\n", " @@@@@@@@@@ \n", " .@@@@. .%&( %@. \n", @@ -395,16 +410,17 @@ "iterations]. The tasks involved include:\n", "\n", "1. (optional) Download, configure, compile SPECFEM2D\n", - "2. Set up a SPECFEM2D working directory\n", - "3. Generate starting model from 'Tape2007' example\n", - "4. Generate target model w/ perturbed starting model\n", - "5. Set up a SeisFlows working directory\n", - "6. Run the inversion workflow\n", + "2. [Setup] a SPECFEM2D working directory\n", + "3. [Setup] starting model from 'Tape2007' example\n", + "4. [Setup] target model w/ perturbed starting model\n", + "5. [Setup] a SeisFlows working directory\n", + "6. [Run] the inversion workflow\n", "================================================================================\n" ] } ], "source": [ + "# Run the help message dialogue to see what Example 2 will do\n", "! seisflows examples 2" ] }, @@ -412,7 +428,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "You can run the example with the same command as shown for Example 1:" + "### Run the example \n", + "\n", + "You can run the example with the same command as shown for Example 1. Users following along will need to provide a path to their own installation of SPECFEM2D using the `-r` flag." ] }, { @@ -428,32 +446,11 @@ "cell_type": "raw", "metadata": {}, "source": [ - "Succesful completion of the example problem will end with the following log message\n", + "Succesful completion of the example problem will end with a log message that looks similar to the following\n", "\n", ".. code:: bash\n", "\n", - " LINE SEARCH STEP COUNT 01\n", - " --------------------------------------------------------------------------------\n", - " 2022-08-29 18:07:14 (I) | evaluating objective function for source 001\n", - " 2022-08-29 18:07:14 (D) | running forward simulation with 'Specfem2D'\n", - " 2022-08-29 18:07:20 (D) | quantifying misfit with 'Pyaflowa'\n", - " 2022-08-29 18:07:29 (I) | evaluating objective function for source 002\n", - " 2022-08-29 18:07:29 (D) | running forward simulation with 'Specfem2D'\n", - " 2022-08-29 18:07:35 (D) | quantifying misfit with 'Pyaflowa'\n", - " 2022-08-29 18:07:43 (I) | evaluating objective function for source 003\n", - " 2022-08-29 18:07:43 (D) | running forward simulation with 'Specfem2D'\n", - " 2022-08-29 18:07:49 (D) | quantifying misfit with 'Pyaflowa'\n", - " 2022-08-29 18:07:58 (I) | evaluating objective function for source 004\n", - " 2022-08-29 18:07:58 (D) | running forward simulation with 'Specfem2D'\n", - " 2022-08-29 18:08:04 (D) | quantifying misfit with 'Pyaflowa'\n", - " 2022-08-29 18:08:13 (D) | misfit for trial model (f_try) == 4.73E-03\n", - " 2022-08-29 18:08:13 (D) | step length(s) = 0.00E+00, 1.00E+00\n", - " 2022-08-29 18:08:13 (D) | misfit val(s) = 5.30E-02, 4.73E-03\n", - " 2022-08-29 18:08:13 (I) | pass: misfit decreased, line search successful w/ alpha=1.0\n", - " 2022-08-29 18:08:13 (I) | line search model 'm_try' parameters: \n", - " 2022-08-29 18:08:13 (I) | 5800.00 <= vp <= 5800.00\n", - " 2022-08-29 18:08:13 (I) | 3193.01 <= vs <= 3821.37\n", - " 2022-08-29 18:08:13 (I) | trial step successful. finalizing line search\n", + "\n", " 2022-08-29 18:08:13 (I) | \n", " FINALIZING LINE SEARCH\n", " --------------------------------------------------------------------------------\n", @@ -472,33 +469,50 @@ " ////////////////////////////////////////////////////////////////////////////////\n", " 2022-08-29 18:08:21 (I) | setting current iteration to: 3\n", "\n", + " ================================================================================\n", + " EXAMPLE COMPLETED SUCCESFULLY\n", + " ================================================================================" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Understanding example outputs\n", "\n", - "As with Example \\#1, we can look at the output gradients and models to visualize how the inversion performed." + "As with Example \\#1, we can look at the output gradients and models to visualize what just happenend under the hood. Be sure to read through the output log messages as well, to get a better idea of what steps and tasks were performed to generate these outputs." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "/home/bchow/Work/scratch/example_2\n", + "/home/bchow/Work/work/seisflows_example/example_2\n", "logs\tparameters.yaml sflog.txt specfem2d\r\n", "output\tscratch\t\t sfstate.txt specfem2d_workdir\r\n" ] } ], "source": [ - "%cd ~/Work/scratch/example_2\n", + "%cd ~/sfexamples/example_2\n", "! ls" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Running the `plot2d` command without any arguments is a useful way to determine what model/gradient files are available for plotting. " + ] + }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -519,19 +533,23 @@ } ], "source": [ - "! seisflows plot2d # to check what models/gradients/kernels are avilable for plotting" + "! seisflows plot2d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The starting model is a homogeneous halfspace but for Example \\#2 the target model is a checkerboard." + "### Visualizing Initial and Target models\n", + "\n", + "The starting model for this example is the same homogeneous halfspace model shown in Example \\#1, with $V_p$=5.8km/s and $V_s$=3.5km/s. \n", + "\n", + "For this example, however, the target model is a checkerboard model with fast and slow perturbations roughly equal to $\\pm10\\%$ of the initial model. We can plot the model below to get a visual representation of these perturbations, where **red==slow** and **blue==fast**." ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -543,12 +561,12 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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eeuopBoB9/vnn3H2ccMIJrHnz5qY7EU6MGzeOhcNh9ssvvzDGGPvtt99YdnY29zvv27cva9u2LbefkpISlpWVxQYNGsTKysps23v37s2OO+44Fo/HTeV33XUXCwaDbPPmzdx+VcXwrFmzWDAYZH/5y19c/ZY6duzIevXqpVyfIAhCFfIMEwSS6bkuuugilJSUYNu2bZg1axYaNWqECy64wFb38ssvx6effoq9e/diyZIlYIxh8ODB+OGHH2rhyP0jEokoPebOneup/6KiIgSDQWzatMnX42aMmSZyaV5h64TI7du349dff0XXrl2l/R133HHYuXMntm/fbirX+tPaZ2dn49hjjxUeE8CfYPbVV1/hq6++wmWXXYZIJCI9FiO9evVCPB7X/d8bN27E77//jhNPPNFWt6ioCFu2bMH+/ftN5W+//TaGDRuGvn37YuHChXqKOyNr165Fz549dZ+8xoknnojKykp88803ysdsZfbs2bjyyisxatQoPPnkk64mT1q/Z4IgCL+gkYUgqhgzZgwSiQSmTZuGpUuXYsSIEWjQoIGwfk5ODgYOHIhJkyahvLwcX3/9dQ0erf+sWrVK6TFkyBBP/a9YsQKVlZVo166db8f86quv4uDBgzjppJP0srPPPhtZWVmYM2eOqe6cOXMQCAQwbNgwaZ9Dhw5FIBCwif45c+YgOzsbZ599tl52/vnno7S0FJ9++qmp7tKlS9GwYUPu5LDnnnsOQPL35obly5cjGAzimGOOAQAUFhYCAFauXGmqxxjDypUr0aRJE+Tk5Ojly5Ytw7Bhw3DyySdj8eLFiMVi3P0UFhZi9erVtgU2tDzJrVq1cnXcGnPmzMGVV16JSy+9FM8++6wrIbxy5Up89913pu+ZIAjCL2jRDYKooqioCN26dcOjjz4KxhhXrIwdOxbZ2dno06cPWrZsie3bt+OBBx5AXl4eN0JXlygqKvKlnzfffBPPPPMMzj33XLRp0wYVFRVYvXo1Hn30UbRr1w5XXnmlqf5bb72FAwcOYN++fQCSWTy0ldHOOeccNGjQAD/88AMuvvhijBgxAu3atUMgEMCKFSvw6KOPokuXLqY+mzZtijvuuAN33nknmjZtqi+6MWXKFFx55ZXo3LmzXvf555/HFVdcgVmzZukLP3Tp0gVjxozB5MmTEQqFcOKJJ2LZsmV4+umn8fe//x1NmzbV299888148cUXccEFF+Dee+9Fq1at8Oqrr+L111/HQw89ZEtPdujQIbz00kv44x//iE6dOnE/v7/85S/Izc1Fr169kJ+fj19//RULFizAK6+8gr/+9a96irqjjjoK5513Hp5++mnEYjGcc845KCsrw9y5c/HJJ5/g3nvv1QXnxx9/jGHDhqGgoAC333471q5da9pn586dkZubCwC48cYbMWHCBAwZMgRXXXUVGjRogPfeew8PP/wwzjrrLHTv3l1v98MPP2DVqlUAgM2bNwOA/t21bdtW/00tWLAAY8aMwfHHH4+rrroKX3zxhWn/2qIdANC9e3dceuml6NSpE7KysvDFF19g2rRpKCgosGWzGD16NObOnYvvv/8+o1dkJAgiw6lNjwZBZBozZsxgAFjnzp252+fOnctOP/10lp+fz6LRKCssLGQXXnihKdODCshAz7BffPPNN+xPf/oTa9OmDcvKymJZWVmsY8eO7K9//atpwQrj8UGQueL7779njCX9scOHD2dt27Zl2dnZLBqNsmOPPZbdcsstbM+ePdzjmDFjBmvfvj2LRqPsqKOOYpMnT7Z5dGfPns3NkFFeXs4mT57MjjrqKBaNRln79u3ZY489xt3Pjz/+yEaMGMGaNGnCotEo69atG5s1axa37osvvsgACLczlvTUnnLKKaxZs2YsHA6zxo0bs759+7J58+bZ6v7+++9s2rRprFu3bqxRo0asadOm7KSTTmIvvPCCyY+rLWQheixfvtzU78KFC9nJJ5/MmjVrxnJycliXLl3Yvffea1uIQ/v8eA/jb27UqFHS/WvfM2NJz3a7du1YTk4Oi0QirE2bNmzcuHFs69attvd//vnns+zsbLZ7927h50kQBOFEgDEXSUMJgiAIIkMoKCjAyJEjMW3atNo+FIIg6jAkhgmCIIg6x9dff43i4mL85z//QbNmzWr7cAiCqMOQGCYIH4nH49LtwWCQZsQTBEEQRAZBZ2WC8BGntGRXXHFFbR8iQRAEQRAGKJsEQfiINrNeBN3OJQiCIIjMgmwSBEEQBEEQRL2FbBJEvWT48OHIzs7Gnj17hHUuueQSRCIR7Nixo+YO7DDjwIEDGDFiBDp06IBGjRohJycHXbp0wd///nccOHDAVn/58uXo168fWrRogYYNG6Jbt2547LHHbAtAGPn999/Rvn17BAIBPPTQQ7btmzZtwvnnn48mTZqgQYMG6N27N15//XXl9/DFF19gwIABaNSoERo2bIjTTz8dn3zyia3eY489hpNOOgnNmjVDLBbDUUcdhREjRtgWY9m0aRNuvvlm9OzZE40bN0bTpk3Rp08fPT8vj9deew19+/ZFbm6u/hk+/fTTpjpvvvkmLrvsMhx33HGIRCLCRS2mTJmCQCAgfMyfP1+v+/XXX2P8+PEoLi5GTk4OAoEAPvjgA26/paWlmDRpEtq3b48GDRrgyCOPxAUXXGB7/6NHj5bu37iIiKhux44dhZ8VQRCEW8gmQdRLxowZg8WLF+Oll17C+PHjbdv37t2LRYsWYfDgwcjPz6+FIzw8qKioAGMMN910E44++mgEg0F8+OGHuOeee/DBBx/g3Xff1eu+++67GDBgAE499VQ888wzyMnJweuvv47rr78emzdvxowZM7j7uPPOO7nCGgC2bNmC4uJitGzZEk8++SQaNmyImTNnYtiwYViwYAHOP/986fGvWrUKp556Knr16oV58+aBMYYHH3wQZ555JpYvX47i4mK97q5duzBw4EB0794dTZo0wX/+8x9MnToVvXv3xpo1a9ChQwcAyZXglixZgpEjR+LEE09EPB7HK6+8ggsuuAB333037rrrLtMxTJ06FZMmTcK4ceNw2223IRKJ4Ntvv0V5ebmp3qJFi7By5Up9AYs1a9Zw39OVV15pWkVPY+zYsdi8ebNp2+rVq7F48WKccMIJOPPMM/HGG28IP6shQ4Zg9erVmDJlCoqKivDzzz/jnnvuQXFxMdatW4c2bdoASH5f48aN47aPxWK2xWuys7Px/vvv28oIgiB8o7YSHBNEbRKPx1lhYSHr2bMnd/vMmTMZAPbGG2/U8JHVD2655RYGgG3evFkvu+SSS1gsFrMt7NC/f3+Wm5vL7efzzz9n0WiULViwgLtgyVVXXcWysrLYzz//rJfF43HWqVMn1rp1a5ZIJKTHOWDAAJafn88OHDigl5WWlrJmzZqxP/7xj47vc8OGDQwAu/POO/WynTt3mhbE0Bg0aBBr0KABO3TokF62evVqFgwG2f/8z/847sv4Xq655hrmZnj//vvvWSAQYJdeeqmwT+0zti7QwRhj3333HQPA7rjjDlP5p59+ygCw6dOnS/f/wQcfcNuPGjWK5eTkKL8PgiAIL5BNgqiXhEIhjBo1CmvWrMG6dets22fPno2WLVti4MCBetnMmTPRvXt3NGzYEI0aNULHjh1x++23u9pvIBDAtddei9mzZ6NDhw7Izs5GUVERVq5cCcYYpk2bhqOPPhoNGzbEGWecgX//+9+m9u+88w6GDh2KVq1aISsrC+3atcNVV12FX3/9Va9z6NAhnHDCCWjXrh327t2rl2/fvh0FBQU47bTTpLaDmkBbUjgcrr45FYlEEI1GbVG/xo0bIysry9ZHeXk5rrjiClxzzTXCpaQ/+eQTdO/eHUceeaReFgqFMHDgQPz000+2ZYF57U877TQ0aNBAL2vUqBFOPfVUfPrpp9i2bZvr99msWTOuhaFXr144ePAgfvvtN73siSeeQCwWw3XXXSfdD4CUUvbNmjULjDHbUtmqfUYiEQBAXl6eqbxx48YAwP3+jDz33HMIBAKUbYUgiFqBxDBRb7niiisQCAQwa9YsU/mGDRvwxRdfYNSoUQiFQgCA+fPnY/z48ejbty8WLVqExYsX48YbbxTenpfx5ptv4tlnn8XUqVPx8ssvY9++fRg0aBAmTpyITz75BE888QSefvppbNiwAeeffz6YYY7r5s2bUVxcjJkzZ2LZsmW466678Pnnn+Pkk09GRUUFgKTw+H//7//hl19+0cVFZWUlLrnkEjDG8PLLL+vvS0Q8Hld6MMX5t4wxxONxlJaWoqSkBA8//DAuuugiHHXUUXqdcePGoby8HBMmTMDWrVuxZ88ezJs3D4sWLcItt9xi6/Oee+7BgQMHcO+99wr3W15ejlgsZivXyv71r39Jj9upPe9CKpFIoKysDN9++y2uvPJKtGjRApdffrl0P0DSL928eXO0aNFCL/vwww/RqVMnLFy4EB06dEAoFEKrVq3wt7/9zWaT8EplZSXmzJmDdu3aoW/fvp76aNOmDYYOHYpHHnkEy5cvx/79+/Htt99iwoQJundaxN69e/Hqq6/izDPPxNFHH23b/vvvv6OgoEB/79dee63pgkHjtNNOE/qkCYIgpNRmWJogapu+ffuyZs2asfLycr1s4sSJDADbtGmTXnbttdeyxo0bp7w/AKygoMBkBVi8eDEDwI4//njT7fNHH32UAWD/+te/uH1VVlayiooK9sMPPzAA7LXXXjNtf+WVVxgA9uijj7K77rqLBYNBtmzZMsdj/P777xkApQfvljmPl19+2dTu8ssvZxUVFbZ6n3zyCSssLNTrhUIh9uCDD9rqffXVVywSibCSkhLTMVttEsOGDWONGzdm+/btM5WfcsopDAC7//77pcd9/PHHs/bt25vsAhUVFeyYY45hANhLL71kaxOLxfTjb9++PduwYYN0H4wx9swzzzAAbMaMGba+GjVqxJo0acKeeOIJ9v7777NJkyaxUCjELr74YmF/bmwSb731FgPAHnjgAWk9mU2CMcbKy8vZ2LFjTd9zt27d2Pfffy/tV7Mkvfzyy7Zt06dPZ9OnT2fLli1jy5YtY5MmTWINGjRgHTt2tH2nZ5xxBguFQtJ9EQRB8CAxTNRrnn/+eQaAvfrqq4yxpNDJz89np5xyCrfeiBEj2OLFi9nOnTs97Q8Au+iii0xlGzduZADYbbfdZip/++23bb7lHTt2sKuuuoq1atWKBYNBk/CYOnWqbX9XX301i0QiLBgM2vyYIsrKytiqVauUHqWlpUp9/vbbb2zVqlXs/fffZ/fddx/Lzc1l5557rklkrl69mrVo0YINGTKEvfHGG+z9999nd9xxB4tGo+yee+7R61VUVLATTjjB5G8VieF3332XBQIBNnz4cLZ582a2fft2dscdd7BQKCT8zIw899xzDAC7+uqr2c8//8x+/PFHNmbMGL39/PnzbW3WrFnDPvvsM/bCCy+wnj17svz8fLZ+/XrhPpYuXcqi0Sj705/+ZPMSRyIRrlC84YYbGAD23Xffcft0I4b/9Kc/sXA4zLZt2yat5ySGx4wZw5o2bcoeeeQRtmLFCvbKK6+woqIidvTRR7MtW7YI+y0qKmJHHHGEySst49VXX1XyIRMEQahCYpio1xw8eJDl5eWxQYMGMcYYe+211xgANmfOHFvdWbNmseLiYhYKhVggEGC9evVSirQaAcCuueYaU5lIyC1fvpwBYAsWLGCMJSczde/enTVv3pw99thjbPny5eyLL75gK1euZADY5MmTbftbtWoVA8Ci0Sj75ZdflI+zoqJC6cGbCKbC/PnzGQD2f//3f3pZ79692XHHHcfi8biprhbV1ibbTZs2jeXl5bHvvvuO7d69m+3evZv985//ZADYvffey3bv3m3qY86cOeyII47QLxo6d+7M7r//fgaAzZs3z/FYp06dyho2bKi3Ly4uZrfeeisDwD766CNp29LSUtaiRQt27rnncreXlJSwrKwsNmjQIFZWVmbbXlBQwACw3377zVSuXSi98sor3H5VxfDOnTtZNBplQ4cOdawrE8NadFn7rWrs3r2b5eXlsdGjR3P71L6366+/3nH/GolEguXk5LALL7xQuQ1BEIQM8gwT9Zrs7GxcdNFFKCkpwbZt2zBr1iw0atQIF1xwga3u5Zdfjk8//RR79+7FkiVLwBjD4MGD8cMPP9TIsa5fvx7//Oc/MW3aNFx33XU47bTTcOKJJ+KII47g1j9w4ABGjhyJ9u3bIzs72zY5SsSWLVscl5XWHitWrPD0Xnr16gUgmXNXY+3atejZs6fNz3ziiSeisrIS33zzDYDk57B3714ce+yxaNKkCZo0aYLu3bsDSKbtatKkicnLO2rUKGzfvh0bNmzAd999p+e9DQQCOOWUUxyP9dZbb8Wvv/6KdevWYcuWLfj000+xe/du5OTkoGfPntK22kRL4/vUePvttzFs2DD07dsXCxcuRDQatdXp1q0bt19W5dVOZdIcAMybNw/l5eXKvw0Ra9euBQBbWrTGjRujXbt2WL9+Pbfdc889BwCu988YS/m9EwRBaFCeYaLeM2bMGDz55JOYNm0ali5ditGjR5uyB1jJycnBwIEDUV5ejmHDhuHrr7/Wc6imE21ykHVC11NPPcWtP27cOPz444/44osv8O233+JPf/oTHnnkEdx4443S/RQWFjouK62h5c51y/LlywEA7dq1M+139erVSCQSJkH82WefAQBatWoFAPjb3/6G0aNHm/rbvn07LrroIowbNw5//vOfTf0CyWwOnTp1ApCcsPX0009j6NChyt9bLBZD165dAQA//vgjXnnlFYwdO9Yx360movv06WMqX7ZsGYYNG4aTTz4Zixcv5k7SA4Dzzz8fy5Ytw1tvvYWLL75YL1+6dCmCwaBNfLrlueeeQ2FhoSlrihcKCwsBACtXrjR9prt27cKmTZtw5pln2tqUlZXhhRdeQK9evfTPVoVXX30VBw8exEknnZTSMRMEQWiQGCbqPUVFRejWrRseffRRMMYwZswYWx1N+PTp0wctW7bE9u3b8cADDyAvLy9lQaJKx44d8Yc//AF/+9vfwBhD06ZN8cYbb+Cdd96x1X322WfxwgsvYPbs2ejSpQu6dOmCa6+9Frfeeiv69OmjR2Z5RKNRYaoytzz11FP46KOP0L9/f7Ru3RoHDhzARx99hMcffxx//OMfMXToUL3ujTfeiAkTJmDIkCG46qqr0KBBA7z33nt4+OGHcdZZZ+nR344dO9pWINuyZQsA4A9/+ANOO+00vfyXX37Bww8/jD59+qBRo0b49ttv8eCDDyIYDOJ///d/TX3cc889uOeee/Dee+/pWRXWr1+PhQsXoqioCLFYDP/85z8xdepUHHvssaYsFnv37kW/fv1w8cUX49hjj0V2djY2bdqEGTNmoKysDJMnT9brfvzxxxg2bBgKCgpw++2361FVjc6dOyM3NxdA8m7EU089hfHjx+PXX39F586d8e677+J///d/MX78eJPw/OGHH/SLmM2bNwOAvqpd27Ztbd/p559/jq+//hq33367MLvIwYMHsXTpUgDQV4ZbsWIFfv31V/2iEADOO+883HXXXbj66qvx888/o0ePHti2bRumTZuGgwcP4vrrr7f1vXjxYvz222/CqPAPP/yAiy++GCNGjEC7du0QCASwYsUKPProo+jSpYut3WmnnYYVK1YoZzghCILQqU2PBkFkCjNmzND9pDzmzp3LTj/9dJafn8+i0SgrLCxkF154oTDTgwik4BlmLLmIQ79+/fQMAxdccAH78ccfTZ7hf/3rXyw7O5uNGjXK1N+hQ4dYz549Wdu2bdnu3btdHbdXPvnkEzZ48GBWWFjIotEoa9CgAevevTu79957TQtZaCxcuJCdfPLJrFmzZiwnJ4d16dKF3XvvvbaFOKyIPsNdu3ax/v37s+bNm7NIJMKOOuoodt1113EnQE6ePNnmid24cSM79dRTWdOmTVk0GmXt2rVjd9xxh+14Dh06xK688krWqVMn1rBhQxYOh1mrVq3YpZdeyr7++mvufkQPqyd3165d7KqrrmL5+fksEomw9u3bs2nTptkWDJk9e7awT+tvgTHGxo4dywKBgGnhE9Hnynu0adPGVHfbtm3s2muvZe3atWNZWVmssLCQDRo0iH322Wfcvvv168dycnKEkzB/++03Nnz4cNa2bVuWnZ3NotEoO/bYY9ktt9zC9uzZY6vfs2dPVlBQIHwvBEEQIgKM0WU0QRAEUXfZt28fmjZtikcffRTXXHNNbR8OQRB1DJqBQBAEQdRpPvzwQxx55JEYO3ZsbR8KQRB1EIoME4QPxONx6fZgMEiz3wmCIAgiA6GzM0H4gFMKMm1ZZIIgCIIgMgvKJkEQPuCUiqxZs2Y1dCQEQRAEQbiBbBIEQRAEQRBEvYVsEgRBEARBEES9hcQwkZEMHz4c2dnZ2LNnj7DOJZdcgkgkgh07dtTcgdVjfvrpJwwfPhzHHHMMcnJykJeXhxNOOAFPPPGEbQLhlClTEAgEbI+srCxu3/Pnz8fxxx+PrKwsFBYW4oYbbsD+/ftt9fbv348bbrgBhYWFyMrKwvHHH4/58+crv4e3334bffr0QXZ2NvLy8jBkyBB9eWYrBw4cwF133YX27dsjFovhiCOOwOmnn47vvvvOVK+iogJ333032rZti1gsho4dO+Lxxx+39ffyyy/j1FNPRX5+PmKxGAoLCzFkyBB8+umntrqlpaWYNGkS2rdvjwYNGuDII4/EBRdcYDvW999/H1dccQU6duyInJwcHHnkkRg6dCjWrFlj6/Pjjz/GlVdeiZ49eyIWiyEQCOiLlVjZvn07rr32WhxzzDHIzs5GmzZtMGbMGPz444+meu+++y769euHwsJCxGIxtGjRAmeccYa+UIeRsrIyTJs2DV27dkVOTg7y8/MxcOBA2/t38ztr27Yt93dm/a198MEHwnqBQADjxo3jfg4EQdQPyDNMZCRjxozB4sWL8dJLL2H8+PG27Xv37sWiRYswePBg5Ofn18IR1j8OHDiA3Nxc3HnnnTjqqKNQXl6OpUuX4rrrrsPatWvx7LPP2tqUlJQgLy9Pf83LqPHiiy/i0ksvxZVXXolHHnkEmzZtwq233ooNGzZg2bJlprrnnXceVq1ahalTp6J9+/Z46aWXcNFFF6GystK0XDGP1157DcOHD8fQoUOxcOFC7N27F3fffTdOOeUUrFq1Cn/4wx/0uvv378fpp5+OrVu34m9/+xu6deuGvXv34tNPP8XBgwdN/Y4fPx7z5s3DvffeixNPPBFvv/02rr/+euzbtw+33367Xm/Xrl3o06cPrr/+ejRr1gzbtm3D9OnTceqpp5pWvQOAIUOGYPXq1ZgyZQqKiorw888/45577kFxcTHWrVunrzw3c+ZM7Nq1C9dffz06d+6MnTt34uGHH8ZJJ52Et99+G2eccYbe53vvvYd3330XJ5xwAnJzc/HBBx9wP6eysjKceuqp2L17N+6++2507twZGzduxOTJk/H222/jm2++QaNGjfT3pK0GV1BQgN9++w1PPvkkBg0ahHnz5uHSSy/V+x07dixefPFF3HbbbTjjjDPw22+/YerUqejbty8++eQTfVVEN7+zRYsWoayszHT8P/74I/785z9j+PDhelmPHj30Zb2NzJw5E88//7ypLkEQ9ZDaXPGDIETE43FWWFjIevbsyd0+c+ZMBoC98cYbNXxkhJULL7yQhcNhdujQIb1MW2WNt9KbkXg8zlq2bMn69+9vKn/xxRcZALZ06VK9bMmSJQwAe+mll0x1+/XrxwoLC1k8Hpfuq0OHDqxbt26ssrJSL9uyZQuLRqPs4osvNtW9/vrrWU5OjnR1NsYYW79+PQsEAuz+++83lY8dO5ZlZ2ezXbt2Sdvv2bOHRSIRNnLkSL3su+++YwDYHXfcYar76aefMgBs+vTpetmOHTtsfe7bt4/l5+ezM88801RuXLFu2rRpDAD7/vvvbe3feecdBoA9++yzpvKXXnqJAWD/93//J31P5eXl7Mgjj2SnnHKKXnbo0CEWCoXYpZdeaqq7detWBoBNmDBB2idj/N8ZjylTpjAA7N1335XWq6ysZMcccwxr06aNbTU/giDqF2STIDKSUCiEUaNGYc2aNVi3bp1t++zZs9GyZUsMHDhQL5s5cya6d++Ohg0bolGjRujYsaMpMqdCIBDAtddei9mzZ6NDhw7Izs5GUVERVq5cCcYYpk2bhqOPPhoNGzbEGWecgX//+9+2Pt59912ceeaZyM3NRYMGDdCnTx+89957pjr//ve/cfnll+PYY4/Vb4MPGTLE9l6127svv/wyJk2ahMLCQuTm5uKss87Cxo0bXb23dNG8eXMEg0GEQiHXbVeuXIlt27bh8ssvN5VfcMEFaNiwIRYtWqSXLVq0CA0bNsQFF1xgqnv55Zdj69at+Pzzz4X72bVrFzZu3IiBAwciEAjo5W3atEHXrl2xePFiJBIJAMDBgwfx7LPP4oILLsAxxxwjPf7FixeDMWY7/ssvvxy///47SkpKpO0bNWqErKwshMPVN+kikQgAmCLqANC4cWMAMN3+b9Giha3Phg0bonPnzvjpp59M5ap5rt3sX9S+cePGpvek5dm29pmbm4tgMOjYJ6D2O2OMYfbs2TjmmGNMUXEey5cvx3/+8x9cfvnllAOcIOo5NAIQGcsVV1yBQCCAWbNmmco3bNiAL774AqNGjdJPjPPnz8f48ePRt29fLFq0CIsXL8aNN96IAwcOuN7vm2++iWeffRZTp07Fyy+/jH379mHQoEGYOHEiPvnkEzzxxBN4+umnsWHDBpx//vlghoQsL7zwAvr374/c3FzMnTsX/+///T80bdoUAwYMMAnirVu34ogjjsDUqVNRUlKC//3f/0U4HEbv3r25Ivf222/HDz/8gGeffRZPP/00vvvuOwwZMkQXcCIYY4jH40oPVbQ+d+/ejVdeeQVz5szBxIkTTeJH47jjjkMoFEJ+fj4uu+wym+d0/fr1AIBu3bqZyiORCDp27Khv1+p26tTJth+trbGulfLycgBALBazbYvFYjh48CA2b94MAFizZg0OHDiAY489FldffTWaNGmCaDSKoqIiLFmyxHb8zZs3R0FBgfIxJRIJVFRUYMuWLbj66qvBGDMtIdymTRsMHToUjzzyCJYvX479+/fj22+/xYQJE3DUUUdhxIgRwvcJJC1EX375Jbp06SKtJ6JPnz7o2bMnpkyZglWrVmH//v348ssvcfvtt6NHjx4466yzbG0qKysRj8exdetWTJ48GZs2bcLEiRP17ZFIBOPHj8fcuXOxePFilJaWYsuWLRg7dizy8vK4K8e5+Z1pvPvuu/jhhx/0sUPGc889h2AwaLuQAZIXxaeddpq0PUEQhxG1F5QmCGf69u3LmjVrxsrLy/WyiRMnMgBs06ZNetm1117LGjdunPL+ALCCggK2f/9+vWzx4sUMADv++ONNt9gfffRRBoD961//YowxduDAAda0aVM2ZMgQU5+JRIJ1796d9erVS7jfeDzOysvL2bHHHstuvPFGvXz58uUMADvnnHNM9f/f//t/DAD77LPPpO9Ha6/y4N0y5/HAAw/obQKBAJs0aZKtzvPPP8/uu+8+tnTpUvb++++zqVOnsqZNm7L8/Hz2888/6/Xuu+8+BoBt27bN1kf//v1Z+/bt9dfHHnssGzBggK2edqvdalUwkkgkWNOmTW3Wgd27d7NGjRoxAOzTTz9ljDH28ssvMwAsNzeX9enTh73++uvszTffZKeffjoLBAKspKREb9+vXz/WoUMH7j6j0Sj7y1/+Yivv0KGD/vm1bNmSffzxx7Y65eXlbOzYsabvp1u3bkrf0SWXXMLC4TBbvXq1sI7MJsEYY6WlpWzIkCGm/Z922mlC28eAAQP0erm5uVwrRWVlJbvrrrtYMBjU6x511FHsq6++4vap8juz8uc//5mFQiHTb4zH7t27WVZWFvf3xBhjoVCInXHGGY77Iwji8IDEMJHRPP/88wwAe/XVVxljjFVUVLD8/HyTH9FYb8SIEWzx4sWOXlURANhFF11kKtu4cSMDwG677TZT+dtvv23yLWtey1dffZVVVFSYHrfeeisLBAK6yK6oqGD33Xcf69SpE4tEIibRcfbZZ+v70MTsk08+adr3t99+ywCw+fPnS99PaWkpW7VqldKjrKxM6TPatm0bW7VqFXv77bfZrbfeyqLRKLv22msd233++ecsGAya/KGaGN6+fbutfv/+/U1C89hjjzV9NhqaGH7ggQek+7/zzjsZAHbPPfewHTt2sO+++44NGjSIhUIhBoCtXLmSMVbtV27WrBkrLS3V2x84cIAVFhayPn366GX9+vVjHTt25O4vGo2yq666yla+fv169vnnn7MFCxawM888kzVq1IgtX77cVGfMmDGsadOm7JFHHmErVqxgr7zyCisqKmJHH30027Jli/A93nHHHQwAe/zxx6WfhUwMl5eXs4EDB7LWrVuzZ555hn344Yds7ty57Nhjj2U9evRge/bssbXZtGkT++KLL9hrr73GLrjgAhaJRGze7nvvvZc1aNCA3XPPPWz58uXstddeY/369WPNmjVjX375pa1Pt7+zXbt2sVgsxgYNGiR974wx9sQTTzAAbMGCBY51CYI4/CExTGQ0Bw8eZHl5efoJ7rXXXmMA2Jw5c2x1Z82axYqLi1koFGKBQID16tWLLVu2zNX+ALBrrrnGVPb9998zAGzatGmmck2oaifUF154wTH6+uOPPzLGGLvuuutYMBhkt912GyspKWGff/45W7VqFevevTvr27evcB/WY5o9e7b0/VRWVtqEuejhlalTpzIAXEFjpWPHjqYI+ZNPPskAsK+//tpWt6ioiBUXF+uvTzrpJHbiiSfa6q1fv54BYE899ZR03xUVFezGG29k0WhU/z4GDRrErrzySgaA/fTTT4wxxkpKShgAdu6559r6uOiii1h2drb+esSIEax58+a2evv37+deQPGOqWvXrqxbt2562VtvvcX9znfv3s3y8vLY6NGjuX1pE8fuu+8+6T4Zk4thbXLqqlWrTOWbN29mANiUKVMc+z/77LNZkyZN9IlpGzZsYIFAwPY/VF5eztq1a8dOO+00xz6dfmczZsxgANiiRYsc+zrhhBNY8+bNTXecCIKov5BnmMhosrOzcdFFF6GkpATbtm3DrFmz0KhRI9skKiA5aenTTz/F3r17sWTJEjDGMHjwYPzwww81cqzaksuPP/44Vq1axX1oaeBeeOEFXHbZZbj//vsxYMAA9OrVC0VFRfj11199PaYVK1YgEokoPUQ5Z53QUmJt2rTJsS5jzDRZ6bjjjgMA28TBeDyOb7/9Fl27djXV/eabb2z+Zq2tsS6PcDiM6dOnY9euXfjXv/6FrVu34s0338SPP/6Io48+Gq1atQJg9y87Hf/OnTuxfft2z8fUo0cP02e3du1aAMCJJ55oqtu4cWO0a9eO60O+++67MWXKFEyZMsX1pFEra9euRSgUQo8ePUzlxxxzDI444gipN1ujV69e2L17N3bu3AkA+Oc//wnGmO09RSIRdO/eXblPQPw7e+6555Cfn4/BgwdL+/nqq6/w1Vdf4bLLLtMnCxIEUb8hMUxkPGPGjEEikcC0adOwdOlSjBgxAg0aNBDWz8nJwcCBAzFp0iSUl5cLF1Xwmz59+qBx48bYsGEDioqKuI9oNAogOUHHOplryZIl+O9//+vrMfXs2VMozK2PwsJCT/tYvnw5AKBdu3bSeitXrsR3332Hk046SS/r3bs3WrZsiTlz5pjqvvrqq9i/fz/OO+88vWz48OHYv38/Fi5caKo7d+5cFBYWonfv3krH27BhQxx33HFo2bIlvvzyS7z33nu4/vrr9e0tW7ZEcXExPvnkE5SWlurlBw8exIoVK0zHP3ToUAQCAcydO9e0jzlz5iA7Oxtnn3229FgOHTqElStXmj477XtYuXKlqe6uXbuwadMmXbRr3HvvvZgyZQruuOMOTJ48WekzkFFYWIhEIoFVq1aZyjdt2oRdu3bZ9m+FMYYVK1agcePGOOKII/Q+Aft7Kisrw5dffunYJyD/na1evRr/+te/MGrUKOkEOyApmoHkuEIQBAHQohtEHaCoqAjdunXDo48+CsYY9yQ2duxYZGdno0+fPmjZsiW2b9+OBx54AHl5ebZoVLpo2LAhHn/8cYwaNQq//fYb/vSnP6FFixbYuXMn/vnPf2Lnzp2YOXMmAGDw4MGYM2cOOnbsiG7dumHNmjWYNm2akihwQ6NGjVBUVORLX5MnT8aOHTtw6qmn4sgjj8SePXtQUlKCZ555BhdccAF69uyp1+3evTsuvfRSdOrUCVlZWfjiiy8wbdo0FBQU4JZbbtHrhUIhPPjggxg5ciSuuuoqXHTRRfjuu+9wyy23oF+/fiYxOXDgQPTr1w9XX301SktL0a5dO7z88ssoKSnBCy+8YEq5NWbMGMydOxebN2/WF6j44IMPsGrVKnTr1g2MMXzxxRf4n//5H5x99tm49tprTe/1oYcewumnn44BAwbg1ltvRSAQwMMPP4xff/0V9957r16vS5cuGDNmDCZPnoxQKIQTTzwRy5Ytw9NPP42///3vaNq0qV73j3/8I84991x06tQJeXl52LJlC2bOnInNmzebUsidd955uOuuu3D11Vfj559/Ro8ePbBt2zZMmzYNBw8eNAn3hx9+GHfddRfOPvtsDBo0yCY2jcJ9586dWLFiBYDqyPVbb72F5s2bo3nz5vqiH5dffjkeeeQRnH/++bjjjjvQoUMH/Oc//8H999+PnJwc02ptQ4cORffu3XH88cfjiCOOwNatWzFnzhysWLFCz5ACACeffDJOPPFETJkyBQcPHsSpp56KvXv34vHHH8f333+PefPmefqdaagK3EOHDuGll17CH//4R3Tq1ElYLxAIoG/fvsKFSQiCOMyoRYsGQSij+QE7d+7M3T537lx2+umns/z8fBaNRllhYSG78MIL9UwPqiAFz7DGihUr2KBBg1jTpk1ZJBJhRx55JBs0aJCp3u7du9mYMWNYixYtWIMGDdjJJ5/MPvroI9a3b19fPcN+8vrrr7OzzjqL5efns3A4zBo2bMh69erFHnvsMZvneMSIEaxdu3YsJyeHRSIR1qZNGzZu3Di2detWbt8vvfQS69atG4tGo6ygoIBNmDCB7du3z1Zv3759bMKECaygoIBFo1HWrVs39vLLL9vqjRo1yuaJ/eSTT1jv3r1Zbm4ui8VirGvXruyhhx4S+ka176NBgwasQYMG7IwzzmCffPKJrV55eTmbPHkyO+qoo1g0GmXt27dnjz32mK3exIkTWffu3VleXh4Lh8OsoKCADR8+nNvntm3b2LXXXsvatWvHsrKyWGFhIRs0aJAte0jfvn2lHnUjsswixt8cY8mFP0aOHMnatm3LYrEYO+qoo9if//xnm7f7f/7nf9iJJ57ImjRpwkKhEDviiCPYgAED2Jtvvml7T3v27GGTJk1inTp1Yg0aNGAtWrRgp512mmlhFcbc/c4Yq55XcOqpp9q2WdEmR86aNUtYZ9++ffpkXIIg6gcBxgxJUgmCIAiiHrN06VIMHjwY//znP3VPO0EQhzfkGSYIgiCIKpYvX44RI0aQECaIegRFhol6gdMKa9pysQRBEARB1C/o7E/UC5zSil1xxRW1fYgEQRAEQdQClE2CqBdY00RZ0XIEEwRBEARRvyCbBEEQBEEQBFFvIZsEQRAEQRAEUW8hm0QaqaysxNatW9GoUSMEAoHaPhyCIAiCqBUYY9i3bx8KCwtpsjKRcZAYTiNbt25F69ata/swCIIgCCIj+Omnn3xfaZMgUoXEcBpp1KgRAOCHH19Cw0YxAEAlq0QlEqhkCQBAglXozytZAoxVJp8jgURlHImq1xWVAcQrk9HleGUAZSyolyeqyisqA6hIVtfrlieCepm+jQHlVc8PJbT61WUVen/mOtZ+tL8AEK9IHk88Xn3FX1EeqioL2OoBQEWFOTqQSJhfl5eFYMXYFw/j/t0SDlc6bLfb66OxhOl1KFTdRyRi7i9c9drYTyRa3V7bv1YvUvVWrH+zQtbyZH/RqtdhQz29LFDdRmsXrTrWcJCZ+gpV9RcJMsQC1XXChvJQINkgFAwjiBACVa+DgRCCgeQBhgIR/XkQIQQDwerXgRCQqEp3V5lIPq+sep2IgyXKq7aZy1EZB+IV1a8T2g84kXwAQHlc75slDOXGvxUV+nNWYf6Bs3jyfbJDcTD9nyZZR3ut/y2r2k9F1Xda1ReLV3/3lVW/2USi+rebKA+YtgFAZaVhuyUTYKXldx+v4P8fVCbU7kBZ+9MIcn7j3Hohfr1wxFxu7S9kOOMEg4xbLxRNPg+F7NsD2o87kvwdBSLJ9xGIhateB01/EQ6Zy7PCCBj/GQAEIiG9HiKR6nZaWTiEgLbUdygMRMP2OqFQ9ZsLR4BguLp+MKy/DoSi1fWCYcPzZHvjuaDScC6oZAkkWIVhW9XvrOp8AkA/X1QYzhPaeaCMBfXyRGXANsbHKwMoTwT1MuO5AjCfL7SfttvzxYF9v+OlP03Qz4sEkUmQGE4jmjUiN7cBGjbKAlA1kKF6wLOKYf15lRgGYBrgNFHcEEBZZdA08BkHJW0QDCeqRbNxcMpGcjDLRnLwqqgEsqrKsqrqa69jAA7Fk38rql6XVwJRVA90USSFbhTVgjSakxS0VacXxOOB6ucVQf25Joqr6yVfh7OTr42i2PqDtYpfP3/QInFsFcC8ukYhHDY+N5z0tX6Mba1COGp4e1pZ1Xkf0aBdCFsFs7HcKHhjBiFcvZ3pZdrzWLDSJIK18lAgiFDVCT4pdKtFrlUMB2HfZhbDcY4YDunPbWIY2UC8vLq9UQjH40B5CEAMiMeTYlivY6iPKFBeYRC51WdwXegGAkC2JnwjSaGbVfVa/xsBq/rnYBWVyS+nIlH9Gsld2QRxFIiXB5L/OLCI4kRA/2dIaOURS72IXt0sjA3len884evyH0Umkq0CmFc/ZHgd5IhcAAhHDYK4qo5xuy5wI3aBa3qtUs8ohIGkqI0ahDAAhMPVYjoUSr4GDGI4bBLM1WI4ahG61c8DoZhADIc5Yrj6XGANnpjqWcQwoI331eeGSGX1eSAuCJ7kAChLBG3itqIS0K7xAwah6/Z8oUGWQSITITGcAQQDIX1wc0skyPTBy/g8HGSIVwYQC1WirEoQR4LJwUn7a+4nWRYNJgcurS/tdVY4OcDZ6yXbV1QmhVy8IqiLu3g8iGgsoYvZcJjpkV2tLlAtHDVRbGwP2MWnSRxLorkqUWKnaLCGWwEMOItgax8qQjgrbN9mq8MRwtV17MLFuD3M2e6FUICjypwIhquFbypEw8noMJIiRhfE4XBSEIdD1eK56nkgEtIFcSASBKuoRCArDHYorr9GJARUJKq3K9QDkqI4GGaojAd0kZdIBHTxFy8P6KKvMh7QxWJlImASkYl4wCQOK/X/JfN3Zo0aq0Z7VeGJX9F+RCLYWj8siQYDPgth2xuy34HSha8I43Zj+3BU3s4njOeMIEK6IA4FgrogNp4PYsFKlFUJYu3ckKzDOxck2xm36eeBUDJ4Yj0PaEjPF+n6MAjCB0gMZxjGQS4UDOvRYZ7QNZKOAU5VEBv3aRS54XClLoiBpIjVRGE8HtDFn1UUA0lhbBSKRmHLE6Y8S4Wq0LXC61/Wp0wEJ9vIhbCxvlUIG8/dViFsjQrzsFosANiiwsa+jMSCldztmkUCgB71BaBHff0gEIqBJcqqOjaIZOPzULg6OmwUuDx426ORZHTYSNUP2S9BDFSLa00QA0nRp0WJw1GWjBIDpjpGUZx8u1VCOl5d14hIHBsR2Su4H5mkHw0nAQyoiWBAYIswXcnxBa4TNiFsjQoD9qiw4blukQCqo8LWunUEN8GT6jbm8V47X4j61bCeL3jXIQSRKZAYrmOIrvZ5270McF4EMVAdJdYEMQA9SmyN8IpEsdYGEEeL9XqWqK9MwKaCSFBbBTDgXgRb26QihEVRYeM2p6hwdZlBrPgUKa4xtOiv9bl1uyw6bBHETkgFMWASz8YIsEqUGBCLYsBgo4BYHJvevoLAFSGLMFsFMJCaCAbUhbBTVJiHyR5hhRcVdooUm8zQ/OeBUMyxjl/4HTzxcjcRMJ8vynx/lwThHySGMwiVW188eFflXgc4Wf8iQQyYbRNaGc82AcBmnUhuq7ZPAPxoMSAWx0a8TKJTiSK7FcCAOxEM+CuEeVqAFxXWj8lQZowKVx+nQcAYTuB+RoSlmCLB0aRvGBBEfTlWCVl0mGOX0HCKDgMSQQxIbROAPUoMmEUxYLZPAHxhDMjFsZ/wxC8gF8CAWQQD6rYIYxnP8qBsj+D9U/Ciwlq/IQexXEPRYdO5QfDceCeRh9vgCe+84jZ4ApjPFwSRqZAYrgXMQjeizxJ2QvVq3+8BzrhvniAG5D5iAFwvsYYxUpzcHrCJTJE4BuxZKbzaI4zw9qP3zxPFEhFsPSZZNNhYZrwL7FUIq0SFjdv49dRFle/C2CiAneCJXdXosBWXdgmAL4gBSG0TgDlKDNgjxQCkwhgQi2MjCUEGCRmy/gBxVomURTCQNiGsFBU22SUsE+eEbQ1+4VDqp1avc0lEvmENWfCEd16xBk+8CuJUIsOHDh1Cebn/ruNoNIqsrCzf+yXqHiSGawjPAxvnap9nlUj3AGfcr1UQG/uz2iYAuSgGzPYJADZhDNgFqDFFm0y4+gFP/AJ2AQyII8HWfnjRYGN5OoSwalRYVibyC/PwLIydBLDIN6zBEcCeosMuBDEArocYgLk+wI0SAzBFigGzKAbEwhjgi1JrmjUnYeuESPhaj0lDJoCtbZxEMJAmIazqFda3cU6ZTsLXhTD2ep4wBlg0uEERzkQ63rnDGjxJRRAD1XW9cOjQITRpXIBDZXu9dSChoKAA33//PQligsRwJsIbEHlWCdXosEaqAxzAF8SA2DaRbGO2TgByUQzIhbGxHytxS5RYFVF/pjoCMSGLAlv7dhLBAD9jhJMQdoKXSs3Yr/G5k0WCh5PwTbuVQmaVUIkOpyiIAUNk2FAXAD9KDLgSxQAchbGxLSAXr6kismBYBTDgQgQD0miwqUzBT+xaCMt8wVHONl50OA3+XxHc84RD8EReZg6e+CWIgWSZ18hweXk5DpXtxfCzZyCi5dv0gYr471hUcj3Ky8tJDBMkhjMF3sDmdLWv4SY6nMoAB0iu+gW2ieo21eUAXxQD6sK4ur7lYsCnKLFI+FqPrbq+3E9sPeerRoOT2/lCmNefij3C2q8sKsxrH6rBE74OL+2a0Tesl1WJW5XosJ+CGLBlmQAgjhIDNiGt1bdGfq0WiuRbN38vInFsRLTQhggVzzFP/AJ2AWztT0UEAz4IYVUs9gg9KsydTOdgkdDg/Z+kaJ/g2er8DJ7I7ibKkAliAHrfqRAJZyMa8U8ME4QREsO1CFcAu7za9zLAeRXEgPiqnxclBsSiWNtmzSThJIw1ZALZb0SZKpwEMKAmggG5LcJcVlU/xKtv/cu3R/BOSrKosIbRIqHBi/h6yjHMsTuY0qtJ6ullCtFhU95hNzgJYsDRRwzIo8RafX0bIIwWa4jEMVAtkDVSnVAnEr4aTgIYSE0Em54r+old2SOs8BbZsOKDN9gJXlBEFZXgiUz4qp4rAOc7ioc8vQOCqBlIDNchZFkl3AxwPFQEMSC+6udFibV+je20OtZtgFwYA865hjV4OYdlqKZlE03M40WjRXYI6zbVaLCxnR9CWBYVth4DLzrM8wvXWFYJQL5AhyQ6LKzjFB0GpIIYgNRHrD93EsWAqb22XWSHEIljwFm8pgJP+AJ8wS0TwNbtjiLY0N7JT+zaHiHzCmuELX3x4AhkU1o1H5DdSXQbPLHW8RI8AdTuKKZCZSSARKrhZWN/oJXwiGpIDGcAsltfMquEnwOcuY04ybrMNgHwo8RaO8Dctrp99WuRMAbEYtTvnMNO2ShEVgxZFNi63UkE28ur2qVBCFujwrKJc24tEk6T7JSQTajTrBKyOqLosE+CGIDUNgG4EMWAMFpsrKMqjnkkEmIRoNLeiCjazLUpKIpg2zZJNNjYlpc+zbMQVokKa2gWCbf2oTRnnPASPPEqiAHnAApFholMhsRwBiEd2BxySAL+DHCAOEIMyG0Tybb2KDHAF8Vae62ucbuxDk98GifK+ZFKTYTMgyzz7orqiERwsq44Ggw4Lahh7kMTwl6RRYWt+BoRlkV8VZZr1gWuReimURADUE6npj3X6vBEsam9EUvEWK8Hd15hvwSvEaFHVyKAAUURbOlHNrHOkxB2wppuTfsrE7OaMPbRRiE7P8i2iYInvLzD5napC2JjP7KVMgmitiExXMOopM5Rudp3GuC8CmKAb5kA5Ff92nYnUQzYE7DzBkprxNhYF/BvopwqsrtzvEHeiwi2bpNFg43bZULYj6iwFS3aKxPBabVMaFFgTRzzJtJ5QUEQA7BNqkuWuUunptUxttXgCmPAZqUw9mHFuHKen4tvOE5Mswp4QRuRHcL22kWaNVdC2IhTVNgNKuI3xQmostz0VquE04JNgN1CwbtbqCKIAXkAxcNaSARRY5AYriVUJkXIrBKp4EUQA+JBDlATxdo2J2Gs7csIL3JspSJFfaxqR1MRv4D5ffLaOYlgQG6LMD73Uwhbo8JuLBKeJs9xO0oKXu4kOoc2nqPDRgSCGIAty0SyzFs6Na2t6bWhD+N2DW7UuKpvDdfZFLzAOwbJ/mVRYNtrlxPrbP5gQC6E3dojRFFhrxYJB1INmlhJNXhiRCSIk/sRB1BSJR4OIhD273cdZ6TOiWpIDNcybgY069W+1wEOkAtiAI5X/QD/VhggFsXWbYBcGGv7NLazUm4YqP08/zvd0hPty40Atm53EsHGOmYd4F4Ip4oW+bX+ddPWFW5WopOhapcA+IIYsKddA7hRYkBNFOttrK8tPyapOAak4lSnQmGsUenHgEh4W4+fV1c1u4SxP8doMOCPEE6VKsGsT55LU+YJPWhiCZ6oWOsA9/Y6gC+IAXkAhdXg/FqCcAuJ4QxBu/VlHdjc4ocgTm5Tv+oH7FFiQDyTmBctBuzCWNsnDyeR7Ccykc053wsix/6IYPtzb0LYa1TYlwlxqaBZI0RWCafosBEvgthQxrNNABZ/sMfsETybhKo4NtWxhvVcCl0NlUgzT/zy2jpNrpP5ipVtEYA7IczDbVRYK/cxSuz1PGDEKXjCw4sgBsx3FAHz+eL3lN4FQaQXEsMZjuhqXzTA8XAriAFIr/q1506imFdPO57q7QGuJYJ3Xj1k0DI1cRfYiuBcD8C9AE5uN/RtOSc7CWFj37xllnn1rMflJIT9wkv02Bec7BIGuIIYsHuIAbltArBFiQGJKAbglD2CJx55kWN9m+EfJR12CdF+Zfv0M7uEcjTYWAbIhbCTPaKW0cZ/7Xwg8w0b62u4vZsIyAUxAOkdxeRz/h1DryTCQQR9/D0nyCZBGCAxXIdRHeAAd4IYgNIgpz0XiWKtb2s9ra52XBpWK4W1rsM5OLnfFO6kq/QPiAd1noh0zC7hUgRb96MqhEU+YRmiqLDIIqH5hfVyv6PI1miwU3SYh8A/DHAEsbG+sUxgmwDgSRTr211kjxAJZOM+0o1MaKcju4SjCAb4QtiYNcKNEDaiGhWuYWwWCUskWcUq4UUQm9pxAiiAPUpMkWEikyExnIGIBjgVL5gfghiA0iCnPQfEohiQR4s1REnZeZFjazsjfukAlSiGKIKqlF3CJxEM2G0R1royIexkj6gVLGLX1SQ6DZFdAkhNEAP8KHFVuZMoTm7zN3uETCA7IYo8u0HaXiG7hNPEOs8iGHAvhI24SaWmYRHHfi+24QU3dxNVBTHgPoASpjUuiAyGxHAt4vbWlxHRAGdEVRADEF71A86DXLLf6te27BEOwhiQZY/gC05RFDkdONkGlLNLSASwtY0bESyqL/MIG7fL7BGiqHBa8DJJTiXNGk8QazgJYkDsIwbsXuKq+iJRDIizRwB2YazXk2SPUBGzNu9wFU5tXQllgR9ZJbMEt55IBAPuhLChzFEIi3zCpn17yCCRBj+xFVHwhIfMP6wiiJN9OAdQgOrzRW1Y2whCFRLDtYCXSRGyAU52xa8iiI1l1qt+wHmQ07DaJwBO9giJMOa1s+IkktOJTHiLBno3Atj62voe0ymEjaikUhNZJDyhspiGfnAW8cuDZ5eQTagzCmLAPKnO1NbiIwbsUWKtftU2rigGuNFiwC6MAUk02GkynN+p1hQn36WSXcL6D5GSCLaU27JGAHIhbMRqj+Bt8yKOPWANnkgX41DIO+xFEANqARStfz+Ih0MI+Ojhjldmhh+cyAxIDNcgbkSwLKuEyhr0bgQxAOFVv9ZeqyMSxVqb5Gs1W4RVKPLEsbEPwF0k2Gqh4OElsizNLsEZX1Uix36JYHsdNSEss0e4iQqnK3IstUrIBDIv2swTt8ZJdYYsEwD4UWKtnUtRDDgL42Qd/gQ5pwwSpgiwx8wRKjhGlBUzSwBwJ4ABNRFs2MaNBgPOQlhmj5B5hd1YJFwIaFe56V0s3AQ4C2IAKQVQkvWV3ypB1DgkhjMM6epCnAGOd8XvJIgBKF/1J59DrwOIRbG1ndbWyRbBixprWCfiieDdBfZb6FrhHa9ovwoaIC0iGPAmhGWp1FSyQyhNnlOxRcjq8MQvzy5h9Q8Dcg+xsQzgR4m1OoBYFANmT3HVdqkwBrjiOFnXOYNEjSy2YcBLZgneP4NNAAPqIhhwjgZb6/AmyzkJYZk9QiZqU5xcl+oCHLzgiVtBbN0OQHq+4J0r4ikuEU8Q6YTEcC1j9Q2btilEhwH3ghgQX/UD8kFO60erZ7/650eLtfY8WwRPPMoEshFVsewFp30DsswSauUyAQyIRbC1bapC2AjPHiETvjKLhOdIscwGwRPI3AiwR0EMcG0TACdKrLWtqmfbJokWA7ALY0DoHcqEDBIi5JPoBLYJtwKYt92rCJa0lQphI5KoMJc0+IV5wRPZecONIAYgvKMIgBslTj5P7ssqigkikyExnIGoDnAy/zCgLoiNda113IpiwB4ttravLuOfJ0UCWcNof1ARrKmilllCvZwnQGtCBFvrGJ/zfMI8e4QsKpzuPMJcq4RRNPPsEl4EMSCNEgMuRLG2XRYtrqpjFYdCcazhMhOEaBKdCE9RZmm6NQXxC7gTwJbttkU0vAphUx8GIawYFU53FglZ8IRXxgueGOEJYkB8RxGQB1CS/aDqdbKfVFe/rIiFgKh/Y0xFTec9JzIaEsM1gEg4uBnMnMp4V/yAWRAD9gFMZZDT+tG2WaO9xkGOJ4yN7bU+kmXmz8SamcJKTa46x8NJG4gFsbP4BWA7WaiKYOs2P4WwEd7vOKWJczxUo77GMpldAhALYqB6Up3+3OIjBmxRYsCFKAbkwhhQFscajiLZiEEAp2ShcNFWdNwA+GITcBbAgD8i2LrNTUTYKHp5k+bcRoodcLojqO9CEjwRtTXeTQQgjBAD8gAKoC6KCSKTITGcoTgNcE5X/DxBbH3udpCzbku+RtVr6H1qWIUxwI8aa/05ieBMGVSdRTE/ApKqAObXdxbB1npuhLCXCLCqkPaCKTrsRiTzBDGgHiUG1EUxYBbWhvq2OoCaODbUl4pNGMQykJZ/Gqf968hm/lvfs6i+RAADDiIYcB8NdiOEBThGhX1aoMMpUOJ0N1FkmQCgHEAB7OcUrZ5sLglBZBokhjMAp1nCvAFO1F5VEANqg5y1nlbXadIcYBfGgF0QOqXf8TKIurwb7HmQlqUM4glfwC5SRfv3KoKtdZ1EMKAmhJ2iwmmxSPAsEG7rehHEgD1KDKiLYmM/1r70srhd/InEMSAWyJz2ymI1VVTSXPHei1N7BwEMpBAJtvaXihB2IZBT9Qs7TaIzBk8cRbKPARTAfFcREAdRaiMdJkGoQmI4wzAOWo7rz0sGOADc22AAhIMc4CyKrXWN27U6PPsDTwTyIscabnNUGhccSTUC4WbQFh2/hsgnl6oABtRFsPV1KkLYTVS4RtKsiYSvqiAG7LYJwB4lBuSiuGq7UaQJhbGxT0NbWTRYKiqtk/NqGtmxaciOTbb8sQFXUWDedpk32CchbIoKp2mJZpUUa3pdQSDFrwAKAOUgSqpUhoJIhP0LL1cmKFRNVENiuIZxivK6bWcd4AB4HuQAvigG5Ff/Wn1jf8Z6Im+wSCADziITqBbMvP36hcpxAGLRC7jzEvMjx+oi2Frf2tYvIex7VFhFyBrKUxbEgDhKDNi9xIBcFBu3V9WxijeblYLXt2mbQCBb26qI0ZpARYzz3qegrU38itq7EcHW11bB6ndEWBWP/YiCJ6rnC9G5AhAHULTXbkRxch8UGSYyFxLDGYJxYEplgLP25WWQA+zRX9FAp7WxCjI34rh6m1xUWvtWFap+oBrhkGeY4vfhhwC2tlEVwYA7a0TGoiqIAecoMcAXxfprixfYKMYEwhiwizuuONb2Ye3DtJ3jP3aLdVlqDT+iy6LjdtgHV/yK+nMSwLz9qIpgQE0IWxBGhT30pVdxGTxJVRADEAZQALkoBuRBFLJJEJkMieEMxYsgBuDrIAdwJsRZBjpjGw034ti8b9knIo8o1xQqFgzZoC+2TXAyTgTtYt/JOiETwYA8Y4RMCKtEhWtEVIuiw5ZtUr+xU5QY4Iti7muJMAbsVgqtHvjiTyiQ9bYc/7EK8bj5tRvR62V/CvtwJX4B+2fLqysTwBqy/MFWgSoTr2myRygtsiEInsj68npHEYDr84U1iAIA+53euAPxSNDXCaHxSrJJENWQGK5lnPIGa6gIYl5/ABy9xADP92tPhSYTxoA7cWzsxylikIkpelSiHHLrhCDjhIIA5rV3I4IB/4WwKsptnFKnVSFdptkqiAF5lBhwL4pNZfZosJI4NtQXCUSu/9gJY/TXq6C14jJyLBS8en+S41IRv6JjknmC9TqK0WDrNst2afaINCy0wd2NZK6JXwEUwFkUa2XcydMBWoGOyFxIDNcCql5hN1f8AJQGOYB/5Q+oD3SAXRgDdiEnEseifqzIosmZgkqU2o3wlfXpJICB1ESw02vpSnMqyy+ngiTSa/MPA2IhzYsSA+5FMaAujAG5OObVt7RzFJVV6KLZelw+o3o8Ok5inCd8Ze1UBDDgbIcA5NFgh+02ISyLCvvgL5YFT7wIYl6fAD+AkuxXHkThlWnjWW3e0SMIJzIo1kYAcrFiFSNuhEwQIVPfoWBYf1T3HzSJKS0djtWLai3TVhcyPoCk2LM+eP1YH7J+3TxU8GMfsveivR/ZZyE6bl4/vM9J+96svmCrJcJqY7BGg2WvnX57qtukuImiWYSFVJQEw/aoH0/wGOuEo2bhpLUx3WIPVT94ZabysP0BJEWg6CFqJ+oLSZFaEw/p+/L6Xnn9OX6unDLud2X5PnnfubWNw3ZHISz7PfsUMZZdhMr+Zx3//wXni+q+7WOO6vmirjJz5kx069YNubm5yM3NRXFxMd566y19eyAQ4D6mTZum19m8eTOGDx+O5s2bIzc3FxdeeCF27Nhh2s+mTZswdOhQNGvWDLm5uejTpw+WL19eY++zvkKR4QxANgHOul0b4IyWCUASFeZEiTWcrv6T+7FHAAC7lcJYDvCjADxrhRFZJNkJY2o1DS+RiFQGbdl70/BinVCJAAP2KDDgHAnmlVlfO16ESdKppeQXlnl9OdulHmKtPSC2TvDqGAWUNVpsbCvMEpHCBDlRtNSIU2S5plA5Vg2PE+yk21RsEABfhPLaOkSLXQthn1eiU60rixBrrwHn84X1XAGony9U7gKqkAgHEfAxtVrCZWq1Vq1aYerUqWjXrh0AYO7cuRg6dCi++uordOnSBdu2bTPVf+uttzBmzBicf/75AIADBw6gf//+6N69O95//30AwJ133okhQ4Zg5cqVCAaTxzNo0CC0b98e77//PrKzs/Hoo49i8ODB2Lx5MwoKClJ924SAAGOs7l+yZSilpaXIy8vD3r1vIDc3xzQI8QY3a5nVP2zdbs1BrNQnrw7Hp2xdu756n3zBxxOjKts04gp1rJSleQKEirjlkYp1QrZNVQAD/ojg5D7VhbBKn7Z9WBfSsP7ueAttONTheog99COsB1QLY5X96G0cBI3TdlNdyX5qEzei3MnGIdsuEpc8AQz4JoIBRWtECtYLwHncTvVcodKnsEzxfGE9V+wr/R2dCm/E3r17kZuba6svQjuP9rlhAcKxBsrtnIiXHcQnj17g+niMNG3aFNOmTcOYMWNs24YNG4Z9+/bhvffeAwAsW7YMAwcOxO7du/X97d69G02bNsU777yDs846C7/++iuaN2+ODz/8EKeccgoAYN++fcjNzcW7776LM8880+O7JZygyHAt4XbGMK8N76ofgNKVv6lMEi3W4EUBAHHkGOB7yER13EQNRB7ldJKuKLOsDk/8AuoCGFATwbwynj/YrRD2hJPHV6GOJlZsUWJALVIsqmesaxVdvKixtS+RuJNFkYV1M3zodpWtwqGuLKrqRvyK+lIUytyJch4Fdaq4uZsI2O8oanUAcVRYWOZwvhCdKw4nEokEFixYgAMHDqC4uNi2fceOHViyZAnmzp2rl5WVlSEQCCAWq/4dZWVlIRgM4uOPP8ZZZ52FI444Ap06dcLzzz+PHj16IBaL4amnnkJ+fj569uxZI++tvpLhI+rhjWwig17mwyDnqowz0AHq4jh5LGKBrKEilHn1gdqdiOHFRuHUxumk4Ub8AmJRqiqMVfzBKhPmfEuppiqIAX9EsbGeqK6GSBwDcoFs7NeVCE7zRMV0oHrMTqJRJHw13AhgUX1BXV+FsAe/sNfgCQBpAIVXjxsw4ZRp+9TLXQZSMoXS0lLT61gsZhKsRtatW4fi4mIcOnQIDRs2xKJFi9C5c2dbvblz56JRo0Y477zz9LKTTjoJOTk5uPXWW3H//feDMYZbb70VlZWVusUiEAjgnXfewdChQ9GoUSMEg0Hk5+ejpKQEjRs39u9NEzZIDGcYqoIYsA9ygLooNpZbRQsvYgzIxTGgNuipCGUjbkVzTeHleFROBiLhC8gFaKoCGFCLBouOI+0LdKgIYkE916IYEAtjWRtrO5F4cxLJvP34KYKd7BjpEtyqEVIn0QvIBaVsPy4Fs2cRnCJK4lfhXMGrxztXaPUAd+cLkTAG7OJYNrapEI8EfM4znDy3tG7d2lQ+efJkTJkyhdumQ4cOWLt2Lfbs2YOFCxdi1KhRWLFihU0Qz5o1C5dccgmysrL0subNm2PBggW4+uqr8dhjjyEYDOKiiy5Cjx49EKqalMoYw/jx49GiRQt89NFHyM7OxrPPPovBgwdj1apVaNmypW/vnzBDYriW4Q5oHgc5QC6KAeeBzlpu3CYSY6KIgBG30QG3ork2cRvxUDkpOEVe3Yhfp22qIlh0XF6tGMmdK4pcESJBDLgTxdb6MpErs0KIvlsVkayhKpZV4HmYvYhdP2/1q4hdDaf/Fafj8iCahXmD3USYRfVTEIRuzhUAlAMogPfzBe9/WnS+SHv6RY/89NNPJs+wKCoMANFoVJ9AV1RUhFWrVmHGjBl46qmn9DofffQRNm7ciFdeecXWvn///ti8eTN+/fVXhMNhNG7cGAUFBTj66KMBAO+//z7efPNNk6/4H//4B9555x3MnTsXf/vb33x5z4QdEsMZSiqDHJDaQOe0zbZdMsipCGUjmX5LTYbbyIcfVgOvKc5EOYPdiGBR/bR4hwGhyOXaHCT1jWJHKIyt7XjfrZNANvah8tuQWS7cYJzclwa/asrHp5FCKj1P/Un6kC6c4VYEO+wrFUSCGLCfA0QBFF5dp2ixtU0q54tMQ0uV5gXGGMrKzBN2n3vuOfTs2RPdu3cXtmvWrBmApPj95ZdfcO655wIADh48CAB6ZgmNYDCIykpatCSdkBiuQUS3vlyXSwY5wJswtrZzFMASwaMqlPX6DvaLuoaXCIiKgExFGANiASxr69aXXCPwosmAsygG1IWxtR2vvUoU2I0VIpXfvpvIs9/48T/rRjyq7E+hP08C2Gn/Plo0/Dhf8AIoWl0NlWixtY21ncr5IpBigCMR8jm1WtxdX7fffjsGDhyI1q1bY9++fZg/fz4++OADlJSU6HVKS0uxYMECPPzww9w+Zs+ejU6dOqF58+b47LPPcP311+PGG29Ehw4dAADFxcVo0qQJRo0ahbvuugvZ2dl45pln8P3332PQoEHe3yzhSN1XHocJsgEO4F/1a3gVxoBcHFvbKwtgRaHkZL84HHArGlXrq9TzIn717R78ybJtnsSzzC4hihJr7QB+W0VhrKEkkK19OYk1N2JZhl9C2k/8iIj6HTGGg/BV7SsVy0YNeYplARSAnxZN5e4i4CyOrX2opG6sS+zYsQMjR47Etm3bkJeXh27duqGkpAT9+vXT68yfPx+MMVx00UXcPjZu3IjbbrsNv/32G9q2bYtJkybhxhtv1Lc3a9YMJSUlmDRpEs444wxUVFSgS5cueO2116SRZiJ1KM9wGrHmGdaQ3TJyup0kbcsZ6Nz2D/BzUnrty4826d5HTQ3SXvaj2kYmfFX78upTVupbtl2WmxdQ8w/XRB8GuLmMfepbiqqXOhOo6YgxFEWvm379iEKnIKJTOh84tXU4X/h5rgCAPXv34ajmYzznGS762/8hnJXj3ECR+KEDWD31vJTyDBOHDxkSTiA0RFfpKttl2R+s7fU6nH5kwsopkszDS9RYFVk0PRXSKZC99q0ieN3uI92+ZaVjEdke9A4kkV5jH4C4H9mEOGsfPBSiyEaUI8pOiPzRdQ2fPLTKYtfrvlU/Xz88zAr9OGWVkG13PJc4nC9UvL+q5wpRfwSRKdTRkbVuo5o2B3AWxdI6HKGjMuDJ+lURZOkcBEWTR2qCdO3Ljcg14tqC4cKO4oeH2XfciGINVXGs4WTLEOFSLIvwTURnOF4/Hy5ePiO3FxXpENSq3SkIYkDtXCGt5yGYIuvXOq55Hec04rEQWMy/MSfBSJwT1RyeI20dQEUQa/UA+S0r1cEOEAsitwOfbH+pDnoavFtwmRRd8Ot98kjlfbr1YKfL1+yqrlN02NSpgig29mtFJQotwqtYFuGTiD4s8FP4pyJIvR5HGgW2mwAK4M/5ws25gtev2+0EUZuQGK5FVAWxVlfDzaBoxItItvWRgmh27DtNoromSau9IsWJhl6PLZ1eZxNuBDHAFx9eBbKG0/69iiy/RXR9IV22kJqe6OcDmXK+SOVcQRCZCo3EtYxK5FfUxoibKLMMlSU/U8VrZKEuUlOZMvz67Grdb+1WENt27tL+wNu/V1KJOBPeSPfFhN/fW4rHW5fOF3Vl0Q2CAEgMZwxuruSd2vPwElFIlVQjC4c7tSH+07VPX/t1mgznBbeixkvWBoryZj61dVHi828j3ecL1X7dWaZSyxEcDPv79TH6dyUM0M8hA/Fic/Dap1tSjUgQ3siUz7JGj8PlpDVfORwjuW6WuCa8UQsXROla8a02zhkEUVvQqFeH8Do4+TkYZYooI1LjsPge61IkNp3CXZW6KnLr0vecIaTy/+23eNWO5bAYc4jDFhpl6gE0CBFELUOCjqgj1AkrFUH4DI3QhJ1MiGIR3iDR5QjdtiWcIOHmAtXzRYrnlVCkEuFIZUp9mEj42BdR56EzZyZT30RpXVpqVkRt34qu6d9MLYvv2hS2JKozi5qY/FsT+/ZMfTtfEISPkBjOFGpjIDscxGc6PzcvQi/TPtN0i3PR5++zSE6X8KwJQUui2TtuRGaqn3M6fLZpEcl19VxRSf8HROZCYri28XtgS4cYq68Rh0x8325Fptvfg1/iWfvsUhTFqQocP4RousUsLU4gWe2sBrMiuFm1TRWtT19EcV04V2jwjjXF4w+HfbZJxMkmQVRDYri28Dow+DWA1aWBtb4hE6RevzdVUcr7HlMRyIm4J0HsRQTVVBtT+zQJ2cM1mixMG5ni5yjLXe5HvlxeH16i1p5F8eF2viCIDIPEcG3gdmDxMqClMnj5KWxpEBUjEolePn8nwZqKncF6PG7FsUtBrCoE3QpG1/U9CrR0CNkEq/C9z5rAuqR6uiK9Kt+VF8EsE6/WNm4WsnAlig+X8wUFTIgMhsRwJuNm8HAzmHkdlHwQtixRlnIfdZFAKGYv9PJ5ehHQbiPNTsLVuC+fPckqYsmvOnpdF6LXq5jzW8xmauTYKvL8eN9eBLVbwaxq01AVx755hVXHiHSdK9z2LdsHBUaIDIbEcE2jMiCoDD5+D5IeBipfhO3hPEAaRKWXz8qTgOYJWbfWB+M+VISxiiBWiA47iZxUtwPqwldVbHoRe+kQsrUljq2iLx2RX5XPWFUwq9o0VMSxkzBOZbsyfo7vbkWvy7GbJcrd9W8hHGYIh330+YaZf30RdR4Sw5mG04DkNAD5KKRdC7hUhW1dv41mFYVuPw+LWHT6/JXFcioCWUUYa32lECVOReg6tnUQwCoCTlX0ptu6UVt9GjGKOL/25bZPVcGsIpKdoshOwlgosFMVxKmO9TUceKmvd/2IwwMSwzWJbFBJZWDzYVBUGshqworhZV+1iVEgpur1dRn1FX1nNpHsViCLRK1ThgjVKLFLROJIKpAlAthJbDkJ30zxNLvq26P/mScEM0EAq2Z+4H2XTgLZFu12EMYyUexrNglTx5KxIs0iWln0Wvup68EO4rCGxHCm41UES9o5DmZ+31I73CLGbgQsDzcCWmVfDiLZMYJsFbdOnmCZKJYJYg+ZJdwKYZHok4knmfitDQ8zkL4sFW7x+ziMYjJdAtgp+mv9vmXiWCSMRaLYbZTYk13Cy7ifgnj27XyRaeM4QRggMZwJiAYJtwObVwFcE7fjVPtyIp6a7ywlwtHkX6/vQROJbiLAon05iWSJX1kYOXYbLRaJW5cRYjeC1y8R7FUAp2LXANyLy8RhIiBCVb8H1feviU2/BLAs+mv8LYiEsaoo9i0S7NfY70EEp/VckQLRWALhmH8XZ0FaBIQwQGK4tnEzuLkcCIWDWhqizUrtjfghamvCSmEUe16OWRPQgHoUWCUC7JTuTBL5Nf4uTMLYS8TXYx5hJ1IVwm5EsBeBm6pPGXAndBOsbi4QEAoEAai/VyfR7BRZVhXAouivSBh7EcW8yYU1uiKdi/OFZwHs5nxRV6xvRL2ExHAmoiqE3Yhgv2+fOZ3c3AhHPwbJuE9X+WHLySrVRS6cPgcVsSwTydp+ZOJYQRgriWJRlJgniH32D3PFsaIIFkWBXQluj/5kFRHoRuhWVAaU69YmkWBypr7Ke9MEMyD+vGQiWSaQVQSwkzBWFcVpF8Sq5wA/RLCPdyX1dofJXQ7i8CToXCU9PPDAAwgEArjhhhv0MsYYpkyZgsLCQmRnZ+O0007D119/bWpXVlaG6667Ds2aNUNOTg7OPfdc/Pzzz6Y6u3fvxsiRI5GXl4e8vDyMHDkSe/bsMdX58ccfMWTIEOTk5KBZs2aYMGECysvNwmXdunXo27cvsrOzceSRR+Kee+4BYx7TsaQicBXrsUSZfXBLxPkTGXiDk1bX2sZY39ouXs5/iPrkPfS+Et4fPLy0S+UYjP0pv2eHz4/3+bv9zkT1DSj/drS+rbg5KTvg1W5gbZdgFcJIsLGu9traXliOhOmh768ybntUH0ul8KFRURlwfBhRqV/TDy/vReWzkX22ou/D03fLKVO9m+DmDoUvuPnfdPP/7uV8IarvowgOhSqTSzL79AiF6ubdFiI91EpkeNWqVXj66afRrVs3U/mDDz6I6dOnY86cOWjfvj3+/ve/o1+/fti4cSMaNWoEALjhhhvwxhtvYP78+TjiiCMwceJEDB48GGvWrEEolLzivvjii/Hzzz+jpKQEAPCXv/wFI0eOxBtvvAEASCQSGDRoEJo3b46PP/4Yu3btwqhRo8AYw+OPPw4AKC0tRb9+/XD66adj1apV2LRpE0aPHo2cnBxMnDgxPR+MyuCmenWfYnRZOIDJIp2y6ICbyK1fUd6a3JcWUXbqT6vnZIXgfc4iz7JTFFg0IY4TARZGitMc+ZXBE6NOdawCRjUKrCpyAHEUUxYNVYnsuo3+xms5WhwOVgcIVI5dixrL6soiyyLrhSh6zPMf8yLDsjLVKDEvQmzFGh1WiharCEqF8T2lc4XbutYxLF43V1Ek6gc1Lob379+PSy65BM888wz+/ve/6+WMMTz66KOYNGkSzjvvPADA3LlzkZ+fj5deeglXXXUV9u7di+eeew7z5s3DWWedBQB44YUX0Lp1a7z77rsYMGAAvvnmG5SUlGDlypXo3bs3AOCZZ55BcXExNm7ciA4dOmDZsmXYsGEDfvrpJxQWFgIAHn74YYwePRr33XcfcnNz8eKLL+LQoUOYM2cOYrEYunbtik2bNmH69Om46aabEAikeALyMrj5NbClMqDJ+tDbSAShK1FcB26rhTXxqiiCRfVkIlkkkHni2EHscq0OAlHsaJ2wCmIfskU4vuZE/oyoRPFURLAbASwSvzKx5yQavQjcssqavdEXCybft5tjDQeZkgjm1REJZDfi2CqMVUSxinVCJojT4hVWSVvmdL5Ix10f3vlCa0+eYSKDqXGbxDXXXINBgwbpYlbj+++/x/bt29G/f3+9LBaLoW/fvvj0008BAGvWrEFFRYWpTmFhIbp27arX+eyzz5CXl6cLYQA46aSTkJeXZ6rTtWtXXQgDwIABA1BWVoY1a9bodfr27YtYLGaqs3XrVmzZsoX73srKylBaWmp6KOM0uKkMbKLbVW7qAGp2B72uwCrgZCOIx50fGuXxzHuovg/Z52T6PBQtF07fE+/WpOr3rnor1dqPm+0p4FYIi+wQ0jqcW+1OtgcNN5YBjXhlgPvQKKsMKj+MpNsG4fXYnN6vG+sF77vgWStM37fl++XZJVR+N053Hmo9NZ5bISyzQVjryGxy1rbG9ola/kwIQkKNRobnz5+PL7/8EqtWrbJt2759OwAgPz/fVJ6fn48ffvhBrxONRtGkSRNbHa399u3b0aJFC1v/LVq0MNWx7qdJkyaIRqOmOm3btrXtR9t29NFH2/bxwAMP4O677+a/eRl+CGEX7bl1ZFf0pnoSfy633EEMlbsQS5kQKdYiwSrHHQ2Lj1kWURZFkcMh+3fCixpbI8bWCXiySLFKlLiGcJPlwVGcuIw4q0aAeRFMUeRTFEFVieimMnEuVRuFWxuEES2aK3uPsWCl8Bh5kWRR9NgaOeZFjK3RYlkE1+l1glVII8RGPFkj9MouLzj9Pl+onCt47QBfLW+RSCUiER99vn72RdR5akwM//TTT7j++uuxbNkyZGVlCetZ7QeMMUdLgrUOr74fdbTJc6Ljue2223DTTTfpr0tLS9G6dWvpsTviZmDzEqWzDmyqA5pb8eskHl0IXVYLEYZASBOoCsfpJJhFIlkkkHni2Gqr4KWAcyuKJZYHkyC22iF8sEsADuJXlrFBIoRTFcEqAtiN+JWJQhWh6Yc/2Gk/kaB5krCXfYZd+IJ5n4nIgqEqjiNBZvrurMJYJIqtVgnea1VBrOIfThnJmF8jIljlfGG8W0gQGUqNieE1a9bgl19+Qc+ePfWyRCKBDz/8EE888QQ2btwIIBl1bdmypV7nl19+0SOyBQUFKC8vx+7du03R4V9++QV//OMf9To7duyw7X/nzp2mfj7//HPT9t27d6OiosJUR4sSG/cD2KPXGrFYzGSrECK7dSzZ5qsQdhLBqgKYJ+pkwlciJl2L3JoYXKtEp8qxOQpmkUiOaiI4zq/PE8HWMt7EObeiOBVBrIpiO9WosFch7FYEqwhgN+JXJhJVxGc6U6yl2nckyKTvQSaURQKZJ455/ViFsVO0WCaKRZFcJ0FsxCiIffEOy3y3brZZbVSibYC3gImbAApBZAA15hk+88wzsW7dOqxdu1Z/FBUV4ZJLLsHatWtxzDHHoKCgAO+8847epry8HCtWrNCFbs+ePRGJREx1tm3bhvXr1+t1iouLsXfvXnzxxRd6nc8//xx79+411Vm/fj22bdum11m2bBlisZgu1ouLi/Hhhx+a0q0tW7YMhYWFNvtESigOYMpCWOTz0hB5u/TtEi8rwPfEuvHTIikseQ/uPlXSmKn4j908jLg4Fuf3JdiXyucn+j5435kofZsGz1Osss2K7GLMR6weT333ikKY5wvW+xB4gTVEvlUNJ5+vtZ2KZ5hXn9e+ojKzHqL3aj1umW9Y9Dk5+Y9Vvy8nXzHPT2x8zXue7LdCuC1tSP5PTecLr0JY9VzBmyei1xGMqwSRYdRYZLhRo0bo2rWrqSwnJwdHHHGEXn7DDTfg/vvvx7HHHotjjz0W999/Pxo0aICLL74YAJCXl4cxY8Zg4sSJOOKII9C0aVPcfPPNOO644/QJeZ06dcLZZ5+NsWPH4qmnngKQTK02ePBgdOjQAQDQv39/dO7cGSNHjsS0adPw22+/4eabb8bYsWORm5sLIJme7e6778bo0aNx++2347vvvsP999+Pu+66K/VMEiIEg5QrISzqz00kWCUCzIv+cgY8aUTVKVLgZQD1Gn0wLrbhZr9O2STCIe5nIIwih8Pmz5YXNbbu0xotFlko4uX2RT6MUWKR9cGwTdk/bOyvBtKwqYgWWTTYbSTYGvnkTWCzIoqWyrNOCDc59ltTVEdoxXUiVR+PLBqsaokwftaiiLE1WqwSKVaJEssixCJc2yVSzLogFMKyu4eyaLDTXUPba87xWy/0PRCOVCLso8+XkWeYMJBRK9Ddcsst+P333zF+/Hjs3r0bvXv3xrJly/QcwwDwyCOPIBwO48ILL8Tvv/+OM888E3PmzNFzDAPAiy++iAkTJuhZJ84991w88cQT+vZQKIQlS5Zg/Pjx6NOnD7Kzs3HxxRfjoYce0uvk5eXhnXfewTXXXIOioiI0adIEN910k8kTXCuoCGGvA5vToJaKAPY6wa6mbq253U9Y0RIhmCBn/Yy44thJGLsRxTLrRCqC2CfRqyJgRVFht0JYZomQiWC/BLBI/IrEpKrYLUvUcGq1kFpqtaQ45W+LBMUT4VQsETJhbG0jEsUy64QbQWy0S4iEsmthrTLGiwInXoSwLGjiVgRbzxeUWo3IYALM85JqhBOlpaXIy8vD3r1vIDfHEE3TBh+/BrdUBza3g5plu7L4lQlfJzFaniEJ26N8b6AJ65LOerlAKFrqB0Kc9ta20bB4u3X/xtdGsWuMEltFrLGecVtVuSk67FBXVO5WxGrlfgnhdIlga31+xglbEbetab8uxG66PMXWyXUyYpIVvsKCfiKct2jdp7Wtdbsminn1rXWNr43LQocMv1djVNcoWEXPjf5hrdxVH27H+XScL7wGTSTnitIDZWh85qPYu3evfgdWBe08OvSFeYg0aKDczomKgwfx2qUjXR8PcXhSa8sxEy5JdWDT6vH8pvrzuH1g0wY3jvdL6vW1tnHKuQskBa/oIWtbU49UjlH18wDkfmPed6Ntt/bH+45FXmKVdHwGlCNQKSCKCnPr+iSEed5WDaNnVeaD5W1Pltn9tcL8womg7WE9RtV8wH55gd3um/cetPchzjMs9yCrfNYib7H1+9Vea1i9xBqi36HMQ2wlbXmH0y2EeZ5gfZthTBLNd9CPLSG3zNUBZs6ciW7duiE3Nxe5ubkoLi7GW2+9pW8PBALcx7Rp0wAAv/32G6677jp06NABDRo0wFFHHYUJEyZg7969tn0tWbIEvXv3RnZ2Npo1a6YvREakj4yySdQLnMSCzCtsbe9lYOM+l0SCnaLAKn4xXtRXFulVsCywipoZWAMRo5dYss9wiP+etEiytW04BJslwlrPYqcIhAxtrJkpjOnajH3xrBMiLzFvdTrAu3/Ya8YJCU4TlVIVwhpWEcyrY63H224Vk9yME4KIr1N0V8VP7KWdNTrrZj8820P1tqpMEZb3GwvZ8wxbrRU8z7ExawXPEqF9b0b7hJN1wmqbcGOZ0JBllxC1cYWLc4ir84VqNFgxEuy3+A2HGcJh/25kM5d9tWrVClOnTkW7du0AJFfIHTp0KL766it06dLFNCEfAN566y2MGTMG559/PgBg69at2Lp1Kx566CF07twZP/zwA8aNG4etW7fi1Vdf1dstXLgQY8eOxf33348zzjgDjDGsW7cuxXdLOEE2iTTCtUnwBienW168QSwdQlhVBHsRwCLxKxCYymLXqyJwgne/loNJLFvh2SV4NgubrcFqgZDYKGT2CZF1wsk24WR1MJRx7RK89oo2CZ6Y5XmFnSJ0TkLYrQiW1fNLALuxU6hurymc/l1E23m2C6u9wm6LEPchs1AY7RMi6wTPNuFkmeBZHVStEso2CYcyblTYSQinKWgiCpiUHihDk3P+4dkmcf785323SSwccVlKNommTZti2rRpGDNmjG3bsGHDsG/fPrz33nvC9gsWLMCll16KAwcOIBwOIx6Po23btrj77ru5fRLpgyLDGYpjVJhXxhvcalIEqwhgjviVCl8XZ3uWojIIGM+yKn1Fgtxj1wUyLxps/UyiEVs02BYxtkyI074TYaTYONFOFCUWTa7TIsSiSXWishrAmkoNqBkh7IcITlX8Ov0cy2tJFEf1iC1/e0SwXRTpBcyflTVqbIwYW/sQRYqNuYtFUeII57kWJRZFiDVUo8OqbVOCZ29ysjH5FDSR3jWMx5Xu+NUGpaWlptcq6wUkEgksWLAABw4cQHFxsW37jh07sGTJEsydO1fajybEw1Xj9Jdffon//ve/CAaDOOGEE7B9+3Ycf/zxeOihh9ClSxeX74xwA4nh2sJp0HJb5rMQVhbBTgLYjfiVnPG9Cl12SHxLMZBl/vmr7iMgOsMDcoFsFb1A9edltVNYs1W4EcUi64SKINbgCWJOtgiuVcKHVGoyj6WTPULDixBOVQTbJtslxNFla1tZmYaT6D1UQ3ojS/v5So4nGhT+i3DFsUwYWzNXuBHFIuuEtY5bQcwTsqplfpJS4MSNEPYqgn0iEk0gGvPxB151nNZVYidPnowpU6Zwm6xbtw7FxcU4dOgQGjZsiEWLFqFz5862enPnzkWjRo2kXt9du3bh3nvvxVVXXaWX/ec//wEATJkyBdOnT0fbtm3x8MMPo2/fvti0aROaNm3q9l0SipAYzkCkg5vq7S43QthvEawigAVnfCdBKhO3bnHblyaeeccoFMhV5cb3zxXGxs9PFi0WiGJTajZZlNhJEBtzEfNErVbmNmIsEciqq82pbNNEdE0IYZEI9ksAi4SmquD120JhvHEiOwaRUBZFka3imCeMRdFikSg2pmdTjRKLBLGGURBraELXi3fYEzK/sCxw4kUIpxI04Z0rMjQy/NNPP5lsErKocIcOHbB27Vrs2bMHCxcuxKhRo7BixQqbIJ41axYuueQSZGVlcfspLS3FoEGD0LlzZ0yePFkvr6xM/iYnTZqke41nz56NVq1aYcGCBSbhTPgLieHaxu3gJmunKoT9GNgAxyiwigAWiV8noZqqJcINmtAVHVMgKywWyJwzv7Iw5kWLBaJYGiXmCWK9PwdBrCERusoLcaQAzyuswbNHVLcTC2EnW4SfItielcF2qFzxKxOdbv4FvNooooa3pDrxjnfMWSE1cSwTxrxosUgUq0aJVQQxb1KdbCENlUiwUrRY4dygHDjRsKbbBDwJYdfnCgCoyJD0mBa07BAqRKNRfQJdUVERVq1ahRkzZugLfAHARx99hI0bN+KVV17h9rFv3z6cffbZemQ5Eqm+YGrZsiUAmMR1LBbDMcccgx9//NH1eyPUITGcKagIX9ng5rRevEQI+ymCnQQwTzSKRKaS4E1nVokq76/oOEQimRdB5kaODcLY5jNWEcUcPzA3Smy1TVjbGQWxBs8/rCGLDltx4S92s4wtb8Kdvsuq/xfrqnKAXAg7RYPTIYKtAlEkflOxTqSCSt8y37BIIFvFsbUP67+LLFrsJIplUWIvgtiKLDpsq+t2NTpVVM4fqncQvd49VD1fHEYwxlBWZr4gee6559CzZ090797dVr+0tBQDBgxALBbD66+/bosc9+zZE7FYDBs3bsTJJ58MAKioqMCWLVvQpk2b9L0RgsRwpsHNIGGFN7hp8G5JpSKERYOaLArsUQALxa+C4PUrUmyeRCfZbyQkjAbzxLFUGMuixSJR7BAlltomjGUaWp8y/7CbiXOprEbHySKh2sa6uhxgFLPuhLDbaLCovhcB7MY2wdt/TaAJRtFx8TzDPHHME8aiaDHPClGWCJo8xcbloXlRYreCWMPqH1aJDmtWiZR8w25WbpMFTqz9pSKE3Ypga45iD4TDlQiH/bvyq3TZ1+23346BAweidevW2LdvH+bPn48PPvgAJSUlep3S0lIsWLAADz/8sK39vn370L9/fxw8eBAvvPACSktL9cl7zZs3RygUQm5uLsaNG4fJkyejdevWaNOmjZ6n+IILLkjh3RJOkBjOdKyDm4o9QiaE/RrYtD5ciGCrSOQKWIkAdSt4VeqbxK9Cm2ohazlOQRTZKo65wthypjdFi0Wi2CFKbLNN2MQvx0OsIbNLCNCtEmnMLWyNBsvEstUewROKboWwnyLYKoDdiF8n0ZvuzBJRgyDlIRLJInGsKoyt/yomy4MkSmwV0LyJczJBzBPGGtbJdGmdLKcidEVtVO4gpiKE3Z4r6iA7duzAyJEjsW3bNuTl5aFbt24oKSlBv3799Drz588HYwwXXXSRrf2aNWvw+eefA4ButdD4/vvv0bZtWwDAtGnTEA6HMXLkSPz+++/o3bs33n//fTRp0iR9b46gPMPpRJpn2CpurR4wwXbh4Ca7yk/3wGY4w7kWwIIBUiZKa9IvrGEVzY7bOPmHrfWM2SxsfRhem3IZa8LVmK/YlEM4bCvTo8TaNl4uYq2+9leWf9iaO9i6RLNgu/W1SNxaI8NOYtgaFVbxCasIYadocDpEME/IikSniuj1a75pluL1TZT3r8ARkvx6hv1Z/n2M9SPC54acwlWRYnM+YXs9bbtWpgliXh5i7a81/7AWHbbmDrbmHLbmGxbVdxz7Vc8VxjLR+UJBCHPvHnoImpQeLEfT0S97zjN86evPIZrjX57h8gMH8cK5Y2g5ZgIARYZrFr+EsAyREDbgWginKIJVBDDXSlGDmSVEqGSQ4EWDTe/REjW2eo2N0WKThcIQKTZ5inlRYmMqNkvEV7dNGD3EUWuk2OIfrqVcwunALyHsFA1WFcFOApgnfmXC182/gWL6bNf70MQyPxpsfj+RIHP0C2uflzUzhVOk2GqdENkmrD5ia4TY2E6E1S5hjQ5brRI1jqoQNuJVCHu4c0gQmcThcbarb6he5cs8wjIh7DSw+SSCbW0zJLOEMUrL27dIIPPEMU8YW7cLRTHHU8y1TohsEyqCWOYftmL1DnvxErsk1aiwSAgb8SqERdFgFRHsVQDL/hX8+jdw04/uAbYcl6o45gljJ1EctV8vcv3BRkEMmG0TPOuDSBBb7RKyyXQy0p1v2DOSO4jVddwJ4XSI4HCkEuGIj55hH/si6j4khusDIiGc4sBmFHVCEexBAKeUXYKzT0cslgbZfngT5AB+erVAJCgWxpxosaoo5kaJeZPr3Apiqwh26R32wzfsZfKcF6wiWUUIO0WD/RDBtsl1nH8Fp3+DmvIMi44lElQTxzJhLBPFsiix1R/Mm1xnFcS8KLCqIOblHnZCtmiHa7za6bxY6VK4e8gqKsHi5MgkMhcSwzVBIg7AZR5Wnwc3bkRYwyqEXUSDVUSwkwB2lVnC0rcvqPQnmSAHwDZJzljXGjGWRYt5othqnbBFiV0IYhsiQeyHXcKHlei43aYYFU6nEFYRwX4JYBXR61e02GidkGWP4O3TKo6twtg6Ic+rKBbZJtwIYtlkORmpTKRzJYpl+YV9JGUhLLl7SBCZCInhDEEppZqVVISwyBqhOLClKoKVBbCP2SXc4phmjZNeTSV7hN0fnDCJbaPA5kaJrV5imY/YInJt0WFVrHmH3YhkhboqkWA30eJ0CGGeLcIpGqwqgq3/Du6yS/DLRcQr5Lf4ebeiZfvQfsaqi2pYhbFXUWy1TohsE14FsVN0WAXfrBEq80Wsdf0KnCgIYVE0WIMditfIHA+C8AqJ4bqASnocN3gQwo7RYI8i2I/sEgBQGfcvx2owzKT75KZX40SOZcLYJooN9gnHKDHPNmGM/hp9xCqC2Ck6LKMGfMPSFGqSDBJe8CKE3UaD3YhgfnYJwbE7iFxVVPvRRDMvEgyoLaqhvX8VUWz1ClujxCLbBE8Qa6hYJkS4yTuc1kl0blKueRXCRjwIYWuZFyJB/uROrzAf+yLqPiSGaxuV5OgyUr3K92NgEwhhJTuFpb2wThWqojeRUBdFoZD55Cfah0gkp5o9QiaKeVFinm3CJog1/IgQe8g77Afa5DkevKWXNbxGhZ2EsKotwq0IdhLAXsRvPJ6eM71x0QPe/sORSq44dlpUw0kUq0SJeYJZJIidRG8q0WFZNNh1pNjr+UGWetMLTucLN3cPCSIDITGciSisSS8d3NIghN1Eg72KYN6AKRKmbsSuE059aWLZeiw8cezkBwZciGJOlFhkm+BaJqIuIlFOk+mMSLzA+iQ6RXhRX1kkmLv8skJUOJ1CmLctVRHM0w4i8asifMvLvN2qj8bMnzdvXyKBzIscy4SxTBSrRIllPmKZIFa1S/BQWabZb6R+Ydm5I12BE4kQNo3pqQpygkgjJIZrC9lKcjykSy9bBjdue2drhI5ACPOiwa5FsIMA5olfJ7EaL/d/GdpwtDrqY90/Txw7CmOFlGrGiXaiKLEmiM31HASxW7uEEdlEuhrMRcwVwZITvyyVmoafQtgpGuxVBPMEsEj8ehW8MmR9akJZJJBlwthqpXAjinlRYicfsVtBzEO6VLPEKlEjKdWk5440Bk483D30SiTIX6zFK2STIIyQGM50eCd8L4ObA6LBza0Q9ksE8wSwiuhNxTusCVrRvjSBzBPHMmEsSqkmmyhntkMk0iaIucjq8KwSvEgxTyh7yC7BjRxzLBKyZZdFUWEefglhlWiwSASrCmCZUI376KG3Eq76ffP2zxPIPGHsRhQbs0+IosR+CGIjKtFhr5knAEj9xe464p0fZEsvpx44cSWEJXNJCCKTIDGcAXBve/EGMreDW4pX+SoDWypp1lIRwH5OmHPqUxO31mPhiWOjMHYSxU4T5US2CenEOhVBbMBVdNj8YakJYBfIfMCA3CJhRBYVdrJHiHASwm6iwaoi2CqAReLXSfimOrHOml3Cur+w4SLSeIxWYazZKbTjEYlimwXCEiXmeYndCmIeIruE6b1zynhWibREg1OZOM1NqekicJKCECYRTNQFSAxnGrIrfVOZwuDGq58GIey3CLaKTpnwrfTRO2wkaJhUJ/IKG4/TKox50WK3E+W0uibbhNPEOqsg5iGyS/DqGKllq4Rpt5z/E5WosBFVe4RfQtiLCOYJYJH49SubhGq/mkgWiWOrMFYVxU5RYpFtQiSIeTjZJXh1ncp4C3AYRbGvGSV4wtjpLmIqgRMHVIQwiWIikyExnMmoWiRUBjfOtrQJYQUR7FUAO4nfRAoR45AhwsXbT1DiFdaOnyeKtTbGSDHPAgFAaIOQCWIpTnYJU11emaJVQoTHhTeMmSSc8gvzRLBKVFi2za0Q5tki3EaDZSKYJ4Bl4rciTcI4YogSW/fPE8dWYWwUxcntlVJRzIsS82wTMkGsapcwwosOWzNLGOsZ8c0CoYrTXURJxDedgRPe3cNU8Du1mmQ6AVEPITFc0xgHKTe3vWQWCcNz0+CmcpXvcmCT1jXUN5anIoJF4jcV0StC1Kcmko3HIhLG1mgxL1KsEiV2I4gdo8OyDBM8satqlRDgNqOEG4xWCplFAuBHhWX2iHQIYb9FME8AqwjfVFKtGbNF8PalCWTehDmrMDaKYu24jJFi40Q7XpRYZJvwKoiNpBIdlmWV8ISbRTaMGO8iygIncY5Y9jFwwrXRlaVg8yCINENiOFNwI5JNKwGpRo8ladQs8CK81m2qE+tEQtgoGlUEsEz8psM/bJxMZ9y3ijDmRYtlolg2UQ6w+4g9CWIjnOgwN++wk1XCGPVN09LLqhijw7KosBHeUssa6RLCKiI4FQGcjvzCTunUjMeiIoytothon3CKEhuFqtE24UYQG5FNpuMJYFmaNStGi4TouVpHhrtvXpdh5ghdpaiwEY9CmFaeI+oCJIYzHdGVvtPgxltCU6vncJVvr+/eRuGHCOYJYBXhG69wL47DEfnCG5rAVRHGTqLYap2QTZQD7L5gZUFs+lBSiA67iAr7idEeYXxu9As7rTgniwobsUaFeXgRwm6jwUYhbBXBPAEsE79+p1oz5hy27lcTxzJh7FYU86LEXgWxEZFdglcH4E+aE4lmnm9Yhl8i2fTcMZjiEBVWCJx4EcKswlvmDYKoCUgMHw7wBjcjvJzCKV7luxXCqYhgkQD2InpFiPrSRDJPHMuEsUgUK0WJHRbcUPYQi+wSgDw6rPSBpWc1Oid/sAyeRcL6XIMXFXayR4hwI4T9EMFu06zJ2skwRn9F+xClUgOqj1smio2eYqt1whol9iqIAbFdwojMO2x9XmcQ3EWURoUNyLJH6CgJYbVouoysEBDzUbGkO/UzUbcgMZxJqFzpex3cfLzKl/mJNXGnEg02imAVASwTv35ZJYz2COs+jdFjq9DVjt9JFKtEiY0iF4Aw8sutK7BLmHCKDousEqIIsdE64SG7hBcBbPQLiywSsgwSxna887SqPcJJCPOiwV5EsBsB7JdVQtSPUSTLUqlpda3RYqPIlUWJrV5imSCWIbNL8KLDPO8wbyJdsj3fN2ycRFcji24AancRub5gcVQYgHPgxAGZ7Y4gMgUSw7WMZw+YDN7gZiSFq3yvQtirCOYJYFXhqzLJLhSW2yMAQ2SXI4ytE+h4ojjIySghihKLBLGGcWKdFZldQhodNvZhjA6napXwIe2aySrhkIdYhvGnzsspLPP51rQQlolgfqo1uSL0P88w3yKhkmO4oiLIjRTzosRuBLFb/zDAn0znJjrMs0844Ut6Na+TsK1wgiVG0hE4IYhMhcRwJuP2Sl80uHGiwrKrfD+EsNtosJMIFgngVLNKOGWQsO5bRRhbRbEsSmxMxSYTxFYbhBe7hAljdJg3UU6GaBJdDWOMCKtYJJyiwlZ7BG9ffghhP0SwSACnI9cwr0+jQOZZJHhZI7TtPPsEL0os8hF7FcRGZJPpAPdCNxXfsK+4vYvoMipseu42cBJPzSrh93LMlFqNMEJiuDaQpc1RvdJ3M7gZ4USDpdkjfBLCfopgaWYJj4twGBfZ4O1Dj/RKhLFRFPPsE7wosRdB7MkuAYXosIpVQoZPwtiYY9i2TTHlFG/iHCCPCgPypZZrQwg7iWCZ+PVbGIeVcgybI8Gqotg4YU6LEot8xG4EsREv0WG3VgkrvlkkZOcF1TRsTnNLDMgCJ+Z67gMnBJGJ0LVRpqOYPN0Jx8HNY6J0kRCujAdcCeF4RUAXlVpb00S8eEB/mPafCJgeXrH2w5vUZ92/9Rh578H6vo19a9uNFw8JyzZhFEbi2+N/v5zv03ixFBdcXFkx3XHgrIwoIoXbuTJPsVNKNRm8qLAR3gpz1rapCuGKiqAuCuPxoKmN2VIRNAnheEXQvnJdVRlvm5HKuPwhQta/tcx6vLz3o6G/f0Mf2mfFa6N/doLvwPyzD3DrGMuMqNxtkMHLfS0iFduP/IuSZx2S17dHhY2kEjhJRwpMgvALigxnAm6EgkeLBADT4OZGTDkNbjwhrKEJP5kI5pVb21j745GqZcLmH7bsS4vq2nzB1qwRhkix1Tph9RIbI8SAeWKdaoQYADc6rMGNDqvi0TeczoU3ZILDjUUi+by6LS97hMge4YRICLuNBjtFgp1Erxdk7YyBf24uYUsZL1IsihLzbBOqEWIeVruEEV50WBWvdoq0TqSTXZi6vIuYSuCEhDBRVyExnIlYz0aqy2m6GNxM8FaZq0UhzIv+8vB7FTpef7wlmt2IYqt1QiaITcfiIIiN2OwSxjLjhDvTPV+XVgnnDy/lyXJG3GSY4GWN0BBZJFSjwkZU7RFuhLCfIlhF/EbK1D7XihhftBn3wRPGVisFzz4hWoVOVRDzULFL8LJQ8DJLAGpWCdtnZhHdNb4ssxHVu4heosKcsppYWCMSZL6mtkvUtTR5RFohMVwX4a06p+FxcJPaI3wWwqrRYLcr0ZWVpe5Li8XMIsMpl7CxjlEUOwlirT4vQqx5iGVIF9pwEx1WnUhn3ea3+HW4ZWzdbr1t7ZRSjdeO+1OXRIWN22tKCDuJYJEAVhW9IkTtjSKZJ4xtkWFbbuGgMEqsKohV/cOm43YRHVaN/LpZjS4tWO8oyu4wuvALW5FFhY3bVQMnBJGJkGc4U7BFgy23vWQDmA+DG287Txh7EcJG/6wsGszz1Rq3WYVwWVml6SEjXsH0hwxZnzbPsOA4ee/Z6CU21tf6MW7TPlNV/7AR7i1LI8Yy1TsOdRCRRUKEMYOEbNKceR9V/fskhI0+W5sH1/Ka5++NlCX0B49QvNLVg4doH7zjER2/yButlZl81AIPsZN/GKj+znjfp+jOgGyypWwhF+tr60RPT4vJWMSt6zScMr+wy7uIfgVOCCJTochwbZFKfkjAPtD5PLh5nQ3ME8L6IUoEobU9bxsgjv46iVzVuuGIXXBraFFj2QIb2nZZlNiabUIWIRbaJQw4RYdluYmrPxRJ/mGjb5i33ZpFwueosd6tRWA4CRIjKhYJXlTY3L+9jYZsspze3kEI631V8J8DfAHMQyRm3cDrIxGuPh7jvrWIsXZ81khx2BLtBfhRYmOZhmqE2IjVLpEs876CnFtfcSZiu8B1MbckWa4WOBEJ4UQ5CWIicyExnMlYBXPcGilQjBZ7GNyM21XtEakIYaclmXki2KsAtgpeWZ/GutoxqIhikxXCZ0EMwDaZzgjPO2x/LrBKOOFl8Q2PpLI0M2C3QThNnAOco8Ky7BE8jFkjALtoNpZZ+7NGgo3wRLCKAA4rXtTGBcrPuA+eMJaJYreC2GiZ0I8rXr04hxWrXcIIzzsMqFklrFitE9bXxpXoRPg2mc5pfonVUie7G+RT4IRH3AchHA1SnmEifdDPIZNIOVosG+g8DG4GakoIW60IVruCzO5g3GZ9eKlnrGvEekw8+4T1fTp9Hm7gnXy41hbO9mS5RBDJ7jhw67tIr+YD1kwSVguE02sjXqLCpr4V7RFuhTDPEqFhtSjIbA3hikrbQxVeW2t73r6tx2e0T5isEhbbhAbPMqHVN+JklzA+56Vas9YV/RYASFOsqaTzS/WizoTKOcJ6HpD9D/Ny0VfhNClOlneY7BFEXYPEcKbj9kpfYJEAUosKm3bpkxA2em5VRbDpOByErBdEffLKeKJYw60gdusfNuLGO2yrK1mS1X5bNQ25uozVPIoGqyCxCRaBRcKKLIOEcTuvD1UhbITnD5b5gnki2IiK8E3FL2zdB69f47FaRbH+vjnRbzeCWOQfNuKUIcTkLZbMM+Yt0iJDNR+xr7i9ILVd8MonWifLHaLGFcbv2nleCUFkGiSGaxHuhAgV4eAmeqcQDXIbFVZFZdKYsQxwJ4Lt+xNHfFUe/PfgvP+aFsSAPDqsivViyTW8KJXX5LYCeAI5VcEhskgYkUUKAb6Is26r4FgoRBFN63OnaLARkfh1I3K9tFUVxbz35CSI9W0OgthUVxIdNuKUUYRXj1e3VoSvKk6WuhTuIgLeAycEkamQGK4LuF15zhrh82Fwc2uP8CqE9begEJmVlXtBxVphLTMeu3b81swYGqkIYiO27BKS58pWCevtUqc7Eini661juBcuKhYJp6iwyqQ5fj2xgAbUo8Gq1gUZbqwTor5Fx8F7D6qC2LoMtQjV6LDKdSIvmqwfn4Lw5YljXspA2XLjSvAuOL3YJzzcRUwlcFKZ4sVDOJj0dPv1CJP6IQzQBLo6itOVvjTq52Fw45WlUwgbcXptpMKjKI5wJtVp+zFOorOWWV+XlVWaJtgZJ8aZyuL2hTZkWCfTmRBkjhBNnrN3bpkUp7LYRk1OpOMICp7wcPILq1okVKPCpjJFn7DII6yh6RzrBDmrCLbiZG2QoSKIrRPqtP0ZJ9Fp/Wh1Q/FKfbtxgp1xch1vYp02gU40oU66Qh1nZTrjc5WJdNY23M/DxcpztYbbC1jJ70AYOOFs16CoMFGXoGujmsLN7WOeB4xnhZBc6QMQZ5GAu8HNGhXmIRvwvAphUXTYSkUF0x9eMfZh7ccpKmx9zbNNyCLEqUSHjahEh23bVFD1Cqc6AbSGEVkkAHleYUAtgimroyqEedFgIyqT5/xA1StsPUaZbUKDf3HBv4jgWU+sbYyIosMyi4OTb1jlQgywpwL0hKvzhsLdFplfGAp5yq1IvMLGMtEqogSRKZAYrm1qUEB4HdyMiKLCGk7CztpGE4484Wvu1/xaRQDH48zxIUJFFMuO2UkQazgJYtP+Zd5hRewXRQ53GHjp+9zadlwguoXMExY8ASKbPJfcbq0vbqthFFRuosL2ehbfq4IQNiITmdp2PybPqU6i4/UtqsMTxDzLhFOqOlt9y2Q6UR9yb3D1c6tVQsUXrJJRQsM3e5DqeUMhiJLqXURZVNhPskL+P9wwc+ZMdOvWDbm5ucjNzUVxcTHeeustU51vvvkG5557LvLy8tCoUSOcdNJJ+PHHH/Xtp512GgKBgOkxYsQI7v7Kyspw/PHHIxAIYO3atW4/LsIlJIYzFd5gx7vt5eJKH4Cvg5tqhJPXBjALYdO+JIJTJIDdCF1ZOyuqopj3XCaIZctKG1GODgu8wdLv1ck37AZRBCsDosUyv7D1tdhLbO+XF/F1skfwhBrvo1MVlrztxnpeJs+ptnfyCmt1eNtUBLFTdFgWUQbEAlj0faugeu1pTQFYm7hdTVL5LqLLqHAGDAUp0apVK0ydOhWrV6/G6tWrccYZZ2Do0KH4+uuvAQCbN2/GySefjI4dO+KDDz7AP//5T9x5553Iysoy9TN27Fhs27ZNfzz11FPc/d1yyy0oLCxM+/sikpBnuK7h4UpflmUglcFNFZ4A5AlhlWiwFVXRq4qxv3DYMOu8at8Ri3/Y6B3mPTd6iDU0D7HVP2xdkMO4kp2+zyrvsBGVRTasSD3E+s44vmAVP3ENwovK8TMImF+rWiRkq80ln5ujwqpo9XmT5ZxsEUbc+odTQbTgBs8rbKwTrqgU+og1D3HQ8pMS+Yd58BbiMHqRTd5gyYp0Tr5hnk84lVXtjPi2CIfqXRuJpQ6Q33FyuhslC5zUZYYMGWJ6fd9992HmzJlYuXIlunTpgkmTJuGcc87Bgw8+qNc55phjbP00aNAABQUF0n299dZbWLZsGRYuXGiLPhPpgSLDmYAoqpbK7WjJ4Ob21noqUWEvQlglGiyL/paVMdcP7vvm7IMXJXZ6bswyYUX0ORrhRYe5ae4Ub3dycZOo39bW34U3RLeSRdE2VQ+nDJXb4UbBm0pU2IsQdpNJgoeWns3tQ4STV9jpvVgxLswhQ+Yd5n4nDtF90bZUrNY1lnJNeN5QmF/CKVO9iyhqw80gUQe8wqWlpaZHWRkn5amFRCKB+fPn48CBAyguLkZlZSWWLFmC9u3bY8CAAWjRogV69+6NxYsX29q++OKLaNasGbp06YKbb74Z+/btM23fsWMHxo4di3nz5qFBgwZ+vU3CARLDdRDubS+Xt7hVLBJuBzeZPULDaVllp2iwSAQ7CVsnZO2t+7SKcxVBrCHzDxtR8Q4b61kRWSWs25LbVaNJdfs+p6NLRMEi4ZQWTYSsDk90OtkijIhEsIqodcKpj1QFsWxCnUq6NV7eYd53ZMWtPYK3+IbbCy6/0wm6xuuiOVU43UU0olrmBm05Zj8fANC6dWvk5eXpjwceeEB4DOvWrUPDhg0Ri8Uwbtw4LFq0CJ07d8Yvv/yC/fv3Y+rUqTj77LOxbNkyDB8+HOeddx5WrFiht7/kkkvw8ssv44MPPsCdd96JhQsX4rzzztO3M8YwevRojBs3DkVFRSl9XoQ7Mud+J8Enheiw7ErfRgqDGy+aCdijwrysEW6FsBU34tdoX5Ch9RmLWSYHxpnNOhFxsEloz73YJXh1jRjTrKXdHuEBlihDIBRLuR8joqgbT5jwBIzMLyyrK5s4Zy4Tp1Iz9iObMOdWCFtRFb/hqrElrvhdG9Oj8Y6BZ4vQtou2af2K7BI8rKnWjPDSrAFiq4R1G+81zwohskf4ZZsw4cVsq3jh6mSpc2uXMF2ou5wfUVv89NNPyM3N1V/HYuIxq0OHDli7di327NmDhQsXYtSoUVixYgUaN24MABg6dChuvPFGAMDxxx+PTz/9FE8++ST69u0LIOkX1ujatSuOPfZYFBUV4csvv0SPHj3w+OOPo7S0FLfddlsa3ikhgyLDmYLbAU/hthfgPqWarUvFwU2WPcLWp2D1OCOmyCsnGiy1N0hWmHOz+hxvHzLbhFNUWGaX4MGLDruNrjhaYlTvB4uiShk+K8btLWulxRk4mQukKb8UMiXwRK3bbBKyCG44nrA9ZNusdVT242bSn6jMapdwEx32gvX3IfOSZySul2LmvEEHv7CKxY43t8RIJuca1rJDaA+ZGI5Go2jXrh2KiorwwAMPoHv37pgxYwaaNWuGcDiMzp07m+p36tTJlE3CSo8ePRCJRPDdd98BAN5//32sXLkSsVgM4XAY7dq1AwAUFRVh1KhRPrxbQgRFhusCIiHCKXe7xK6KRcJLVFjvxxIVdhKMgF0IG5EJ4FQwtrdGj8vKmClKrB2TFiV2ihDzItLWBTlUFuMwRoeNE+lMbQQLcFi3AYIocXkFEI2YilgigUCoZhbY0JDdUhYJXFXhaxU8qjlneRPnjKisNAfYo8JOC2pYy3kimNvGhxR4xj6sEWRepNgYBRb2yZlQp0WHVdGiw+ay6ol0xu2iBThU4NUX9SFbiMO3CXJuSNEWIURwF/FwnTgngzGGsrIyRKNRnHjiidi4caNp+6ZNm9CmTRth+6+//hoVFRVo2bIlAOCxxx7D3//+d3371q1bMWDAALzyyivo3bt3et4EAYDEcL1AdqVvJZXBzU36MJEodiuE/ViGWdSnUcDyrBNG24RIEBv7FNkl+MdgzixhxFgmskpYSYs9ogZXoVNFJZOElz6k9V14hmVrKIgEr1sh7IcI5iGyVViFrMgW4dYuoWeUsGSWMOLGKmHFbodwJ5QzBrd3ZzjzS5wsdSp3EY3w7gw6BU6cCAf8/X7cHs7tt9+OgQMHonXr1ti3bx/mz5+PDz74ACUlJQCAv/71r/jzn/+MU089FaeffjpKSkrwxhtv4IMPPgCQTL324osv4pxzzkGzZs2wYcMGTJw4ESeccAL69OkDADjqqKNM+2zYsCEA4A9/+ANatWqV2hsmpJAYziRkZ0qRB0zhtpcjCtFk2eDmdAtMlE/YWuZGCDuJYDciWeQj5olaa5TYiFEQy/tICmJRdJiHbBsPJ/FrixqL8Cp4E3EglL7hxe3kJSuyyXPW16IsErzb8yp5hQF+VFgjXUI45GJcSEh+O+F4whdBzKurAi/Nmkz06u0s4tdpG08ci6K/ZZVBxIL8/VcigSBq56JRmGNY4YJJ1S9cly0SbtixYwdGjhyJbdu2IS8vD926dUNJSQn69esHABg+fDiefPJJPPDAA5gwYQI6dOiAhQsX4uSTTwaQtFi89957mDFjBvbv34/WrVtj0KBBmDx5MkI1fOeNsENi+DAllSv9VAY32aQ5Dd7kOT+EsNcoscwiIYoSa4LYOqnO2E7FLsFDdSKd0CphwSqMeUKZVSQQiGRWVNjr7Hv+ynT2elYB7DL4Zd6nQgYDGTx7hGwpZasQ9ksE89rwhDEvSqwqiPU+OGUa1ugwD69WCcA+ic6KTDjXBizhkO7Lh7sBTtFftyk5DzeLxHPPPedY54orrsAVV1zB3da6dWtTZgkV2rZtC8b8v/tJ2Mmgf3fCEZEHTDGtWiqJ1AG1iXMieAJYBRUhLJsE5xZVoW08LmvaNae+ku3Nk+lUUtYp49I3LiRdnkOfcLMMLpDaamMiv7CbiXMyr7CGKE8wb/U2DZEQDlVUehLCbvqx7lslkwUv8i1LtQbYJ9IZUbGpyFD1mvOyk7hpX2sIfh+8+SWOljoFv3A6LBJA8mLG7wdBaNDPoSZR8Xb5uIBBKlf66R7cVKLCqkJYFdFSzrw+VfalIoitbZ2OV8WTDSgswFGF7TfAE8puBVOGi2RALFysWIWMD9rRdZTYKSrsVgj7JYJV+5UJYjcLb7jBKauEzM7i9NH4mVEiIbO+VeFb/uE0ecVT4XCxSBCHPySG6xipeMAA9clzXuqILBIyAehVCDtFgzXha3yItolIVXx7qa+hmq3DittbmUJSObEqCADlriA/Dtep01y+LS9+YR68vMJW3ESKAbEQlhGOVyo9ZKQiiKX9WqLDKqvSub3wcJNn2oib/NYZiegCljvjNHVLHfcuYh1YjY6ov5AYPsxxzjMrPjk7iWNVi4SGV6sETwjzUBG4snYq+7biJjps7dONVcKIsR4v44eGShYR1+K5Dq1CpyqUvQokDdmqdCIhx1tgQ0PkFXayIMiEsIrIdVNfRRBz+3XpjTYis0qY6qUQjUxDQD1z8Cly7HbM8MMiQRDphsRwJuJkp3DhAXO9a4EgS8dkCJWosBsh7OfxyPapapew1le9CJB5s2X4dkvS5dLetU1NRuechJbXiXSpRoXFnl53IthNe6cotFN0OFWrhBGRbzjViY2AWCA7XWwlmM/KWslm5/5iVeUiORVLnV8kfb7Mx0faDpWog9DPoT6QwpKbPHhX+k4WiXTkA9bwQwgb+5J5fkWv03FcKYlbhQsjt7lDawsV36UIP/IOiwSV24lbKpPLVKLCboSwX6gK4lSjwyKrBA+ZLYWHdbfWn7/bOwL1mRq9OCeIGoDE8OGGD/f53Fzpu40Yc324KUSFVQWn09LLKv3Ks0OoH4fxr9slmr1MogNcXPDIxIxK1CnDl2cWYZtn6NY9IpnQZRV0MosEDxURbcVPIezUp0wQ+xUdVl1yWeTx9kqdtE3IrEyCuz5KF8Yp3HkkYUxkOiSGMxWnCEuKM/q9TLTyc0BzGzX1IoRFAlhVGKv0zy2XWCWcSFdaIiu+TbTLENxOpEslCugkzFSFm4YbkagSFVYVwl6yTvglslUvBKzwPlsvVohUf/6OUzHqysQ6CU5jhJvJcwSR6dCiG4chXiZKuRW6svpuLBKqUWErbrNAONVVWQxDq69aF+CvSpdubItsqK42l8Gk4r/0Q/eLoox+eFI13ApEL0KY14ZXJl+BrhJx6ypyFZXSNn7DW3BDWt9hhbqKygAinJXlMg4/MrXUYAo2Py/ko6FKxEL+XcTHfeyLqPtQZLguk8ZBzcuVvurCG26xtvNLCKu0cxMdVrVKqOA0iU41o4QbZBMwhSn9DiOsmtBthNdPYawh8wq7xU0UWCVFm3y7ulVCb6O4AEdNUd88xCp3i1K5o0RWCSKTITFcn/HgAZNNnlPeLUdguokKi0h1kp6qIFbZD88qYW1v9Q3XGH6tUGfFZdTKt8UGqnB7nq7JW9nWVedkUWCvWRbcpkJzwq2NwutCH07vV/VnZZzQmEp6NSfqRG5hnxbGcTPJtlJgmyCIugCJ4UymhlcUcpqEVRuoCtx0ZqvwCy/HSCeVuo2TkPMqfGtCpDqRjkl6MtwudCIi3RHfw8EvrIpfd6UIorap20bCuoiPq3PJ4F3RZ9qkKVn0VITbLA9ucesJtrYpK2OIxbydIBLxAELh1NOxBQ19WP3D9RU/BJAx2ug2rVq68ZoLWIV0+YHDFZWI18Bv062/uCapRAJBpJ71wi1+5KRXxa8L+nCQIeyjr9vPvoi6T2aN6ESdoj7NGvZilRC1zUhkoqmG71CIyNSImyytmipesytkMiLfcKbBu1Byu2z3YUMNimSCyCRIDNd16thqYXWB2rZc+J3bmVAnkyZN+Tl5LlXSZbUgCILIBMgmQaSFmlh5rib6zxTiFQGEI/XjvRL+UBsCtiZSrDmlSSOSpCsDTG3NY0gux+xvfwShQT8HokbwYyJcnbAb1DA0wY4gao6yBJ0yCeJwhP6zCSFuU6YRZrxMECRSo06kvTqMqOmMEkTNk2kTrwkiHZAYJgiCIAiCIOot5BkmhARDzLfocDgSULJKyOpFIoE6ZZUIhylKWdOEg4yiwzWIdVnmwx0/lwOuKwQiwYyIDkeCzNcls+vE8ttEjVG/RjKixtDy7rrN2et1P4c7oslzwRTzEhOHL+meyFZb+6TJc2oEQunJX0xjDnE4QmK4rhON1PYRHHbUtsAOhuQnm1QX5qir1EQkJ5pBI2JFrFrMxMM1vzCDkdoQ1rVJVu1+3ARB1DD1a4SrRwSy0u+ASVWU+WUjqAnxGrHsQ7RP3upz1rYZiUzs1LIQy3SiMXsKK7fRy5pYia2mMQp4o7DPNDLpAqimCEQE34eoPAUokkzUBcgzXI9w8n6FQgyJDMsgYfUQp9s37EVYq7ZxqudHxNd64snkpZiDgRAqWc0sJhENpr6gRjjM9CWZI5FKpSWZg+H0rMCeiARNeYTj4SA3s4O1ntd98UjVL+z2AoB3gcG7ELG1y9ClmAHUylLMfhKOMsTLa+acEfJ5OeYQeYYJA5l7pjxcCbq4/kh3RM5DFIDnXXW6rW/bLUcUGiOq6RSkXvA7sms91lhM/G+YlqhKGqI/ANz9tjOA2pxAownBRA1MQEvF4uCmbW1bKSIGsRyW/N+kOxJME7MoGkzUPUgM12XSKJaNg1mmeVRF4jRVQexXhBdQs4D4JeDD0cz6fmoLv7WYW6uDLAKp2QRUxK+ojpNvWBap9SJUZW28RoV5700rs0aKtc8s066x/IxOpo2wPx+a1W4nu9PkFBQhgUxkMiSGD0Nq69a4Jpq1SKc1owRP/PktGr1GlUXtnKLCxnZ++oV5FyDGk4nbaLwKQh8h0jczXZVQIP2/6Ux0lMi8tm4FbiISVGqjWk9GKn5hlQuGmrA+HLZeYsFFVSZbqggi3dTYr3/mzJno1q0bcnNzkZubi+LiYrz11lv6dsYYpkyZgsLCQmRnZ+O0007D119/beqjrKwM1113HZo1a4acnByce+65+Pnnn011du/ejZEjRyIvLw95eXkYOXIk9uzZY6rz448/YsiQIcjJyUGzZs0wYcIElJeXm+qsW7cOffv2RXZ2No488kjcc889YKzuXtk6DXRur9rTcZUvs0pYX8tEpiZunYSxUx3ePtI9Wc8PkWv9rnmTKevTiY/3Vt0KHaM3NWy6HW8XZV5Tf/kxiU4lYquJXdHDyz7ciueanjBo/U6su68z1oYMCpOHFMYqUUpIL2h5hv18EIRGjY1IrVq1wtSpU7F69WqsXr0aZ5xxBoYOHaoL3gcffBDTp0/HE088gVWrVqGgoAD9+vXDvn379D5uuOEGLFq0CPPnz8fHH3+M/fv3Y/DgwUgkqk9UF198MdauXYuSkhKUlJRg7dq1GDlypL49kUhg0KBBOHDgAD7++GPMnz8fCxcuxMSJE/U6paWl6NevHwoLC7Fq1So8/vjjeOihhzB9+vQa+KSqcLJApHgbTCaGRLfdeQLYq4XCDx+uSh9GYWx9uEUm0E1CnhPtdsq7LPscVU4oKiemw5lMPLGpimKZdQCQp1jjidB0LoShIoRFdg6n96kC7zM1XpAYt6tMrvOKk5av1d9j1P25QemCWXLnKJNtdQShQo1dZg4ZMsT0+r777sPMmTOxcuVKdO7cGY8++igmTZqE8847DwAwd+5c5Ofn46WXXsJVV12FvXv34rnnnsO8efNw1llnAQBeeOEFtG7dGu+++y4GDBiAb775BiUlJVi5ciV69+4NAHjmmWdQXFyMjRs3okOHDli2bBk2bNiAn376CYWFhQCAhx9+GKNHj8Z9992H3NxcvPjiizh06BDmzJmDWCyGrl27YtOmTZg+fTpuuukmBAKZlXHBRCQIWGePR0JAhbcTg+oqdKr1eCvMhcMBxOOaxSKAsjLGrctrm47sEn5NmHOKXgPyyXM8jCcaN9F55Uiw7CJM5QIslDmRK4D/78AjKwwcMmR9UG2nEY0lUF7G/+y0jBIVsRAiZQkkwkGE4pWIR4II+7CyFy9jhCi7RCp4EdmqFgmnSHFNLLTBu1tw2N1AiUaA8ooa320wzIA0ZFUhCL+olX/1RCKB+fPn48CBAyguLsb333+P7du3o3///nqdWCyGvn374tNPPwUArFmzBhUVFaY6hYWF6Nq1q17ns88+Q15eni6EAeCkk05CXl6eqU7Xrl11IQwAAwYMQFlZGdasWaPX6du3L2KxmKnO1q1bsWXLFuH7KisrQ2lpqemRibiZFMFDFql08g0bUY3syl6r9qOK6uQ8L1Fht6RkRVHIGFETuaj9IORwa9jthCa3AscUeTR8JxGHKKUXjKJQNTos7Csc9C1KLOpHNSpsqiP5ArT3rDJ5zm3k17rbOvLzrxX8sNX5baWLBSoRC/r4CGRuyj2i5qlRMbxu3To0bNgQsVgM48aNw6JFi9C5c2ds374dAJCfn2+qn5+fr2/bvn07otEomjRpIq3TokUL235btGhhqmPdT5MmTRCNRqV1tNdaHR4PPPCA7lXOy8tD69at5R+ICKfomnACROqTnIy3251uffk52BlFJG8imqmuQBCnIopl7VUEuQpO7TS/sNfP2rfvQ7aqoYdbsJmONSJYGxOn3NoFVOwS1XW9i2JZWychbBTwflgkNHgXHKK0an5MtBN9tE5WCN8nfarcdZHdvUlh4pyqrS4dE3sJoiao0WG/Q4cOWLt2LVauXImrr74ao0aNwoYNG/TtVvsBY8zRkmCtw6vvRx1t8pzseG677Tbs3btXf/z000/SY68JbJOpXPjAVFH1iPGixF6iw6IyrT83wtiprpMQFkWF3Vgk3PqFjSccWVo1x+9eUHa4wBMrbpfZVbk9L5tEp/3VIpzWFGu8KKlKdBhwJ4iT9dVEsVYvlVRtIiHs9H5l+GGVcLrwOYz/HdRxOCf4eSeRIDKFGv3Xj0ajaNeuHYqKivDAAw+ge/fumDFjBgoKCgDYo66//PKLHpEtKChAeXk5du/eLa2zY8cO23537txpqmPdz+7du1FRUSGt88svvwCwR6+NxGIxPVuG9vAbYYorzlW/29vgxsii6GpfFn20RjatVgknZNFhN4JYwyiMRQ/hsXAm2cmEsHW/1jZuo8leJyv6Jm5T8Q77OOPdaYWuVCcqWdvLPj5RRgnedi+oREqdPLgqGSGMYpf38NK/qnVD70cSKbZaJGQiWDR5zlTHBxEt+p35kXM4GPApbaGX/1mVqLDkPOLqTmIdjxo7ZcQaPXo0AoGA6XHSSSeZ+ti+fTtGjhyJgoIC5OTkoEePHnj11Vf17Vu2bMGYMWNw9NFHIzs7G3/4wx8wefJkW7Yrwn9q9TqYMYaysjIcffTRKCgowDvvvKNvKy8vx4oVK/DHP/4RANCzZ09EIhFTnW3btmH9+vV6neLiYuzduxdffPGFXufzzz/H3r17TXXWr1+Pbdu26XWWLVuGWCyGnj176nU+/PBD0w9w2bJlKCwsRNu2bf3/IIyEo5Jt/maQSEU4aVf7bm/NO0WHvQhiv9OdqQhv27EpeoVFUWG3FgnTiUdykrGdyHhRn8MwHBYLccSqy7ep8rGk4hv2Eh221QuHuEI0HavBiZdlVrNHpBIVtuJ04SHb7pRWze3dAxlOfvdMw/Gc4PJOoq9WuqrlmP18uMEpIxYAnH322di2bZv+WLp0qamPkSNHYuPGjXj99dexbt06nHfeefjzn/+Mr776CgDw7bfforKyEk899RS+/vprPPLII3jyySdx++23p/4BElJq7Cx4++2346OPPsKWLVuwbt06TJo0CR988AEuueQSBAIB3HDDDbj//vuxaNEirF+/HqNHj0aDBg1w8cUXAwDy8vIwZswYTJw4Ee+99x6++uorXHrppTjuuOP07BKdOnXC2WefjbFjx2LlypVYuXIlxo4di8GDB6NDhw4AgP79+6Nz584YOXIkvvrqK7z33nu4+eabMXbsWD2Se/HFFyMWi2H06NFYv349Fi1ahPvvvz/1TBLpmmnP8Xi6FbrG+m6u9q3b3ESHRSLWrSDWyv1YgS5VIexnbmLPS19bTlieL4J8WsUqXcSCgkig4knOKo5lN1JEk+h42/UyB6sED5F4lNklAH5k1k3uYBmyaLAXIewlKqz/deEXNtVxm1eaU593gZXsOwMintKosPc5JtIV5xTuJOrbDwOrxJAhQ3DOOeegffv2aN++Pe677z40bNgQK1eu1OvEYjEUFBToj6ZNm5r6+Oyzz3DdddehV69eOOaYY3DHHXegcePG+PLLLwEkxfTs2bPRv39/HHPMMTj33HNx88034//+7/9q9L3WR2pMDO/YsQMjR45Ehw4dcOaZZ+Lzzz9HSUkJ+vXrBwC45ZZbcMMNN2D8+PEoKirCf//7XyxbtgyNGjXS+3jkkUcwbNgwXHjhhejTpw8aNGiAN954AyGDdeDFF1/Ecccdh/79+6N///7o1q0b5s2bp28PhUJYsmQJsrKy0KdPH1x44YUYNmwYHnroIb1OXl4e3nnnHfz8888oKirC+PHjcdNNN+Gmm25K74eUziiCVSClMJU61at9txkheILYSRS7WVrZqT/psQiEsEpU2Ip+QaHqwY7yL1q84HrypdNtcR8u/Hy7fQx/xJAVP6wSvOiwKBLsRRDrbV0IY5VFOHj7crvSXKoLbzhF4GXbrcNfOidNOll9VAiEYs6V3KBgq0v1TuLhnG/YmhFL44MPPkCLFi3Qvn17jB07VrdXapx88sl45ZVX8Ntvv6GyshLz589HWVkZTjvtNOG+9u7daxPVhP8EWF1eVi3DKS0tRV5eHvb+tgi5DasGs0Q8mXQ0EQdLlJleA0g+j5cnX8cTVY+qbeVxIB4HS1jK44lk7sh4AkzLJ1xRCVZRCXYoDqblIK1IJMuqXvO2JQ8hgERVzuB4eQCV8eTzykQACe151d94hfl1wlDX+LqsrLKqPjP9tT435gzWcg9raPmHrVhzD/sBT8w6WSM0MRwWiGJVi4QmbMMRpj831tW3V4nhUFWZfrKKhPTngawwApGgbZt24gtEgkAkmBTD2gkyGkk+D4f1skCo6nU0bCpHOJQUvuFo8mIuFK7+i6qTuLGs6m8lS/5OK1ki+UDyb4JV6GX6diSQqIwjwSpRURlARWXVb68ygLLKICoqA4gbyisqq7YlglX1oZeXVwKHEubXWrvyymSuYa1+clvyebwiiHg8+RmWl4UQ13//VRFNy3YA+mutjva3surfNlKWPJCQIR+wlneYV2YtN/ZhJRxPz4ITIsFtFcIqUWGt3BoVBviRYU3cahcc2utIpNIQOWam7Vp5JFgteCPBajEcDSYju6ZtIa28uiwSZIiFKvW7DlpZuGolM+0uhfY6EmQIBYIIBcO6GA4GQvojFIgknyOkX/Rp20znBMO5wXa+MJ4rAPP5oupckWxnKNfOFVX1WUVCP1cAhnNC1XmEGc4j2mvjNn3cTwQQL68+F1jH/8p4ALsPxtH97WXYu3evq/k02nn0yx9noGFutnI7J/aX/o4eR12Pn376yXQ8sVjMlFbVyLp161BcXIxDhw6hYcOGeOmll3DOOecAAF555RU0bNgQbdq0wffff48777wT8Xgca9as0fvbu3cv/vznP+Ptt99GOBxGgwYN8Oqrr+pBQSubN29Gjx498PDDD+PKK6/07b0Tdg4/s2B9QvE2tuwKX+Vqn2eV8BqJ5HmGVfzDQFKM8iat+ekbFvWlKoStfVmfO2WQMAph2zbO7UdXfmGPuJm0mYm4DUAa64vaqlolVHBKO2YVk9YIsShK7HZSmwxRf7z9exHCRkQWCR7/v713j7OjKPP/P2fmzExCQoaEkIRIIAhuBCIgoDh4CQEJZLmuFxA0Er/8UFRAFlcF3BV0xYCiiLhEVARU3LgKrAgSCQLxAgEkZA2iIEq4JSGIySSEZGbOmf79cVI91dVPVT3V3ecymef9es0rc7qrq/ucnOn+9Kc/9RSV2eay3VSSCH0Kw4nVuZ4kWp4i1Tsqod9oFPGjbmymTZuWKIm6YMEC6zG4KmKdcsopOPbYYzFz5kwcf/zxuPPOO/Hkk0/ijjvuiLf/93//d6xfvx533303fv/73+P888/He9/7XqxcuTK1r9WrV+OYY47Be9/7XhHCDaC1Q4Ejifby0B2+Sbk85AJ7KHW0D7nDqJ3EIm1qrVJH25AbnNp2aF17exS7wwpqlrlyR4TKQAlt5QiDlRLayxGqlVLcVr3u6mqL3eGhbYdmlNN/12eVU6JTd4n1WerM/hQhbrFLSJPi2yGEQ6teuAbOKah11oiEK+7gyRIDcNcYbiBtpfbYHTbpaItiN9e3nFo2qn3IHXb1Z1IuD8Zub7ysYzB2fBVqNjrVXrVR/5oz0unos9KpmerM5eY6gO4LSDq5oW6xT0y7RLA6ZmodFY/wTbJhc4WTbej6wq6bG1/el/oTsd4gOfoqMvLDhnvd8Ey36LpmqPO+jfh6UI6Axk98x4Jyhm2oilgAcMghh+Dhhx/GVVddhWuvvTbVdtddd8Uee+yBv/zlLwBqLu83v/lNPPbYY9hvv/0AAAcccAB+85vf4L/+67/wrW99K9529erVmD17Nnp6evDtb3+7kPcpuBExPFwpt9ceefmWOUgIZcuUzeXOKH78pVAnuFCUILaJYJsgBpCYsrnWl4pfWEoe5XSKbWXTuEKYdpfpeITCdIV9VSQ4+OtM5xxM12LTL/vobKvFH2yv9WmZ9XW66KWmXu7oGMTAQLi1aE7RDOQTxIA9OlGUU0w50VmEsKtKRogrrLfhZLZdD0yoShKUYG7ooDmnUdJZi0pwYVwjuIaJzTzxCeRWI08ZVFURi+Lll1/Gc889h1133RUA8OqrrwIA2tqS3/v29nYMDg593i+88AJmz56Ngw8+GNdff32qvVAf5FNudXJcwLJOsMEVYTbxxikX5svVAunoAVW+zBadyIo1ilEupQbLcYSwb9AcwBto4otIuKIvwSX0ChJNhQ/6QVi9V9vofxuhg6hcUQnTwbRVltAJiRdQ6xQquhA6oM2HrU+uELZt46sgoeC6wvEyXSQ7/m+LnIGwJSpLAPRMkcQNrTlw1jeIzgcVlShqIF2REQn1E4KrItYrr7yCf/u3f8MDDzyAVatW4b777sPxxx+PiRMn4l/+5V8AAK9//eux99574yMf+Qgeeugh/PWvf8VXv/pVLFmyBCeddBKAmiN8+OGHY9q0abjiiivw0ksvYe3atc6Zb4ViGF62zvaKem7KpNTeXhsUodPZMTQwAkg9+jLv9m13+NyoRKg7TMUluA4xAGdsAkg7uTbH2MQ79TMhwFMiPUAIcwbN6a+zRiRceeH0BY8QTZQTbJuK2VUbOwOuiAQFN+ZgPg12PR3W1+m/u6ISoe4wFZfgOsQAEuuA9OA6IOni2hxjG94JPgih6xLCVE6YI9hdWWzKOeZEJHyC19SA1I1VaJ3ahkUluPEIn0tsPC20PUmknGDbsuGMqoi1Zs0adHd3Y//9948rYm3ZsgUrV67E97//fWzYsAG77rorZs+ejR//+MdxRayOjg784he/wAUXXIDjjz8er7zyCvbee2/ceOON8SC8u+66C0899RSeeuop7Lbbbon9S62D+iJiuJUhoxDEiS40HqGLX8YJTkUlXCc9X3ZYJ1QQA3RsQmEK49o+ctYcZohgdZy+3xW2eIQP1Z470QaJLy88DAbDtZfaUI14bm+5LUJFE8im8HXlhkOiEuVyFFeWiPe9TTD7ssM6oYLYXKfWA7QoVvsoAp8Iptq4csKAv64wVUHCJLSsne4McgxQrvhtmDscYqLUKR4RYp60amaYy3XXXWddN3r0aPzyl7/09vG6170ON998s3X9/PnzMX/+/CyHJ+REYhLbIc5HX4H1ZCnRxX385YpL6G4pV1DaplA2IwxZUf1w3WDOcbuqR+R1hRW2iERd4hItNhEHNfFGPWoK67gm2PBVN3DFJbJMWFHpaCOFqFl1Ii+2Pm37t63nlFHjopdTi5cZsRQg+f8bkhfmfI+KEr71nJKZrAZjDpQtsOKQOkcNdydYGFmIGG42tgFI1HJODqzscQAt63wnP9cJjjXwK4cgDhXF5k/Wdmo/ZjbYdXw+IRwaj3C5wpyIhPNCZq6jSi7ZyqqZ2MoAWL7foRf/PNPamoKFmx/1lVgbciv52eFEmxyCmCNK9bYhApmzjUuE+46ZEsI6RbnCnIiELy9sfneo7wF1Q9Zeoj/nwkRvnoGrqWsEf0Imn7HiNk9CDjJNe6mt8B9BUMi3YTjje7TNvNu3neBiYcZwhznuZqgg5opi1+x1QFr8+rCKbUdW2HzNEcKhZHaFHVniVF6YE5eoY6SivcQv7UY9tjbFi5n1dBXT0LfVPyZdILmmBTbdYd9gOv33UEGs1tlEsWt2N0rscsUyRwSbx2rLCOvvOTQeQbnCZl+A/cYnNCJh27buhNwEFvAkxzXo2neucbnDodEwQWg0IoYbRdBJjRiQxDmpOWrEciZgyOMOJ9oxHvdzBLHttW06Z644dm1nE8EhQt0lhHXq4Qpz8D72TD1tYH53m1xmzZfpNN+2/ljcLprs2wNud5iCEsQKriDmiFG9bZ5pj/U+QsQ4dexcIRwK5b5Tb5kbkeAMnjOhvnt5nmbUDc+TRJ1Q80QRPJ5BEFoAEcPDlNTja8ejL+7dPrU+izucaENNJ8xwiH2usFpmqydMiVyO8HX1bzsO/T34hHDWeITPFY6XWSISzvrCWZzeBg64U9PZ6nDcOZc4Dim1ZS7L4w5TbXUR6BLEWUWx3j70hyJLdjhECBflCiu4EQnbNkD6uxRaSaJQKJHNidWZf7OO3HBW88TpDrdK2TlBIBAx3ETIOqxkiM4Uvh7nLu/dPrE+7try+Msm8nRskQG1XBeTtT78YlRfnmdaZlcfnGVU+TSAJ4S5kK7wNrJEJBKYF8bUhZN5AW4SvmynSzxzohK2foG0O8yNS+htXYLY5hID/gF0RQ2iCx1A1ygh7HKFbQPnbBEJX17YB9WeupELiQJlJjgewZu50meeKNS5irqJz0p7W7nwH0FQiBje3uDe7fuyYA53OFQQJx7ze4QikJ6gwiaKbcLXFLacH1cfrmMxBTxXCOvkcoUZA+fsvwfmhTkX2BwXGGpwEbUslQs2BjCF5IZtUQlqIJ3LHfbFJbiCWM8Qc11iwD6ATm+f58eEK4LzCGEuruiJS+wqXBEJW1+11y3idDLqfPueJCbacs4d2841PndYIZUlhFZHxHCr43PonCc1zx09+O5w6MmsCEHsE8X6sjyuMNWXbZ3t+HSH24yCUEI4NB7hygqHlkFKvjHH94dbScJHCzkwNnGUxR12ZVxdpdZcgtj83ecSuwbQ5ckKm4RkhykRrJdP4wphrius43OFFdxyeukbqbAbscLhPI3J8yQx0Dyh1its5okgtCIihluJwMfOIXf7ie2orKnHHVZw3WF9nS78bFUmKPEIuEUxJzJRtDtMHQ9H3IcIYRNTCPuywjG2iIRLJIVGcAqefc6HWQ7Jl+UsJx6Jh12Mfe5wvA+LO2yLSyTb8gQxRxT7BtFxRDK3vW2f5sQelBuc+j2HEOa6wkPL/BEJX144lIbNPJcF7UmiMyoRtwlzh+sRlxCEeiBiuJUxxbHrMXWOu/28J7gQQZzoxxC+ppCkRLFLGBfpDJt92URwiBAud0RWIazjikfEOFzh4IiELy8cSkFZ4rwigvvI21dVguMO5xXEtrJrNpcYSItigD/hRqhIdvWtjoPjBqfeYwYhbLaLP2OHK2yLwNgiEq4bJ98NGKd+bWEC2fxShDxJDI1KMN3h4KdTTNrQXviPIChEDDeLvILBJYwtd/u+x+mcE1zRgjjVVnOJ1TpTMNqEcW0fxTnD1H7U8XCcbd0Jt90M6H0kPitLPIIUv1ldYeaF0RvNaVAMwhzwkh40Zxcw+uPukKiEyx32XfNDBLG+3CUeTZcYoMUo4M4K2+BuY9uneXw2ca/fAHCEsI4rU+z6vwl1hW3fGbOvulHwAFVn7IljnqhlLvPEMdakXdxhoYURMdwqpO7wjcfPLrFiEzVFucPMOrZcQWyLTXBEsUsYU+KYi9mPzwW2HaP5foE6CGHi/yPEFbai3WClIzj1Fbx5XRpXdtMWlcjiDlMfo80dptp0MMWgvo36nRLFLmFsClWdLBNu+ASwKYJ9bjDAF8K+eAT1/xLiCnNwVyQx3GHjP6YIJzhVfcgUy1mfJOY0TzhjTaT2sNDqtM7IFsFOuR2oaBfYzjLQXwFQEy1RlT/6utTRhmhgMPW7ub40qoxoa4Vc11aOMFgpob09QrVaQrkzQqW/5qyqdW3tEQarpVj0VSuleB1QE4iVgeQ2ABLtgSGhOFgdcm51IanaKfIIYh3bLHFUrtcmgoF0PjjVPqMQpi5EvgoTQ+0cEYnEwTtOD3Uuq9ZWasdgNPS9bkM7BjH0uqMtwsCgpZpIW4TKtnUdbcBA2lxM0NkG9A8i1V7tQ60fVQbUn4RaptqXOwZRGWhDuTyISqUNnV1V9Pe1o1yOUIm/87U2HR2DGBio/V/p7QFs26Z2AJVKWyz2Kqq98RpIitDB5J+sUxADQEdf1duGwvYwwHRybSIYsJRFI5xgrhCmXOGhZfRNELXMdiNlQk3D3LKUy0Bl25dDv57Yftf+EBLXgo52YKDqvHaYbfTrRR5KpbZC89clmY5Z0JBvQyvieuyc824/XuZwh115Mc4ACWoKTlt0gHKJVXvKKTbFqO4YU+4tB18f1L7NtubxU/ngxOeg9ceqHKFhCmH+gEhGRIL7BMLEEMdkDe06wR3g5HrsDdDuMOUG2sQXp9waVWXC55Sq7Sin2BSfukvLSa9whbCrX+pYUq+N9+gSwsk4RTYhzK0gERqRCBmg2dBMqutJIlUjnAk3O2x7mpi1GpEgNBoRw62Ay2VzrdPFb0BUgls5gnuCCxXE+nogHSOgRLFNGNuc2pAfCp/4th0vJwai92kTwjpB8YisrrAtIuG6kIZUkijQSXYNUOqwiBNfVMJXOaLTEFlFCWJbbIIaXKe25QpjIC1is/6YcI/D9r70z0h9Fvpno39mnMoRQFoIu1xhzsA5+3cmm7ArtKqE0zBhlkv0mSfM7LBz7IKGCGKhlRExPJzg3O1ndYfjdvQJLkQQU4PqOAPMXKJY39bl3LpEsoltO5f7bDs+6vj17fV9mm0oIRwSj4hxjea2ucKhNHAaZoVLROjCRH9sXbY8Fg9xh819mILYhksQm4PqALdLbIriEGFsE8ih+PoklxnHaopgPR8cIoR1qJuWeH+GEKaywq5yaiHo30FOJQlFQ5zjhGESaJ5sg/OkkGxf5+oSglAkkhluBm3ldLhP0V4Gqirb1QlU+r3dkblhTxaMzA4bWS8zP8zJhFW3ZXtVjljPA6scce1tRkO54G0XvsFKKRaUep5YMVihc8NAOjus9heKK2ZBORvmlMohIhjIJ4RdsRa6RB7hClsvipaLqIuCqkq0lzpQjQbodW1lVLf97bhywz70bVU8ksoOD2WD0/tKtxnKG9syxADiHLGe/1WCcGDbNkAtMwwgkSeubZ9cr/an0PPE5roisPVHCVabEwwYNwFE+TRXLeYs8QjfPaKCE5Hg5oULc4P160JqJ9r1xNXOR2cH0F/7uyt1tCMaqJLZYc5YE+paAf+lzElbqb1Qd72l6z8LDUfE8HAhIW71wRCWgRGK0BPctpOXl0BBDCAeWAcgNbgOoEUxQAtj1U6RJSvMgSOAqXZZhLC+LlgIOyIUXleYE5HQt9MjD3Uuq2YOpLORHDSnC92h31WbrvZB9FXbtq1PD7Ab1Q5srdLrzMF0oYIYgHVgHYDU4DrAL4r1Nqo/ClMk++CIaJ8AptpQIrjWzi6EE066Rwi78t4uVzg0ImFrY1aSaBr69cA26Fq/flDbbYMcWB1qnniqlQhCM5FvZ5MpaqCRNQsWL3PkTbPEJQBnZMKVIwbSWWJOBIFyYc2frFB9+Y6BamebljkRCdGiJEUKYff/JyWCmQPnssLMCXMdGv2xsu1xNCcqMbTMnh12Dabj5oeBdGQCsMcmqCyx2paKT1DRAypOoR9LyA+FbR+uY1KYkQjdDdZjEUUKYXeFifT788VosqB/v9tLjuotXPS/K/13W4Y/JCrhyQ4PrbOcezzxOkFoVVrkFlYAkHzcZXv0Zbnbj7HFI7ZBucND6wLjEoDVIQaQKL0GwOkS194m7RSr9kDalVWu8dBHWIxDTLm/tn1QGWZbe1csAgjMCGvYL0KEK2wZOEcus148tQuv7eLcIHxxCcodTq6v/SlQTrAtLsFxiIF02TUA1tgEMCT8zOhErV3aCdbFp+pPQQniIiHrKVPZXme5tbQbrPdTpBCm8+DJbQCQ1UlsEQlbXrjps5tRTwk1uO5wfL0ALwrhdYgFoUURMTxcsUUlqPUqKlGPExxACmIAidqSHFEMJOMTgFsYA27RCqTFcuIj8myrsAnsLCIY8OSDAacQzhKP8LrCajtuFYk6YotF2JbbhK4r4wsgjkpQ7WKha8QlQgWxvk89CmGLTQBwimKAL4x1TJEcimvGN/NYFC4BXNvGLYL1bVyubqgQplxhtY3uCpvrzN/rQV0yrFnMEy1aF2OMNbHlh13XC/TleyuSGRbqiYjhVkcfRBdytx9vn+MExxDEANIDJbYto1zi2ltKTtIBIDHIDki7xQAtjHUGiUF0XMGrcDnLVC6ZGqQXIoKB/EKYFY9wucK2gXPxMkteuM7YBLA+iI6iq20QfYPbRCPhBLuyw6agzSOIgfTEHABIlxjwi2IAXmGs+tTxidlQbI4zOWEGUwTr/VJuMOAXwj7M7akYhHKAbQPnqGW2vHA9xFapvQtRdZuq5DxJpPCZJ/EywzyhHp0oiLEm6etFtsGugtAIRAw3E/2kZfudgrrbpwbSBZzgsgpiIB2v0Jf5XGIg7RQDSLnFtY/FXUGi6DqWIbPQpWae60y+5sYi9GVZhDAZj1BQFSRU/+b0ywAtjHX0gUJNGDTUXmpDNVLuIeUEpwfS0VGI5DrXNZ/aziaIAffAOmDIJQbAEsWAWxirPl2YYtkkJF7BEcC1PpN/D5wpqM1YhL6MEsJ54hE+V1hhi0j40IWx7ffkBhahG4Jt0PU2YvPE5Q4zBtPZrhVmW/TRFWIEoRUQMTxcoASy6wSXaEef4PS4BAVHEAOgc2GESwykRTEAMj6h2gJp8WkTxyZUuTUTbhUKjgAG7E6w2d7lBgO20mhhQjjlCicOlHCFFVREwpYXJih69jndITanZTZxOcHUsjxxCcAtiAF3jhhIu8QALYpr63nCWGeAqCCRNUts24d5TEP7sQtg8zh8brC+PIsQ5gyac7nCvkxxy+SFqXKcPvMkbrdtGWOsCauUGpC8XnSJ3BBaF/l2Nhqf6+tzBMjyaY4smOMEF2M5wQF+QQzAGZtI9AGwRDGQFsZqG4AWpkog62Qpt+arTewTwEC4CDaX10UIc1xhauCcr8qELzaR0TF21RoG/PWGVVTC5Q5zBtPlEcR6f2ZsAggTxQC8wlj1k3g/BdcYVtirTaT/PrgiGHC7wUAxQth0han9+1xhc9826pZL9T1JDDVPXGU5Mz5NBJIZYmzN+ZZLHcVU49D6EwSFiOHhgO9uP15WO9kVeYID3IIYgLs9kHIKAFoUA3ZhrG+j0DPCWSbY8OGKXZgCGChOBOvLOXniYCEc6gorfMI3Z57Y5/r6ag4rUepygknhbLjDRQliwO4S17ZJRieApCgGkpligBbGtXa0OKbw1RsOmaSDEr+Av9awTQQDbjdYX68L0axC2HSFE8dIuMJDx0X8/Xtu/OouvkLNE447XLQgFoQWhfzr/cMf/hDc0b777osyd6YqIUFiUIQi9G6fcofNNhlPcAAtiAHjMZgRm1DruaIYgFcYA3ZxTEENqlOEZIwp8Qu4BTCA9AUgixusbees+xzCNiFMusJGG1I0Nygj7BPAKjfMdYcVpjucRxAD6UF1ahnXJQbSTjEApzAG7OJ4aBtqUGm4W2zrX2EbpJdXBAONE8IcV9gXkVDUxRGmzv+kAHaYJxx32Ha9UO1DBDEwdN7P6QwLQj0hr2gHHnggSqUSooiZp2xrw5NPPonXvva1hR6cAPfdfog7nEMQA0Qu2HLXHyqK1ToqK2wKTZs4ViiRnPz4whxjW98K85hs+7A5weY6yg1O/O4YWJfcR6ArTKFcYaqNcn2pvDAljCmXuCAB7XKQXTWHTXe4KEEM0NM2c1xiIOkUq3VULphTTi1da7g+5cC44hdwC2AgTATX2kTEsm3bMDLCLlTfLlfYbKtD5YUpYVx4rphrnigMd5gca2JCXW98ghhIGShZkdJqQj2xXp0efPBB7LLLLt4OoijCzJkzCz2oEQElcl0nL+puP15nuMOcuATAFsQA/Hf9SLrEahvAIYqN7dV6WxyCEqJVLSvsE7KhUPtTeAUwkE8Ea9v7HOSs8YjMrrCiTmXWKDeYWqZyw3pVCUUscB3uMAeOIAbSZddqy9IuMRAmigG/MAb8tYZt9Pe15yq9ZhuMR7nPXBEMuLPB+vKEeHYIYa4rTGG6wlkiEkVCPklUhJonJqHmCeAUxIB2zt8q1SSE1oX8C541axb23ntv7LTTTqxO3vGOd2D06NFFHtfIxlVKRwnmkBOcwjWYLlAQA/aBcmYUwoxb6Ot0Ua2v19u4ssIuwVoUNneZdDocAhgIE8GpNo7yabmFsMsVNnFc+EMrSfhiECHthxzd4tzhWr92QQz4YxNAMivsE8VD2yeP3XSMAbs4TrRxlFLjCGFO9Qlb9MIlgKn1PjdYX160EM7iCrsiEqQjXKQbWW/zJKcgBownivWY8l0QCoK8qt17771BnfziF78o5GBGJK4TmlrHqTOZ9wQHOAUxED5QjopOAEmnWK033WLVv45NHJu4csIU3CiF9TGfaxY49doiglOvHW5wYnmIEM6K2t4VkVDUwSV2VZRQUQnXBBw2d7hIQQxkc4kBWhTr2ytMx1i1AewitBJQdzgUV+aY+hOhJsWwxSHM9pQI1rfXawjXQwhzXGFFXUqq+aoPAdnME8v1IqYAQQwkDZSSp761IDQTGfHWIjgffSnU3X7gCS63IAacd/2Ae6Ac5RSr7QHCDTbFpUUcJ7bRyDv5hjfbRk1vTGxnzgjHEcHmclYsAnAL4VBXOEREcx4P560ywXCPzYF0pvCtlyAGkrEJvZ3pEgN+Uay2r7Wt/esTxgr9zyDLILlQXH8mPgEM5BPBgFsIdxB9hUQjXJiuMCci0ZAyXiHmiQVndtghiAGwDJQ8tKG90BuOptaDFloO719xFEX46U9/invvvRfr1q3D4GDyJHvLLbfU7eBGJAWc0JxkEcQA3yUGvKJY3z5+TQhj1c4mPn0iuRBs+7bsl54S2RKHMPrnZIobJoRtrrC68BfsBHMEr2oTGq3gkkUQA/bYBMATxWqd/tUx3WKzLx2XQDYZYGrkkD8p13TIIQK41j6bCNbbcIQwtc88rrCJKypha+tulOF6ULR5AlgFMQCvgVJiDsgXhGbgvaJ94hOfwLe//W3Mnj0bkydPRqkk84sHk0c4mOI47wkOcAtiwHvXD8DvEgOkKFbrXcJY9af2Y5LqvwG4BLdPAJPbZxTBteXE4LYsQrjeZBhU5Ks1TLU1B9JldYcBtyAGkKoyUVvmdompNnp/QHoQnSmM9b4UlHNs0m+I3zz3ja79uPom/jzYItjslxuL0PukhHDWeITNFVYuY90rFGw777OeJIaQVxADXgMFW4o7XEEoGu/V6oc//CFuueUW/PM//3MjjkcogjyCGAi66wfs5dTUOt8gOWrQHCUwXQI50Y5rf2lw3WXquGzbZ60ukdrW5QYDYUJYJ68rbLyOB88V4Bor99c3E10oeQRxbd1QrALgucSqT9WeEsW1daWUEDQdY8Aujof6GfrdJ2Cz4PtTsfyJOAUwvV7r0+MG67/r/WYRwo2iEOFsmiVZzRMdlyAGWE8UAaSvF+XhbaQtXLgQCxcuxKpVqwAA++23Hz73uc9h7ty5AID58+fjxhtvTGxz6KGHYtmyZfHrb3/72/jRj36E5cuXY9OmTVi/fn2qUMH69etx7rnn4rbbbgMAnHDCCbj66qvZBQ2EbHivWt3d3VI/uF7YHn3lPcHphApiwHnXDxAnOYsoBozqEZ5BcrZZilwCWd+XIm9swrYfzj5CBDC1L68bDKRjEfoywC2Ei8gJNwEzIhH/G+gO64QKYgDO2ARAu8SAWxSrtqYwBui8MPX11P8E6pkaMnH9qdC5Yb4ABsJEsN6/+v8C6IywKYR18rrC5r8qLxwvb3ZW1fc0UYMsz8l4oggg7RLnpK3UVnCd4bBj2m233XDZZZdh7733BgDceOONOPHEE/Hoo49iv/32AwAcc8wxuP766+NtOjuTg45fffVVHHPMMTjmmGNw4YUXkvs57bTT8Pzzz2Px4sUAgA9/+MOYN28efv7znwcdrxCG96p/ySWX4POf/zy+973vSfm0vJjiVlHEo68sd/yJ7SyPwYBcohhgVI8AyKLssQj2DJzjiNesOOMRtnU5q0t4RTBAC2E9/hAihHV8rvAwwheX0NtwBDEAa2yi9rtfFAPJTLHZVrU3RSPlGqu2gFuUmvjGMmX5c7I50JTbyhpcl1EEA9mEcBE54ZbBZ55QOKpLsAQxwDdQhinHH3984vWll16KhQsXYtmyZbEY7urqwpQpU6x9nHfeeQCA++67j1z/pz/9CYsXL8ayZctw6KGHAgC+853voKenB0888QRmzJiR/40IJN7T3nvf+17893//NyZNmoTp06ejoyM5Knb58uV1OziBgHOCc93x+wQxYD/J6W1hufN3VI8ALMIYYIljc5u6DpwzcO6LIX4Bf3WJzCIYCBfCOrZ4BIUlItEMfO6wTqggBpL5YCWOKJdYb1v73S6KAbdbDNiqR6RFmU0g65iZYaBYsWtiE4+hAtjcxvGn4nSDbW05Qji5P7cr3FBM8yRk0LXraSJXEAPuJ4oAKYpLDRzjEcLGjRsTr7u6utDV5a6XXq1W8ZOf/ASbN29GT09PvPy+++7DpEmTsNNOO2HWrFm49NJLMWnSJPaxPPDAA+ju7o6FMAC85S1vQXd3N+6///5CxXC1WsXtt9+O6667Lo5kjGS8p8X58+fjkUcewQc+8AEZQNdIbFEJV1tyMg23IAbgvusHWC4xYHeKa+v8wlhvRw6Oy1A9wpUfziSmc1aXcAng2nqLCAbYsQiAIYRtOWGdAFe4yLywwswNh1STMOMSOhxBrC/TM8GUS6y3rf1ea2sTxYDfLaa20bdV+6TQ3299MsNut9TuEqeXuQQwtU2IG6y319tyhbAtHkFhi0gUSkhFCZs7rBMQrwPAM1AA+/UiJ/WajnnatGmJ5RdffDEuueQScpuVK1eip6cHW7duxdixY3Hrrbdi3333BQDMnTsX733ve7HHHnvg6aefxn/8x3/giCOOwCOPPOIV14q1a9eS4nnSpElYu3ZtwLuz88QTT+B73/sevv/97+Oll17C7NmzC+l3uOO9ct1xxx345S9/ibe97W2NOJ4RjzMqkfUE5xDEtX1a7voBtigGYH8kxhwkR4ljvb23esRAWiAFCd4A58KaGWZUlqjtq0ARbCwnq0a4hLCOa4IN0xV2kSNWEVRRwuIO61Az02URxADfJa5tO7SNLxZhCmN9e307wC42fSK5aDJXl/AIYHPbtCDmi2CzPRV/4AhhnRBX2MwLZ4Iz8YbCJZipp4mueJ0uiAGegaL3CaSfKrYgzz33HMaNGxe/dgnXGTNmYMWKFdiwYQNuvvlmnH766Vi6dCn23XdfnHLKKXG7mTNn4pBDDsEee+yBO+64A+9617vYx0MZjlEU5TIiN2/ejP/5n//Bddddh/vvvx977rknPvaxj2H+/Pmpm4GRiveKNW3atMQXRagTrhNe3hMckBTEAO+uX9/OXG6783e5xUBKGNfa2MUxEFA9osDHcBwR7coqBwtgoDgRbLbxCWFXPMIlajkucIBTnLfWsCsukUUQAyBjE7Xfs4li6rVLGCso51jBKbFWT1x/KtR7AXixiSwi2NzOFYsA+ELYNcFGiCscFKtgOMGpqISOKz7hi9cBxRsoA62Zvx43bhxb43R2dsYD6A455BA8/PDDuOqqq3Dttdem2u66667YY4898Je//IV9LFOmTMGLL76YWv7SSy9h8uTJ7H4Uy5Ytw3XXXYf/+Z//QbVaxbvf/W5ceumlmDVrVnBf2zveK9VXv/pVfPrTn8a3vvUtTJ8+vQGHNMLgTKVJLdO3CxXEAO+uH7Cf5NQ6XRSr/ap1gFsYA6Q4rrVN1x5OrNdGAdU7O+wboGcfSEfEJkIEsLneHPAWIoQtApoUwlQ8wuEKxxGJOuKcmpkQxlkEMYBE2TV9vb597fdkdAKwi2IgKaRrr3mxCDNnrGNzkF3UY9INhU34AjzxSy0zndlQEWy2yyOEdTh1hV2zzuV+3B9qnlBPE6l4XVEGCpAWxdshURShr49+kvvyyy/jueeew6677srur6enB729vXjooYfw5je/GQDw4IMPore3F4cddljw8b31rW/FTjvthC9/+ct4//vfj7Fjxwb3MVLwiuEPfOADePXVV7HXXnthhx12SA2g+8c//lG3gxupkFEJ1wlOxyaIAftjMMB9ktO3BZJOsVqnizjCLQYIYQxYnwU3s4KEDfcgOktsgnKrqceF9RLB5nJKCCf6dsQjsixTOBxmlyMcuo6KWHAFMZCsQwyA7RKrflQ7XbRxhHHt2JLvz5cZdolPxVbjo8t638jZlyIkM0wtp6IJ9RDBrjYKXQi74hFON7jI/HBW80QniyAGwgwUte22dii3545J1CszzOWiiy7C3LlzMW3aNGzatAmLFi3Cfffdh8WLF+OVV17BJZdcgne/+93YddddsWrVKlx00UWYOHEi/uVf/iXuY+3atVi7di2eeuopALUM8o477ojdd98dEyZMwD777INjjjkGZ555Zuw2f/jDH8Zxxx2XafDc0UcfjSVLluDSSy/Fc889hw996EPYa6+9gvsZCXgVxde//vUGHMYIhHvi8i1z3fED9EnOfAwGuE9yalsgub3qw+YWA/QACiNKoXCW32lCBQkSz/6tI6ZDBTDAF8Fm21AhTOWEKfHKdYULKMNGi9rkQDqAjk1Q+eEsglhvq9oAYaIYQMItVn0qTGGs90N91WwCWUevIBEiYrlw4hih940+AQzwRbDZNlQIUwPmXPEI3zIXRYg7r3niepqorw8RxGo5wBfFMMa3DDNefPFFzJs3D2vWrEF3dzf2339/LF68GEcddRS2bNmClStX4vvf/z42bNiAXXfdFbNnz8aPf/xj7LjjjnEf3/rWt/D5z38+fv2Od7wDAHD99ddj/vz5AICbbroJ5557LubMmQOgNunGN7/5zUzH/Itf/ALPP/88brzxRnzve9/DggUL8Pa3vx0f+tCHcPLJJ0u5XI1SFMmE4fVi48aN6O7uRm/vzzFuzDbRoN/VK8FKLQOGTnC6GPZtrw+o07fTH1MlftfaGAXX9XWxKKb6MPuxtQGG4hS+dmq/xKA4L6Ez0GUQ2M4yQTYHxBS/VFuHAAYKEMH6ayoaAdDxCH25tiwhhqkohWOZ7uqq3xPLkF6mRyXIbdQyTUjrA+r0kmt61QX9d31yDiWKXW3MdbXXiZep9sCQOHb1Y+sva5si4fzZ2N1g+rITIoCpfooQwYBdCFPxCOp3PSJBTbZB5Yvj36nzvecakBDDIdsXdb0wzv/U9WLj5j6M/+dr0NvbGzQOKXEdHTeGvZ2/383o7j4++HiGM/feey++973v4eabb0ZHRwdOPvlk/L//9/8S5eFGKqQY3rhxY9CXY9OmTYm7H6EGWwzryykxbGurLw89wZmvmaK4dlwFCWOAFse+bQgyiWYLQfUwXY/+OOIXSAtgoh1bBJvrfG4wECaEteWkENbberanRKz+uy5o1TJKDHO2zyOIAbsoptoWJYxt/aXXO1fXHZ8odlW3MMUv4BfAVJ+uuEPRQhjwiFnQVSRyiWF9uc88YbQtTBADbFG8cXMfxs+5WsRwC9Db24sf/ehH+O53v4tHH30Ug4NNPom0AOTzzPHjx2PNmjXsYtGvec1rsGLFCpm2uQi0iEPi8ZevvI7rERgw9BgMsD8KA9LRCXMdYI9QqL5NcWdGKfRjoAQjtxSPdoKuS0F3bsaNeg++PvIKYIAvgs3XBQjhRuCKSgAg4xK27X2RCfU7kM4JA+nohGoHpGMRtmoStWVpsWdmjBVUrEKH6r+Z+Eq6Ue8RSH8etb78/adFc7gINl+HCGEdSgjbyB2NCKk3TMUlzOXciB1Ajx2honbb1qfOYXmoVvgl5rj9bedceuml6O/vj6MZixcvxsKFCzFt2jR88YtfxEc/+lGsWLGiuQfZIpBXtiiK8N3vfpc98nBgwOHuCUlCTmScPswTHDCUIQbsJzkgPbgOoEUx4D3RWYWx3qe2rVUkmpljikbXrfQdj8J1PAzxCxACmNrWJYLNfs19FCSEva5wwehi11VZwmzrEsQA4ok5ACREMSV0dXGlD7JT2+qCS88VJ/tOHisljuP9WcSjTyS78LnMijy1im3HraDeb22f/uPwCWDALoLN9q6KERwh7BO1Nle4UDjmSRZBDNgNFPXaJYoBq4kiNI7//u//xpe+9CUAteoW7373u3HqqafigQcewMc//nHcdNNNOPDAA5t7kC0CeeXafffd8Z3vfIfdyZQpU1JVJgQGjBNW8AnOXGc7yQH2O3+Af6Lb1sY80SUGUsTtLOJY349LULrc5EbAFd/U+3Nsn0kAU22yiGCgaY6wz9nN04cpiAHEg+oAWF1iQA1qc7u/eik2c1u9vdrGFG0ucWxur+MSyRRm/CKryA3ZJ2A//qHjsC1Pb+eaHlmhC2BqG5sIBtwD5ThCOMQVZhEyyYZBbkEM+A0U87U5oNpmorRvn+XVWplVq1Zhv/32A1CbQO2f/umf4mjE0Ucf3eSjay3IK9yqVasafBiCC/YJDij2JAf4T3RUG6QFHimObftJtWEI5UbiOta4jf1YWeIXCBfA1GtTxGYRwgYsVziHeLY5uzZ32CWIAQS5xEA2UQzQbrG+jYISx0P7oz8Tn0g2+1eEilgOnGNQZM0TZxHA1HbcSARgd4OBcCHsywq7fk/umCFiXeKZK4gBvoEChJsoAADLzKpC3Rg9ejS2bt0KALj77rvjChU777wzXnnllWYeWsvRuACg4MdxgmMJYmodkO8kl1gWKIxVO9AC0CqQE9sTGeRWgSHOrY8Gbe+JI4CpfecRwWZ7x7pME2zUyWHm5oddsQkAQaJYvTadX8AtjNV2AC2O9f1R23BywSGCuQiCZjp3HJM9NuGOQLi2d4lgIJsbbL4u3BHOg+1pokkeA0VvG2qiAPS5LYTBav6Iodnfds7b3/52XHjhhTjuuOPw05/+FL/61a8AAE899ZRMw2zQoipjOyfLXb1rnUsQm+ttJzkgmREDsgtjhZkz1tvDnSEjM8hcQmc7yuk4e7NwLjFvu0AwM8aFimDP+pQQbkBWGLC7w0B2QQwgkyimXvuEMRAmjvW+OLGGZg+k40Yv/NEJnvi19RUigONlTDfYfM2dZKNuWWHAOf7EOfg61EABijFRytv/gLVW48orr8Spp56KT3/60zj77LPjEmpbtmzBRRdd1OSjay1EDLcajhNV6o7fdRKj7vr19eZJjtomizAG3E6ATSAb24YOtkiUe6u3uLXhc7BdzkhIzpha5hPBQP2EsO9YMhAy85xLEANwxiaAcFEM0G4xYHd9OeLY15eJy1FuBlxH2llqLUD82vryiWByAg2mG0y9tsUjUvsoYva0APMkJYiB4q4XoSaK0BT22GMP3H///anlxx57bBOOprURMdwKBAyYcApigBbTgP8kB9hPdADvZMcZIGcThLaYhYuKfuKv04k3NKLBeRQYOMDOupwSnT4nmNrOI5K9QrggV9gpgI0ya1xBzHrNFMUATxirZQBfHAPpwXg2uGK5WXDFuU34AmHi17Y8VAQDxQphlytc2LTCnqeCma4X5nogu4kCFCeOpbSaUEdEDLciWU5wgF3wutro7bIIY8B9svMNkMuSJ8sinPMSmnfzHZuzDFuA+AV4LjC1PaNNsBAuUChTojVEEAMOV9h4rfoH0qIYCBPG5jJ9ubNKhEMcAnyx3Cr43g+QLTrhWu6LQgC0SA0VwbV98YVwLvEbEoMj1rMEMWB3iak2vmuFvn0846U4xULrYr1SHXnkkfj4xz+Od73rXeT6v//973jzm9+Mv/3tb3U7uBFFhhMcgLCTnGoD+NtlOdnpcNyAkMFx+sxGeQdi5CFUgPvcENf6EPEL8ARwQLugaESDCBHE1HqbKE4sM0QxkE0YKyjnWF8HuEWhy03mos+gF0KefSo4DrZP4IeIX4DnAgM80etyg23bNJy8BorqA0iLYr2dz0QB7NcLQWhhrN/We++9F0uXLsVnP/vZePYSnWq1imeeeaauB7ddw5l8wxd5QMaTnN5Ob2teQLKc7FwCOd4+4LEZp/RaMwh95Odr77pw2MQvYHdeixTB3P7qkBX2zSxHtaEEMQCnKKaW6eIpRBjH6zwC2bbObJM1DpEosVaAqM1zLADP0Xa1sQlfgBa/AE8AhyzjCOHQ6EUmqGhdVgMFyH+9sBkpgIhjYVjg/HYuXLgQn/rUp/CHP/wBP/jBD9gz0gkZ4ZzgiHbOkxzgP3lRbc32+jaUOOOc8DhCOe5vmAzCCD0+zgWhKPEb2N5aNi2rEGZEJDiTbWQVxAByiWJ9uSlwqCiFIqtAVnCEsm9boL6Z4jxRDc62LuELhIlfwC5As4pg67YZ4hEsccy4DpBYDBQgQBQD2Y0UYOh8Vs5ZymywUnBpNckMC0M4r1Ynnngi3va2t+Gkk05CT08Pfvazn+G1r31to45t+yKj0HW2A/wnOUtb68nLFoWgttG3cwm4EGcgRDC3GiHOh+vzUrjEpGtfgYI5lwiuA6T4ZQpiALlEMXu5RRwD4QI5bsMQyhS6AG5GnjjrPn2CF7CLXkWWQWohy7OK4KZBuboAeV1hmyh6v2bftutFA84TglAk3m/sPvvsg4ceeginnnoq3vSmN+HHP/4x3vnOdzbi2EYuNkEMBIligHmi0/s39+GKQpjb6bgem1FsT4/SOO9Xwblo+D6TjKI5SAS79tMg0cwRxLZ2LlEM2AWwvo4SQTbnOF7vcJAVHKFMbpdRPNeL0OOPt2N+V7JWZ8iyzjaJRogQ5rrObJimSGhbr4libuNygV3XCxHIQgvD+nZ2d3fjjjvuwIUXXoh//ud/xuWXX47TTjut3sc2MrBlh22PwFztAeuJDnAIY3M7lwNMbUv1wznxhQrmUPTyPzbqsV9F6MmfcyPg69PTh3MGuSJEcEHY4hM2QQyAdIkB2ikG7MLY3CZkXWq9Q8BxhLJJVfubySo+G0HIe1Jw3VWfoMwqjF2zyOUVwb59swm5XvgMFIBnopjbGNuxrxfbg9EhbLdYv52lUin1+rLLLsMb3/hGnHHGGbjnnnvqfnAjhiyCGHCLYsApjAGPODa3d13cuELZ7LdIp4D6LLIK3Xo4GKEXAu4x5BG/vu0bFNMIEb7O5YQoVu0BWN1iICmM9W0UQQLYIXq4Qjm1XQbh3ApkjQ9whWMeYQyEC+B4XUFC2LqPgNrz3m0yXC+c1wpzO2J78hyQ97srdYaFOmL9dkYR/ejtlFNOwYwZM3DSSSfV65hGJoGOb7yNIlAYAxlPeFRfoS5wEQ5BlmPIQj3cjHo4xmAIX05fOZ3nov8fXIIYSAtcwC+Kqe1MUeQTx2YfPtHlilr4tgFaKJOagyzOaMg2eYQvp496xDQy4apEVKfrBXVuyXy9EIQWxHrluvfeezFhwgRy3YEHHohHHnkEd9xxR90ObESS9SSntlX4TnQKxgkPCDjpOfrOLZJcj/oaSdGiO8P7YInekP6LyC1z+7FtGjD9srkOcItiwC2Mqe1twskVrXD1xxVEIQJ7OJP1vYVsl1f8Av6bkHpFNrz4SnO24vVimD3VEEYW1m/nrFmznBvuvPPO+OAHP1j4AW3XcB59cU9yAO9EB/gFtgnzpGfijV2Ewo1pNJOCRXmQ0M16HAVFMIL7y4FL9LLWO4Sxvr0O1ZdLYHGFsms/27MAtpH1PXPEbpb9cFz4vPEMFr7rhSv+oPehaPL1otSer7RaVO1HVC3u7yOqMsaUCCOGFlUY2zFcQazgnugA/snO13cW5xc5hNw2MjnQLUbezyBFns8gi0ith6hm9sutOQz4RbHC5xjH7ZgC2dVviDjLKpzz4vt8Fc0U5aEiV5EphhEQPykqxxzSF2+HDFEMtNz1QhBaieGnNkYa3BMdwHq0Rfbtg/sYLYQ6iOmWpRVyxyZZj6nOApsjiFU7wC/uOOIY8IsiX8zCuW1O4RyKmXfWKUKE1fPYKfIec5a8deg+i8w2JwgZSMc1UfS+TfJeL1yCeRiaG8LIQb6dzSDLSGFf+RrXvnyEONVZqIeYHu7UO1pQ1OfbBNHNFbp6W0WoOGZvxxRUoQ5zXhottG002kkuakBhI7LLubcr4nrBnW3N9/ea51rRqlE3QYCI4ebhG+DAgVvujHssWeAcv5wEw2nkzUKLVuIIEcXmNjpZRLULV3/1rPrQaKFdBM2oglH0Z5LbmS4qPwxkv174/saLEsv12haoHWORcQuZjlnQEJXSbDgDHLKQVeCEniBGusPbKrTSDUfB3wlXSbTQ7V00O09blDs9XGi2kK/n/uvWdxEmCkWjrheC0KK00BVUYM3sVm9aSVQJrUUL3PiEDmorqu+s5HW1hdb+XJp2bHnjDEURcr1oa93/R0Fo/tVN4NECQkQQWpmihEleUa3TykJupDIi/k9a8XrRisckCNuQb6fQ0hQpTIpmRFxURyDy/yoILchgpdhYhkQ8BA0Rw9sZrSwetze2t896RIpAqX8q6Iwg97LR56/t7XwpbF+MnL/8YYyctAQfRQjZYZNvHY4CVlyocJoxfqHI71aDhXUzztsh+5TritDKiBhuMYo+YdTzBCQnt/pSVLmvIvr37a8u4rhRorcVhepwFPw+QsVho/5f6iW6zf/DOojjIs/B9T6fD0aDde1fEPIgYrhFyHMiynsSq8dJkKqJKqRxlcvK+v9SRCmxUHGr95VbGBclBOshplpFpLaigAfcwrKRn12I8OR+lnlFs/7+cwjj7e1awaZacJ3hVvlbFlqCtkbtaMGCBXjTm96EHXfcEZMmTcJJJ52EJ554ItEmiiJccsklmDp1KkaPHo3DDz8cf/zjHxNt+vr6cM4552DixIkYM2YMTjjhBDz//POJNuvXr8e8efPQ3d2N7u5uzJs3Dxs2bEi0efbZZ3H88cdjzJgxmDhxIs4991z09/cn2qxcuRKzZs3C6NGj8ZrXvAZf+MIXEEVRcR8KaieX0EdN5k+WbbjbD6Ka6SfLcWzvP0V+vq6bjTz/37Y+QsiyTUzWC5QaXKP/hO6X85P1WIr+KeK9ZPlphffO+b8t8j253ltWMnzP8/wt5vnbL/JaIQitSsOc4aVLl+LjH/843vSmN6FSqeCzn/0s5syZg8cffxxjxowBAHz5y1/G1772Ndxwww34p3/6J3zxi1/EUUcdhSeeeAI77rgjAOC8887Dz3/+cyxatAg777wzPvnJT+K4447DI488gvb2miN12mmn4fnnn8fixYsBAB/+8Icxb948/PznPwcAVKtVHHvssdhll13w29/+Fi+//DJOP/10RFGEq6++GgCwceNGHHXUUZg9ezYefvhhPPnkk5g/fz7GjBmDT37yk4V8JiEnp6L7jNtnOEEV6Q5Uo4HC+mo1zGlxi3Z6ff93NtfZdhwhNXw57u9gVA1ziUOFSSh5hHZethcXqtHvw+aghvyf+Bxd23viuLfmcYS4x9UK2yGuxzWgEdcKfT9NdZUFwUMpKtrqZPLSSy9h0qRJWLp0Kd7xjncgiiJMnToV5513Hj7zmc8AqLnAkydPxuWXX46PfOQj6O3txS677IIf/OAHOOWUUwAAq1evxrRp0/CLX/wCRx99NP70pz9h3333xbJly3DooYcCAJYtW4aenh78+c9/xowZM3DnnXfiuOOOw3PPPYepU6cCABYtWoT58+dj3bp1GDduHBYuXIgLL7wQL774Irq6ugAAl112Ga6++mo8//zzKJVK3ve4ceNGdHd3o7f35xg3bkxiHdfRLaINEHYiy3LSKkrUDucTZlG5WVNEF71v7kxmnD6LahPkunIIFW2NFNfbiKp9ubbfHim1d2XfOEv0IDT6wN0Ht19Gf4VeBzh91elasXHjq9h15/ejt7cX48aNC9iudh3d8OcvY9yOo9nbefvdtAU7vf7T7ONZuHAhFi5ciFWrVgEA9ttvP3zuc5/D3LlzU20/8pGP4Nvf/jauvPJKnHfeeYnld999N1avXo2xY8fisMMOw+WXX47Xv/71cZsnn3wSn/rUp/C73/0O/f39eMMb3oAvfvGLmD17du73LNhpWEzCpLe3FwAwYcIEAMDTTz+NtWvXYs6cOXGbrq4uzJo1C/fffz8A4JFHHsHAwECizdSpUzFz5sy4zQMPPIDu7u5YCAPAW97yFnR3dyfazJw5MxbCAHD00Uejr68PjzzySNxm1qxZsRBWbVavXh3/MWTF+7jJ80iK89gqT2zBpBoNeH84/YZGCZoda2jk8eb5vEP+L7nfDbNPX5u6ExoP4PTle9wd+Hg9qvYF/QTvb3v8yfEZ5v3/CvouUPtwERLnyAn3epLnfFDUtWI4s9tuu+Gyyy7D73//e/z+97/HEUccgRNPPDEV5fzf//1fPPjggwl9oTj44INx/fXX409/+hN++ctfIooizJkzB9Xq0Gd57LHHolKp4J577sEjjzyCAw88EMcddxzWrl1b9/c4kmnKALooinD++efjbW97G2bOnAkA8X/05MmTE20nT56MZ555Jm7T2dmJ8ePHp9qo7deuXYtJkyal9jlp0qREG3M/48ePR2dnZ6LN9OnTU/tR6/bcc8/UPvr6+tDXN3SS3rhxY6oN58SVZR3gv6P3bc89WQU/XitALLWKY2y6nFmPK7Qfsz31f0U5yma/lEurf2980QprZCOyRyJc6wDwhIWLvNtz+thGkJubR+i06gC5vJiOachnZLiorv8L0mGm9kU5s9zog+rP5e4OVnINvMt1PfCYJVm3BbJdK1rlHJ6V448/PvH60ksvxcKFC7Fs2TLst99+AIAXXngBZ599Nn75y1/i2GOPTfXx4Q9/OP59+vTp+OIXv4gDDjgAq1atwl577YW///3veOqpp/C9730P+++/P4DaE+lrrrkGf/zjHzFlypQ6vsORTVPE8Nlnn40//OEP+O1vf5taZ8YPoijyRhLMNlT7ItqoRInteBYsWIDPf/7zzmN1Yb1zz3hSc23HOZmxHqkFnOCKOhk2YyCGEol534MShZx+dAFpa6+34Qhknzj2CeNcojcLLlGYRwR7tmWL3qLjHXn20aroYjH0c9CFpOtzYArllEg2+/SJY0rY+kRxTkFMdplBJGe9VgD+60XR14pGYppWXV1diSfCFNVqFT/5yU+wefNm9PT0AAAGBwcxb948fOpTn4rFsYvNmzfj+uuvx5577olp06YBAHbeeWfss88++P73v4+DDjoIXV1duPbaazF58mQcfPDBGd+hwKHhYvicc87Bbbfdhl//+tfYbbfd4uXqjmft2rXYdddd4+Xr1q2LHdkpU6agv78f69evT7jD69atw2GHHRa3efHFF1P7femllxL9PPjgg4n169evx8DAQKKN+Vhi3bp1ANLuteLCCy/E+eefH7/euHFj/CX3ESqEXY+zbLhOaHlchpA2cduCBG21Tg5au3HxynO8uqgMcYA5Aphq4xLILnFsE8amKHa5xDZBnEkoZxGzGQWwV/xyKylwaOZAvkai/oZC3y9XPHOEskckO8WxSxiHiuIMgjj4mlCgCM4jfusmeisDQKXAm+1K7T2a1+iLL74Yl1xyCbnJypUr0dPTg61bt2Ls2LG49dZbse+++wIALr/8cpTLZZx77rnO3V5zzTX49Kc/jc2bN+P1r389lixZgs7OTgA1o23JkiU48cQTseOOO6KtrQ2TJ0/G4sWLsdNOO+V7v4KThonhKIpwzjnn4NZbb8V9992XihnsueeemDJlCpYsWYI3vvGNAID+/n4sXboUl19+OYBa3qajowNLlizBySefDABYs2YNHnvsMXz5y18GAPT09KC3txcPPfQQ3vzmNwMAHnzwQfT29saCuaenB5deeinWrFkTC++77roLXV1d8d1XT08PLrroIvT398df1LvuugtTp05NxScUvjvKkJNYUSLYdlLLejJjieIA4Vi0mK1mLOzeXkrG54s4LiWoOZ+Hz3kOEcAu91f/PtiEcYgorttMdK4cbxHt4RHALgFXZBY06/es0u9v0wzKnUO/Z634wHWAbf27+nKI4yBh7BPFIYI4oKqEjZDrRRYBnOWppbk+atFJN5577rnEADrXNXzGjBlYsWIFNmzYgJtvvhmnn346li5dii1btuCqq67C8uXLvU+y3//+9+Ooo47CmjVrcMUVV+Dkk0/G7373O4waNQpRFOFjH/sYJk2ahN/85jcYPXo0vvvd7+K4447Dww8/nDAKhWJpWDWJj33sY/jRj36En/3sZ5gxY0a8vLu7G6NH10aIXn755ViwYAGuv/56vO51r8OXvvQl3HfffYnSah/96Edx++2344YbbsCECRPwb//2b3j55ZcTpdXmzp2L1atX49prrwVQy+nsscceidJqBx54ICZPnoyvfOUr+Mc//oH58+fjpJNOikur9fb2YsaMGTjiiCNw0UUX4S9/+Qvmz5+Pz33uc+zSamY1Ca7ordeJLVMMI0fuDOCLyqwittUwRbWzLcMpclV+sAnR0OW26hVUe+p4yHbMZUHitoC2VgGcVfwW5RjnEbetEKPIKuZ08ezC97fi2r9rW8t21qoWtv1Q+6Da2o6FaJvnelGECC7KmVb0bnwFe03+ePZqEo9dinE7jmJv5+1301bsNPOzwcej8853vhN77bUX9tlnH5x//vloaxs6/1erVbS1tWHatGnWQff9/f0YP348vvvd7+LUU0/Fr371K8yZMwfr169PHNPrXvc6nHHGGbjgggsyHafgp2HO8MKFCwEAhx9+eGL59ddfj/nz5wMAPv3pT2PLli342Mc+hvXr1+PQQw/FXXfdFQthALjyyitRLpdx8sknY8uWLTjyyCNxww03xEIYAG666Sace+65cdWJE044Ad/85jfj9e3t7bjjjjvwsY99DG9961sxevRonHbaabjiiiviNt3d3ViyZAk+/vGP45BDDsH48eNx/vnnJ2IQ9SDPia0IAZzlUZtP8IYI3YFBf8m6VqSjrXZPyXmvSjDbPjddJJufuytyYcsjU8spt5hyiimXmOMQc5eRcMVtvURwVvHrE71cwZtV3FYanMksa/+XWaMQrs/E5zJzHWDXgDiL+6u+L1a3mHKKqcGBnHZMihbCea8XeY2S7YEoitDX14d58+bhne98Z2Ld0UcfjXnz5uFDH/oQqw8AePXVVwEgIarV68HB7cMwalWaVmd4JOBzhjknGM6JjXtSKyKO4RK+PiEYInSHiyhWIriIti5X2eYi25zjPG4t5RSb7TgOMWt/HDGaQwhbS3BR5HWjFT7RyxGOeYVtvYRxmXEzk3d7n8vscpMDnFdre6It6RRznV+zHaMN69rAEMJ5RHDR14tNG7fgn3b9RHZn+P8+X7wzfMDF7OO56KKLMHfuXEybNg2bNm3CokWLcNlll2Hx4sU46qijUu2nT5+O8847L64z/Le//Q0//vGPMWfOHOyyyy544YUXcPnll+M3v/kN/vSnP2HSpEn4+9//jte//vWYNWsWPve5z2H06NH4zne+g6uuugoPP/wwDjjggMLev5CkKdUkBBqfEOac2Ip+tGYTvy7h6xOyIUK30qKiuLxN2HLeS4enrctVtrnItjwylT12OcNUTphyik1Xl+MQFz5oztXGEJhsEVyEALaJX5/o5QjWIkRtJWeMoqxdJrIejxLBru1VG99gOOrzVgLZVvnBlv+lsr+E+0s6xVmd3wIH0yXaeK4XWUVwiAB2XS+GexTuxRdfxLx587BmzRp0d3dj//33twphilGjRuE3v/kNvv71r2P9+vWYPHkyVDTSmwAAiuhJREFU3vGOd+D++++Py8FOnDgRixcvxmc/+1kcccQRGBgYwH777Yef/exnIoTrjDjDdcTlDPtOOEWc2LI4CUCYAHaJQZ9QDBW6fYNNmyMmpquNf0IvM1xjm1tsW065x5RrnDXba742XWKfQ+zrz+sM+1zhLEI4jwim2oWKX5+AZInignPB/Zb+Ogv2R8qM/nxusW29ze2lnOM8mV6jHcslNvsJXB96Lg+9XhTlPAP09YK6VmzauAX7TP3XYesMC9s34gw3iDzlZkJPbFlOatwTGkCLXJfw9YneUJHbjAiFEqfcY+1qG3S+b5uzbHORKfeYco0px9h0i13urfm6Gg0EOcR1rTBBESqEs0YwKAEcKn69opgheG0itiiK6l+Jat97Kpftn4vNTbY5yJRz7HKMKbfY5hQbLjGZJTYd54JrC8ddFyyEQ4U2wL9e6Oex4RJ9E0YmIoZbANfJp94nNvOkxnV/bSc2lwD0CcnQk2UjIhQhcQjAL5qVs0wde7ktYonjrMLYJop9r83YBCcyEa/LI44DhWxCCGdwlFNtuAI4VPy6BCJXjBbtFBeFcoJ978MlllUfNhHMEcccYeyKRphtjPXWAXY2cojlkIFqrutF0deKWv/860Xu83W1WmwWvlpgX8KwR8RwE8g6CtclhIsWwVwBbDvB2cRg0dEJbr8h6BEF7vH4RLNLJFMC2eUchwrjEFHMzfzWY50zL1ykEA4VwVwBTC7LKHyZYjdqkQt6SVXz4TjBgDumYRPIHHHMEcY+UewSrS6XuAHucEgZzKxC2OcE5zVMBKEVETHcZLgnt6xCOK8I5gpgSuTliU7kOZEOBI7T6DAOPWTfSoz6IhFUnzaBTEUsKHHsE8YuUWyLNrgEsisy4XKHM+EafJZVCOcVwXkEcEbhyxa7jS6rptgmPjnHWWpvt79Xm0imHGTKOeYIY64oplxil2DWybouA1zjxHl9CDBNshgm+nmsVQdDCwIgYrilsMUjuELYdWLLK4LNE1mI+LWdBDmiM1TYZiHrPjra3O/BJZRdkQj9s+W4xmaUwiWKXS6xzRV2CWIOuXPEDsfYKoRD3OBQEcwRwDbxaxGDTjEZInQbFZ+wRRnItnbBbHWUXeKYausSxnlEsSs2oUUmrFUm6pgd1rFdL7hCOI8I9l0rgNo5rS/KOQC6UnBMolk3kEJLImJ4mMERwllPbEUJYOpk6K46YV3l7LNZDIlRexubULYJZJs4dgnjEFHMcYmzCGKbO1y3gXSaUM0khF1ucKgI5ghgQpxahW8RA+xC+stCmRmHAPyCudxuF8gccWy6xub+8ohiShA71pGD6jISeo4vUgi7TJMiDBNBaFVEDDeRPCe3ep7YfCc1jgC2iV+XgOQI3r5q806wXe32wW8Kl1CmBDIljm2RCFMYh4hin0vcCIeYjS0iUaQQdrnBeUUwVwBnGWDn2s5FPz3lLotO7f+Zu+8yIxJhyQCbn1VKHNuEsdkmiyi2CWK9HUcQ29zhJrjGQPj1Iqtp4jJMJEMstDIihhtM1hJreYRwESI4qwCmRKGz4gRT7DbyxBrHFzzH1tVOl1OzCWSbOOZEItT/DVcU+1xiPTYR6uoWkh22RSE4k3DY4AhhrggOFMBs8WsTjD7RmUfc2vZhq+cbsi8lnF3l0gIHyOmfpVcYU24xRxTbBDFAC9aihWzGPHE9jBPb9SJEBFNtqyKGhRZGxHCL4coKm+uLOrGFiGCfAA4Rvy5xyRW79cwUq4F1vmNxiWVKIFPimNoXJYyziuJQl9jmCIe4w4VGJUJd4TxCuEgRzBXANvHoE6KtkqFUwtJ2vDaRbItdWHLA6vNNZY2zimKfIAaGhK9NsHLc4QIhqz1kFMJZ3OBQEVwYlWqxmXjJDAsaIoZbgJCTG7WsKCHMFcFZBTAlFvNmifO0p9ArS/j6c4llm0C2iWOXMDajFFlEMcclziKIFXXJCPtc4axCOIsb7BDBXgHMFb82Icm4aEcDzbmwlzqY0yxT762zgyeOCWHsdYspUUxlivXf9TiEmSNWMOIPZH44p5PMmTHOtT5ECOcVwTbDRGISQisjYrhJcE9urmoRgF8IN1IEpxzmAPHrEp5ZRW4/Y7vOVFk1Xt+1iINrHS2Q9c+EyiBTwjhUFOsD7bIKYoVPBFPuMBWVKEosp2aYA4oRwo0Qweb6QPHLEryNKL8CxHdsrmNyCmVKIJvi2CeMOW6xLoqpgXZclzhr7pdyh+vkGAN846QeponrqeHAYOO+moKQBRHDDSBrTtjWh/q9nkKYK4J9ApibI3YtV3DEbSihfXbGrm16XYdlnS0C4RLGZpQiRBTbXGIzNuESxL7KEIW4w9RAuaKWFS2EbSK4CAFMiEWn8GWoiqhOyqNk+5KbdLSR74EUyFTEwieMHW5xQhTr8QmbS5xXECuKriPsMT/i3TridN7JNDxCuAgRLAjDARHDTcZ1cuO6x1mEcKNFMHVStJ0ofQJ1axOeCI9S12vLsXVanGLKQTadY0oY29ximyimHGCfS2wTxAquw1vXyhIaXleYWsYVwqEiONWPI3phCmCu+HUoiRCxG23Nl7MsjdJmXPPs1ymWCYFc6mhPfx6ma0wJY0c5tYQo5rjEHEGsoJxgYlk9ohKJrhzXBu4TxKKFsEsEVwYLmI65vwL0FxjF4k59LowIRAy3CCHTbALux131PLEl+gkUwNT10SYuOYK3Ea6DurbbjscmkikHmdIJLmFMucU2UexyiUMFsS8/rOA4wZnjEa68MNcprpcQLkgEpwSw5QvtEqBZha6tz5I5HSNzH0owU/1aBbIhjq2usfoczUF4NlGsxSfYLrGZIzYFMVV2jesO53SMXdeGrMYJJYR9Tw+zimBFfxPLYgqCDxHDDYQTl6AeeZnbux53cYVwvURwVgFsE5tcwVtkhELPEfsm1qCOe1Q7LY5dwpgaNJdVFJuC14xNhApihRK2LoEbtylqembXtMwuV9isIwzwhXCRIjiDALYJVZcoLSoWEdqPErq2YyuNKqf6JMWxTxi73GJTFHNdYio2ofAJYoVax3WHTQLcYtf0y3EbpnGiqJcQdl0vBKEVETHcorgeeSmou3xFHiHMOam5XGDzesoVv67rcIjYDR21rIQgd1++3LD53kxxTAlj16A5ShTrmWJdSFMusRmb8AliBZUftlHvqAQZkYh3vu0iT4ljta4RQpgrgo0vDiVAbeLSK1YbVVVim1j1Oczm+7CKY58wNsui6W6xSxS7XGKbQ0xFJhRUftikjgPkAJ6pEmqcKLgxulARPDBYyv8kr1oBKgXGJFw32sKIQ8RwE3CdqLjbcO7yuUI4y4mNau8TwaZIzJIZrkd5Hm6fSixSx+eKRejv2yWMKbfYJopdLrGZJfYJYv1zUHWIzfywwnSHC6kSETKxhil8OfGIPEI4hwj2ucCmKKQEsFX8ekRv/QfQOfbf0W6NS+jvkYpWJISxUbHCKor1+ETZELyGS0wKYmAoR2wTxC6B63CHrRQgmM2IRIhx4nqCSAlhnxsceq0QhFZDxHALoE5irvyXDfPkRgm7UCGcRwRnEcCUuPQJ1HpUlrDRqTmvJjaBbItF2ISxyy32iWLTJQ4VxKQrbMQlCos9uMjj1LjiEYoihHCoCA4UwKSItQhPruDNK4z1HLGrL6tQJlxknzB2ucUpUexyiY0scSo2YeaIKUGsMOMSWcRvA6Zg5hgniqxC2GeaiAgWhhsihlsY847fPLlR8QiFeXJzCeGsJzauCObEJmzi11tZok5PurQB9J4KErRA9sUigKHPyBTFgBl/SEchgNr/D+US23LElOg111FtGo7h+sYRCVccwsR0hclJLhhCOMANdjnBuhDMKoCtWeIMasOV8Q3tP45E2AbPEeLYJ4wTohhI3Bk6RbHpEvtiE2Re2BDElDBWmIPpjNes3HCDsBknVJWHUCHMMU0qTT6tCIILEcPbAbaTW1Yh7Dux2USwzwU2RSUlJK3VJRj6J68DoQ+kd+1P6QV7BYlkdQifMLaJ4qQzrP+edInNLLEtR6yLXqrKhCmIbe6wLSqhcsPmILq6zE4HpMWxKx6hUOIoqxAOdIPZIpgpfutRWSJrH5wKEqkIBJB8rw5hzBHFiYF2lEtMxSZsgljPECtMQewaTNcAbE8RucaJ64mbeb1wCeGspkkeomo1Xd4wZ3+CoBAx3GJkPbm5HncpQoRwqBtsc4J9ApgcXOe4Huc5sZrua2jfsaNrHB8ljn3C2MwYZxXFtthEqCBuBHUTxT5McVugEA4VwSnhaIjgVIyiiZUlbLgqSNgEcqmjzS2MjSgFSxRzXGIqNsERxJRj3ERCxpdw8BknRQhh8zrQyGibIITSOn/tIxTzTr9ozJNbViGcRwRnFcBFVZfIu623egQhjl3C2OUW+0SxGZ2wxSZCBXFWd7jhmAPnfK6wTQhrWIUwFYug3OCcIpgjgJteXaJj6P/aVUGCqh5hbkMKY4tb7BTFHJeYik2ECmKfO1zHqESW2UuzGichQph6eugzTZoxWZIgcBExPAwo6uQWIoRDTmxZRbB5fecOrvNtUwR6ZIJbPUJtp78vShjroljvnxLF+qQelEtsi01QgtjEJohDKLSqBEEqL1wUFZVHDhfCPjfYKoIdLnA9K0uw+jBITb7hqR5B9W0TxzZhTLnFbFFMucQBgjiFGZlwteUSUEEiRARzqklkIa8QdkXoBKHVEDHcJFyPvbKc3OothKkTWxYRnFUAc0VvZSB7gfdyx9BObPtT12vujHM2YWwTxeYAOtMlNtdT7q9NEJvusA2fO+yisHrDIeI31BUuWAiz3GCLE1xkdQlr+wxw+gmtHgEgIWj1Nk63mBDF+vYl4w8hdoldOWJDEFsH1KVee9xhDjkrSoRUHSrKOOEIYZ9pUsn71axU6XEAefoThG2IGG4h8pzcQskihH1uMFcE+wbWUW0Sx55D8Lpw9auEMuUEA7ZYRLKN+gxcotjnElOxiTyC2BeXoOBEJbKuC6oUwWlbDyGc0Q22iWCvAM5QWm2wku284KOtPPSdsM8sl796RCxyVX+awKZcYmtsQnd09RwxRxBzyq2ZNLCEGgWnnFozhHCdY+yCkBsRww0m9FFWI05uWU9sNjc4qwimTpgugVqp1EcU65TLg+RxuMSxb1KNLKLY5hIXKYgpQibiqDsu8csppRZCXiFcBxFsE78+4Vut5hPG7e1D3w1qX0ogs8RxQPWIZBQi6RLrTnNCEAOJ2ERKEJvYBLELzkQcigJno3NdO1zrQoyTkDbm9SLk6aEgtBoihlsAavBc3pNbPYSwzw3miOCsAtgnfPv7ihNjnV1Dnz2133J50CuOTceYLKnmEMVmdEIXu7pLbBPEWeBkh11RCUoUFzJZhysy4ZxkI6MrnEEIU25wJhHMEMA28ZtF9Fb6Syh3unPitn6VSLYJ5KKqR6REsSU2YcsRkxlin8sb4g5niUoAhQhl6jrhqkPPGTRXW26091wvuKaJILQqIoZbDOeUmgEnN50ihTDlBhchgk1xaRO/IaK3wnhUXC6nhYBtH0ok68dGOcfljsGU8HVNqrG1kizPRrnErtgEJYiLcIddE3E0Yka6ePAcBeUSM1zhvEKYErVcNzhrdQlKbLqEb6U/TBSHtlfimToGSiCTwjijKLa5xK4csVcQc/PDiQ/NIaYJkZurogQxvoS6TlDtGmGccITwwGApc6Rv6ECrQ3+vRSCZYUFDxHAT4UYmqIgELXrpeEQoeYUwVwRzBLBL/HLErg9fH7pYNo+ls6vqFMaUW2zNBBsusV6OTRfEqr8iBbGOyx2mohKUG5xpEB13wBzVjlrmqCmcblsfIZxnYB2QFsGU+PQJ2aKyw3pWmNonJZBNYWzGKWyi2IxA6MupLLE3RxwiiClctYddk3A0KDdMusOO2UmLNE64QlgQWh0Rwy1C6J2+DjXBRlF3+a4TW9yW4QaHiGBKAPtEa5GD6vSqEtR+lUDWj5MSxllFsS02UaQg1uG4wzpUVKLuuWHSCSYiEllcYaotEY2I11mEcJAbbBHBWQVwiOgd9MQp2trTT0ts/SuBqx+TTRjb3GJf5QggKXBtsQmzHBxbEOtw3GHONM02ckYjfPXoXcYJNe2yyzgpUgj3D9KROEFoFUQMN4gsdSN19Dt9V3F01wmvnkI4VARnFcAhoneA0bajI32GpvZhE8imMDajFGa+WI9QKFGcEriES6wLYiA9sI4jiBOfDcMdTrbPF5UIEss+l9gVkdChXGFmPMLEmhEOFcIZRXCIAPaJXR8hYtk8hrZy5BTGlFvMFcXO2IQvMuGCU3+Y4w7X3ljaDS5wEJ2O75riMk4GiGuHaZzQfdb+DRlPUlhmWEqrCXVExHATCJlaU7/Tz+oKu9aFCmFuLCKrCDYFsEv8csSuD18fSizbBsyZwtjmFusRCt0pplxiM0tsCuLE8VsEMYVrQg7KHeZGJWwU4hb7hLErIqERu8KJdmHxCJ8QdsYotPbmcl1Q6iKYK4Bd4rVaUEyiXYtJUPtrs2SFTWEcJIqJcmrW2IRDEAPgxyU0WO6wjYwCOOvEGfo1xTW2hLvMVXLThe16sbUC9In2FFoYEcNNRn/slWWSDe4yvUakokghHOoG66KRK4BdwrXIMmtKuFL7pMSxKYwpt5hyim0usS02YYtMUPjiEsm2bnfYF5XQRW9d4xI+YZyoEcxwhYlt6yKEc4pgUwDbxC9X+PoiFW3EoFKqb5tANoWxGaWwiWI9U+waKAekYxMuQRwUl+C4wzao3HAOiphRzjW2RP+9nsaJZSZxQWgpilMQQqHY7vSznNw4d/lFC+HKQFss+iqVtlgM9ve1xyKxUiklhLC+DVATovpP3G5bf/qPDbU/6seGq3/yeLYd99D7Tb6v5Hse6i/xXh2fpfq8bf8nA8bFyVxO/X9XiO+C7warpQbC6Hlhz+NOliusk1EIR1srLCE8WCnFQrFaLcXCsNJfSghhvR1QE5y66KxWSokfczvbjw/u9tb9bztOdazmdvr71N+/3s5bqs74nK3/N6n/D9Xe8Z3QoL87xA0Wg0R1FD3qw5g8RhfGtt9dteiB5FPERhkn+rViuGeGFy5ciP333x/jxo3DuHHj0NPTgzvvvDNef8kll+D1r389xowZg/Hjx+Od73wnHnzwwVQ/DzzwAI444giMGTMGO+20Ew4//HBs2bIl0eaOO+7AoYceitGjR2PixIl417veVff3N9IRZ3gYwz256dju8nWyCGHKDc7qBJturE/sZsW3ra3esHKO9eM0HeNyxyDpFOuZYsoltsUmfA6xLz8M8N1h1yQcTccnHBJChRehIKda3kaIEI63ceSJOW6wKYB1KIfWJ3ArA/luYsodRlacyAmbx6ZcY3X8bdoAOp9TrFzikPrCQQ5x4sNhusO+qIQtN9ykmej0G1ffDS3XOMkjhAthYAAILAPo7S+A3XbbDZdddhn23ntvAMCNN96IE088EY8++ij2228//NM//RO++c1v4rWvfS22bNmCK6+8EnPmzMFTTz2FXXbZBUBNCB9zzDG48MILcfXVV6OzsxP/93//h7a2oe/lzTffjDPPPBNf+tKXcMQRRyCKIqxcubK49y2QlKIoatGr3vBn48aN6O7uxvoN/4uxO44CULuTV67vYFRNzDOvfoCaM6wXT9frReq1halyavpACNegubwnt7xC2HSBTUJLrdm24aLHIyh0cWzbxhyQpw+808u0qb707VVb/XqtohBqmYpBDi2P6Hbt6WUd2jZKDJfbosTyoWW137vaBuPccIe2vL3UFsck2tAeRyPaSkO/t5c6aq+RXtdWak9Oq2z8Hrto1cqQANZ/r/QnZ53TIxDUJBv6eiIrzIpHFCSEixDBNgEcInzNfG8IpkC29dNuvNYH3+lt9Yk/2ok2sYjtGPr7V8tUNjh+bbQtdbSl2iBu2z4kaJUYLrcPCd9t60rt2rLOcnK92r69PBSTaCsPiWPt91J7F7lc/R6f/41rgfq9Gg2k2uizlKqniHpd38pgiZxkQ78mUFlhVzwiy7Wif/Or+M7RZ6K3txfjxo0Dl/g6+uPTMW6H4mIoG1/tx/hTbgw+Hp0JEybgK1/5Cs4444x0/9uO++6778aRRx4JAHjLW96Co446Cv/5n/9J9lepVDB9+nR8/vOfJ/sU6ofEJJqE7VFXHmxuwND65L9AcULYjEUA9kiE3tYVgVBQ0QYqypA3N+zrz3UcCipCMdQ2GZ0wt48/E8KN4UYmEsfriEnoj0JdeUHu8kJgPC4OJfGY2+EKJ7axPWL37csihM1YBJCMCujrbVEIRSpyMFBK/OiExCV8bc32tv2a7W0RCvO9mNEJvT/qs9WxRiYcbZJ38ERkxvdUwRaV0OM73NrZGchzzUg6xen1A8R6qoya2b5ujnALUq1WsWjRImzevBk9PT2p9f39/fj2t7+N7u5uHHDAAQCAdevW4cEHH8SkSZNw2GGHYfLkyZg1axZ++9vfxtstX74cL7zwAtra2vDGN74Ru+66K+bOnYs//vGPDXtvIxURwy2OLS9si0i4Hnnp66lsah4hHB+LJoSHlvnd4FABbKLndvP+JPq1iGPz+Mw2uii25YnN96Z/PiGCWMeWHzb7jPfLyATbSi35MoqKkOopLGx5YZZYSQsfcrrlwHiESwgrdCGssLnBuog0BSZX/JqYGV/fjwlXHJvtzfdjvlczT6w+Kz1LDLhddxJLflgn/r+nviPm73koQBj7agwrOBEJqo3vBtk1CVP6GIZ+d92UtwIbN25M/PT12We+XLlyJcaOHYuuri6cddZZuPXWW7HvvvvG62+//XaMHTsWo0aNwpVXXoklS5Zg4sSJAIC//e1vAGrZ4jPPPBOLFy/GQQcdhCOPPBJ/+ctfUm3+/d//HbfffjvGjx+PWbNm4R//+Ee9PgIBIoZbEpd4CDm56biyX8l2xQlhjhtsE5l6n2Ybn4DV4Ype1zaJdQ5hbH4OqfdKuMSmoNbbcS8otgF1yTbp9raLX8Vy0+VyhYt6umGFKyb6tchCiCvscBXrIYRtbjAlguPD9QjgeF9McevD149NHHNFscslNj83nyDmDqhLrqMep9DucOK71G+50aqjE+yDe2PqqiEMFGecKAoVwioOVeQPgGnTpqG7uzv+WbBggfUQZsyYgRUrVmDZsmX46Ec/itNPPx2PP/54vH727NlYsWIF7r//fhxzzDE4+eSTsW7dOgDA4GDtQ/jIRz6CD33oQ3jjG9+IK6+8EjNmzMD3vve9RJvPfvazePe7342DDz4Y119/PUqlEn7yk58U8CEKNmQAXYvgEhN5Hk1TJ7fk+tq/1F1+XiEcr3O4wYp0vWFDhHrEaxZc2yUm2SDKqAFDx6hyv7ZSakDtfXd0JAfMVSql1OA6fWCdPqjOhFNyjao9bOtPwR0856o3XFh5NZe4qFoECQfKFdag4hFZhTCVD3a5wfFyQ1zq2FxfG3kn4TBnpKMGyunHFQ+Q23bcKl+sr1d9tJcjDFZLiXJsqj6xPmGHObCOM1COnJWOGEwXl1rTSQye8wyko3ANnAuoQewyRrjXDKoWfa3N0O9cV5jaRxbjpJV47rnnEpnhrq4ua9vOzs54AN0hhxyChx9+GFdddRWuvfZaAMCYMWOw9957Y++998Zb3vIWvO51r8N1112HCy+8ELvuuisAJJxkANhnn33w7LPPAgDZpqurC6997WvjNkJ9aM1v53ZKiHvmutMPiUiYyylXOEv+NFQI29xgXwaXdGcZDu9ghf9DYdsHuYxwiql1VJbYliNOfHaWi0tIXEJfVvvd/SSB2waoQwzCuiOHOA6JSJi/E65wSEY1rxC2ucEuh1XfLjW4TittllcIU/3ZyrvZjpN6H/r2+j709UU4xFSbBFR2mAu3xFodsvAU1GQbCs5TxFBXWP99uAphAHGpNPXjEsMmURQ5YxX6+unTp2Pq1Kl44oknEm2efPJJ7LHHHgCAgw8+GF1dXYk2AwMDWLVqVdxGqA/iDDcRbgYMcLvDISe35HbpZaGDIGxCONQNNgVwaj8O0ZsXWx+6sUNOsqG5vEDSKaamZy6XB+PPQrnEZgk2n0OcKqVWGaow4ZqQw+YO62XW9OXU7HOKQkuvmR9+yKNmhwCxRiSYrjAAazwiQQYhHOoGm04wJX5t1GMGOnOfytXV3V7A7RSbLrFeiq1Nc4HN8msch1jHbAMw3GGqzBq0GekA9wQcBU++oWMaKtxInUlWV9g0Tqg+bUI47wDnaKBK/s3m6S+Eiy66CHPnzsW0adOwadMmLFq0CPfddx8WL16MzZs349JLL8UJJ5yAXXfdFS+//DKuueYaPP/883jve98LACiVSvjUpz6Fiy++GAcccAAOPPBA3Hjjjfjzn/+Mn/70pwBqwvyss87CxRdfjGnTpmGPPfbAV77yFQCI+xHqg4jhFsI80XHv9E3ynty4d/khQpgjgs1tKQHsE78dGef8HOhKP9bX90UJY5co1qMTpiAGkrEJfQa7EEGsQ03ZTEUi9GW+qZcV+mx0JvpMdEA6IpE7MmH+h+uD50z6s98Zka4wsT60wgRHCBcpgrnit6+PPv6uLlqwmP1Ss8+FiGI9OuETxInjsAliDVdcgqw1nLgLtMUjtN9NfFMzNxDzuuCrRe/CN/jWNcNc0UK4FXjxxRcxb948rFmzBt3d3dh///2xePFiHHXUUdi6dSv+/Oc/48Ybb8Tf//537LzzznjTm96E3/zmN9hvv/3iPs477zxs3boV//qv/4p//OMfOOCAA7BkyRLstddecZuvfOUrKJfLmDdvHrZs2YJDDz0U99xzD8aPH9+Mtz1ikDrDdcSsM2zWjfTVGDZrRpr1hTm1hfWxIrY6ka7sV6OEcFYRnFX8cqAEMpCOApadtYWHfqdqC6u6xGobVy3icsdgfM2m6g9TtYdtdYc5NYf1+sJUvWGVGW5vK5O1httLHUOvt61P1Rk2awhXVW3gPnq9EsPVSmIQjFlf2FlbWJ9kQ3N9fTWFQ+MRRQlhnwi2CWCb4M2KTSibrjGQzBnr6xO1hTvo5aq96kOtUxnidmM5VVcYqNUfpmoPU+tTdYctNYdjZ7hcpusNqyxwuXPoRNFepmsNM+oMmzXpqfWuevR6fWFXbWF9uX5t0CtIUHG6EOOkf/OrWPSuD2WuM/yPG04tvM7whPn/navOsLD9IM5wi2E+9nLd6ZtwIhKUK6xDxSOKFsKuSIT+mhLAPvHbXskmAKrl9Oeq70sXxuq41LWOcopdLrEepzAdYn1QHYUtLgH43eHQqISJ6R67BtHZKGxwnQ2zagQjIsF1hRNkFMJULKJIEcwRwJUBt/9R7vCLa10Ym06wOlbKKdZdYjM2oc9kpw+sMwfVhcQlfO4w6RQD1qhECnNdwOC4rPjGnpjXjCxPEbmuMLXMdr0Y6G8N91wQKEQMN5m8JalSYjnw5EYNmnNVmIj7IwbLxdtbRLO+DHC7waYQtongrOKX048ukClh7BLFyaoR7tiETRCHVJgwq0sAyd9VZQmAH5VwRSco6iZ0zRyx+doQwIm8sAkxcA5wZ4X19ZQwDnGEzW04Qtgngm0C2Cd6bdi200UyJYxT8QgiPqFHJ8zYRCGCWEPFJRIQ2eHackZUAkZuuAgY4jnLNYKbF3atC43T6b/bjJNc6LZ1ERTZlzDsGf5Bnu0Qs5KE704/z8lNJ0/2S68aobcLFcJmhYeOvmpKCLdXBuMfk/LAYKYfCtt+zGMyj5l6b2YFDROzFrE5MYdZYQLg1R421/scH2p7wP1EwkbIAFGdeCrmEFx5YddAuwBXmDPpQ0g0IlQIm1Ub+voGU0K4MhDFPzb0NtQPZzsd8zhcs86ZlTOG+k4vqxqfoVllokoNHHTUFbb9Xyd/Z95IAenvnLm+jlUkQsaXAGlxzJlsxwbXOHFVGRKEVkPEcIPJWobKdTIzKfrkxo1HUNGIuA9CCOslynwi2CZMOaKWg68frig232Pq99RnSERLiLJrJtT/E3Uzk1jv+OrpTxQ4mN+xupdXSw2k8+wvJVzcVSRMKFeYWk9Vj6DgCGHbhBQ+EewSsiFi17WNuR21nCOKbZ+H/plQdZRduKZspv7vvOsVrumZU68Z3/8GT8ph3sAW9RQR4BkngjCckG9tC1DE7F2mOM5zcnM5jiFCmHI1KwNph5MjgnVCHF3OD4VtHzZRTL2XLIKYwuUOK2z/3zZs3wNzHb2+RR0en2DR8UQkAH7dYW5O2CeE42WW0ms2EayTxeXlCmWfMLYdZx5BzHWHKQHtc/1T6G3yTsPsqnpSJ/L+XXKeIuY1TgShVREx3KJQj71C7vRr64d+Dzm56WQtjeMScC4hrHCJYBOOuHXhE8iUMDbb6SJed7ltNwA2QcyNSwB+d5gTlTAJeQLRdIInSUi2Zz02Z7jCQGOEcLwvhiA1lxctkrmimHovRQtiHZ87nCkqYX5vzEy666arIKjIEfU0JjRSZ67P6won+nIMsM5CVKllwwv7qUghLWEIEcMtDOdO33dys7Ut8uRmywmTcQmmENZxObQ+8RuaE/b1T4liHZ9LTF0QuIJYJ8QddkUlQpMllDimZkvM/bSDeqRMuW2+wXMpAew4LosrTK3nxiNsuISwHi+g3GAdboTBxgBzkB3XFTaPUT9+MwaioASxwiaIdTK5w1miEkB+x7gJ+G5wuecAVwUJILtxIgitgHxrWwSueMiTBw5t7zq5hQhhTjRCoYtLbkxBbxsieEMH0pnb2tbbBLGiKLcdoC9kNnfYxNw2/WShhV1hF468MAB7RMKC73F7qCvsE8IKlxsc4g4PDETWH996Sixz9s1xibmDCylcg+nIgZAWdzhF6N0hNXCTupGr02A66kY079Mc30BrgK4goVO0KywI9Ua+oU0gxDWjBEnonX5oRIJzcuPiEsJ6tMAUlBwRXMTAOZOQQXQul5gSxLYqE0B+dzg0J2y251QooSpK+EaxswgZWES15cw8l6GihC1LHB8KVTKNKYRd2wBpIazDEcEuMRuKrS+uKFZkFcS+uIROYlnoQEnzS29+ZzhxiAKcY9v1Ic/TlpBInfnaN7YECDNOBKEVkTrDLUSeEfm+k1tyXbaTW2g8wieEFT4RbOISv0VOuqHvp6LVMG2vDCbalwcG4/X6uo6+aqImcVu59lmYM9apOsJmXWFVf1jHbAOYdYPpSTaoKZzrha3ecHAd4jxumke4pIQPYxIOTlaYiynsOELYJ4oBd+yhEpiRLJfT70n136HVHFbHUTaWqdf67319g4m6xHotYZ24rrBRg1ivVRzva1vtYR3WJBtazWGTaKBam40u3gkx5bJrMo4GwhlfAoTlhU0aYZz4iLZWEJWK65PzVEgYOcit2jCAeuwV+gjbPGllObkl+mMKYZ28QpgzgC4rvkF4vgF0WRxi7qPDLO6wKyrhyw1zv29Ni1FQ7hslfj15YdfFsJ6usCv/yhHCLifYpFKJ4p9Q9G3N7TlOcRaH2Da1tAnlDpPZYcZAOnMdB+fELia2Jx91rEPMhfsUkYO4wsJwRr6hLQaVAePiu9Mv8uTG7kOrI2ziEpPc7LBPAKsoBvXjwiWKue9BPwYTUxD74hI6eWZzcj0OBeh6wy1dUUKDJVBMEeTJA5twXWFOTlihD5aL+2aISsAiSpkCuK8vSvz4CBHFvt+pWfOyZK3jvl2VJeD+v00/JTBPnMzJY5o4uI57sxr6txzyFBFwu8LVwFrmgtBImv+MR2DBeexFLUu7gMWd3LjxCGqwHEdEmuuo9To+getrryIN1P7MWAQAMhrhi0youEQorngFwItKmOSJTujTOOsUPiWzzVXjCg+ukNkGp/QWxxX2YbbPKoR1XOKXI3apNl1d1N/9tliEFqMw4xO2mAQVmaCiDxzMqZoBxNM0u3BFJVLTOpPxiIDIRNY/eAuhuWFK/IbkhU1sTxEb4QpHA4OIysWND2HVnhZGDHKr1iSyTlVrkte1a+TJjRKreYUw1+nlHp+tL05FCd9yhS0uwXGHs0YlzHUULRWFyAoZobDXFwbgHmhFlFPTyeIKu4QzRwibbqzNBQ5xfW24+rA5xb7jp7LOts8mqzvMiUqYsKISDagpnJU8f6uup4jhTxTtxokgtCryDW1BbCP0uSc771O/Ak9uXFfYVUeYWsYVwj7Klar1x4VLFNuO0yeIXcfLiZ9Qjj0XX26Y3sZyHK0ikHMIE1P4+CbaSO1aF16BrrCJcoW5QjhxHA4R7CLLDHQuUawTIohD4hIUrrrDQGA8IrWe+/ShdQUyUMxNbshTxMR+JCssDBPkmzrM4Dz2AsLywo08ubkGzFHLbdMf24RliODltKX2lUUQ6/0BdLk1wH6TkWxTSrUH/AMhXRTxxDBPNRR3x4FigxDJbGETt/dHJFwijesKU2IwqxB2ieCsM9BRUPvJIoiH+tsWRfLcTDjL0zFuRFwZYm9uGAjPBNdpSmZXVCLkRjUkUufalju2JM9YB0GoNyKGW5TwahHZ7/Q5cGZO47jCiiwRA5sI5opfH65+sgpi1zIFxxX2DaRz9ZGuKex+DdgvqlR2vRCyDhy1uXLUd8EjgPJkCDkupgvblMcKjhCmtufOROc6LlsflCDWj8sniEPiEhRZoxKFkvXpREht7QLgfrW5VYds6DfqZsWh3FSqtf/Hon6G4WyCQv0QMdxEshZR5wpf350+Jy9cZP5LCcKsQtikCAFsoyhB7IpLmPrPlh2m2rjIkxt24fre+aqg5HaN6+SyZckLUxEJnVBX2CcUXUKYdGmZAjhULFNtfS6xTRCbyyiHXMc1Mx31f+AiFYMxS+6R9SUDx3jUWey6/p5sN6x0ycSw/YY8RRSE4YSI4RYmby1hCpcYyhKR4LrCPrc0ixC20T4wGPRjgxLbPkFM9pMzg5A1KsGB+j5QsZuWxubOEQLGmxN1PDLxRSSyusIKloA1hHBIH64IREh+mOMS69imdLbhqsOs8H3+ReWGAVgdxKBaw8MI85xQ5FNEQWhVhtlVT1BkfexlwnEKizy5UcLQJRZN8elyg33i1oZPGIcIYm5cwpYddk1rGprX9s04JdRwxSU4Yol6XJ/FFVbYXOGsQjhrVMIljG0uMXWsvmP0ucOuCErooMU8N0ONJM/Uy4D9b50zvsTf99Dv9XqKKAiNRr6pTcZ30gsvkp5eFnKnn+XkltcVti2nhDC5TUYRHNJXVkHsWsbBF5Xg5oYpqIoStouo63toq35SOI5Hz1aXjvjO5BE83IhEcL+eqZVdQjhEsGbFJbR1bIKYiktkPTZfVIKbG+Zkxq0DL13xLE90K6r2OddzyTNBkw9XpI61PfkUMd/NeDQwWPiPIChEDA9jiipxxTknuCISXHwiMVQIFymCqb5NfIKY7CeDO0wR+ln7qolQH5vtoxzujjKnkoSr9jAn/sAp/xXqCsfLHJNpZBXBqlax+ePCJbp1OILY3NZ0hzkD6fLGUrZ3bNcH6u+ZU2ox0XfADblEJIThgIjhYUDoCH7qsVfeO/1E/zlPbpycrY5NCLu3GWT/WI+TENsuQVyUO8x1eG254UZSqFDmDDrK4cjp5HGFuCKM+wjf5wrrcOoH2+CIXk4brojPepwufFEJW27YxDeIrrbM8h1xVZDYjioUhJTnpJCIhDCckG/rMKeowU6cO33Xyc0XkcjjCpu4B725BW6WbXyCONE2sKyai6y54VT9UEZFiXo+McybfywExht0TcRhE1ZFOpY+V5iKR/i2B9Iz1nFxbecTxCHusLk9p+5waBxle30k7oso2W5WQ54M2bBF6op4iigIjUa+oS1APcRC6GOvrITmVH1i0RePsA9yCxfBIX24BLF1AhDH1cVXZk2nmY8Zh52GCKn7WnDtWVeW1RaRCHGFk/viC+G82ERx3jwyNzvMrSphyw2b8LLCNlfY9WSiNQbfFUnKQA+tVlPguUsyw0I9ETE8THDPBsRzSUIee/kGO+S90+dEBkKEcJFk6c/rZjscch1ubphb47Oe5/umZYnr+CjadYG0DZ4rospBiCvMEcJZ3WAXHEGc1x12kXW665bKFjd4sg0XnMHWgjBSEDG8HZPnsVeiH6b7GxKRKIqihbCrX25cImssQifUceduRwlY2wWwqAGaDcdWFzbEAdxGo4VUUU6uD6quMMfprYdDHErTxG3oxBsNwndTWs8b4pBInUQlhFam3OwDEBpL3jKaWWdFA8IiEhxXOEQIm9tXO/zHXK4MolLO74BXGPuyUam0oVwWu2a44BJqnIgEvV2YK+zC116tL3c4nkQNROhwrK8MRPH2fX0Rurq2zR5XiVAul8i25r99fYPo6mpDtVJCe9n/HvV2g5US2mzbDFSBjqFzWDQwiJLx9xltraA0KvDS2F8BOou9nOaetbEgzFNv1hv1vER9FUQo7kYo6msdl15oPnKr1iJwTnwupy7r3X8e1yDrSbEIp9gnhH2zzHFmoLP1mzwOOjvscofV+w/JDesMDNADV0zq+ciz6a5xxnwmq8awI0ucqFZQUFWJ0HJqru1Cq0C42oa2rydZazq7KkoIvMpDeRFHWBgOyLdUSJClkkS9yStY87QvMoaRJz5RxEAU6sJnG2hZVJWSahT2aLmoCQlCHmn7ohIcQeWbCIJD3oiEq/JD9gkusg3Ms2WHudvbyJob5pJp4FwL4BOwTb95FYQWR8Twds5wGRDhikiYhJRB4xKyXb0m+lBkzguLA7Pd4YpINArufpt1fDquG5BUFYkWmXp5OMAdbB06ZbwgtAqSGR5mtMJsYC7RFTJ4juOUNlKktg8MklliX3a4XKmiUq45tx19VQx0JV3cvLlhYXiR173MEpEIqQdcFL7sMAWVG0610TLHHGwZ4Up/CeXOBgj0SgUoMy6lgxWgbXhccn0mCufGu+iSkNFAhKitOCMiaoGbN6F1kCv0dgRXC9ZjQIQv71pEhQXA7grX261tBs0aqOIjdEbEutGfMTfMmYDD0cZXVs1F6OC5PBS5j3oer+0zUQMOKVqqXNowp1E16QWhlWmRq5oAAFXuCCqhbnBFNaddUTcAWchzb9AKTx8KIWR65jo/MrcJu+EUQaDgDtjzTSPtot45YR9RwZOzNIuixgEIwvaI/HUIAoN61TMOxTbxhg9K622HZnrdcM1opijareQKyEbV/uX0GbLfoicFyc12InpbkVZ9yhXCwoULsf/++2PcuHEYN24cenp6cOeddwIABgYG8JnPfAZveMMbMGbMGEydOhUf/OAHsXr16kQff/3rX/Ev//Iv2GWXXTBu3DicfPLJePHFF8n99fX14cADD0SpVMKKFSvq/fZGPMP/GyoMK6gMccjgOYrhHJFopnsstA4hwrBVXWKhuWz3FSMGqsX/BLDbbrvhsssuw+9//3v8/ve/xxFHHIETTzwRf/zjH/Hqq69i+fLl+I//+A8sX74ct9xyC5588kmccMIJ8fabN2/GnDlzUCqVcM899+B3v/sd+vv7cfzxx2NwMH0d+PSnP42pU6fm/tgEHsMjzS/kIs9jbxkdXB9kUJ0gCMLw4fjjj0+8vvTSS7Fw4UIsW7YMZ5xxBpYsWZJYf/XVV+PNb34znn32Wey+++743e9+h1WrVuHRRx/FuHHjAADXX389JkyYgHvuuQfvfOc7423vvPNO3HXXXbj55ptj91moLw29Gv/617/G8ccfj6lTp6JUKuF///d/E+ujKMIll1yCqVOnYvTo0Tj88MPxxz/+MdGmr68P55xzDiZOnIgxY8bghBNOwPPPP59os379esybNw/d3d3o7u7GvHnzsGHDhkSbZ599FscffzzGjBmDiRMn4txzz0V/f3+izcqVKzFr1iyMHj0ar3nNa/CFL3wBUTSyXJmODnEuBUEQhO2TjRs3Jn76+vy1zqvVKhYtWoTNmzejp6eHbNPb24tSqYSddtoJQE27lEoldHV1xW1GjRqFtrY2/Pa3v42XvfjiizjzzDPxgx/8ADvssEO+NyewaagY3rx5Mw444AB885vfJNd/+ctfxte+9jV885vfxMMPP4wpU6bgqKOOwqZNm+I25513Hm699VYsWrQIv/3tb/HKK6/guOOOQ7U69MjjtNNOw4oVK7B48WIsXrwYK1aswLx58+L11WoVxx57LDZv3ozf/va3WLRoEW6++WZ88pOfjNts3LgRRx11FKZOnYqHH34YV199Na644gp87Wtfq8MnU1862lpbwJulyELhTK3caihXuJpzumdBEISRQFQZRDRQ4M+2iNq0adNi46y7uxsLFiywHsPKlSsxduxYdHV14ayzzsKtt96KfffdN9Vu69atuOCCC3DaaafFLvBb3vIWjBkzBp/5zGfw6quvYvPmzfjUpz6FwcFBrFmzpvYeowjz58/HWWedhUMOOaQOn6Jgo6Exiblz52Lu3LnkuiiK8PWvfx2f/exn8a53vQsAcOONN2Ly5Mn40Y9+hI985CPo7e3Fddddhx/84AfxI4Uf/vCHmDZtGu6++24cffTR+NOf/oTFixdj2bJlOPTQQwEA3/nOd9DT04MnnngCM2bMwF133YXHH38czz33XJzJ+epXv4r58+fj0ksvxbhx43DTTTdh69atuOGGG9DV1YWZM2fiySefxNe+9jWcf/75KJW283xWnah0tDlrD1fK7Zlyw4KbURKIamk6Okrs3HC5oyS5YSFFuS3a/nPDdeC5556LBSuAhHNrMmPGDKxYsQIbNmzAzTffjNNPPx1Lly5NCOKBgQG8733vw+DgIK655pp4+S677IKf/OQn+OhHP4pvfOMbaGtrw6mnnoqDDjoI7e01Q+jqq6/Gxo0bceGFF9bhnQouWsaWevrpp7F27VrMmTMnXtbV1YVZs2bh/vvvBwA88sgjGBgYSLSZOnUqZs6cGbd54IEH0N3dHQthoHZH1t3dnWgzc+bMRDj96KOPRl9fHx555JG4zaxZsxJ/GEcffTRWr16NVatWFf8BCAKDcnnoRqIsEZaGwZm8gZr4IQ9dXTxhQ018ETJpBRdOn/XYb8PoKHaSCGGIVj5XqeoQ6sclhjs7O7H33nvjkEMOwYIFC3DAAQfgqquuitcPDAzg5JNPxtNPP40lS5YkRDYAzJkzB3/961+xbt06/P3vf8cPfvADvPDCC9hzzz0BAPfccw+WLVuGrq4ulMtl7L333gCAQw45BKeffnod3r2gaBm/aO3atQCAyZMnJ5ZPnjwZzzzzTNyms7MT48ePT7VR269duxaTJk1K9T9p0qREG3M/48ePR2dnZ6LN9OnTU/tR69SXV6evry+RN9q4caP7TRu0t5Wl1nCTsUUuzBnoONGMZkYgOMkRW5tWj9WwKbezaw2XRpXrWmu4q6uNrDXMdXlb1Q12zUCnC2NK2HNnr2sv+CYjlNJ2IpK72gel1nDBRFEUX/OVEP7LX/6Ce++9FzvvvLN1u4kTJwKoid9169bFVSe+8Y1v4Itf/GLcbvXq1Tj66KPx4x//OGHwCcXTMmJYYcYPoijyRhLMNlT7ItqowXO241mwYAE+//nPO4+1FSl3DOauA9lWds9CVy23FVJGrFJuI2v+VjvahnWJNYpWdVO6CpwStRmUOtpYs9DZaGuP4lno2spRUH1hJWobIW5bVUCb2Nzkri77OaloF34kM6p9eMxCN1gpYbCtuCcPoXXBL7roIsydOxfTpk3Dpk2bsGjRItx3331YvHgxKpUK3vOe92D58uW4/fbbUa1WY2NtwoQJ6OzsBFCrHrHPPvtgl112wQMPPIBPfOIT+Nd//VfMmDEDALD77rsn9jl27FgAwF577YXddtst71sWHLTMbeKUKVMADDnEinXr1sWO7JQpU9Df34/169c721BFrF966aVEG3M/69evx8DAgLPNunXrAKTda8WFF16I3t7e+Oe5557zv/FAXK4ddxxZ3vFm+qN6EzUYTrmirvJhHOc0ZHBc3oF0WbevlIeco7yDAYUAOut3L19q4qDMcrn1ogaNiD+ofRS1L060RaDp9Hz9XdeA7ZUXX3wR8+bNw4wZM3DkkUfiwQcfxOLFi3HUUUfh+eefx2233Ybnn38eBx54IHbdddf4R8UzAeCJJ57ASSedhH322Qdf+MIX8NnPfhZXXHFFE9+VoGgZMbznnntiypQpiVp9/f39WLp0KQ477DAAwMEHH4yOjo5EmzVr1uCxxx6L2/T09KC3txcPPfRQ3ObBBx9Eb29vos1jjz0Wj+AEgLvuugtdXV04+OCD4za//vWvE+XW7rrrLkydOjUVn1B0dXWl8kfC8MAlhLNEJBLbD8NqF0J2GvFY3xSMtshBEcLS1oe5T1amuA5CX3eJ29r5n32pqFGlZWY/bS33IJaNTxyT23QNA7s5gOuuuw6rVq1CX18f1q1bh7vvvhtHHXUUAGD69OmIooj8Ofzww+M+LrvsMqxduxb9/f148sknvYPxVb8HHnhgnd+d0NCr9CuvvIIVK1bEUws+/fTTWLFiBZ599lmUSiWcd955+NKXvoRbb70Vjz32GObPn48ddtgBp512GgCgu7sbZ5xxBj75yU/iV7/6FR599FF84AMfwBve8Ia4usQ+++yDY445BmeeeSaWLVuGZcuW4cwzz8Rxxx0XP4qYM2cO9t13X8ybNw+PPvoofvWrX+Hf/u3fcOaZZ8YC9rTTTkNXVxfmz5+Pxx57DLfeeiu+9KUvDbtKEqEnMdsJrN5OgO6o6k4rhSlOdaodbcFitVGl2bgZYioewbmw+P6PQr4LXe0jz/mx0R4gsPJACVo9a+sTm/UQxFm39eWFs5LnRoPj9lvbeM5JzaaZOf+y9n8idemF4UpDb1V///vfY/bs2fHr888/HwBw+umn44YbbsCnP/1pbNmyBR/72Mewfv16HHroobjrrruw4447xttceeWVKJfLOPnkk7FlyxYceeSRuOGGG+LSJABw00034dxzz42rTpxwwgmJ2sbt7e2444478LGPfQxvfetbMXr0aJx22mmJxxXd3d1YsmQJPv7xj+OQQw7B+PHjcf7558fH3Gp0tAFFR2Z9WeKsWWNfeTUTKg9syw7r2wDuqZo5ItglvGvr6YukS/janGKucaRfcMoOccARv6MyXuPLzItve6kjqN9Sexeiqr/ovZfODqB/IN0/MVAulSHuaA+eqrXcEaEykBR9KlvcXo5QdeQTs2Z7Q7ZT4jRkPy4hnMUVdm3vI8TxFdLYyq51tgH9ddav5fIgKpX8hkO1WkK1WtyNVZF9CcOfhorhww8/3DmDW6lUwiWXXIJLLrnE2mbUqFG4+uqrcfXVV1vbTJgwAT/84Q+dx7L77rvj9ttvd7Z5wxvegF//+tfONkXRhnYMwn0BLqKO5KgyoGuBIkQ0VxRTg+hsA+vMesNZBLHaLiuUEHb1F5oXVoLZtd1IzOaxKJeBSuMrr+gD5nwi10RVlOAMoiuXS6hUom3bldDXV/vdJ4B99Yo5AjqrA+3bnopI+HLCLie43OEXyD5Xv7BseB3y65xrQj3oaIsw4LjOFDHgWhBaDflGb8eEnuc5wsv1uF45myGD6HRaafAZRwj74hxA4/PCvgoU1ONUm4PMdX9bDsv/CyV8fGIotGpB3ioHoY4pJSI5YtX1E3p8rm1CIxKqr9BKErpodv4fMMqkWbPEnWFPORpFvSMSrj8R/XpQTjyxIsoIyo290MKIGN4OqMfJ0JcDy3ti44hEU2xSrmyl3OaNMoSSpT9dyFMRidC8MCVqbRNuuG5Q6qnFm5ZTrGN+0yWO9eoE+mN7nzjjQAlK3Ul1ZYezCOIscIQwJy5Rj2Nz0VJl2NobP4jOXk88vSzLQDlB2B4YvsNbRxi+R1cmVO1IMx/WiLyYiS8qMdDVjo4+u7iz1RPmRCZ8+AbmJdtmc4XVMjMiMYwHmgsObLnhvPWGze2ofpTo5E7z7CLLLHcJAe+ISHChbjBslSRcZdXMG54sTwxIuFUlhhF5Y3WdXVX09xVz81rtL6GCAjPD/ZIZFoaQ+8AWoK3U/HgAZ4IH8tGXw8kE3FGJLO4w4J4lLour69vOJ4SLcoVthJYoMj8e86kv5f6EuEctjU2QUG/EeGRuPh4PFUScDKvr8X98WB20iPRFDlwl0LK6sbZtSSfbUkFCfw+uvsyIhBK/1OA5zmddF1q8qoSJ7elNEX/XiSdVdX6SKAj1Zrhd6kYkrhm/qBJYtNgxXKMMOTBqvYmZG6bwCUZz2xBBXGvflvpxrXfhG4Bne59ZxT+g3WAQFxBbJYlmXWw4UQn2zR7nEXKTxEi7JRbhcys5hEw24Su15qsAwRHFql1IqbZQlzdrybaQSIr+f5Zyg807RCpLbC2z5vieNvj72V4q7hLuOz2FRihEAAvDCRHDw5iiBjhxNFoRd/s+gVikIE7thyl+9b7pjHJ2kW8uMyMSLnc+9LM2L1zma9IotbrDLZS5dGEdOJde7n1UzhhopeMSacrZVK+V88ktXeaaqCKLMNXFLvXjgrO/PK6wifnZ+WipfHCTsV0fuH/PLvFrO1fVcwr5wUqp8B9BUIgYbjI+1yxU8HIGRbgmXrI9+nJhRiVsVSV0ONEBriAuetIMewyDF4/I4wqbUC68bfBcPS9ELUETBh8BSYFlG0QXr8/5+F4Xhdy4hE2gFjmFsq0/lxB29WXDjEi4CJ55zozFsLLDxTu9pfauQvppL3CggVlvPOuTRNd67vVEEJqBiOFhSmHlMRn9+ErmhBDiDgM8QQzkF8Vqe5sbHJoTti333QRwIxIcUmancYGjJtywuUaum7IiH9VmpdRuES3E98WXDQ7NCruiEjZRZ7rDukAMjUu4tskril3b+4RwXlfYRH2mvhuOxA1LvQSYKw7RArli298xN1bn7pteLjPRCcOZ5l/FBCuhj6c5j765j774d/vukx7XHQ4VxD5R7BPH3HbUflx5aK4rnDciYcsLD7sBb/WEqAsbKnT19rbcsGtZvI7xuN9XIs2MS1CC2CeKOcLT15ZaxxXCIa4w5zPj1BfW/w99Nz5kjWFb/MZ2A1YAzRxUnfVJYpb1gtAqyKVzGMIVyb6pdjm6gHu3b4tKUNiEb4ggBnjlzUzRy3WQbYLbPA7u8evLuJOL+CISHHyVJKibI8o9AtzfuyIf2ZKUOx3rcuzbVVGC8YjcFZXwuZKu7DAnLlHrIzwikWfSDWp9FiEc6goPbcf7bF0zz1kn1VDri7ijrHOkpw3276ZtwDX1dCf0rerni5C4VmhFHIrBwRIGqwX+5JzNVdi+EDHcRFx3/+GusL+9q41+UuTe7XNPcJQ7zMnQcgWxyykOxSWCswhh1/s0XeHQiITtAlTEY8+iZ59zXbxrDQoWD9T3wecKOv+v3DVsOe6ly+n0xSV8gjiLKOZiE8rUfkOEcLIvtyvMGaAIOCISrhsbTi7YNvvcdlZbOORJorcviUoIwwgRw8MMSqRwcmCuR18hd/uuqhIcdzgkLkG1p0RpvG1GYezbjhLBoUI4qyvsi0jY0D9GTiUJLq4yf02hsz5ixPZonRuV8DmYZmUJHa4g5rjEqo8QYcxxiknxHSiEOVMvU4RGVCj81USoO8TAG27X04w6Ocf2ChK8Zcn19kF0NvOEOjeFmieC0AxEDLcgeQckFV8vMvwEZ6ssocN1U03xqfpzZnc1gev7seFzg7nvgboBCHGFFb4pml3/777Bc1kuliZe9zcrNtfY5soRy82qALkn2CCqSoS4wzpUXIIjiAG7S8wRxlliEjY3OKsQ1vG5wtTAOerztNUXDh0oya4kUacbMhuheWLqiSC1zBWr8yRLAPDNE0FoRUQMDyOoExjncbZbJNG/u2abozDdYQpfXMJc7nOJVZ9ct5WD6o/qkyuEbduEDJoDhj7TrBEJzgVMh3vRLDpCUSjUzY2xzFdT2JYb5jqSNnc4JC7h+p0SxFQtYpcoDsElsM39cgbLJWeqo+MRNlxVOjgRCVde2Jclru2kdWMReeqBc8YSUOvoG3S7eZKHagWoVkoF/uQ+JGE7QsRwk2gvWTJoFriPp1NT8TrqR9rO/b6qEh2Gi2k+0g+NS7hEJcclBpIiNlQc+7aj9sk5ZtckIjqmK+waOBc665x+4Qq54PmwXXgLHwVve5xcp/JVNifRFpVQsOrcElBxCa4g9rnECl3McsQxpz0lwF1CmMpEu+IRIa5wrvJpntrDnJur2jLL97TeA0s9cGJ1voSIfp4oyjwRhFZDxHCLETIq31dH1oR7t6+gsmBZT3BUXCJEEHNFscIUx64fCtW/71h84l0di8IXjzBvLHyusMJ2kXK1o15T+fOWdoI1yFJX5sCnnDWFuQPpuO6wTqggBuwusWvWOlPscsWyrW9TmJvH6xPC3HgExxXmRCSC60rbBs81AdvNJtcVDp/Eyd+ea560W6rUCEIrIGJ4GECXxPGfpLji1yekinCHgXBBzBGaNtGaBVdfvtiGTSS7hDAXyhU21wH2iETo41PuBTO038JcY64rnIpH8HPDvhJr1EA67tTBrrgEVxD7XGKAJ4x9uPog3WmGiHcJYS4uV5gTkXAKYMYNEnnjZfte2p5u1Nk15lUYSr7m5obrbZ5QyHTMQj0RMdxC5BmAFPLoyxaV4GbBuCc4ShAruFMZ25xZnzA2fzjtKDiutE3EU0JYJ48rHDrRRkIoMwbPmYQI38JEb4hYyJvl1EUT01EEaHdYwakskVUQU69tohhIitqQHwqbCM4qhHXq4QpbcWSJU4PnWmBWOdvfFTXgmorV+Z4kumJURZkngtCqiBhuAiFigTOAyXwdcrdP71Pr25Fj9bnDiWMk8sMuQexzial2LkIcZJvgDskO24RwSPWIZDu3K6xwlVTTSZdbc3+HAPoCW8hUzCFlpqi21Gh+Uxw7BtGFCieXG5nlUX+oIHa9BobEqk0YZ8HWJ7X/UCHMrR5husjcAY3c/2vv9yD1neLPVjfcsJknOqHmiQhioZURMTzMCH00neVuXxFygvPFJfTfXYI4tMSaaudyjH34tg/NDnOFsE5WVzhex4hIcEqpJtcPj6xwClOQeHLDOtaoBLFeOZHKmeQO5HK5nBxBbHttm87Z/PHB2cYmgvMKYQrX50q5wkNvxB+RcApgjrDlllULeMoRYpZQY0w4sTrXk8TQEmsu84RTE10QWoHWrRMzwmgrtWMwSrqvHW0RBjxTRna0AQOD+mv7Nnpbvd2oMrB1W5mZzjag39Ba5fIgKpU2dHZV0d/XjnI5QsWRtxpqV9uu3DGIykDtjKl+V+fwwcqQeOzoq73/arkN7ZXaQSjBWdbepC48VTudLIKYwia8XW1ChLAvHsFxhW2PLG03Pa4LnW0a5sR+mQK58KoSAFDuBCr9xrIyUOHXSCp1tCMaGPo7K40qI1Jf/o52YNu6Ukcbom3fOf13RVs5wmClhPb2CNVq8m+hrT3CYLWE9nKEaqUUty13RKgMlJLbqzbbxJy+HVATkX196vtQW1YZiMjX+jJzuU5Wx5hTKo16HSKEuQMPXa4wK+Zii0ikSikYN1IcgVznqZhDKbdFqGjXBPO1eQ3R0a8H+u/UNvp5PnUMHYMYyPwOalQGSqiguKcd6m9REABxhocV5mPqrHf7IVkwZ21hizucaONwiIEwl5iTE86Dqy9O3eMihfBQu4hspxNaW9j1RMAXwaEIqYDCJqOgSA1scogXjmiiBtK5ssPcuISOTSDqZddMl5iKStic2tAZ6Gzb29bbXpvHXbQQzuIK20j+/7u/Q+nvWP3FL1WKkxpjEvo0x5UbDhlnwnGHpdSa0MqIGG5R6j0LnUneE5wtLqEvCxXEgF0U29xf1yA6348JNztslmgLFcIUrJsQT8zFFZGwtaPapte3yKNPU+j6Mp16e08cAvA/To/Fmicu4csP622AZGUFMzbhEsX6MpeLy/3xbe86DlME64I2RAj7aDcEsu//rNCIhAvXVMwNhDMWgFoXap4ouGMhBKHVEDHcYKi7ec4jZZ9jV04In/C7fWp93LdnUoiiBLFees0nioH8WWGKkOwwJYL18mlcIeyLR7gqSFCuMCciYb49TkTCRd2mYo53kNN949aKJcqoudxhl2ijyoVRos81qE4tt2WJa325ndssrjC1PSenzHGDU8sZrrnNFdaJxS3hCmeKSOiE3GjZqHN8IvWkMOBJYtHmSbxOssPCMKG1wk0CgNpj5+qgPQdp5oJdmS993ah2YOs2DUZlwajssKtvKjvc0TGIgYG2VM641n4oQwwAlYHk70BN86i3bmaJAXdeuChBrGOb7U7H1Gk2wV+EEPZVkBhaRrvCPmeH2h7wX1gpQmdZVJTauxBV+9yNyu1ARbsp6ywD/RV6vdFWzw3bssHqd3K9li1WqOxwuTNCpX8oJ2zmh4GhvLAtQwwglSMGkMoSK8xMMUDnhbMIYm4fpkC3ieDUOkIIh8YjfBUkqGX233VLNENeWKeOdYSpMSYhmLlhHVs2WF13qPX6MpUdpsaa5GGwWsJgqbic72BVMsPCEOIMDwNCHkvb7vazuMNU6ZyQPJhL/Jnb2FxiwD5dcpF5YU6f1DGYkYgihLANMx5RhCus//9nyQvr1GXQHBDsqJETIig4UQmfO6yWGe6wLS7BiQXoy1JtHS4xkHZja/3mywvb+qH2a4tEuI5dd8SLEMK5XeHEm26nfzcxK0lkiVYU5BabsbqQv2XbjbMtKhGvd1SW4EwxLwithIjhJmMKCPNxc4hDxxEvvixYlhOcq55kqCB2iWLALoyBfHlhm6Cm9qeOi8oGm++jXB5kCWGdkEFzPlc4FOeTYqPfQmoMZ8EUEK4BTPo6zekrBUyyQa63xCXqKYi5otgUxoqQvLBNQNv2QYlgXyxCf+9UXMRVOULH9n9CtrGs50YkmjF4Lu9Npis3nNU8oda7zkmC0OrI13YYk+Vun1oWcoILEcRUhlgXePqjf44odgljShxnwdUndQwcNxjwO8LceAT1f+lyhTkXOFdeuOjBcoW7x65BdIFOHdcdplxIF1xBbBOGHFHsEsY+kWzDtz21b5doN91g10BCKm8dEo/wucJm+9rvjohE4kAcAti8UTNel9q77NsyMf+GzGourr/ZosyTeH1Z9ZteZ14rOjrFHRZaF8kMtzDtpTZUI01cOXJeerarq30QfdW21HKqhjC1TGWHXXnhRB9G/WG93qSZIdbbA0gsN/PD5msgKUbNWHVRgljHNY2ybZlPBAN0NIIrhF1Cl7oQhkYkXE8jzHXmhTj1pCOv+NVD5ADIWsMKMzesY8sQE19yV3Y4UZfYaG/WHlb54drbSGeIASTqEAMgc8QAEu2BpFjUM8U6VaIWeKgg1nFOjOHIBQO0G2wup4RwlniEwuXsWwfO2SISrhurFp51rqttEH2D/v9zfRwKdd73ZYcV5lgTPT9s+atlM1gpoVpgneFBR618YeQhznATMQcYuYSDq7wVR9RQWTCfO6zwucM6VIa4wxIZoFxitS3lFJsiVHeMixirYvbnc4HJY22wEKZiLVld4YQmYLpLhROSoXTVENYfZwdEJbh1hUPjEvo6Kj7gck1NwZgSmoYba7Y1f3xwt1P7dcU3bO9L/zzM0mup7ZhCmPw/VBTgCie+U66Z5xpYVs0Vq3NXHBpa7otKUO4wVXeY+zRREFoREcMNJO80mwozN0y2YWTBzGV5TnBJcWsXxPo2ajt9uUsU68sod5YSsyE/Jvq+fOLcJvT195uIjgQIYRtmPIK6+XGVUwvBJY5zlVULuYtxtU3EIyxRCdvvnoypucwWl8griPX1QNpBpUQxFVGgRKqOTez6xLKtb/JYjOO1vRdqsF1iu4xCOJMrTMHNBLvc4QKrSriuIa4Mfx7zxLXMHGviu14IQqsiYngY4HLibHf7dD9Dv1PusCL0BEeJW10Q23LELvFIiWKXMHaJZA6+flwCmPM+OgwxPdSGFsI6nAEqHGefag+4Loj2753rwlu3qhKJAyhGYIS6w3kEMTWojppxzuemcoRx3J4QyFl/dFwC2BTBLpfb/ByyCGGFVQhzXWHOjZPtpsvEJYADvrtZbzRDn97kcYep8wtFXkE8WC0V/hPCwoULsf/++2PcuHEYN24cenp6cOeddwIABgYG8JnPfAZveMMbMGbMGEydOhUf/OAHsXr16nj7f/zjHzjnnHMwY8YM7LDDDth9991x7rnnore3N26zatUqnHHGGdhzzz0xevRo7LXXXrj44ovR3583ZCL4kMxwi8CtG6nnhpM5r6HfqTZUdpiqO2zmwcz8sCsTZmaC9TrEVI4YQGo7AIk8scLMFcfLB9Jn4iJcCOcMcIZgTU1CQkQizD71mpuUEObGI1yD5ihXODQi4coL1532MlC119uO0TPAWm641N6OqKpywmWgoopodwD9A2R22FVXmKo9nMBoZ2aIAaTqEAOIc8S1t5ysRwwgrkms0DPFCjNbrENlh0NwusXEOnPyDNuMfIA7Hwy4Z5hTOGeSc0UoSCdAi0i4qkjEbbTldZ5Uw4R7zdBzw8lrA5UN5mWHOWNNOLXqhxO77bYbLrvsMuy9994AgBtvvBEnnngiHn30Uey2225Yvnw5/uM//gMHHHAA1q9fj/POOw8nnHACfv/73wMAVq9ejdWrV+OKK67Avvvui2eeeQZnnXUWVq9ejZ/+9KcAgD//+c8YHBzEtddei7333huPPfYYzjzzTGzevBlXXHFF0977SKAURVGDr3Ajh40bN6K7uxvrN/wvxu44CgAwGFUxiGp8EqtGA/Hvg9HQ8kFU44k3qtEgBgZL8UmqMliKT276MvX7wCDiE15ftS2xXP2rTmRKDKt1/XGbUuK1GjOUblf7VxelSrgqUVtbpk8IMNR2wBCzaluF3oetjQtKLCu4otmWdXOJYCCbEE5ES5hC2HRoOtqGhLC5bUfbUPuu9sFY+HZofQ0ti2IxXG6L4u062iK0l9riKE8b2mM3uK009Ht7qaP2Gul1baX2IaE7WKn9rgbKVStDk24Yy+PfK/2110rMVqpDYlcNoqtUhsSwvr5SrYlh9TtQm4Rj25dZCV01UC4WvgPVoXW2Ntva6cuUUK1qTpQaWKevB5ITAZgilhrwo4Sxr12R2Eqd5RXBQIAQ5sYjOtodbn5b2hXu7Bj6XYnhcvuQGC6Xh/LC5bLWtn1IDJc7h5zh9m0ZrG3rSu1dQ+205ep327XAvF4k2mHbusFKPODadb2oxNeD0tD5e7AUD7rWrxfm+V6/XoReKzZvehU3zD0Tvb29GDduHLio6+jDb5+LseVsE/lQvFIZwJt+c2fw8ehMmDABX/nKV3DGGWek1j388MN485vfjGeeeQa77747uf1PfvITfOADH8DmzZtRtjxp+MpXvoKFCxfib3/7W6ZjFHiIM9wksswgpN+1+0YJh7rDoXfvNocYGHJ7k1Uj7C4xgIRTDCDlFgO0Y6ywCeQsLrFroAdVPD6PCNa3L0oIm/hc4aF2YbnCwjErR3BIVInQ3F+ybZg77KocYbZJuMUOhxhAXGkCAOkSA0hVnKh9PENOcfyWPI6xCVco+2r7mvt2beuajc4Vi9DXZxXCJiXqj0FBDZzjRCQSbbTBcw12irOgn/dVFSLKHXZXjuA/TWxFNm7cmHjd1dWFri53CbxqtYqf/OQn2Lx5M3p6esg2vb29KJVK2Gmnnaz9KCFuE8KqzYQJE5zHI+Sn9f9aRwC6MNZ/16dl9sUgzN8Vepk1RREnuGTbIUEMIDUVJ4DEdJyq/JpqC9hFca19WhirPhX1GqlsmznJJYCBtAjnxiKA/ELYdIX1dZTI5UQkOOhZ4axTMStYUzLrcKISVFu1PzVFM3FX6ItLcAUxAGdsQq0HaFEMDJVi09HLsulQrjFH5NqwiV9bv1TVC6o95QbrbfIIYVeFCWdWmBORaCKJawbaY3dYL8dpM0+oEp3UMvWnoP9JmOZJkCDO+Z6rBZdWUzeZ06ZNSyy/+OKLcckll5DbrFy5Ej09Pdi6dSvGjh2LW2+9Ffvuu2+q3datW3HBBRfgtNNOs7rOL7/8Mv7zP/8TH/nIR6zH+Ne//hVXX301vvrVrzLflZAVEcPDGF8WzOUOF3GCA+hcmJ4jBpBJFANuYQzYhSoVrfDBmS6UHNyWQQSbfVETanAywonjsAhhyo0JcYWpZfrguVyVJLjojrH+uy9TrDvBSgD73OFtWIVuDkFcO3y/SwzQorj2lpNusepTYXONi8Ymql0C2NzOJ4IByzTLGYUw6Qrr8QgTqkJEp8UR1l3gAqtHFIHPPPG5wwrb00Tu9aJVneHnnnsuIVhdrvCMGTOwYsUKbNiwATfffDNOP/10LF26NCGIBwYG8L73vQ+Dg4O45ppryH42btyIY489Fvvuuy8uvvhiss3q1atxzDHH4L3vfS/+v//v/8v47gQurfVXK5DY7vYpXO5w0Sc4gH4MZotNAOaEG2lRDPCFcW3b9BmWI2x9uJxmcgrlHCIYCBfCnJHc5rU/xBWmljV8AB0XT1TC6Q4rQYy0O5xHEANItVXLEpNpGC4x4BbFQNotBtJRCpOsOWKfk2wbXMcVwQDfDdaX5xXCJSJCQbnCQ+uYVSRM2nSHuYtcnkc8c54kUmR1h6nB18n29utFwHOehqKqQ3Do7OyMB9AdcsghePjhh3HVVVfh2muvBVATwieffDKefvpp3HPPPWS/mzZtwjHHHBM7yx0d6Rux1atXY/bs2ejp6cG3v/3tHO9O4CJiuIWwPfqioO72fe6wwneC4wpivS89cmGLTQBplxhIimIAXmEM2MVxPaDEL+AWwIB7gJ0rFqEv4wjhkHiEzxVW+CISeh3shpRSA5JOsGsmOoCejY5yh411RQliIB2v0JeZsQkATlGs2gJpYVz7aNxVJPLEI3RCZqEz98kRwYA7FgGEZYQpyEFzJtvWeSfasOWFC8AWnwsZb0JFJULdYdd4Evr87zZQtieiKEJfX03mKyH8l7/8Bffeey923nnnVPuNGzfi6KOPRldXF2677TaMGjUq1eaFF17A7NmzcfDBB+P6669HW9t2+MG1ICKGm0Ay49WBajRgbeu726cG0nHc4SIEMZDOhQG0SwzYRTGQjE/U1tuFMWAXqGZ1ihBsfSpsg/HyimCAjjXkEcKuCw/lCpv925a56gsDSWFciEj2RSESApkQu9t+Z7nDRFyCgiOIAaTFM0C6xIBfFAN0HMIUoHo1Cs5sc3mgJvUgS63lEMH6cj336xLCrHiEQglhriscL/N8txtdZs2SG1ZwnWC1jDPWxHatqLVNXy/yOsN9/YPoqBZnfvQF9nXRRRdh7ty5mDZtGjZt2oRFixbhvvvuw+LFi1GpVPCe97wHy5cvx+23345qtYq1a9cCqFWc6OzsxKZNmzBnzhy8+uqr+OEPf4iNGzfGg/d22WUXtLe3Y/Xq1Tj88MOx++6744orrsBLL70U73/KlCmFvXchjYjhFsR350+JXY477IpL6HAFMeCOTejHAKSjE0B6cJzpFtfapIVxvM4Qvz5BG4K71nD6op9HBJvLXbPLcYWwyxU211ED53wRiZC8cJAw9glgquoEJWQ57jBjMB3H9aVqELtiE6oPtZwSxUAyUwy4hTFAC1QAwRMM6Nj6jNczBDBgF8EA3w3Wl2cRwuSgufigy4l15MA5Shj7hG/IJBsBrm+W64TLPKEc4Kzmib6t3nY48+KLL2LevHlYs2YNuru7sf/++2Px4sU46qijsGrVKtx2220AgAMPPDCx3b333ovDDz8cjzzyCB588EEAiKMWiqeffhrTp0/HXXfdhaeeegpPPfUUdtttt0QbqYJbX0QMNwjficu33nW3zznBhcQlqDaUIAbcsQm1nnKJgbRTDNhKqRmj5BODhOofk6COQeGrNWwTwYA7FqGvL1IIu6ZV9rnCCmqq8LpHJUgBTEQlKFHscocVhjucVxADcLcHUtGJ2ttMZoVtbjFAC2OFmRH2CVourriFTwBT2/vcYIAQtihICJuusH5cel1hwB+RUNR58FzIk0QF1zxR2MyTvIK4VTPDXK677jrruunTp3vF6uGHH+5tM3/+fMyfPz/L4Qk5ETHcRLwCmcgND52Ywk5witATXK3ftCAG4IxN1NbbRTFAxycAfyk1mzCtbZvdAXP1q+DUGQbCRTDAHyiXVwi7XOH4+CkR7IlIDLXLUFaNO9scd1vl/Ia4wwUJYgDO2IRaT0Un1DrT+TXdYoAWny6BXDTU/oFAAQx4RXBquSGEncKZK4QNV5jE1YbKC1PCuA7xCeo6UpR5kkcQA0kDZXvMDAvbDyKGWwzyxEbc7SvqfYIDeHf9gN0lBniiGEgLY8AujoF0GTWOoOXgq0jhE8BAuAhOtwkXwqHos80B9MA5VxWJejrCZK1hl2h2ZX5Nd7gAQQwQuWDD7TVdYn0btZxyiuN1SEciTLGpz2xnE6hActY7Lq7+FObxALQYL0wEa9vmEsIKWzyC4wpT4rZgwUuZItR1Iq95whkwV9uOJ4iBpIGyNftHAACoDESoDBZ3k1epSuxAGELEcIviutt3ZX59J7hQQQzw7voBeF1i1WZo26HjtgljIC08ObWGi8RVsSJEAJvrfSJYX57YziGEQ11hClMg6+hTMJvUTRhTAljFJlxRCdMd9s1QZ+IRxADoKhOA1SVW2wAOUWxsH68HnRWmxCiQFMkAT9i6sO1HESqAqfVcN1hfRm5jCmEK3yC4RFuGc+yISCTKqtWZEPPEdS1xjTXhCGKAfqIoCK2IiOEWwJUDc5VYM4Uv5wSnwxHEAP8kR7nEQLqsjt63ausTxoBdHJvYpma2wS3PZssnU64sVwSbbV1uMFCsEOa4wgpXRMIlggudlMM1XbMSzIyKEGx3GHAKYiCZC3YNlDNdYoAWxYntFYYwVm18WWGfeM2DK4bhE8BmG3N2uCxucGI5JYSZ8QiWK6xQDrCrpJrLJS7AQXZF7TjmiSLUPAHcghhIGyiDYadlQWgoIoYbjOvk5TyxbbvbL/IEB/gFMeDPEQN8Uay2rbUb6l9BzWFvE8cKs6JE3trDvkF5tkiCSwADPBGcXr5tW73caR2FsLk8a0Qis0vsErwKTr7Y4w4XIYiB9Ex1+jKXS6x+B5KiWG2vrzeFsd5GQTnHLmyTcIRmjVPCV+ERwEC4CE61IQbW1UUIm64wx1Guw0A6zrUjj3lSpCAG0gZK3phEteCYRFViEoKGiOEWJEtR9awnOMAtiAH3Xb+5Xh1TbTktigHaLQbSwrjWH/3efSK5CFxZXGr2N6q9LQ5Ra+8WwYBvMo10XzYhnBXlCrsiEkNtMwyeIztiCF4zKsF1h4sSxIAzRwzYB8rpojh+rX1RSLcYSItMizjWibdNfHR8IeDqO8YSRwgRwKnXgZlia0YYyDZgjoNyhZX45bi9OYWyr6IEYDdPfIOsixLEQNpAKYszLLQwIoabhG+GOSB9t889wRUpiAH/SY5aX1ueFsXx9g5hXNuGFpt66be8Ii8EzrTHOi4XuLYNXwTb2nCEMLXPIlxhkxAnOKQtOYiOi80d5rTVBTFAD6oDrLEJgC+KgbQb7BLGejvrrGsDQ+cWlpjl4pnljdqXKYCpdjYRbK5zucG15QFCWN9HqCucJeJQcL3h+PoQYJ6YcKJ1WQQxkDZQtmQ6QkFoDCKGm0yeE1kIWQUx4D/JUS6xwoxPqO3VOiAtjAF7VMIlSvU+Q/H1q7DpCuJ67xTA5voQEaz/rvdJCeGs8QgT0xVWgrbhUzErN9h8bXOHuXEJwC6IAbLKBACrSwwgFZ2otadFsVpHucGmmKTEsbkNd2rirLgENiV+qW1CBtex3WCAL4S58QgfptNruMSNGjxXL/MEcAtiAM4nisD2MemGsH0jYrhFUI++zLv9ok5wOiGCuNaed5IzRTFAZ4pr60spsWg6xoBdHOvoT4G5otaHz0yzXO8t0QmeCAayucFANiHsQrXh1hbm0DDh7KMIQQwEu8SARRQDXmEcbwNabJr9czDjE1kdZK74JZdlrC5BusFAOh+sLwsRwjo2V9gcONegGsL1II8grq3zGyh5nWEprSbUExHD2yG+O36AFsQAnHf9td/DRLHZTrU1hTGQFpKUOFZs1Z521zsuYTsGgCd+qXahIjj9ezIWAdAZYdfEGr54hJkVDsV0ketGqDsMhAtiwJkjBmiXuLbcnge2DZIjHWLii+4SyPr+KLji19U3pz9yeb1EMJBdCOsUGY8oAGWGKHFctHmiEyqIAQQZKILQiogYHkZwT3A6IYIY4J/kzN9toli1A1zVI5JCzSaOVXvALVDrhTs3nE8Am+19IhjIJoRD4xE6toiE+lcNnitM/G4TtXFu2BaVcOESxHEbhiDWl5k5YoAtigFPSTXAKYzNtt64BPhiloNPQNeruoRVBANuNxggB8uRQtiWE6awDZwLGUjXJHxPE/U2HEEMgGWgDGSfHFQQ6k7r/sWOILh3+y5cd/xcQQxkO8mp3wF7Roxyi/X2aht1TCYugWwSmhsOdSxswpFdXSKDCNb3S4ng9LZ8IUztI6SCRGGYYjcEmzus4xpQ5xPEgD02obcFvKK4ts5dPUK1Ca0e4RPJReHtv6DqEuYfQZAbDGQXwjq+eISDOC/cQHHsc4cpuIIYSOaDzSeKAKzXC0FoZUQMNwFOTUjrtowTHFcQA3De9av1vpOcLorV61T1CIcwprbRt1XvyYbphud9HMdxSYOrSzgEsLmNTQQDfiGst+UKYVs8gqIuA+fyiF+XO+wqteYRxACSZdf0bczYBJByiQG7KK6tcwtjs41q56seUW8RnMIxUM8am2BUl2A5wYBfBBvL2UKYE4/I4gLXsf4wxzRxPU3kCGJ9mc9AUW1rbfKL4oFKhIECM8NF9iUMf0QMN5AsgyE4JzpqIg6OIAbgvOvX1wNg3fm73GKAHlVsE8fmtiYcoZwXn7C2neCpkdO+2EQeEWy2p+IPHCGsw3GFzYhEvUhFJciDKTOmaCbELSGIa/vU6hADtEusloeKYoAljAH34Ljg6hEDjPNPhkoUodUl6ByxwwUGihHBZhuOEE4cgxGP0AkRxzmcYvNJorOtYZ7oZBHEQLiBovoQh1hoZUQMtxhmVIKCOsEVIYj1bcy7fgBeUVzbfmg7biyCI451uGXWisRbXcKiH3wC2FxmitKsIthc7hLCOq5BcxxXOG5TVKyCI37JSAQRl/AJYiAxqA4AHZvQt1PLAbso3rZOF3cpYQykohS1dmlxDIRVj0hUjchYci3vYDt7ljhAAAN2EQyECWG9aoRPCLviEQ5RyyqpFuAUB9WmJ64f1NPEUEGsr7c9JXSJYkFoVUQMtwisqTYdJzidEEEMgLzrV8tDRbG5ndqWE4uwiWOFOSCvGbhqZdrEOcMEY4tgc9sihLArHuGqCJE7KsEZAMfdjsobhwpiIFllAqBjE0DaJVZ9q3W6aOMIY8DrGg+1pwVyoo1WQaLI6ARnMJ5zf8S6lAAG6ieCAb8Q1nHFI6hlVPuceWH3U0G7eULF8bIIYgAsA6X2e23flCiuaOe0LFQqQKXAp4CVfIcjbGeIGG4yrrv9Ik5w+u+UIDbbmic5wC6KAfsjMQUljGttku/VVXrHJ5IptjKeBiuyFIMPzQzTy+wCGChOBLvaUELYFY+gBLArIpFZMBM5YnI2OipvTC2zCWIgLWwdsQkgUBQDSbdYX7+tjSkGSdcYcArkoW39pdaKwj+IzpIZ5ohfICmAqTYOIZtZCFOOMEDHI8wKEj4C88Icg4S7jrrOcAUxEGag1PrDttfp85ggtCIihhtAqCCgsmC+E1yIIAZA3vUD6ZOc2latM/PA1CMxgCeM9X4c1/5MFSTyzHaUdzY617o8AtjcPt2WL4T1tpQQ1uG6woUMqnO5viHLqPwwJYhTv1tiEwBfFKu2huhNucVq33obpMUi6RybNKiKBIm33Jrle5FXABPrc7nBPiGs43GAGzXrnI7LPAHc8TogKYgBBBkogIhiYXgjYrhJ+E5c1DIqLpFFEAP0Xb9qA/BFce213S1WmFEKqh+9v9r+0uuodkB98sNcXeEWxcRkF8TjwkaJYPN3mxCm4hFcV5jrKBeG7gS78sPmep8gBsJFMRAujIF0lEJraxWTYArlBuA6RgD0ewPSn4OtbVYRDBQnhKkohM/tLTg2QTu8bvPEF68rwkAB/KJYBtAJrYyI4RZFP8Fx7/iBpCAGEA+qA0De9QPpkxzAF8W110PHRQljIC0AKXE8dIzuz4YrlouEsy9bVQuO+KX2YfbHFcFm26xCWMcniusidn1RCcoJtm3vEsSAOzYBpKMT2rqEKDb71NurbUyxR4ljwC6Qtb69ItQg4lSTyNBvjGuiCoB+n9R2rnq/20gIYGobbjY4RAjbaIIrzDVP9N+pawXgF8RA2kAB+KK4CPr7okLd5X4prSZoiBhuAfQTlK/8mvVkl/MkB/BFsWprRiBq7aEtSws4lzjW90dB7aPR+E7ulPAF7I8IOVlilwgGeG6w+ZojhH2i11dOrVCBrAlYqyAm2qYEMTA0qA7gucRAMjoBOJ1iwOIW6/1p25HiGLALZGobG0Z95bqJXBOb6HX1xRDAQA4RbPZnE8GAXQh7XOGEEA5xkD34rgs284Qbr6v1kbxWAG4DBeCLYr1PQWhFRAw3mJBaw5wTXBZBDKRPcgBfFOttVXufMK4tp0UhFavQ4QjlRuI6VkUe8Utt7xLAvvY2EQzwhHCIqM1cUi3LxBuuPkxBDGR3iQHaKQaSolhb7xTG8T6I0y/lHOvbuESmol97ZB4qYjlwjoGz76zil9q20xSz7lgFOx/MEcINnnrZZp746g67rhUAUk8UgbSBAvBFsdm2vUXO34JAIWK4idhOTrYTXF5BDPhPcgAtigH6RKe3V9tQ2eDafmAsR2p7HZuLzIVT2zJr34B/QAh3EJ2rv6JEMOAeKMcRwjZXuPCIhM3Z5bjDrvywKzYB2F1iwC+KAb4w1vuN2xLOsdGPV1y6HOV6wBXa1Hvy9MESwEBxIhhwxyIYQjiXK1ywoOZeKwAEGSjqtc39tZkogtDqiBgexnAFMQD2SU5/bTvRATxhrLbNkg0OHX2sjlEni9DNkknzV5jKLn4BvwA2t3OJYMCdD266EA6ALYgBf2zCbJsSwZ4cMOAWxtvaUSKPdI7jbSwOstFv4hgbje/44nb242OLX8AvgKl9ma+zuMG+dRwYIjnU9MhingDIZKAAyaeK6nVqALVxrdC3z0qlEqFSKk5cVyIR6sIQIoZbiNATnPk65K4foE9y1GtyQJxDGNu21be3nRhDc8E+dzkPodlk18neHpuwDLprSwt5X3QiRAQD7nxwlphEETELumNPtQiFa0CdLzYBuKMTQDI+oV4DPGFsttPaWgWyuX8Tl5vcDBhinBS98fZM8Wtr6xPAgNsNBjILYasr3AS4+eEQAwUIv16Y14qBksxyIbQuLXIWHblwM8Qhghiw3/UD/pMcQN/9A35hDNDiWPXhi0Rw3IMBzQVuxGC6EEfDH53ILn5t2+cRwUCYELa5wtysMFsYM0VvaiIOl+h1xSYAOjoB0Jli/XVimUcYA3ZxrG3jEo0sodxEnIJX4RLwlPi1bUOWYXO4wHEbhwimtuEKYZM6Zou5A6+d1wePgQKEi2K1TOoKC8MJEcNNwHnicpzgTEEM0Cc18zX3JAfwT3SA3fWlxLHZhw73xNnsUckhJ3fXMVLC17UPjgAGihXB1GtuPKLIWec465yC2NyWik0AaZcYcGeK4zaODLBN7NrEHhWtMLcFU2x6iAU1QRH9A+A51iHCF3BUosjgAgNhIphYnxLCBVaQoOCKXnMwXaiBAoSLYiDseiEIrYSI4RYgZGpN10mOEsiJ15aTHJD9RKcvt1aKsIg/bpas1U6mIWI8VPi6+ucIYMAvgoFihXDmChJ5MAQvSxADfFEM2DPF+nbWgXAZBsjZhCHgF8ouKumbiCDBmyeG4XpPnP5DxC/Ac4GBcBFMtHEKYR8BQplrnpht1d8s54ki+TqHiQKkrxd5z9+VAckMC/VDxHCDCCmp5jrBAby7fsAvigHeiQ5ITt6hsIljfZ1NIANuoQi0/ohk3/ErskYnbMs5AhgoXgRThLrNQfhiDYQgBpAcVAf4+9Db6J8jxy1WhIrjxDq/G8wSlTr9uqgv8DQfehwhx+CKfYSIX4AngG3b+4QyGEKY0UdWKNHKvV54rw3a32sWEwWghbEgtDoihpsE5648VBAD8N75p9owTnS1/fFOdlkFsg5XLLcKXMfDd3EIEb9AdgHMXWYK4aZUjggUxIDFJQbcgtcmnM12psgKFceAWyCn2gSconUHOKtoDSGLyObknF1tXKKSK4Bt/WRxg6m+6hCP8FG0gUItc5kogNtIAUQcC62NiOEWIosgBuAUxeSdvuPuH6BPdID9ZFc7BvcJzxyQZ6PogRdUyTVF0bEL7sne1y5E/AL2mEJWEVw7Br8Q9rnCmeBMvuFzd0EIYks7a3zC1k5vC/DFsd4fSwQHfJbNriqRZSAfZ5tQ4asoWAArMgvhAsSyzzwBwq8XHAFsXZbhemE7r3EZGIgwUGBMYkBiEoKGiOEWhzrBAQgWxb5l+nJT5NhOdoBbINeOye8KJKtDZD9BDRDCN1TwFuFecPvwXRxs4hfIL4Bty6lYRFYhXJiTTFWWoESzLzahtwP8Ytfm9pr/Ly5xDLgFstl3JhHcQlUlQo+FEx9wCV8gTPy6tskjggP7LIIsghjgP1VUuEwUtU8d1/VCEFoV+bY2EfJkxjjB2dqFimLXcn2d7WQH2E94PpFcO878j8+KEtIUefvjOiG+i4ZrgFqI0HUtzyqCQ2CLY4bQdbYDSFEMgI5PGO2tLjBXHJvbusQcRyhT+wRaSwSbhIpAn+BVuP5WQsWvZzu2CLb1n0MIs68NTEEM8A0Us61PGKfaizgWhiENqNIquGA/ykY76cbZ3D3qMTfVXl/OXaeOxfxJHENb2fozdJxtwT8mHW1R3X6Sn2n2Y3V9FuZscL7Plfv/xVlee1/274pJEW50+gACLpA2N84mRCwDn6wih9pG9U/tR9+Gsy3VT7mT9+PaZyv+ZH1/3M8t9LN3/b9pqO8HOUAu9PtHUadya6lllrED1LiArNcL23LX9WI4s3DhQuy///4YN24cxo0bh56eHtx5553x+ltuuQVHH300Jk6ciFKphBUrVqT6OPzww1EqlRI/73vf+xJt1q9fj3nz5qG7uxvd3d2YN28eNmzYUOd3J8itWgMJqihhaWtziQFYnWLA7gCY24WsM9v4TnZ5HAKO09wI8robRU1OkWe9rUKE1U0OEMGcY2Nhyw5TDrFqD7CcYsDhFuvbKGyuscLlHof0Q8Fxl4cLoX873BslX7+efpyTZmRxm0OFcMANofW6YHGIAQRfL/Rrhd7e3Ib6O3etL+U8d1cLLq1WDcwM77bbbrjsssuw9957AwBuvPFGnHjiiXj00Uex3377YfPmzXjrW9+K9773vTjzzDOt/Zx55pn4whe+EL8ePXp0Yv1pp52G559/HosXLwYAfPjDH8a8efPw85//POh4hTBEDLcArhMckD5p+U5y1DY2YWxuZ27rE8CuNql2gc4AJ47RTBoSGWC09a33lkcLFMHObYqsOOESxEA2UUxsZ4ohrzg2+3B9N7lCOUvfw5Us8QHu58Do2yl+fX1kFd4FDJrLvDzwemGeL1xGirmtb/1w5vjjj0+8vvTSS7Fw4UIsW7YM++23H+bNmwcAWLVqlbOfHXbYAVOmTCHX/elPf8LixYuxbNkyHHrooQCA73znO+jp6cETTzyBGTNm5H8jAsl2eKYdnrhcY58oBrILY8Avjql+fKLHli3jwHWaW4k8IrBIYQxkF7/x+gwiOM8698FYBDFgd4nVdoBbTCs84hhgCmSqv1AH2Nd3Ubimum4kWcR+wDF6hS+3vzzucwNnorNdKwC7KNa3o7bNcr2grhWF3ig3mWq1ip/85CfYvHkzenp6gra96aab8MMf/hCTJ0/G3LlzcfHFF2PHHXcEADzwwAPo7u6OhTAAvOUtb0F3dzfuv/9+EcN1RMRwg8kiejnrucKY2t4mnrgimeoz9MQXIrRbnUYJY5/oDenTd+OR15124hK1gF8QK1xOsYIjjom+XKIqSCjb9lEvB5jzuXGpt0udUYizBG/ofopyonNGOJxdZ7yWuK4V+rZxmwzXi1zjCCxsQQUocIz0FtT+NjZu3JhY3tXVha4u+ju1cuVK9PT0YOvWrRg7dixuvfVW7Lvvvux9vv/978eee+6JKVOm4LHHHsOFF16I//u//8OSJUsAAGvXrsWkSZNS202aNAlr165l70cIR8RwE/BlhznrAYdodlR/0Lc34Z70FL6Tn4u84nm4kPd9cQVv6P44rjtLRBfUxt+JQxArfMJY9aMTKhaJfrliLLNo5sK5GSiaOjvJwUJXEXpcBcYwgvt0dZH3WuBwfAH/tcLsI9E28HqRlc7OTkyZMgXnr/1d4X2PHTsW06ZNSyy7+OKLcckll5DtZ8yYgRUrVmDDhg24+eabcfrpp2Pp0qVsQaxniWfOnInXve51OOSQQ7B8+XIcdNBBAIBSKV0iNIoicrlQHCKGm0Tek5zextuOccIz+zOh+g89+eURz9sDRVwssnxuIXETtpguuB2vM+10FSKMAb441uG6yBQ5RDOHlLAGGh9xICjyPZLkeY9ZxWm9hDW3O8bA69Drha2t7VwRIpJtfWc9/40aNQpPP/00+vv7M23vghKZNlcYqAlzNYDukEMOwcMPP4yrrroK1157bab9H3TQQejo6MBf/vIXHHTQQZgyZQpefPHFVLuXXnoJkydPzrQPgUfzz54jmJCTHBB2onO151Z98PXv7KPAk+H2TBFiMWu2OnTfdW3vi0qkOg8Qxqp/Cl88w0eRMQQbdRbWdaURAr0o8Zn1WBsgsrmViLjXC7Otr73rHMO9XuQ5140aNQqjRo3KvH29iKIIfX3EjSmTP/7xjxgYGMCuu+4KAOjp6UFvby8eeughvPnNbwYAPPjgg+jt7cVhhx1WyDELNCKGm0xouTWd0JOdSVaxnOqnAPEsDFHkwMG8/w9Zt8+0XaggjnfGjD7Y9skhi7McSiOEdSvRqEoZRX5+TRTeIdcK1V4nz/Uiq1DO0q5VueiiizB37lxMmzYNmzZtwqJFi3DffffFJdD+8Y9/4Nlnn8Xq1asBAE888QQAYMqUKZgyZQr++te/4qabbsI///M/Y+LEiXj88cfxyU9+Em984xvx1re+FQCwzz774JhjjsGZZ54Zu80f/vCHcdxxx8nguTqznZ5li+Waa67BV77yFaxZswb77bcfvv71r+Ptb397Yf1zHnG5tjPJKq5dFHEyrAe2yEdeWvnEXY8bjUIc6iKOy1U2jX0gORxdG0UIKt972h7LqGWlWTcA9fw/KOA9hTi/rm1N8horof0NR1588UXMmzcPa9asQXd3N/bff38sXrwYRx11FADgtttuw4c+9KG4vZpMQ2WQOzs78atf/QpXXXUVXnnlFUybNg3HHnssLr74YrS3D32+N910E84991zMmTMHAHDCCSfgm9/8ZgPf6cikFEWBladHGD/+8Y8xb948XHPNNXjrW9+Ka6+9Ft/97nfx+OOPY/fdd3duu3HjRnR3d6O39+cYN25Mpv034qTSqieuVj0uRau73/U+voa+/zziuJ6EimqhPrTqjUSDRH2jzpV59rNx42bsPP7d6O3txbhx4wo8KkHIj4hhD4ceeigOOuggLFy4MF62zz774KSTTsKCBQuc2xYhhl20uli0MVyPOw+tLpxtDKvjblXBLGxfDNPYSrPPuxs3bsb4nU4SMSy0JMPzr7pB9Pf345FHHsEFF1yQWD5nzhzcf//9qfZ9fX2JMH1vby8AYOPGV+t7oHWk2SdQgWZYiVRB2K7IPmBqe8d1vVDXQfHfhFZExLCDv//976hWq6mSJpMnTyYLYC9YsACf//znU8unTTulbscoCIIgCMOFTZs2obu7u9mHIQgJRAwzMOsQ2gpgX3jhhTj//PPj1xs2bMAee+yBZ599Vv74G8zGjRsxbdo0PPfcc/JIroHI594c5HNvDvK584miCJs2bcLUqVObfSiCkELEsIOJEyeivb095QKvW7eOLIBtm8axu7tbTpRNYty4cfLZNwH53JuDfO7NQT53HmIKCa1KW7MPoJXp7OzEwQcfHM8brliyZIkUwBYEQRAEQdgOEGfYw/nnn4958+bhkEMOQU9PD7797W/j2WefxVlnndXsQxMEQRAEQRByImLYwymnnIKXX34ZX/jCF7BmzRrMnDkTv/jFL7DHHnt4t+3q6sLFF1/snOtcqA/y2TcH+dybg3zuzUE+d0HYPpA6w4IgCIIgCMKIRTLDgiAIgiAIwohFxLAgCIIgCIIwYhExLAiCIAiCIIxYRAwLgiAIgiAIIxYRw3XkmmuuwZ577olRo0bh4IMPxm9+85tmH1LL8Otf/xrHH388pk6dilKphP/93/9NrI+iCJdccgmmTp2K0aNH4/DDD8cf//jHRJu+vj6cc845mDhxIsaMGYMTTjgBzz//fKLN+vXrMW/ePHR3d6O7uxvz5s3Dhg0bEm2effZZHH/88RgzZgwmTpyIc889F/39/Yk2K1euxKxZszB69Gi85jWvwRe+8AUMt7GnCxYswJve9CbsuOOOmDRpEk466SQ88cQTiTbyuRfPwoULsf/++8cTM/T09ODOO++M18tn3hgWLFiAUqmE8847L14mn70gCACASKgLixYtijo6OqLvfOc70eOPPx594hOfiMaMGRM988wzzT60luAXv/hF9NnPfja6+eabIwDRrbfemlh/2WWXRTvuuGN08803RytXroxOOeWUaNddd402btwYtznrrLOi17zmNdGSJUui5cuXR7Nnz44OOOCAqFKpxG2OOeaYaObMmdH9998f3X///dHMmTOj4447Ll5fqVSimTNnRrNnz46WL18eLVmyJJo6dWp09tlnx216e3ujyZMnR+973/uilStXRjfffHO04447RldccUX9PqA6cPTRR0fXX3999Nhjj0UrVqyIjj322Gj33XePXnnllbiNfO7Fc9ttt0V33HFH9MQTT0RPPPFEdNFFF0UdHR3RY489FkWRfOaN4KGHHoqmT58e7b///tEnPvGJeLl89oIgRFEUiRiuE29+85ujs846K7Hs9a9/fXTBBRc06YhaF1MMDw4ORlOmTIkuu+yyeNnWrVuj7u7u6Fvf+lYURVG0YcOGqKOjI1q0aFHc5oUXXoja2tqixYsXR1EURY8//ngEIFq2bFnc5oEHHogARH/+85+jKKqJ8ra2tuiFF16I2/z3f/931NXVFfX29kZRFEXXXHNN1N3dHW3dujVus2DBgmjq1KnR4OBggZ9EY1m3bl0EIFq6dGkURfK5N5Lx48dH3/3ud+UzbwCbNm2KXve610VLliyJZs2aFYth+ewFQVBITKIO9Pf345FHHsGcOXMSy+fMmYP777+/SUc1fHj66aexdu3axOfX1dWFWbNmxZ/fI488goGBgUSbqVOnYubMmXGbBx54AN3d3Tj00EPjNm95y1vQ3d2daDNz5kxMnTo1bnP00Uejr68PjzzySNxm1qxZicL6Rx99NFavXo1Vq1YV/wE0iN7eXgDAhAkTAMjn3giq1SoWLVqEzZs3o6enRz7zBvDxj38cxx57LN75zncmlstnLwiCQsRwHfj73/+OarWKyZMnJ5ZPnjwZa9eubdJRDR/UZ+T6/NauXYvOzk6MHz/e2WbSpEmp/idNmpRoY+5n/Pjx6OzsdLZRr4fr/2cURTj//PPxtre9DTNnzgQgn3s9WblyJcaOHYuuri6cddZZuPXWW7HvvvvKZ15nFi1ahOXLl2PBggWpdfLZC4KgkOmY60ipVEq8jqIotUywk+XzM9tQ7YtoE20b1DJc/z/PPvts/OEPf8Bvf/vb1Dr53ItnxowZWLFiBTZs2ICbb74Zp59+OpYuXRqvl8+8eJ577jl84hOfwF133YVRo0ZZ28lnLwiCOMN1YOLEiWhvb0/dza9bty515y+kmTJlCoC0G6J/flOmTEF/fz/Wr1/vbPPiiy+m+n/ppZcSbcz9rF+/HgMDA84269atA5B2lYYD55xzDm677Tbce++92G233eLl8rnXj87OTuy999445JBDsGDBAhxwwAG46qqr5DOvI4888gjWrVuHgw8+GOVyGeVyGUuXLsU3vvENlMtlq+sqn70gjDxEDNeBzs5OHHzwwViyZEli+ZIlS3DYYYc16aiGD3vuuSemTJmS+Pz6+/uxdOnS+PM7+OCD0dHRkWizZs0aPPbYY3Gbnp4e9Pb24qGHHorbPPjgg+jt7U20eeyxx7BmzZq4zV133YWuri4cfPDBcZtf//rXiTJId911F6ZOnYrp06cX/wHUiSiKcPbZZ+OWW27BPffcgz333DOxXj73xhFFEfr6+uQzryNHHnkkVq5ciRUrVsQ/hxxyCN7//vdjxYoVeO1rXyufvSAINRo3Vm9koUqrXXfdddHjjz8enXfeedGYMWOiVatWNfvQWoJNmzZFjz76aPToo49GAKKvfe1r0aOPPhqXnrvsssui7u7u6JZbbolWrlwZnXrqqWTJo9122y26++67o+XLl0dHHHEEWfJo//33jx544IHogQceiN7whjeQJY+OPPLIaPny5dHdd98d7bbbbomSRxs2bIgmT54cnXrqqdHKlSujW265JRo3btywK3n00Y9+NOru7o7uu+++aM2aNfHPq6++GreRz714LrzwwujXv/519PTTT0d/+MMfoosuuihqa2uL7rrrriiK5DNvJHo1iSiSz14QhBoihuvIf/3Xf0V77LFH1NnZGR100EFxCSshiu69994IQOrn9NNPj6KoVvbo4osvjqZMmRJ1dXVF73jHO6KVK1cm+tiyZUt09tlnRxMmTIhGjx4dHXfccdGzzz6baPPyyy9H73//+6Mdd9wx2nHHHaP3v//90fr16xNtnnnmmejYY4+NRo8eHU2YMCE6++yzE+WNoiiK/vCHP0Rvf/vbo66urmjKlCnRJZdcMuzKHVGfN4Do+uuvj9vI5148/+///b/4PLDLLrtERx55ZCyEo0g+80ZiimH57AVBiKIoKkWRTG8jCIIgCIIgjEwkMywIgiAIgiCMWEQMC4IgCIIgCCMWEcOCIAiCIAjCiEXEsCAIgiAIgjBiETEsCIIgCIIgjFhEDAuCIAiCIAgjFhHDgiAIgiAIwohFxLAgCIIgCIIwYhExLAjCdsf06dNRKpVQKpWwYcOGXH0dfvjhcV8rVqwo5PgEQRCE1kHEsCAILUm1WsVhhx2Gd7/73Ynlvb29mDZtGv793//duf0XvvAFrFmzBt3d3bmO45ZbbsFDDz2Uqw9BEAShdRExLAhCS9Le3o4bb7wRixcvxk033RQvP+ecczBhwgR87nOfc26/4447YsqUKSiVSrmOY8KECdhll11y9SEIgiC0LiKGBUFoWV73utdhwYIFOOecc7B69Wr87Gc/w6JFi3DjjTeis7MzqK8bbrgBO+20E26//XbMmDEDO+ywA97znvdg8+bNuPHGGzF9+nSMHz8e55xzDqrVap3ekSAIgtBqlJt9AIIgCC7OOecc3HrrrfjgBz+IlStX4nOf+xwOPPDATH29+uqr+MY3voFFixZh06ZNeNe73oV3vetd2GmnnfCLX/wCf/vb3/Dud78bb3vb23DKKacU+0YEQRCElkTEsCAILU2pVMLChQuxzz774A1veAMuuOCCzH0NDAxg4cKF2GuvvQAA73nPe/CDH/wAL774IsaOHYt9990Xs2fPxr333itiWBAEYYQgMQlBEFqe733ve9hhhx3w9NNP4/nnn8/czw477BALYQCYPHkypk+fjrFjxyaWrVu3LtfxCoIgCMMHEcOCILQ0DzzwAK688kr87Gc/Q09PD8444wxEUZSpr46OjsTrUqlELhscHMx8vIIgCMLwQsSwIAgty5YtW3D66afjIx/5CN75znfiu9/9Lh5++GFce+21zT40QRAEYTtBxLAgCC3LBRdcgMHBQVx++eUAgN133x1f/epX8alPfQqrVq1q7sEJgiAI2wUihgVBaEmWLl2K//qv/8INN9yAMWPGxMvPPPNMHHbYYbniEoIgCIKgKEVyNREEYTtj+vTpOO+883DeeecV0t+qVauw55574tFHH81c1k0QBEFoTcQZFgRhu+Qzn/kMxo4di97e3lz9zJ07F/vtt19BRyUIgiC0GuIMC4Kw3fHMM89gYGAAAPDa174WbW3Z7/tfeOEFbNmyBUAtsxw6850gCILQ2ogYFgRBEARBEEYsEpMQBEEQBEEQRiwihgVBEARBEIQRi4hhQRAEQRAEYcQiYlgQBEEQBEEYsYgYFgRBEARBEEYsIoYFQRAEQRCEEYuIYUEQBEEQBGHEImJYEARBEARBGLGIGBYEQRAEQRBGLCKGBUEQBEEQhBGLiGFBEARBEARhxCJiWBAEQRAEQRixiBgWBEEQBEEQRiwihgVBEARBEIQRi4hhQRAEQRAEYcQiYlgQBEEQBEEYsYgYFgRBEARBEEYsIoYFQRAEQRCEEYuIYUEQBEEQBGHEImJYEARBEARBGLGIGBYEQRAEQRBGLCKGBUEQBEEQhBGLiGFBEARBEARhxCJiWBAEQRAEQRixiBgWBEEQBEEQRiwihgVBEARBEIQRi4hhQRAEQRAEYcQiYlgQBEEQBEEYsYgYFgRBEARBEEYsIoYFQRAEQRCEEYuIYUEQBEEQBGHEImJYEARBEARBGLGIGBYEQRAEQRBGLCKGBUEQBEEQhBGLiGFBEARBEARhxCJiWBAEQRAEQRixiBgWBEEQBEEQRiwihgVBEARBEIQRi4hhQRAEQRAEYcQiYlgQBEEQBEEYsYgYFgRBEARBEEYsIoYFQRAEQRCEEYuIYUEQBEEQBGHEImJYEARBEARBGLGIGBYEQRAEQRBGLCKGBUEQBEEQhBGLiGFBEARBEARhxCJiWBAEQRAEQRixiBgWBEEQBEEQRiwihgVBEARBEIQRi4hhQRAEQRAEYcQiYlgQBEEQBEEYsYgYFgRBEARBEEYsIoYFQRAEQRCEEYuIYUEQBEEQBGHEImJYEARBEARBGLGIGBYEQRAEQRBGLCKGBUEQBEEQhBGLiGFBEARBEARhxCJiWBAEQRAEQRixiBgWBEEQBEEQRiwihgVBEARBEIQRi4hhQRAEQRAEYcQiYlgQBEEQBEEYsYgYFgRBEARBEEYsIoYFQRAEQRCEEYuIYUEQBEEQBGHEImJYEARBEARBGLGIGBYEQRAEQRBGLCKGBUEQBEEQhBGLiGFBEARBEARhxCJiWBAEQRAEQRixiBgWBEEQBEEQRiwihgVBEARBEIQRi4hhQRAEQRAEYcQiYlgQBEEQBEEYsYgYFgRBEARBEEYsIoYFQRAEQRCEEYuIYUEQBEEQBGHE8v8D4d1fBLHaaTMAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, - "execution_count": 19, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -562,12 +580,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In the following gradient Vs kernel, we can see how the 5km x 5km smoothing blurs away some of the detail of the raw graident. The blue colors here suggest that the initial model needs to be sped up to best fit waveforms (and vice versa, red colors suggest slowing down the initial model)." + "### Visualizing the Gradient\n", + "\n", + "We can look at the gradients created during the adjoint simulations to get an idea of how our inversion wanted to update the model. Gradients tell us how to perturb our starting model (the homogeneous halfspace) to best fit the data that was generated by our target model (the checkerboard).\n", + "\n", + "We can see that our gradient (Vs kernel) is characterized by large red and blue blobs. The blue colors in the kernel tell us that the initial model is too fast, while red colors tell us that the initial model is too slow (that is, **red==too slow** and **blue==too fast**). This makes sense if we look at the checkerboard target model above, where the perturbation is slow (red color) the corresponding kernel tells us the initial model is too fast (blue color)." ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -579,12 +601,12 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "" ] }, - "execution_count": 20, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -594,9 +616,22 @@ "Image(filename='g_01_vs.png') " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualizing the updated model\n", + "\n", + "After two iterations, the updated model starts to take form. We can clearly see tha the lack of data coverage on the outer edges of the model mean we do not see any appreciable update here, whereas the center of the domain shows the strongest model updates which are starting to resemble the checkerboard pattern shown in the target model.\n", + "\n", + "With only 4 events and 2 iterations, we do not have quite enough constraint to recover the sharp contrats between checkers shown in the Target model. We can see that smearing and regularization leads to more prominent slow (red) regions. \n", + "\n", + "If we were to increase the number of events and iterations, will it help our recovery of the target model? This task is left up to the reader!" + ] + }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -608,12 +643,12 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "" ] }, - "execution_count": 21, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -622,6 +657,411 @@ "! seisflows plot2d MODEL_02 vs --savefig m_02_vs.png\n", "Image(filename='m_02_vs.png') " ] + }, + { + "attachments": { + "tape_etal_2007_fig9.jpeg": { + "image/jpeg": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Re-creating kernels from Tape et al. 2007\n", + "\n", + "The 2D checkerboard model and source-receiver configuration that runs in this example comes from the published work of [Tape et al. (2007)](https://academic.oup.com/gji/article/168/3/1105/929373). Here, Tape et al. generate event and misfit kernels for a number of individual events in [Figure 9](https://academic.oup.com/view-large/figure/31726687/168-3-1105-fig009.jpeg) (shown below). This exercise is meant to illustrate how kernel features change for a simple target model (the checkerboard) depending on the chosen source-receiver geometry. \n", + "\n", + "![tape_etal_2007_fig9.jpeg](attachment:tape_etal_2007_fig9.jpeg)\n", + "\n", + "*Caption: Construction of a misfit kernel. (a)–(g) Individual event kernels, each constructed via the method shown in Fig. 8 (which shows Event 5). The colour scale for each event kernel is shown beneath (g). (h) The misfit kernel is simply the sum of the 25 event kernels. (i) The source–receiver geometry and target phase‐speed model. There are a total of N= 25 × 132 = 3300 measurements that are used in constructing the misfit kernel (see Section 5).*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Choosing an event\n", + "\n", + "The Event ID that generated each kernel is specified in the title of each sub plot (e.g., Panel. (a) corresponds to Event \\#1). We can attempt to re-create these kernels by choosing specific event IDs to run Example 2 with. \n", + "\n", + ">__NOTE:__ Our choice of preprocessing module, misfit function, gradient smoothing length, nonlinear optimization algorithm, etc. will affect how each event kernel is produced, and consequently how much they differ from the published kernels shown above. We do not expect to perfectly match the event kernels above, but rather to see that first order structure is the same.\n", + "\n", + "To specify the specific event ID, we can use the `--event_id` flag when running Example 2. For this docs page we'll choose Event \\#7, which is represented by Panel (g) in the figure above. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "usage: seisflows examples [-h] [-r [SPECFEM2D_REPO]] [--nsta [NSTA]]\r\n", + " [--ntask [NTASK]] [--niter [NITER]]\r\n", + " [--event_id [EVENT_ID]]\r\n", + " [method] [choice]\r\n", + "\r\n", + "Lists out available example problems and allows the user to run example\r\n", + "problems directly from the command line. Some example problems may have pre-\r\n", + "run prompts mainly involving the numerical solver\r\n", + "\r\n", + "positional arguments:\r\n", + " method Method for running the example problem. If\r\n", + " notprovided, simply prints out the list of available\r\n", + " example problems. If given as an integer value, will\r\n", + " print out the help message for the given example. If\r\n", + " 'run', will run the example. If 'setup' will simply\r\n", + " setup the example working directory but will not\r\n", + " execute `seisflows submit`\r\n", + " choice If `method` in ['setup', 'run'], integervalue\r\n", + " corresponding to the given example problem which can\r\n", + " listed using `seisflows examples`\r\n", + "\r\n", + "optional arguments:\r\n", + " -h, --help show this help message and exit\r\n", + " -r [SPECFEM2D_REPO], --specfem2d_repo [SPECFEM2D_REPO]\r\n", + " path to the SPECFEM2D directory which should contain\r\n", + " binary executables. If not given, assumes directory is\r\n", + " called 'specfem2d/' in the current working directory.\r\n", + " If that dir is not found, SPECFEM2D will be\r\n", + " downloaded, configured and compiled automatically in\r\n", + " the current working directory.\r\n", + " --nsta [NSTA] User-defined number of stations to use for the example\r\n", + " problem (1 <= NSTA <= 131). If not given, each example\r\n", + " has its own default.\r\n", + " --ntask [NTASK] User-defined number of events to use for the example\r\n", + " problem (1 <= NTASK <= 25). If not given, each example\r\n", + " has its own default.\r\n", + " --niter [NITER] User-defined number of iterations to run for the\r\n", + " example problem (1 <= NITER <= inf). If not given,\r\n", + " each example has its own default.\r\n", + " --event_id [EVENT_ID]\r\n", + " Allow User to choose a specific event ID from the Tape\r\n", + " 2007 example (1 <= EVENT_ID <= 25). If not used,\r\n", + " example will default to choosing sequential from 1 to\r\n", + " NTASK\r\n" + ] + } + ], + "source": [ + "# Run the help message to view the description of the optional arguemnt --event_id\n", + "! seisflows examples -h" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run command with open variable to set SPECFEM2D path. Choose event_id==7 and only run 1 iteration\n", + "! seisflows examples run 2 -r ${PATH_TO_SPECFEM2D} --event_id 7 --niter 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Comparing kernels\n", + "\n", + "This workflow should run faster than Example \\#2 proper, because we are only using 1 event and 1 iteration. In the same vein as above, we can visualize the output gradient to see how well it matches with those published in Tape et al." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/bchow/Work/work/seisflows_example/example_2a\n", + "logs\tparameters.yaml sflog.txt specfem2d\r\n", + "output\tscratch\t\t sfstate.txt specfem2d_workdir\r\n" + ] + } + ], + "source": [ + "%cd ~/sfexamples/example_2a\n", + "! ls" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Figure(707.107x707.107)\r\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "! seisflows plot2d GRADIENT_01 vs_kernel --save g_01_vs.png\n", + "Image(\"g_01_vs.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the above figure we can see that the first order structure of our Vs event kernel is very similar to Panel (g) from Figure 9 of Tape et al. (2007). Our kernel shows some additional low-amplitude sensitivity, most prominently at the ring of alternative blue and red on the edges of the domain. From experience this is likely due to the `Pyaflowa` preprocessing module attempting to window and fit very late arriving waves that are caused by boundary reflections from the edge of the domain. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example \\#3: En-masse Forward Simulations\n", + "\n", + "SeisFlows is not just an inversion tool, it can also be used to simplify workflows to run forward simulations using external numerical solvers. In Example \\#3 we use SeisFlows to run en-masse forward simulations. \n", + "\n", + "To motivate this use case, imagine a User who has a velocity model of a specific region (at any scale). This User would like to run a number of forward simulations for N events and S stations to generate N x S synthetic seismograms. These synthetics may be used directly, or compared to observed seismograms to understand how well the regional velocity model characterizes actual Earth structure. \n", + "\n", + "Although this could be done manually, if N is large, this effort may require a large number of manual tasks, including the creation of working directories, editing submit calls, and providing book keeping for the external solver. SeisFlows is here to automate all of these tasks." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No existing SPECFEM2D repo given, default to: /home/bchow/Work/work/seisflows_example/example_2a/specfem2d\r\n", + "\r\n", + " @@@@@@@@@@ \r\n", + " .@@@@. .%&( %@. \r\n", + " @@@@ @@@@ &@@@@@@ ,%@ \r\n", + " @@@@ @@@, /@@ @ \r\n", + " @@@ @@@@ @@@ @ \r\n", + " @@@@ @@@@ @@@ @ @ \r\n", + " @@@ @@@@ ,@@@ @ @ \r\n", + " @@@@ @@@@ @@@@ @@ @ @\r\n", + " @@@@ @@@@@ @@@@@ @@@ @@ @\r\n", + " @@@@ @@@@@ @@@@@@@@@@@@@@ @@ @\r\n", + " @@@@ @@@@@@ @@@& @@@ @ \r\n", + " @@@@@ @@@@@@@@ %@@@@# @@ \r\n", + " @@@@# @@@@@@@@@@@@@@@@@ @@ \r\n", + " &@@@@@ @@@@( @@& \r\n", + " @@@@@@@ /@@@@ \r\n", + " @@@@@@@@@@@@@@@@@\r\n", + " @@@@@@@@@@ \r\n", + "\r\n", + "\r\n", + "================================================================================\r\n", + " SEISFLOWS EXAMPLE 3 \r\n", + " /////////////////// \r\n", + "This is a [SPECFEM2D] [WORKSTATION] example, which will run forward simulations\r\n", + "to generate synthetic seismograms through a homogeneous halfspace starting\r\n", + "model. This example uses no preprocessing or optimization modules. [10 events,\r\n", + "25 stations] The tasks involved include:\r\n", + "\r\n", + "1. (optional) Download, configure, compile SPECFEM2D\r\n", + "2. [Setup] a SPECFEM2D working directory\r\n", + "3. [Setup] starting model from 'Tape2007' example\r\n", + "4. [Setup] a SeisFlows working directory\r\n", + "5. [Run] the forward simulation workflow\r\n", + "================================================================================\r\n" + ] + } + ], + "source": [ + "# Run the help dialogue to see what \n", + "! seisflows examples 3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run command with open variable to set SPECFEM2D path\n", + "! seisflows examples run 3 -r ${PATH_TO_SPECFEM2D}" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "You will be met with the following log message after succesful completion of the example problem\n", + "\n", + ".. code:: bash\n", + "\n", + " ================================================================================\n", + " EXAMPLE COMPLETED SUCCESFULLY\n", + " ================================================================================" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Understanding example outputs\n", + "\n", + "This example does not produce gradients or updated models, only synthetic seismograms. We can view these seismograms using the `seisflows plotst` command, which is used to quickly plot synthetic seismograms (using ObsPy under the hood). " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/bchow/Work/work/seisflows_example/example_3\n", + "logs\tparameters.yaml sflog.txt specfem2d\r\n", + "output\tscratch\t\t sfstate.txt specfem2d_workdir\r\n" + ] + } + ], + "source": [ + "%cd ~/sfexamples/example_3\n", + "! ls" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, we have set the `export_traces` parameter to **True**, which tells SeisFlows to store synthetic waveforms generated during the workflow in the `output/` directory. Under the hood, SeisFlows is copying all synthetic seismograms from the Solver's `scratch/` directory, to a more permanent location." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "export_traces: True\r\n" + ] + } + ], + "source": [ + "# The `export_traces` parameter tells SeisFlows to save synthetics after each round of forward simulations\n", + "! seisflows par export_traces " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MODEL_INIT solver\r\n" + ] + } + ], + "source": [ + "# Exported traces will be stored in the `output` directory\n", + "! ls output" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "001 002 003 004 005 006 007 008\t009 010\n", + "\n", + "AA.S000000.BXY.semd AA.S000009.BXY.semd AA.S000018.BXY.semd\n", + "AA.S000001.BXY.semd AA.S000010.BXY.semd AA.S000019.BXY.semd\n", + "AA.S000002.BXY.semd AA.S000011.BXY.semd AA.S000020.BXY.semd\n", + "AA.S000003.BXY.semd AA.S000012.BXY.semd AA.S000021.BXY.semd\n", + "AA.S000004.BXY.semd AA.S000013.BXY.semd AA.S000022.BXY.semd\n", + "AA.S000005.BXY.semd AA.S000014.BXY.semd AA.S000023.BXY.semd\n", + "AA.S000006.BXY.semd AA.S000015.BXY.semd AA.S000024.BXY.semd\n", + "AA.S000007.BXY.semd AA.S000016.BXY.semd\n", + "AA.S000008.BXY.semd AA.S000017.BXY.semd\n" + ] + } + ], + "source": [ + "# Synthetics will be stored on a per-event basis, and in the format that the external solver created them\n", + "! ls output/solver/\n", + "! echo\n", + "! ls output/solver/001/syn" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# The `plotst` function allows us to quickly visualize output seismograms\n", + "! seisflows plotst output/solver/001/syn/AA.S000000.BXY.semd --save AA.S000000.BXY.semd.png\n", + "Image(filename=\"AA.S000000.BXY.semd.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Vq5dQKBTC29tbjBgxQiQlJUntsbGxZtv5+uuvTcao7S5Yb7/9tpDJZCY1hIeHCyGu351pyJAhQqVSCaVSKXr37i02b94svffAgQPCw8NDej137lzh6+srPDw8RNeuXcVHH31ksq2+ffsKDw8P4efnJ95++21RXV0ttb377rsmd0qqb3/++c9/CgcHB5Oab3xvc/p7DUFBQVLbiy++KH02MTExorCw0OS927dvFwEBAcJoNJqNe+zYMREZGSnc3NzEoEGD6rwDVo3FixcLHx8f4eXlJV555RWTz66+77FbMVZtd8EyGo3ihRdeECqVSvj6+or333/fpP3HH38UnTt3Fu7u7mL06NEmt9WdNWuWmDVrlvT62rVrYuTIkcLd3V2EhoaKPXv21Lof+/btM7sLVnh4uMm/ofr2p77vsb9/b8fHx4u2bdsKuVwu7r77bpGcnFxrTUJYdjcyIrq9yYS44dwJIiIiIiKiW4inYBERERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdUwgBARERERkdU42boAe1ZdXY0rV67A09MTMpnM1uUQERERUSsnhEBxcTHat28PB4eWOdfAANIEV65cQceOHW1dBhERERGRiaysLAQEBNi6jFoxgDSBp6cngOt/wUql0sbVEBEREVFrp9Pp0LFjR+k4tSWy2wCSl5eHf/7zn9i3bx86duyIuLg43HfffWb9ysrK8OSTT2Lr1q1Qq9VYtmwZHnvsMbN+999/P/bv34/y8nKLa6g57UqpVDKAEBEREVGL0ZIvD7DbADJnzhy0b98e+fn52L17NyZMmID09HSo1WqTfrGxsdBoNMjOzkZKSgoeeOABREdHo2vXrlKfLVu2QK/XW3sXiIiIiIhaHZkQQti6iIbS6/Xw8fFBZmYm/P39AQB33303Zs6cialTp5r09ff3x5YtW9CvXz8AwNSpU9GlSxe89dZbAIDy8nL06dMHn376KYYOHdqgGRCdTgeVSgWtVssZECIiIiKyOXs4PrXLGZBz585BpVJJ4QMAoqKikJqaatKvsLAQubm5iIyMNOl37Ngx6fXSpUvx6KOPWnSRjsFggMFgkF7rdLqm7AYRERERUatjlwFEr9ebJTqlUomioiKzfo6OjpDL5Sb9ak63yszMxMaNG3HixAnk5ubedLtLlizBO++80+i6q6ur8ddffyE3NxfV1dWNHoduDUdHR3Tq1AnBwcG2LoWIiIjotmWXAUShUJjNPuh0OigUCrN+RqMRpaWlUgi5sd9LL72EhQsXws3NzaLtzp8/H3PnzjXZpqW34U1NTcWrr76Kq1evWtSfbCcsLAwrVqxAu3btbF0KERER0W3HLgNIaGgotFotcnNz4efnBwBISkrCzJkzTfqp1Wr4+fkhOTlZugYkKSkJERERAID9+/fj999/x5w5c2A0GmEwGODn54dff/0VYWFhZtt1dXWFq6trg+vV6XSYM2cOOnfujIULFyIkJASOjo4NHodurcrKSiQnJ+O9997Dc889hw0bNrToO0gQERER2SO7DCAKhQJjxoxBbGwsPvjgA+zZswcpKSkYPXq0Wd+YmBgsXLgQ69atQ2pqKhISEnD06FEAwJkzZ6RTobKysjBo0CCcPHkSbdq0adZ6f/31V5SUlGDZsmXw9fVt1rGped19991wdXXFnDlz8Ndff6F79+62LqnFOHjwIP71r38hLi4OUVFRti6HiIiI7FTLfD67BeLi4pCVlQUfHx/MmzcPGzduhFqtRnx8vDTDAQALFiyQLlifMGEC4uLipNmNtm3bws/PD35+flIw8PPzg5NT8+ay9PR0BAQEMHzYiTvuuAMAcOHCBRtX0rK8/vrrOHz4MN59911bl0JERER2zC5nQADA19cXO3bsMFv/+OOP4/HHH5deu7u7Iz4+/qbjderUqUG34G2IqqoquLi43JKxqfnV/F1VVlbauJKWQ6vV4vDhw2jXrh327dsHIQRPTyMiIqJGsdsZkNtJSEiIdI3K382YMQNubm5md/i6kRACCxcuRFBQEBQKBYKCgvDiiy9K7Xl5eRg1ahTkcjnCwsKwd+9ek/cvXboUvr6+8Pb2xquvvoobHw2TmJiIqKgoyOVyDB48GBcvXpTaysrKEBMTA09PTwQGBmLdunUm465evRoBAQFQKpWYPn06KioqpLZZs2YhJCQEMpkMR44cseRjAnD9zmUymQwKhQIKhQIdOnQwuTPZ0aNH4evri7y8PGndypUrER0djezsbPj4+ODEiRNSm9FoxB133IGvvvrK4hpao6SkJBiNRrz88svIz8/n7BARERE1GgOIjR0+fBh5eXk4ceIEzp8/b9JWXl6O77//HnK5HJs2bapzjG+++QabN2/GgQMHoNfr8dtvvyE6Olpqv/Gp8cuWLcOECRNQWFgIANixYwdWrlyJo0ePIjU1Fdu3b8fXX38N4PpzT8aPH48XXngBGo0G/fv3x5QpU6Rxb3zK/Pr16/H000/j7NmzAIDk5GTMnTsXW7ZsQVZWFjIzM7Fo0SLpvTUH/ZY8f+XvXF1dodfrodfrcfDgQXz++efYvXs3AKBfv36YOHEiXnrpJQBATk4O3njjDaxatQodOnTAwoUL8dRTT8FoNAIAPvzwQ6hUKkyfPr3BdbQmKSkpcHZ2xrhx4wBcv36KiIiIqDEYQGwsPj4e48ePx7333mt2qti2bdvg7e2NefPm1XsaWWJiIkaNGoWgoCAAQGBgoBQU9Ho9tm7digULFkAul2Ps2LHo0aMHtm3bBgBYs2YNnnnmGXTu3Bn+/v6YN28e1q5dC+D6XcIUCoU0C/PWW2/h+PHj0izImjVrEBsbC6VSiQEDBmDMmDFYv349AODbb7/FpEmT0KdPH6hUKrz55pvSuAAwe/ZsDB48uMl3AwsODsaAAQNw+vRpad2SJUuwb98+/Pzzz3juuecwZcoUKZA9/fTTcHV1xccff4ysrCwsXrwYq1at4ulEN5GcnIxu3bohODgYLi4uSE9Pt3VJREREZKcYQGyosrISGzduxKRJkzBp0iSzkLF27VpMnDgRjz76KA4cOIDLly/XOk6/fv2watUqrFixAidOnDB5yOHNnhqflpZm9qT4uto8PDwQEhKCtLS0Op8yX9+4GRkZKCsra/DnVJ/09HQcPnwYffv2ldYplUp88MEHePTRR3H06FEsXLhQapPJZFi1ahUWLFiAadOm4YUXXkDXrl2btabb0fnz59G1a1c4OjoiODiYp2ARERFRozGA2NCuXbtQXV2NoUOHYty4ccjMzERiYiIAQKPRYOfOnZg0aRI6d+6M3r17m11jUWPq1KlYvnw5EhISMHDgQPj7+0vXNNT11Piap8H/vb2+thvbb/aU+drGrVnfVAaDAV5eXlAqlejSpQvuuusukwACAH379oVWq8WoUaPg6elp0hYREYEnnngCWVlZ+Ne//tXkelqDixcvSjNsISEhnAEhIiKiRmMAsaG1a9di/PjxcHZ2hlqtxvDhw6VZkI0bNyIwMBC9e/cGgFpnSG40bdo07N+/H0VFRYiNjcWTTz6J1NTUmz41/u/t9bXd2H7jU+YtHbdmfVO5urqiqKgIOp0OeXl5yM/Px6uvvmrSZ86cOZg6dSo2bNiAtLQ0szHCw8MREhLCu5NZoLq6GpcuXZICSEBAAK5cuWLjqoiIiMheMYDYSHFxMRISErBhwwbpWST79+/H+vXrYTQaER8fj6ysLKlt8eLFSEpKkk5xqourqyueeeYZqNVqnD592uSp8TVufBp8eHg4kpOTLWorKSlBeno6wsPDTZ4yb+m4wcHBcHd3b8KnZq5NmzYYN24cdu3aJa3buHEjzpw5g08++QSvv/46Zs+ebXJnL2qYvLw8GAwGKYD4+fmZfD8RERERNQQDiI1s3rwZbdq0wZkzZ3Dy5EmcPHkSaWlpKC8vx5dffonDhw/j119/NWm77777ap0F+eabb7Bz506UlJTAaDRi7dq10Ol0uOOOO0yeGl9WVoaEhASTp8bHxMRg5cqVyMjIQG5uLlasWIGYmBgAwD333AO9Xo/Vq1fDYDBg0aJF6NOnj3QgWvOU+eLiYhw5cgQJCQmYNGkSAGDy5MnYuHEjTpw4Aa1Wi3fffVcaFwAqKipQXl4OIYTJ1w1VVFSErVu3Sk8s12q1ePHFF7Fy5Uq4ubnhpZdeglarle7sRQ1Xc9OBGwPI1atXTa41IiIiIrKYoEbTarUCgNBqtfX2W758uZg4caLJumHDhom3337brO+zzz4rnJ2dxT333GPWtmnTJhEUFCSqq6tFeHi4WLt2rRBCiO+//170799fqFQqoVQqRe/evcUPP/wgve/atWti5MiRwt3dXYSGhoo9e/aYjLt48WLh4+MjvLy8xCuvvCKqq6ultmPHjonIyEjh5uYmBg0aJDIzM6W20tJSMXnyZOHh4SECAgJEfHy8ybhff/21aN++vVAoFGLatGmivLxcahs8eLAAYLJkZGQIIYR49913xf333y/1vf/++8W7774rhBAiIyNDABAeHh7Cw8NDeHt7i/Hjx4srV64IIYSYNWuWiImJManj8OHDwtfXV+Tl5ZnUNmLECLPPuEZ0dLTJZ9iafffddwKAKCgoEEIIsXnzZgHA5PMkIiKilsHS41NbkgnBc1MaS6fTQaVSQavVml2sfaP33nsPiYmJ2LBhgxWro6bo06cP3njjDYwdO9bWpdjcxx9/jJdffhnl5eWQyWT4/fffMWDAACQnJ6NHjx62Lo+IiIhuYOnxqS3xFCwiqldubi7atWsnPSvFz89PWk9ERETUUAwgVsKJJvvBvytTV69eRbt27aTXNV/n5OTYqiQiIiKyYwwgViCXy6HVanlgayeKiooAwOQZJ63Z3wOIXC6Hh4cHCgoKbFgVERER2SsGECvo06cPCgoKpIcMUsv2008/QSaTSc9gae2uXr0qnXZVQ61WQ6PR2KgiIiIismdOti6gNYiOjkZUVBTmzZuHUaNGoUuXLnB0dLR1WfQ3lZWVOHXqFHbt2oWHHnoIbdq0sXVJLUJubi6GDh1qss7b2xuFhYU2qoiIiIjsGQOIFTg6OuL//u//8Nlnn2Hfvn3YtGkTT8dqgRwdHdGpUyc888wzmDp1qq3LaRGEEGanYAGcASEiIqLGYwCxErlcjpdeegkvvfSSrUshsphOp4PBYDALIJwBISIiosbiNSBEVKerV68CAGdAiIiIqNkwgBBRneoKIJwBISIiosZiACGiOuXl5QEAfH19TdZzBoSIiIgaiwGEiOpUUFAAmUwGtVptsr5mBoQ3UyAiIqKGYgAhojppNBp4eXmZ3TZarVbDaDSiuLjYRpURERGRvWIAIaI6aTQaeHt7m62vWcfTsIiIiKihGECIqE51BZCaU7IYQIiIiKih7DaA5OXlYdSoUZDL5QgLC8PevXtr7VdWVoaYmBh4enoiMDAQ69atk9qOHDmCnj17wsvLC23btsW0adOg1+uttQtELV5BQUGtAcTLywvA9eeEEBERETWE3QaQOXPmoH379sjPz8eyZcswYcKEWm8LGhsbC41Gg+zsbKxfvx5PP/00zp49CwDo0qULfvrpJxQVFSEzMxPV1dVYuHChtXeFqMXSaDTw8fExW69UKgEwgBAREVHD2WUA0ev12Lp1KxYsWAC5XI6xY8eiR48e2LZtm1nfNWvWIDY2FkqlEgMGDMCYMWOwfv16AECbNm3QoUMHAIAQAjKZDBkZGVbdF6KWrK5TsDw9PQEwgBAREVHDOdm6gMY4d+4cVCoV/P39pXVRUVFITU016VdYWIjc3FxERkaa9Dt27Jj0+tKlS+jZsye0Wi0UCgV+/PHHOrdrMBhgMBik1zz4ottdXQHEzc0NTk5O/DdAREREDWa3MyA1p4DUUCqVZtdv6PV6ODo6Qi6X19kvMDAQRUVFuHr1Kl599VWTUPN3S5YsgUqlkpaOHTs20x4RtUwFBQW1noIlk8mgVCp5G14iIiJqMLsMIAqFwuw3rzqdDgqFwqyf0WhEaWlpvf0AoG3bthg5ciSmTp1a53bnz58PrVYrLVlZWU3cE6KWq6ysDOXl5bXOgADXwzxnQIiIiKih7DKAhIaGQqvVIjc3V1qXlJSEiIgIk35qtRp+fn5ITk6ut1+N6upqpKen17ldV1dXKJVKk4XodlVzi10GECIiImpOdhlAFAoFxowZg9jYWJSVlSEhIQEpKSkYPXq0Wd+YmBgsXLgQxcXFOHLkCBISEjBp0iQAwI4dO3DmzBkIIZCTk4M333wTQ4YMsfbuELVINQGktlOwAAYQIiIiahy7DCAAEBcXh6ysLPj4+GDevHnYuHEj1Go14uPjTWY4FixYIF2wPmHCBMTFxSEsLAwAcPXqVYwcORIKhQLR0dEICAjAypUrbbVLRC1KQUEBgLpnQDw9PRlAiIiIqMFkQghh6yLslU6ng0qlglar5elYdNvZvHkzHn74YeTn59c6C/Loo48iPz8fP//8sw2qIyIiotrYw/Gp3c6AENGtpdFoIJPJpKee/x1PwSIiIqLGYAAholppNBp4eXnB0dGx1nYGECIiImoMBhAiqlVBQUGd138ADCBERETUOAwgRFSrup6CXoMXoRMREVFjMIAQUa00Gk2dt+AFrs+AlJSUwGg0WrEqIiIisncMIERUq5vNgNTcWUOv11urJCIiIroNMIAQUa0suQYEAE/DIiIiogZhACGiWllyDQjAAEJEREQNwwBCRLW62TUgCoUCAE/BIiIiooZhACEiM2VlZSgrK6t3BqQmgJSUlFirLCIiIroNMIAQkRmNRgMA9QYQDw8PAJwBISIiooZhACEiMzUBxJJTsDgDQkRERA3BAEJEZiyZAXF3dwfAGRAiIiJqGAYQIjJTUFAAoP4A4uDgAA8PD86AEBERUYMwgBCRGY1GA5lMBi8vr3r7KRQKzoAQERFRgzCAEJEZjUYDLy8vODo61tuPMyBERETUUAwgRGTmZg8hrMEZECIiImooBhAiMlNQUGBRAOEMCBERETUUAwgRmbnZU9BrcAaEiIiIGooBhIjMaDQaqNXqm/bjDAgRERE1FAMIEZkpKCjgDAgRERHdEgwgRGTG0lOwOANCREREDcUAQkRmLL0InTMgRERE1FB2G0Dy8vIwatQoyOVyhIWFYe/evbX2KysrQ0xMDDw9PREYGIh169ZJbdu3b8edd94JlUqFgIAAvPPOO9Yqn6jFKisrQ3l5ucUzIAwgRERE1BBOti6gsebMmYP27dsjPz8fu3fvxoQJE5Cenm524WxsbCw0Gg2ys7ORkpKCBx54ANHR0ejatSuKi4uxaNEi3HXXXbh69SpGjBiBzp07Y8qUKTbaKyLbKygoAACLZ0B4ChYRERE1hF3OgOj1emzduhULFiyAXC7H2LFj0aNHD2zbts2s75o1axAbGwulUokBAwZgzJgxWL9+PQDgsccew3333QdXV1cEBgZi/PjxOHbsmLV3h6hF0Wg0AMAZECIiIrol7DKAnDt3DiqVCv7+/tK6qKgopKammvQrLCxEbm4uIiMj6+1X4/Dhw4iIiKhzuwaDATqdzmQhut3UzIBYehesqqoqVFRU3OqyiIiI6DZhlwFEr9dDqVSarFMqlWa/idXr9XB0dIRcLq+3HwB89tlnyMnJwbRp0+rc7pIlS6BSqaSlY8eOTdwTopanZgbE0iehA+AsCBEREVnMLgOIQqEwm33Q6XRQKBRm/YxGI0pLS+vtt337dixYsADbt2+Hu7t7ndudP38+tFqttGRlZTXD3hC1LAUFBXBwcIBKpbpp35p/S7wOhIiIiCxllwEkNDQUWq0Wubm50rqkpCSz06fUajX8/PyQnJxcZ78DBw7giSeeQEJCArp06VLvdl1dXaFUKk0WottNQUEB1Go1HBxu/t9DTQDhDAgRERFZyi4DiEKhwJgxYxAbG4uysjIkJCQgJSUFo0ePNusbExODhQsXori4GEeOHEFCQgImTZoEADh58iQeeeQRxMfHIzo62tq7QdQiWfoQQuB/p2BxBoSIiIgsZZcBBADi4uKQlZUFHx8fzJs3Dxs3boRarUZ8fLzJDMeCBQukC9YnTJiAuLg4hIWFAQA+/PBDFBQUYOzYsVAoFFAoFBg5cqStdomoRbD0IYQAZ0CIiIio4WRCCGHrIuyVTqeDSqWCVqvl6Vh02xg7diyqqqqwffv2m/bNyclB+/btsX37dowaNcoK1REREVF97OH41G5nQIjo1uAMCBEREd1KDCBEZKIh14DU3OKa14AQERGRpRhAiMhEQ2ZAHB0d4e7uzhkQIiIishgDCBFJhBANmgEBrt8JizMgREREZCkGECKS6PV6VFZWWjwDAly/DoQzIERERGQpBhAikmg0GgDgDAgRERHdMgwgRCQpKCgAgAbNgHh4eHAGhIiIiCzGAEJEEs6AEBER0a3GAEJEksbOgDCAEBERkaUYQIhIkp+fD2dnZ3h6elr8HoVCwQBCREREFmMAISLJtWvX0LZtW8hkMovfwxkQIiIiaggGECKS5OXloW3btg16Dy9CJyIiooZgACEiybVr1+Dr69ug93AGhIiIiBqCAYSIJDWnYDUEAwgRERE1BAMIEUny8vIaPAPCi9CJiIioIRhAiEjS2BmQ0tJSVFdX36KqiIiI6HbCAEJEAICqqipoNJpGBRAAKC0tvRVlERER0W2GAYSIAFx/BgiARl2EDoCnYREREZFFGECICMD1068ANHoGhAGEiIiILMEAQkQArl+ADjR8BkShUABgACEiIiLLMIAQEQDOgBAREZF1MIAQEYDrAcTd3V0KFJaq6c+noRMREZElGECICMD/ngEik8ka9D7OgBAREVFDMIAQEQDg6tWrDT79CmAAISIiooax2wCSl5eHUaNGQS6XIywsDHv37q21X1lZGWJiYuDp6YnAwECsW7dOaktLS8OwYcOgUqnQrVs3a5VO1CJduXIF7du3b/D73N3dIZPJGECIiIjIInYbQObMmYP27dsjPz8fy5Ytw4QJE1BYWGjWLzY2FhqNBtnZ2Vi/fj2efvppnD17FgDg7OyMyZMn4/3337d2+UQtTmMDiIODA+RyOQMIERERWcQuA4her8fWrVuxYMECyOVyjB07Fj169MC2bdvM+q5ZswaxsbFQKpUYMGAAxowZg/Xr1wMAQkNDMX36dHTp0sXau0DU4mRnZ6NDhw6Neq+HhwcvQiciIiKLONm6gMY4d+4cVCoV/P39pXVRUVFITU016VdYWIjc3FxERkaa9Dt27FijtmswGGAwGKTXOp2uUeMQtTQVFRXIy8tr1AwIcD2AcAaEiIiILGG3MyBKpdJknVKpNPsNrF6vh6OjI+Ryeb39LLVkyRKoVCpp6dixY6PGIWppcnNzAYABhIiIiG45uwwgCoXCbPZBp9NJT2S+sZ/RaERpaWm9/Sw1f/58aLVaacnKymrUOEQtTXZ2NgA0+hQshULBAEJEREQWscsAEhoaCq1WK/3WFgCSkpIQERFh0k+tVsPPzw/Jycn19rOUq6srlEqlyUJ0O7hy5QoAzoAQERHRrWeXAUShUGDMmDGIjY1FWVkZEhISkJKSgtGjR5v1jYmJwcKFC1FcXIwjR44gISEBkyZNAgAIIVBeXo6KigqTr4lam+zsbLi6usLb27tR7+dF6ERERGQpuwwgABAXF4esrCz4+Phg3rx52LhxI9RqNeLj401mOBYsWCBdsD5hwgTExcUhLCwMAHDx4kW4u7tjxIgROHv2LNzd3TF8+HBb7RKRzdTcgrehT0GvwRkQIiIispRd3gULAHx9fbFjxw6z9Y8//jgef/xx6bW7uzvi4+NrHaNTp04QQtyyGonsRXZ2dqNPvwIYQIiIiMhydjsDQkTN59KlS026qxsvQiciIiJLMYAQETIzMxEcHNzo93MGhIiIiCzFAELUylVWVuLy5cvo1KlTo8fgRehERERkKQYQolbu0qVLqK6u5gwIERERWQUDCFErl5GRAQBNDiDl5eUwGo3NVRYRERHdphhAiFq5jIwMyGQyBAYGNnoMhUIBACgtLW2usoiIiOg2xQBC1MplZmYiICAALi4ujR7Dw8MDAHgaFhEREd0UAwhRK5eent6k06+A/wUQXohOREREN8MAQtTK/fXXX+jWrVuTxuAMCBEREVmKAYSoFTMajThz5gy6d+/epHEYQIiIiMhSDCBErVhmZibKy8ubHEBqLkJnACEiIqKbYQAhasVOnz4NAJwBISIiIqthACFqxdLS0qBQKNCxY8cmjcOL0ImIiMhSDCBErdgff/yBXr16QSaTNWkcNzc3yGQyzoAQERHRTTGAELVif/zxB/r06dPkcWQyGTw8PBhAiIiI6KYYQIhaqcLCQqSnpyM6OrpZxlMoFAwgREREdFMMIEStVGJiIgA0ywwIAM6AEBERkUUYQIhaqX379qFdu3YICwtrlvE8PDx4EToRERHdFAMIUSu1d+9e3HvvvU2+AL0GZ0CIiIjIEgwgRK2QRqPBH3/8gXvvvbfZxmQAISIiIkswgBC1Qps3bwYAPPjgg802Ji9CJyIiIkswgBC1QuvWrcOQIUPg5+fXbGNyBoSIiIgswQBC1Mqkpqbil19+wdSpU5t1XF6ETkRERJZgACFqZRYvXoyAgAA8+uijzTouZ0CIiIjIEnYbQPLy8jBq1CjI5XKEhYVh7969tfYrKytDTEwMPD09ERgYiHXr1pm0r169GgEBAVAqlZg+fToqKiqsUT6RTezcuRPffvst3nnnHbi4uDTr2AwgLduVK1fw5ZdfYt68eZg1axaef/55vPfee9i6dSvOnz8Po9F4y2swGo04c+YM9u/fjx07duDw4cPIzs6+5dslIqKWxcnWBTTWnDlz0L59e+Tn52P37t2YMGEC0tPToVarTfrFxsZCo9EgOzsbKSkpeOCBBxAdHY2uXbsiOTkZc+fOxe7duxEaGoqxY8di0aJFWLBggY32iujWOXToECZNmoT7778f06dPb/bxeRF6y5Samoo333wTP/zwAxwcHNC5c2d4eXmhrKwMmZmZ0t+Zu7s7wsPD0aNHD0RGRqJHjx7o0aMH2rdv3+hbNV++fBmHDx/GoUOH8PvvvyMlJQVlZWVm/Tp27IgRI0Zg4sSJGDJkCJyc7PZHExERWUAmhBC2LqKh9Ho9fHx8kJmZCX9/fwDA3XffjZkzZ5qd1+7v748tW7agX79+AICpU6eiS5cueOuttzB//nwUFRVh5cqVAIBffvkFM2fOxIULFyyqQ6fTQaVSQavVQqlUNuMeEjWd0WhEdnY2kpOTsXHjRsTHx2PAgAH48ccf4enp2ezb+/jjjzFv3jyUl5c3+9jUcEajEUuXLsXbb7+Njh07Yv78+Xj44Yfh7e0t9RFC4MqVK0hNTUVKSoq0pKamorS0FADg5eUlBRG1Wi0tLi4ucHR0hKOjI2QyGXQ6HYqKipCXl4ezZ8/i7NmzuHr1KgAgJCQEd955J3r37o2ePXsiMDAQcrkchYWFOHv2LA4dOoQtW7bgwoULaNu2LSZMmIBHH30UAwYMgIOD3U7UE912hBAoKSlBcXExdDodiouLzb42GAzS/w2Ojo5wcnKCi4uLtLi6upq8dnFxQVlZGTQaDQoLC1FYWAiNRiMthYWFKCkpgVwul85m6dKlC7p06YJu3bqhQ4cOzfY8q9uFPRyf2uWvmc6dOweVSiWFDwCIiopCamqqSb/CwkLk5uYiMjLSpN+xY8cAAGlpaRgxYoRJW0ZGBsrKyuDu7m62XYPBAIPBIL3W6XQAgMjISLMfknXluuZab41tcNv2ve2qqipUV1cDAIKDg/Hee+9hzpw5cHZ2rnMbTeHh4QGDwYCqqir+BruBSkpKkJKSgqKiIlRVVcHX1xchISHw8fFp1HiXL1/GY489hsOHD+O1115DbGxsrafcyWQydOjQAR06dMDw4cOl9dXV1cjMzDQJJFevXsX58+eh0WhQVFSEiooKGI1GGI1GCCGgVCrh5eUFHx8fhIaG4t5770XPnj0xYMCAOu+21qFDB/To0QPjx4/He++9hz/++APr16/H+vXr8cknnyAwMBAjRoxA3759ERYWhvbt20OhUEg1lpSUmBz41Czl5eUoLy+HwWCQvi4vL0dlZSWEEKiurq71z7rW1fa51fW6vramvr7Z15au43tuzXta2muZTGay1LauZn11dTV0Oh20Wq20FBUVmbwuLi6GXq+Xfq7UxsnJCa6urtL/DUajsd7+tXFzc4OPjw+8vb1NlpqQ8ueffyIjI0M6bVShUCAsLAzdunVDWFgY2rRpAw8PD7i4uKC8vBxlZWUoKytDSUlJnYter4fBYEB1dbXJUvN/wY2vG8LSYNSQAGVJ34Z+5rZgl0cJer3eLNEplUoUFRWZ9XN0dIRcLjfpV3Onnr+PU/O1Xq+vNYAsWbIE77zzjtn6CRMmwNXV1Wx9Xd8kzbXeGtvgtu13205OTggMDERoaChCQkJu+W+IPDw8AFw/mFapVLd0W/assrISKSkpOHbsGBITE3Hs2DGkpqbW+gMjLCwMDz30ECZPnoyoqCiLxt+9ezcef/xxuLm5Yf/+/Rg0aFCDa6w5Vatz584YM2ZMg9/fGDKZDH369EGfPn3wn//8B4cOHcKGDRtw4MABfPnllw36germ5lbr4uTkBAcHBzg4OEAmk0l/3vh1bW03+vsBSG0HJDfr09T31Hxd27qbtdtqXUuo4VbW1dJe14TnG0P039fduF4mk0m/RFCpVFCpVPDy8oKfnx9UKhWUSqW0eHp61vmnq6trrf9mKisrUVFRAYPBgIqKCpPFYDDAzc1Nml11c3PDzVRWViIzMxNnzpzBX3/9Jf25e/duFBYWoqqqSurr5OQEd3d3yOVyeHh4mC3+/v7w8PCAm5sbHB0dpf8jalsa8nPU0rDSkFBjad/y8nK8//77Fo9rC3YZQBQKhTT7UEOn00m/Gbuxn9FoRGlpqRRCbuz393Fqvv77ODXmz5+PuXPnmvTv2LEj3nrrrRY7xUVkLQwg5qqqqnDmzBn88ccf+OOPP5CYmIg///wT5eXlcHR0RI8ePdC/f38899xziI6Ohq+vLxwcHJCXl4fU1FTs378fX3zxBf7zn/+gV69eeOKJJzB58mST06hq6PV6vPXWW/jggw8wYsQIrFmzBm3atLHBXjedg4MDBg0aJIWnkpISXLp0CTk5OSgtLZV+CCsUCnh6epocAMnlcp62RdSCyGQy6VSruo6vGsrZ2RmhoaEIDQ2t9YG6NcHG3d29Vc7I63Q6BpBbITQ0FFqtFrm5udLUflJSEmbOnGnST61Ww8/PD8nJydI1IElJSYiIiAAAhIeHIzk5WeqflJSE4ODgWmc/AMDV1bXWmQ4i+l9wb20XogshkJ+fj8zMTGRkZEh/JiUl4eTJk9JF16GhofjHP/6BiRMnom/fvujVq5fJ7OyNOnTogF69euHxxx/HJ598gl27duGrr77CSy+9hJdffhnjxo3DQw89hE6dOqG4uFgKKnq9HsuWLcPLL798Wx2Ee3h4oHv37ujevbutSyEiO1ATeKjlssuL0IHrpz15e3vjgw8+wJ49e/DPf/6z1rtgvfLKKzh9+jTWrVuH1NRU3H///Th69CjCwsKQnJyMe+65B3v27EFISAjGjx+PgQMHWnwXLHu4yIfIWo4fP45//OMf+PPPP9GrVy9bl9OsSktLceHCBZw7dw4XLlyQgkbNcmPo8vT0RKdOndCjRw9ER0cjOjoad9xxR7PMCl29ehVr167FV199hbS0NGm9Wq3G5MmTMW/ePHTq1KnJ2yEiIvtlD8endjkDAgBxcXGYNm0afHx8EBAQgI0bN0KtViM+Ph6LFy+WLkhfsGABZs6cCX9/f6jVasTFxSEsLAzA9YvH33//fYwePRo6nQ4PP/ww/v3vf9tyt4jsVs0pWPb8NPSaU6b+/PNPnDx5EidPnsRff/1l8qwKuVyO4OBgdOrUCUOGDEGnTp2k1506dYJarb5l19u0a9cOL7/8Ml5++WUUFhbi8uXL8PDwQKdOnW6rGQ8iIrq92e0MSEtgDwmTyFouXbqEoKAg7Ny50+Tuci1VaWkpkpKSTMJGcnKydBvh4OBg9OrVC+Hh4QgNDZVu+9i2bVve8pGIiFosezg+tdsZECJqWW68CL0lKi4uxp49e3DgwAEcOnQIJ0+elG4ZHBERIV1z0atXL0RFRcHLy8vWJRMREd2WGECIqFm0xIvQ8/PzsWnTJmzduhW//PILKioqEBwcjIEDB+KJJ55A3759ERERwZtLEBERWREDCBE1i5onY9s6gFRVVWHnzp34+uuvsW3bNlRXV2Pw4MH4z3/+gzFjxiA4ONim9REREbV2DCBE1CxkMhk8PDxsdhG6VqvF559/jg8//BCXL19GVFQUli9fjsmTJ8PX19cmNREREZE5BhAiajYeHh5WnwEpLCzEf/7zH3zyyScoLy/H5MmT8fzzz6N3795WrYOIiIgswwBCRM3GmgGkoqICH374IRYvXoyKigo899xzeP7559G+fXurbJ+IiIgahwGEiJqNQqGwSgBJTEzEE088gbS0NMyaNQtvvvkm/Pz8bvl2iYiIqOn45Coiaja3egZECIH33nsP/fv3h7OzM44fP45PPvmE4YOIiMiOMIAQUbNRKBQoLi6+JWMbDAY89thjeOWVV/DKK6/g6NGj6NWr1y3ZFhEREd06PAWLiJqNUqlEUVFRs49bVlaGhx9+GL/88gu+++47PPLII82+DSIiIrIOzoAQUbNRKpXQarXNOqbRaMSkSZOwf/9+bN++neGDiIjIznEGhIiajUqlgk6na9YxX375ZezYsQPbt2/H0KFDm3VsIiIisj4GECJqNkqlslkDyKZNm/Dhhx/i448/xv33399s4xIREZHt8BQsImo2zXkKVnZ2Np566ik88sgjeOaZZ5plTCIiIrI9BhAiajYqlQolJSUwGo1NHuu5556Dm5sbPv30U8hksmaojoiIiFoCnoJFRM1GqVQCAIqLi+Hl5dXocfbu3YsffvgB3377LXx8fJqpOiIiImoJOANCRM2mJoA05TSs6upqzJ07FwMHDsSjjz7aXKURERFRC8EZECJqNjUBpCkXom/fvh2nTp3CwYMHeeoVERHRbYgzIETUbFQqFYDGBxAhBJYsWYJBgwZh4MCBzVkaERERtRCcASGiZtPUGZBDhw7hyJEj+PHHH5uzLCIiImpBOANCRM2mqdeAfP755wgJCcHIkSObsywiIiJqQRhAiKjZKBQKyGSyRs2AaLVafPfdd5gxYwav/SAiIrqNMYAQUbORyWSNfhr6hg0bYDAYMG3atFtQGREREbUUDCBE1Kwa+zT07777Dvfeey86dOhwC6oiIiKilsIuA0hiYiKioqIgl8sxePBgXLx4sc6+6enpGDhwIORyOXr37o2kpCSpbdOmTejXrx9cXV0xe/Zsa5ROdNtrzAyIRqPBvn37MH78+FtUFREREbUUdhdADAYDxo8fjxdeeAEajQb9+/fHlClT6uz/2GOPYfjw4dBoNJgxYwbGjRuHqqoqAIC3tzdeffVVzJw501rlE932VCpVgwPI9u3bUV1djbFjx96aooiIiKjFsLsAsn//figUCsyYMQNubm546623cPz48VpnQc6cOYMzZ85g/vz5cHNzw7PPPguj0YjDhw8DAO699148/PDD8PX1tfZuEN22GnMK1ubNm9G/f3/4+/vfoqqIiIiopbC7AJKWlobIyEjptYeHB0JCQpCWllZr37CwMLi4uEjrevbsidTU1EZt22AwQKfTmSxEZKqhAaSiogI///wzxowZcwurIiIiopbC7gKIXq+XnjVQQ6lUQq/XN6mvJZYsWQKVSiUtHTt2bNQ4RLcztVqNoqIii/sfPXoUJSUlGDZs2K0rioiIiFqMFhdAhg8fDjc3t1qXRYsWQaFQmM086HQ6KBQKs7Ea0tcS8+fPh1arlZasrKxGjUN0O/P29oZGo7G4/88//wxvb2/06tXr1hVFRERELYaTrQv4u927d9fbvmvXLqxatUp6XVJSgvT0dISHh5v1DQ8Px5kzZ1BZWQlnZ2cAwKlTp/DKK680qjZXV1e4uro26r1ErYWPjw8KCgos7r93714MGTIEjo6Ot7AqIiIiaila3AzIzdxzzz3Q6/VYvXo1DAYDFi1ahD59+iAoKMisb1hYGMLCwrB06VIYDAbExcXB0dERAwYMAAAYjUaUl5ejqqrK5Gsiajxvb28UFxejsrLypn11Oh2OHDmCoUOHWqEyIiIiagnsLoC4urpi8+bNWLFiBby8vHDo0CGsWbNGap89e7bJMz2+/fZb7Ny5E15eXvj888+xefNmODldn/hZs2YN3N3d8e677+KLL76Au7s7Fi1aZPV9IrqdeHt7AwAKCwtv2vf333+H0WjEkCFDbnVZRERE1ELIhBDC1kXYK51OB5VKBa1Wa3axO1FrdfDgQQwaNAinT59Gt27d6u27YMECfPDBBygoKIBMJrNShURERLcvezg+tbsZECJq2WpmQCy5DuTo0aPo27cvwwcREVErwgBCRM2qJoDc7E5YQggcPXoU/fr1s0ZZRERE1EIwgBBRs7I0gGRkZKCgoIABhIiIqJVhACGiZuXi4gKFQnHTAHL06FEAwD/+8Q9rlEVEREQtBAMIETU7b2/vm14DcvToUXTu3Bm+vr5WqoqIiIhaAgYQImp2ljwNndd/EBERtU4MIETU7Hx8fOoNIBUVFfjzzz8ZQIiIiFohBhAianZt2rTBtWvX6mw/deoUDAYDAwgREVErxABCRM3O398fubm5dbYfPXoUzs7O6NWrl/WKIiIiohaBAYSImp2fnx9ycnLqbD969CiioqLg5uZmxaqIiIioJWAAIaJm5+/vj6KiIpSVldXazgvQiYiIWi8GECJqdv7+/gBQ62lYhYWFOHv2LAMIERFRK8UAQkTNzs/PDwBqPQ0rMTERANC3b1+r1kREREQtAwMIETW7+mZAjh49Ci8vL4SGhlq7LCIiImoBGECIqNl5e3vD2dm51hmQo0ePom/fvnBw4H8/RERErRGPAIio2Tk4OMDPzw/Z2dkm64UQOHLkCPr372+jyoiIiMjWGECI6JYIDg5GRkaGybpz586hoKAAd955p42qIiIiIltjACGiWyIkJATp6ekm637//XcA4B2wiIiIWjEGECK6JUJCQnDhwgWTdb///ju6d+8OtVpto6qIiIjI1hhAiOiWCAkJQUFBAbRarbTu999/5+lXRERErRwDCBHdEl26dAEAnD17FgCg0WiQkpKCgQMH2rIsIiIisjEGECK6JSIiIuDo6Ig///wTAPDzzz+juroaw4cPt3FlREREZEsMIER0S7i7uyM8PBzHjx8HAOzatQvh4eEICAiwcWVERERkS3YZQBITExEVFQW5XI7Bgwfj4sWLdfZNT0/HwIEDIZfL0bt3byQlJUlty5YtQ7du3eDp6Ynw8HBs3rzZGuUTtRr9+/fH/v37UVFRgYSEBIwaNcrWJREREZGN2V0AMRgMGD9+PF544QVoNBr0798fU6ZMqbP/Y489huHDh0Oj0WDGjBkYN24cqqqqAACOjo747rvvoNVq8emnn2L69Olmtw0losZ76KGHcO7cObz11lvIz8+v998qERERtQ4yIYSwdRENsWvXLrz44os4ffo0AKCkpAS+vr44ffo0goKCTPqeOXMGffv2RV5eHlxcXAAAQUFBWLNmDe6++26zsQcOHIi5c+fi4YcftqgWnU4HlUoFrVYLpVLZxD0juv0YDAZ0794dGRkZGDNmDLZu3WrrkoiIiG5r9nB8anczIGlpaYiMjJRee3h4ICQkBGlpabX2DQsLk8IHAPTs2ROpqalmfYuLi5Gamorw8PA6t20wGKDT6UwWIqqbq6srdu7ciRUrVmDNmjW2LoeIiIhaACdbF9BQer3eLM0plUro9fom9Z01axbGjBmD7t2717ntJUuW4J133mlk5UStU9euXdG1a1dbl0FEREQtRIubARk+fDjc3NxqXRYtWgSFQmE286DT6aBQKMzGsrTva6+9hkuXLuGzzz6rt7b58+dDq9VKS1ZWViP3koiIiIiodWpxMyC7d++ut33Xrl1YtWqV9LqkpATp6em1njoVHh6OM2fOoLKyEs7OzgCAU6dO4ZVXXpH6LF++HNu2bcPBgwfh7u5e77ZdXV3h6urakN0hIiIiIqIbtLgZkJu55557oNfrsXr1ahgMBixatAh9+vQxuwAdAMLCwhAWFoalS5fCYDAgLi4Ojo6OGDBgAADgq6++wscff4xdu3ZBrVZbe1eIiIiIiFoduwsgrq6u2Lx5M1asWAEvLy8cOnTI5OLW2bNnY/bs2dLrb7/9Fjt37oSXlxc+//xzbN68GU5O1yd+Fi5ciJycHHTr1g0KhQIKhQKLFy+2+j4REREREbUWdncb3pbEHm5zRkRERESthz0cn9rdDAgREREREdkvBhAiIiIiIrKaFncXLHtiNBoBAJcvX26xU1xERERE1HrUPIKi5ji1JWIAaYLz588DACIiImxcCRERERHR/5w/fx7/+Mc/bF1GrXgRehMUFhbC29sbWVlZnAEhIiIiIpvT6XTo2LEjNBpNi33MBGdAmsDR0REAoFQqGUCIiIiIqMWoOU5tiXgROhERERERWQ0DCBERERERWQ0DCBERERERWQ2vAbGi5ORk/PLLL7h69Sqqq6ttXQ79jaOjI4KCgjB8+HB06tTJ1uUQERER3ZYYQKzkyy+/xMqVK+Hj44POnTu36AuDWqvKykocPHgQX3zxBZYuXYp7773X1iURERER3XYYQKwgMzMTK1euxBNPPIFZs2bBwYFnvrVUFRUVePPNN/H2229j4MCBcHV1tXVJRERERLcVHglbwYEDB+Du7o4nnniC4aOFc3FxwezZs1FaWoo//vjD1uUQERER3XZ4NGwFeXl58PPzg4uLi61LIQsEBQUBAPLz821cCREREdHthwHECoQQnPmwIzV/V7xRABEREVHz41FxCxASEoJ+/frV2jZjxgy4ubmhqKiozvcLIbBw4UIEBQVBoVAgKCgIL774otSel5eHUaNGQS6XIywsDHv37jV5/9KlS+Hr6wtvb2+8+uqrEEJIbYmJiYiKioJcLsfgwYNx8eJFqa2srAwxMTHw9PREYGAg1q1bZzLu6tWrERAQAKVSienTp6OiogIAcO3aNUycOBHt2rWDt7c3Ro8ejUuXLln0WWVmZkImk0GhUEChUKBDhw545513pPajR4/C19cXeXl50rqVK1ciOjoa2dnZ8PHxwYkTJ6Q2o9GIO+64A1999ZVF2yciIiKipmEAsbHDhw8jLy8PJ06cwPnz503aysvL8f3330Mul2PTpk11jvHNN99g8+bNOHDgAPR6PX777TdER0dL7XPmzEH79u2Rn5+PZcuWYcKECSgsLAQA7NixAytXrsTRo0eRmpqK7du34+uvvwYAGAwGjB8/Hi+88AI0Gg369++PKVOmSOPGxsZCo9EgOzsb69evx9NPP42zZ88CuH7L4blz52LLli3IyspCZmYmFi1aBAAoKSnBXXfdhdTUVOTm5qJLly6YPn26xZ+Zq6sr9Ho99Ho9Dh48iM8//xy7d+8GAPTr1w8TJ07ESy+9BADIycnBG2+8gVWrVqFDhw5YuHAhnnrqKRiNRgDAhx9+CJVK1aDtExEREVETCGo0rVYrAAitVltvv+XLl4uJEyfW2vbMM8+IadOmieHDh4u3337bpG3jxo2iU6dO4t133xX33HNPneM/88wz4t///netbcXFxcLFxUVcuXJFWjdo0CDxzTffCCGEePTRR8XSpUulti+//FIMGTJECCHEzp07Rbdu3aQ2vV4v3N3dRWZmphBCCD8/P3HkyBGpfcqUKeKdd94RQgjx2muvidmzZ0tte/fuFcHBwbXWePbsWaFQKOrcvxtlZGQIV1dXk3UTJkwQH3zwgfRaq9WK9u3biz179oiHH35YvPDCC1JbdXW1GDBggPjggw/EpUuXhI+Pjzhz5ozZdqKjo8UPP/xgUU1ERERELYWlx6e2xBkQG6qsrMTGjRsxadIkTJo0CfHx8Sbta9euxcSJE/Hoo4/iwIEDuHz5cq3j9OvXD6tWrcKKFStw4sQJk2sXzp07B5VKBX9/f2ldVFQUUlNTAQBpaWmIjIy0qM3DwwMhISFIS0tDYWEhcnNzLX5vVFQUMjIyUFZWZlb/4cOHERERcfMPrBbp6ek4fPgw+vbtK61TKpX44IMP8Oijj+Lo0aNYuHCh1CaTybBq1SosWLAA06ZNwwsvvICuXbs2attERERE1HAMIDa0a9cuVFdXY+jQoRg3bhwyMzORmJgIANBoNNi5cycmTZqEzp07o3fv3mbXWNSYOnUqli9fjoSEBAwcOBD+/v7SNQ16vR5KpdKkv1KphF6vr7W9vrYb2/V6PRwdHSGXyy0et2b9jbKysvDaa69Jp2dZwmAwwMvLC0qlEl26dMFdd91lEkAAoG/fvtBqtRg1ahQ8PT1N2iIiIvDEE08gKysL//rXvyzeLhERERE1HQOIDa1duxbjx4+Hs7Mz1Go1hg8fLs2CbNy4EYGBgejduzcA1DpDcqNp06Zh//79KCoqQmxsLJ588kmkpqZCoVBAp9OZ9NXpdFAoFABg1l5f243tCoUCRqMRpaWlFo9bs76GRqPB/fffj9dffx1Dhw618FO7fg1IUVERdDod8vLykJ+fj1dffdWkz5w5czB16lRs2LABaWlpZmOEh4cjJCSEt0YmIiIisjIGEBspLi5GQkICNmzYAD8/P/j5+WH//v1Yv349jEYj4uPjkZWVJbUtXrwYSUlJ0ilOdXF1dcUzzzwDtVqN06dPIzQ0FFqtFrm5uVKfpKQk6ZSn8PBwJCcnW9RWUlKC9PR0hIeHQ61Ww8/Pz+L3JiUlITg4GO7u7gCuz4Q88MADeOihh/Dcc8819mNEmzZtMG7cOOzatUtat3HjRpw5cwaffPIJXn/9dcyePdvkzl5EREREZDsMIDayefNmtGnTBmfOnMHJkydx8uRJpKWloby8HF9++SUOHz6MX3/91aTtvvvuq3UW5JtvvsHOnTtRUlICo9GItWvXQqfT4Y477oBCocCYMWMQGxuLsrIyJCQkICUlBaNHjwYAxMTEYOXKlcjIyEBubi5WrFiBmJgYAMA999wDvV6P1atXw2AwYNGiRejTp4/0oL6YmBgsXLgQxcXFOHLkCBISEjBp0iQAwOTJk7Fx40acOHECWq0W7777rjRuRUUFxo8fj4iICCxevLhJn2NRURG2bt2K7t27AwC0Wi1efPFFrFy5Em5ubnjppZeg1WqlO3sRERERkY3Z+ip4e9aUu2ANGzbM7K5XQgjx7LPPCmdn51rverVp0yYRFBQkqqurRXh4uFi7dq0QQojvv/9e9O/fX6hUKqFUKkXv3r1N7uB07do1MXLkSOHu7i5CQ0PFnj17TMZdvHix8PHxEV5eXuKVV14R1dXVUtuxY8dEZGSkcHNzE4MGDZLugCWEEKWlpWLy5MnCw8NDBAQEiPj4eJNxv/76a9G+fXuhUCjEtGnTRHl5uRBCiP379wsAQi6XCw8PD2m5ePGiEEKId999V9x///3SOPfff7949913hRDX74IFQHqPt7e3GD9+vHSXr1mzZomYmBiTOg4fPix8fX1FXl6eSW0jRoww+4xr8C5YREREZI/s4S5YMiF4bkpj6XQ6qFQqaLVas4u1b/Tee+8hMTERGzZssGJ11BR9+vTBG2+8gbFjx9q6FCIiIiKLWXp8aks8BYuIiIiIiKyGAcQKZDKZybM5qGWr+btycOA/DyIiIqLmxiMsK/D19UVubi4qKipsXQpZ4OLFiwCu32GLiIiIiJqX3QaQvLw8jBo1CnK5HGFhYdi7d2+t/crKyhATEwNPT08EBgaaPMzvyJEj6NmzJ7y8vNC2bVtMmzbN7EF5zeHuu+9GWVkZvvzyS86EtHAVFRX49NNPIZfLER0dbetyiIiIiG47TrYuoLHmzJmD9u3bIz8/H7t378aECROQnp4OtVpt0i82NhYajQbZ2dlISUnBAw88gOjoaHTt2hVdunTBTz/9hA4dOqC0tBSzZs3CwoULsWzZsmattVOnTnj66aexcuVKbNmyBZ07d4ajo2OzboOarrKyEqdPn0Z5eTmWLl0KV1dXW5dEREREdNuxy7tg6fV6+Pj4IDMzE/7+/gCuzzLMnDkTU6dONenr7++PLVu2oF+/fgCAqVOnokuXLnjrrbdM+pWUlODpp59GeXk5Nm7caFEdDb3LQHJyMvbt24fc3FzOhLRAjo6O6NSpE4YNG4ZOnTrZuhwiIiKiBrOHu2DZ5QzIuXPnoFKppPABAFFRUWZPCS8sLERubi4iIyNN+h07dkx6fenSJfTs2RNarRYKhQI//vhjnds1GAwwGAzSa51O16C6IyMjTWohIiIiImpt7PIaEL1eb5bolEql2fUber0ejo6OkMvldfYLDAxEUVERrl69ildffdUk1PzdkiVLoFKppKVjx47NtEdERERERK2DXQYQhUJhNvug0+mgUCjM+hmNRpSWltbbDwDatm2LkSNHmp3CdaP58+dDq9VKS1ZWVhP3hIiIiIiodbHLABIaGgqtVovc3FxpXVJSEiIiIkz6qdVq+Pn5ITk5ud5+Naqrq5Genl7ndl1dXaFUKk0WIiIiIiKynF0GEIVCgTFjxiA2NhZlZWVISEhASkoKRo8ebdY3JiYGCxcuRHFxMY4cOYKEhARMmjQJALBjxw6cOXMGQgjk5OTgzTffxJAhQ6y9O0RERERErYZdBhAAiIuLQ1ZWFnx8fDBv3jxs3LgRarUa8fHxJjMcCxYskC5YnzBhAuLi4hAWFgYAuHr1KkaOHAmFQoHo6GgEBARg5cqVttolIiIiIqLbnl3ehrelsIfbnBERERFR62EPx6d2OwNCRES3Xn5+vq1LICKi2wwDCBER1SouLg6+vr5YtGiRrUshIqLbCE/BagJ7mOIiImqM6upqBAYGIjs7G2q1Grm5uXBxcbF1WUREdBP2cHzKGRAiIjKTmpqK7OxsLFmyBIWFhTh+/LitSyIiotsEAwgREZnZv38/XFxcMGfOHHh4eODQoUO2LomIiG4TDCBERGTmxIkTiIqKgqenJyIiIpCammrrkoiI6DbBAEJERGZSUlIQGRkJAIiIiEBaWpqNKyIiotsFAwgREZmorq5GamoqevToAQAIDw9HWloaeM8SIiJqDgwgRERkIiMjA2VlZYiIiAAAdOnSBSUlJcjLy7NxZUREdDtgACEiIhPnz58HAISFhQEAAgMDAQCXLl2yWU1ERHT7YAAhIiITGRkZcHJyQocOHQAwgBARUfNiACEiIhMZGRno2LEjnJycAAA+Pj5wd3dnACEiombBAEJERCYyMzMRHBwsvZbJZAgMDGQAISKiZsEAQkREJjIyMtCpUyeTdR07dmQAISKiZsEAQkREJjIyMkxmQADA398fubm5NqqIiIhuJwwgREQk0ev1yM/PNwsgbdu2xbVr12xUFRER3U4YQIiISFJzmlVQUJDJ+nbt2uHq1au2KImIiG4zDCBERCS5cuUKAEi34K3Rrl076HQ6lJeX26IsIiK6jTCAEBGRpCaA+Pv7m6xv27YtAPA0LCIiajIGECIikuTk5ECtVsPNzc1kfbt27QCAp2EREVGTMYAQEZHkypUraN++vdl6BhAiImouDCBERCSpK4D4+voC4ClYRETUdAwgREQkuXLlitn1HwDg7OwMb29vzoAQEVGTMYAQEZEkJyen1hkQAGjTpg3y8/OtXBEREd1u7DaA5OXlYdSoUZDL5QgLC8PevXtr7VdWVoaYmBh4enoiMDAQ69atk9q2b9+OO++8EyqVCgEBAXjnnXesVT4RUYsjhKjzFCwA8PHxgUajsXJVRER0u3GydQGNNWfOHLRv3x75+fnYvXs3JkyYgPT0dKjVapN+sbGx0Gg0yM7ORkpKCh544AFER0eja9euKC4uxqJFi3DXXXfh6tWrGDFiBDp37owpU6bYaK+IiGynsLAQBoOhzgDi7e3NAEJERE1mlzMger0eW7duxYIFCyCXyzF27Fj06NED27ZtM+u7Zs0axMbGQqlUYsCAARgzZgzWr18PAHjsscdw3333wdXVFYGBgRg/fjyOHTtm7d0hImoRcnJyAJg/A6QGAwgRETUHuwwg586dg0qlMvkhGRUVhdTUVJN+hYWFyM3NRWRkZL39ahw+fBgRERF1btdgMECn05ksRES3i5qHENZ3ClZBQYE1SyIiotuQXQYQvV4PpVJpsk6pVEKv15v1c3R0hFwur7cfAHz22WfIycnBtGnT6tzukiVLoFKppKVjx45N3BMiopajrqeg1+AMCBERNQe7DCAKhcJs9kGn00GhUJj1MxqNKC0trbff9u3bsWDBAmzfvh3u7u51bnf+/PnQarXSkpWV1Qx7Q0TUMly5cgU+Pj5wdXWttb0mgAghrFwZERHdTuwygISGhkKr1SI3N1dal5SUZHb6lFqthp+fH5KTk+vsd+DAATzxxBNISEhAly5d6t2uq6srlEqlyUJEdLvIycmpc/YDuB5AKisra51FJiIispRdBhCFQoExY8YgNjYWZWVlSEhIQEpKCkaPHm3WNyYmBgsXLkRxcTGOHDmChIQETJo0CQBw8uRJPPLII4iPj0d0dLS1d4OIqEWp7xa8wPVrQADwNCwiImoSuwwgABAXF4esrCz4+Phg3rx52LhxI9RqNeLj401mOBYsWCBdsD5hwgTExcUhLCwMAPDhhx+ioKAAY8eOhUKhgEKhwMiRI221S0RENnWzAOLt7Q2AAYSIiJpGJngyb6PpdDqoVCpotVqejkVEdi84OBiPPfYYFi9eXGv7hQsXEBISgp9//hn33XeflasjIiJL2MPxqd3OgBARUfO52VPQgf+dgsVb8RIRUVMwgBARETQaDSoqKuoNIEqlEo6OjjwFi4iImoQBhIiIbvoMEACQyWRQq9UMIERE1CQMIEREhJycHAB1PwW9Rs15xURERI3FAEJERFIA8fPzq7efl5cXioqKrFARERHdrhhAiIgIOTk58Pb2rvMp6DU4A0JERE3FAEJERLhy5Uq913/U4AwIERE1FQMIEREhJyfHogDCGRAiImoqBhAiIrI4gHAGhIiImooBhIiIkJOTc9M7YAGcASEioqZjACEiauVqnoLOGRAiIrIGBhAiolZOq9WivLzc4gBSVlaGiooKK1RGRES3IwYQIqJWruYZIJZehA6Ap2EREVGjMYAQEbVyDQkgXl5eABhAiIio8RhAiIhaucbMgPA6ECIiaiwGECKiVu7KlSvw9PSEh4fHTfvWzIAwgBARUWMxgBARtXKW3oIX4DUgRETUdAwgREStnKUPIQQApVIJgDMgRETUeAwgREStXEMCiJOTExQKBWdAiIio0RhAiIhauYYEEIAPIyQioqZhACEiasUa8hT0GiqVijMgRETUaAwgREStmFarhV6vR0BAgMXv4QwIERE1BQMIEVErdvnyZQBAx44dLX6Pl5cXZ0CIiKjRGECIiFqxrKwsAA0LICqVijMgRETUaHYbQPLy8jBq1CjI5XKEhYVh7969tfYrKytDTEwMPD09ERgYiHXr1kltaWlpGDZsGFQqFbp162at0omIWoysrCw4ODhY/BwQgDMgRETUNHYbQObMmYP27dsjPz8fy5Ytw4QJE1BYWGjWLzY2FhqNBtnZ2Vi/fj2efvppnD17FgDg7OyMyZMn4/3337d2+URELUJWVhb8/f3h5ORk8Xs4A0JERE1hlwFEr9dj69atWLBgAeRyOcaOHYsePXpg27ZtZn3XrFmD2NhYKJVKDBgwAGPGjMH69esBAKGhoZg+fTq6dOli7V0gImoRsrKyGnT6FcCL0ImIqGks/5VXC3Lu3DmoVCqT20ZGRUUhNTXVpF9hYSFyc3MRGRlp0u/YsWON2q7BYIDBYJBe63S6Ro1DRNRSNCaAqFQq6HQ6CCEgk8luUWVERHS7stsZEKVSabJOqVRCr9eb9XN0dIRcLq+3n6WWLFkClUolLQ39oU1E1NI0NoBUV1c3+v9SIiJq3ewygCgUCrPZB51OB4VCYdbPaDSitLS03n6Wmj9/PrRarbTU3D2GiMgeCSEafQoWAJ6GRUREjWKXASQ0NBRarRa5ubnSuqSkJERERJj0U6vV8PPzQ3Jycr39LOXq6gqlUmmyEBHZq4KCApSXlzdqBgQA74RFRESNYpcBRKFQYMyYMYiNjUVZWRkSEhKQkpKC0aNHm/WNiYnBwoULUVxcjCNHjiAhIQGTJk0CcP23f+Xl5aioqDD5moioNbh06RKAhj0DBOAMCBERNY1dBhAAiIuLQ1ZWFnx8fDBv3jxs3LgRarUa8fHxJjMcCxYskC5YnzBhAuLi4hAWFgYAuHjxItzd3TFixAicPXsW7u7uGD58uK12iYjIqi5cuAAA6Ny5c4PexxkQIiJqCru8CxYA+Pr6YseOHWbrH3/8cTz++OPSa3d3d8THx9c6RqdOnSCEuGU1EhG1ZOnp6VAqlfDx8WnQ+xhAiIioKex2BoSIiJrmwoULCAkJafCtdOVyOZycnHgKFhERNQoDCBFRK5Went7g068AQCaTQaVScQaEiIgahQGEiKiVSk9PR0hISKPe6+XlxQBCRESNwgBCRNQKVVRU4NKlS40OICqViqdgERFRozCAEBG1QpcuXUJ1dXWjTsECwFOwiIio0RhAiIhaofPnzwNAk07B4gwIERE1BgMIEVErlJqaCrlcjqCgoEa9nzMgRETUWAwgREStUGpqKrp37w4Hh8b9GGAAISKixmIAISJqhdLS0hAREdHo9/MULCIiaiwGECKiVkYIgbS0NISHhzd6DM6AEBFRYzGAEBG1MllZWSguLm7yDEhJSQkqKyubsTIiImoNGECIiFqZpKQkAEBkZGSjx1CpVAAAnU7XLDUREVHrwQBCRNTKHDt2DG3btkVgYGCjx6gJIDwNi4iIGooBhIiolTl27Bj69u0LmUzW6DG8vLwAgBeiExFRgzGAEBG1IkIIKYA0BWdAiIiosRhAiIhakXPnzqGoqIgBhIiIbIYBhIioFdm7dy+cnJwwYMCAJo1TE0B4ChYRETUUAwgRUSuyZ88e9O/fH56enk0ax8XFBe7u7pwBISKiBmMAISJqJSoqKvDLL79g2LBhzTIen4ZORESNwQBCRNRK7NmzB1qtFmPHjm2W8fg0dCIiagwGECKiVmLdunUIDw9v0gMIb8QAQkREjcEAQkTUCly9ehWbNm3C1KlTm/T8jxvxFCwiImoMJ1sXQEREt95///tfODk54amnnmq2MVUqFQoKCpptvNvF/v378e233+L8+fNwc3NDr169MHz4cNx9991wcODv/YiI7PZ/wry8PIwaNQpyuRxhYWHYu3dvrf3KysoQExMDT09PBAYGYt26dSbtq1evRkBAAJRKJaZPn46KigprlE9EZDVnzpzBf//7X7z00ktQq9XNNi5nQEyVlJRg4sSJGDJkCPbv34+2bdvC0dERX331FYYMGYKgoCC8+uqrSEpKghDC1uUCuP4z8tSpUzh8+DBSUlJQWFho65KIqBWw2xmQOXPmoH379sjPz8fu3bsxYcIEpKenm/1wjY2NhUajQXZ2NlJSUvDAAw8gOjoaXbt2RXJyMubOnYvdu3cjNDQUY8eOxaJFi7BgwQIb7RURUfPKz8/HuHHj0KlTJ7z++uvNOjavAfkfvV6PYcOGISUlBevWrcOkSZOkU92EEDhy5AjWrFmDr776CsuXL0d4eDgmT56MkSNHIioqCo6OjlarVQiBHTt24KOPPsIvv/yCqqoqk/aOHTuiV69eiIqKQlRUFHr27ImQkBCr1FhdXY0rV64gKysLHh4e6NixY7OG5rpUVlZCJpPB0dGx2U5RpNubwWBAYmIiDh48iHPnzkGj0UAulyMgIAB9+/bF8OHDm3y789uZTLSUX8M0gF6vh4+PDzIzM+Hv7w8AuPvuuzFz5kxMnTrVpK+/vz+2bNmCfv36AQCmTp2KLl264K233sL8+fNRVFSElStXAgB++eUXzJw5ExcuXLCoDp1OJ/0AViqVzbiHRERNU1VVhYSEBMydOxdlZWX47bff0LVr12bdxrvvvosPP/wQ165da9Zx7U11dTUeeeQR7NmzB/v27UOfPn3q7FtZWYndu3fj22+/xZYtW1BaWgq1Wo0BAwZIB/s9e/ZEaGgonJya/3eEV65cwYwZM7Br1y70798fMTEx6N27N5RKJbRaLS5evIhTp07hzz//RFJSEnJzcwEAcrkcERERCA4ORlBQEAIDAxEQEAAfHx+o1WppcXd3r/cAvrq6Gvn5+bh8+TKysrJw4cIFkyUjIwMGg8HkPd26dcPYsWMxffr0ZvseTklJwdq1a/Hrr7/ir7/+kmbyHBwcoFAo4OXlBZVKZbLcuE6pVMLDwwMeHh5QKBRo164dgoODpQd0NlRpaSkuX76MoqIiCCGkGTJnZ2eLl5YYnIxGI/R6PYqLi1FcXAy9Xg8hBJydneHi4gIXFxe4ubnB3d1dWlrqaYoFBQX4/fffcejQIRw8eBCJiYkwGAxQKBQIDw+Ht7c3ysvLcf78eVy+fBlubm4YO3YsXnjhBfTv39+qtdrD8aldzoCcO3cOKpVKCh8AEBUVhdTUVJN+hYWFyM3NNbnjS1RUFI4dOwYASEtLw4gRI0zaMjIyUFZWBnd3d7PtGgwGk/8YdTodAOCjjz6Cm5tbvTU3JOfdrn1tvX1762vr7dtbX1tvv6X01el0uHjxIhITE6HVajFs2DB88cUXCAwMtHgMS9WcgiWEaJEHP9by0UcfYcuWLdi6dWu94QO4fkA5atQojBo1CgaDAUePHsW+fftw5MgRfPPNN8jOzgYAuLu7o2fPnrjjjjsQHR2NYcOGISgoqEl1/vbbb5g4cSIcHR2RkJCABx980OzvbcCAAXjsscek19euXcOpU6dw6tQpJCcn4+LFi/jjjz+QlZVV6ynLMpkMrq6u0sGli4sLgOs/P8vLy1FWVobq6mqpv7u7Ozp37ozOnTtjxIgRCAkJQefOnREYGIjS0lKcO3cO+/fvx6effoply5ZhwoQJiI2NRXh4eKM+g5MnT+Lll1/GL7/8gjZt2mDo0KF46KGH4OfnB5lMhvLychQXF0Or1UKr1aKoqAharRbZ2dlIS0uTXut0OhiNRrPx1Wo1OnfujJCQEGlf/Pz84OTkBIPBgGvXruHq1atSALt8+TIuX74MjUbTqP25kaOjY4MCi6WLk5MTqqurYTQaTZaqqirp64qKCulz0el00telpaUN3o+ah5y6u7vDzc0NTk5OcHJygqOjo/Rnzb56e3ujbdu2aNu2LXx9fc3+9PX1haura53bEkKgoqICpaWlKCoqgkajQWFhITQaDfLz85Geno7z58/j9OnTOHfuHACgXbt2GDRoEJYtW4ZBgwahZ8+eZr8syMzMxMaNG/HFF1/gzjvvxMCBAzFv3jyMHj26STOJ1dXVKC4uRlFRkbQYDAZUVlaioqICFRUVqKystIuZabucAfntt98wffp0nD9/Xlr373//G0VFRfjkk0+kdVlZWQgODjaZXv7888+xZcsW/Pjjj7jvvvswffp0xMTEALj+mykXFxdcu3YNvr6+Ztt9++238c4775itVyqVFv3wtfQHdEN+kHNMjmnv2+aYzTumXC5HUFAQIiMj8eCDDyI6OtriWhpq7dq1mDJlCkpLS2v9pU1rkJ6ejsjISDz55JP48MMPmzyeRqORZiBqltOnT8NoNCIyMhJTpkzB9OnT0aZNmwaN+9lnn+HZZ5/FwIEDsWHDBrRr165JdVZXVyMvL086YKs5aCstLZUOhGoWIYT0W243Nze0a9cOAQEBCAgIQLt27Sz63i4vL8eaNWuwcOFCZGdnY9q0aXjnnXfQsWNHi+q9du0a3nzzTXz++efo1q0b3n77bYwdO1YKSA1Vc+BaUlKCkpISXLlyBRkZGcjIyMCFCxeQnp6O9PR0ZGVlmfwCQSaToU2bNtL+d+zY0eRrtVoNmUwmfSaVlZU2X6qqquDg4GBy8F+z1KxzcXGRZoZuXDw9PaU/PT09oVAo4ODgIB0oGwwGlJWV1bvUFnhqQo9Go8G1a9ekpaSkxOzvSqFQwNXVVQoyAKSxy8vL6/wFj5OTEzp16oTQ0FCEhoaib9++GDBgADp16mTx/8fV1dXYvn073nvvPfz222/o2LEjpk2bhokTJyIiIqLW2Z7KykpkZmbi/PnzOH/+vBSCzp8/jwsXLqCystKibQNo0TMgdhlA/vzzT4wYMcJk2v+5556DXC7HsmXLpHWFhYXw9vZGSUkJ5HI5AOD999/HsWPHsGHDBjz00EMYMWIEnnnmGQDXp9fatGlT5w/T2mZAOnbs2KL/gomIbpVt27ZhzJgxyMnJgZ+fn63Lsbrq6mrcd999uHjxIpKTk+Hh4XFLtqPVarF792788MMP+P777wEAkydPxty5c2/6TBej0YiXX34ZH374IebMmYP//ve/cHZ2viV1WoPBYMCqVauwcOFC6HQ6PPfcc5g/fz68vb1r7V9RUYH/+7//w4IFC+Dg4IB33nkHTz/9tNU+A4PBAI1GA6PRCGdnZ7Rp08aq1/u0NqWlpcjLy0NeXh6uXbuGvLw85OfnS0GqqqoKQgiTU77c3Nwgl8vh5eUFb29vqNVqeHt7w9PTs1lPB0tMTMQXX3yBdevWobi4GGq1Gl27doWvry+MRiOKi4tx8eJFZGdnS7OELi4u6Ny5M7p06YIuXbogJCQEbdu2hZeXl3RKoJubm8mMo4uLC8rKyqBWq1v08aldBpCaa0AuXrwo/dDjNSBERNb122+/4e6778bp06fRrVs3W5djdZ9++imefvpp/Pzzz7jvvvusss38/Hx8+eWX+Pjjj3H58mUMGzYML7/8MoYPH272W9nMzEz885//xMGDB6UAcrsoLi7G+++/j/fffx+Ojo6YO3cuJk2ahK5du0ImkyErKwvff/89PvroI1y8eBGzZs3CggULGjxzRNTcysrKcOTIERw8eBAZGRnIy8uDs7MzPDw8EBgYiKCgIISEhKBLly4ICAhoVGC1i+NTYaceeeQR8dRTT4nS0lKxdetWoVarhUajMes3b948MWrUKKHT6cTvv/8uVCqV+Ouvv4QQQpw6dUp4e3uLP/74QxQVFYl7771XvPnmmxbXoNVqBQCh1Wqbbb+IiOxFUlKSACCOHDli61Ks7uLFi0KhUIiZM2faZPsVFRUiPj5e9O7dWwAQERER4uOPPxb79u0TW7duFbNnzxZubm4iMDBQ7Nu3zyY1WsPVq1fFc889J9zc3AQA4ebmJjw8PAQA4eTkJB577DGRnJxs6zKJrMoejk/tcgYEuP4ckGnTpmH//v0ICAhAXFwchg4divj4eCxevFi6IL2srAwzZ87E1q1boVarsWzZMkyePFkaZ/Xq1fj3v/8NnU6Hhx9+GJ999lm9FyzdyC4SJhHRLXLp0iUEBQVh586dJjf0aAnKyspw4sQJHD16FElJSdJdlrRaLSorK6FWqxEcHIzBgwdj4sSJ6N27t8VjCyEwatQoJCUlIS0trdF3PmoOQgj8+uuvWLFiBbZv3y6dzx4QEIAnn3wSL774Yqv4+VRaWopff/0VZ8+ehRACQUFBuPvuu+Hj42Pr0oiszh6OT+02gLQE9vAXTER0q2i1Wnh5eWHDhg2YOHGiTWupqKjA4cOHsWvXLvz88884efIkqqqq4ObmJt3WtnPnzvD29oazszMKCgpw+vRp/PLLL7h27RruuusuLF++3KLbZX7zzTf45z//iYSEBIwePdoKe2eZ0tJS6fkZHTp0aNV3JiNqzezh+NQub8NLRES25+npCZlMZrNbPpaXl+Onn37CunXrsGPHDpSUlMDX1xfDhg3DjBkz0K9fP0RGRtZ7wXFVVRW2bduGhQsX4s4778SUKVOwfPnyOu8SlZKSgmeeeQbTpk1rUeEDuH4HtLCwMFuXQUR0Uy3zaS9ERNTiOTg4QKlUSg9xs5azZ8/i+eefh5+fH8aPH49z587h9ddfx4kTJ5Cbm4v4+Hg8/fTT6N27903vduTk5IRx48YhMTERq1atwo4dOxAWFoa4uDiz5zykp6fjgQceQJcuXRAXF3crd5GI6LbGAEJERI2mUqmsFkBOnDiB0aNHIywsDOvXr8ecOXNw+vRp/Pnnn3j99ddxxx13NPq2mY6OjnjyySdx5swZTJgwAXPmzEG/fv3w9ddf4+DBg1ixYgX69OkDNzc37NixQ7q1OxERNRwDCBERNZq3tzcKCwtv6TYuXryIiRMnIjo6GufOncNXX32FS5cu4d1332322//6+Pjg888/x+HDh+Hp6YkZM2Zg0KBB+Ne//oWxY8fiyJEj6NChQ7Nuk4ioteE1IERE1Gg+Pj4oKCi4JWNXV1fj448/xuuvvw4vLy98+eWXmDp1qvQ041vpzjvvxL59+1BQUICcnBwEBga22Is5iYjsDQMIERE12q0KIBqNBo8//jh27tyJOXPmYPHixTYJAD4+PryVKxFRM2MAISKiRvPx8cGZM2eadczz589jxIgRKCoqapHPGCEioqZhACEiokZr7hmQv/76C/feey88PT1x/PhxBAcHN9vYRETUMjCAEBFRozVnAMnJycGwYcPg4+ODn3/+uc5ncRARkX3jXbCIiKjRfHx8UFZWhrKysiaNU15ejrFjx6K6uhq7du1i+CAiuo0xgBARUaPVXKDd1FmQN954A0lJSUhISED79u2bozQiImqhGECIiKjRmiOA1Dzob9GiRYiOjm6u0oiIqIViACEiokZragAxGo3SU8dfeuml5iyNiIhaKF6ETkREjdbUAPL//t//w6lTp3DkyBE4Ojo2Z2lERNRCcQaEiIgaTaVSwcHBoVEBpKKiAm+++SYmTpyIfv363YLqiIioJWIAISKiRnNwcIC3t3ejAsi6deuQnZ2N2NjYW1AZERG1VAwgRETUJD4+PsjPz2/Qe4QQeP/99/HAAw8gPDz8FlVGREQtEa8BISKiJmnTpk2DZ0D27duH5ORkfPDBB7emKCIiarE4A0JERE3SmKehf/311+jatSuGDBlyi6oiIqKWigGEiIiapE2bNsjLy7O4f3FxMTZv3oxp06ZBJpPdwsqIiKglYgAhIqIm8ff3R25ursX9N23ahLKyMkyZMuUWVkVERC0VAwgRETWJn58fcnNzIYSwqP+GDRtwzz33oGPHjre4MiIiaokYQIiIqEn8/f1RWVkJjUZz074lJSXYv38/xowZY4XKiIioJWIAISKiJvH39wcA5OTk3LTvvn37YDAY8MADD9zqsoiIqIWyywCSmJiIqKgoyOVyDB48GBcvXqyzb3p6OgYOHAi5XI7evXsjKSlJatu0aRP69esHV1dXzJ492xqlExHddvz8/ADAoutAduzYgZCQEISGht7qsoiIqIWyuwBiMBgwfvx4vPDCC9BoNOjfv3+9FzI+9thjGD58ODQaDWbMmIFx48ahqqoKAODt7Y1XX30VM2fOtFb5RES3HUtnQIQQ2LFjBx544AHe/YqIqBWzuwCyf/9+KBQKzJgxA25ubnjrrbdw/PjxWmdBzpw5gzNnzmD+/Plwc3PDs88+C6PRiMOHDwMA7r33Xjz88MPw9fW19m4QEd023N3doVKpbhpATp8+jYsXL/L0KyKiVs7uAkhaWhoiIyOl1x4eHggJCUFaWlqtfcPCwuDi4iKt69mzJ1JTUxu1bYPBAJ1OZ7IQEdH/7oRVnx07dsDd3R2DBw+2UlVERNQS2V0A0ev1UCqVJuuUSiX0en2T+lpiyZIlUKlU0sJbSBIRXefv73/TGZAdO3bg3nvvhbu7u5WqIiKilqjFBZDhw4fDzc2t1mXRokVQKBRmMw86nQ4KhcJsrIb0tcT8+fOh1WqlJSsrq1HjEBHdbjp06IDLly/X2a7T6fDbb79h5MiRVqyKiIhaIidbF/B3u3fvrrd9165dWLVqlfS6pKQE6enpCA8PN+sbHh6OM2fOoLKyEs7OzgCAU6dO4ZVXXmlUba6urnB1dW3Ue4mIbmfBwcHYv39/ne0///wzqqqqGECIiKjlzYDczD333AO9Xo/Vq1fDYDBg0aJF6NOnD4KCgsz6hoWFISwsDEuXLoXBYEBcXBwcHR0xYMAAAIDRaER5eTmqqqpMviYiooYJDg7GlStXYDAYam3/6aef0K1bN3Tu3NnKlRERUUtjdwHE1dUVmzdvxooVK+Dl5YVDhw5hzZo1Uvvs2bNNnunx7bffYufOnfDy8sLnn3+OzZs3w8np+sTPmjVr4O7ujnfffRdffPEF3N3dsWjRIqvvExGRvevUqROEELXekfDG2+8SERHJhBDC1kXYK51OB5VKBa1Wa3axOxFRa5KRkYHOnTtj586dGDFihElbUlISevXqhZ9//hn33XefjSokImod7OH41O5mQIiIqOXp2LEjHB0dkZGRYda2Y8cOKBQK3HXXXTaojIiIWhoGECIiajInJycEBgbi/PnzZm07duzA0KFDeRMPIiICwABCRETNpEePHkhJSTFZV1BQgMOHD/P6DyIikjCAEBFRs+jZsydOnTplsm7Hjh2orq7G6NGjbVQVERG1NAwgRETULHr27ImcnBzk5eVJ6xISEtC3b1/4+fnZsDIiImpJGECIiKhZ9O7dGwBw7NgxAEBpaSl27tyJMWPG2LIsIiJqYRhAiIioWYSEhCAwMBB79uwBAHz//ffQ6/WYPHmyjSsjIqKWhAGEiIiahUwmw7Bhw6TrPj799FMMGTIEwcHBti6NiIhaEAYQIiJqNlOnTsW5c+cwadIkHD58GPPmzbN1SURE1MIwgBARUbMZNGgQJk6ciE2bNmH69OkYOXKkrUsiIqIWRiaEELYuwl7Zw6PuiYisTQiB3Nxc+Pn5QSaT2bocIqJWxR6OT51sXQAREd1eZDIZ/P39bV0GERG1UDwFi4iIiIiIrIYBhIiIiIiIrIYBhIiIiIiIrIYBhIiIiIiIrIYBhIiIiIiIrIYBhIiIiIiIrIYBhIiIiIiIrIYBhIiIiIiIrIYBhIiIiIiIrIYBhIiIiIiIrIYBhIiIiIiIrIYBhIiIiIiIrMYuA0hiYiKioqIgl8sxePBgXLx4sc6+6enpGDhwIORyOXr37o2kpCSpbdmyZejWrRs8PT0RHh6OzZs3W6N8IiIiIqJWy+4CiMFgwPjx4/HCCy9Ao9Ggf//+mDJlSp39H3vsMQwfPhwajQYzZszAuHHjUFVVBQBwdHTEd999B61Wi08//RTTp09Henq6tXaFiIiIiKjVkQkhhK2LaIhdu3bhxRdfxOnTpwEAJSUl8PX1xenTpxEUFGTS98yZM+jbty/y8vLg4uICAAgKCsKaNWtw9913m409cOBAzJ07Fw8//LBFteh0OqhUKmi1WiiVyibuGRERERFR09jD8andzYCkpaUhMjJSeu3h4YGQkBCkpaXV2jcsLEwKHwDQs2dPpKammvUtLi5GamoqwsPD69y2wWCATqczWYiIiIiIyHJ2F0D0er1ZmlMqldDr9U3qO2vWLIwZMwbdu3evc9tLliyBSqWSlo4dOzZyL4iIiIiIWqcWF0CGDx8ONze3WpdFixZBoVCYzTzodDooFAqzsSzt+9prr+HSpUv47LPP6q1t/vz50Gq10pKVldXIvSQiIiIiap2cbF3A3+3evbve9l27dmHVqlXS65KSEqSnp9d66lR4eDjOnDmDyspKODs7AwBOnTqFV155ReqzfPlybNu2DQcPHoS7u3u923Z1dYWrq2tDdoeIiIiIiG7Q4mZAbuaee+6BXq/H6tWrYTAYsGjRIvTp08fsAnQACAsLQ1hYGJYuXQqDwYC4uDg4OjpiwIABAICvvvoKH3/8MXbt2gW1Wm3tXSEiIiIianXsLoC4urpi8+bNWLFiBby8vHDo0CGsWbNGap89ezZmz54tvf7222+xc+dOeHl54fPPP8fmzZvh5HR94mfhwoXIyclBt27doFAooFAosHjxYqvvExERERFRa2F3t+FtSezhNmdERERE1HrYw/Gp3c2AEBERERGR/WIAISIiIiIiq2EAISIiIiIiq2EAISIiIiIiq2EAISIiIiIiq2EAISIiIiIiq2EAISIiIiIiq2EAISIiIiIiq2EAISIiIiIiq2EAISIiIiIiq2EAISIiIiIiq3GydQH2zGg0AgAuX74MpVJp42qIiIiIqLXT6XQA/nec2hIxgDTB+fPnAQARERE2roSIiIiI6H/Onz+Pf/zjH7Yuo1YyIYSwdRH2qrCwEN7e3sjKyuIMCBERERHZnE6nQ8eOHaHRaKBWq21dTq04A9IEjo6OAAClUskAQkREREQtRs1xakvEi9CJiIiIiMhqGECIiIiIiMhqGECIiIiIiMhqGECIiIiIiMhqeBG6lZSWluKzzz7DL7/8gtzcXPDmYy2Pg4MDOnXqhAceeABTp06FgwPzOREREVFzYwCxAqPRiOeeew7nzp3Dgw8+iJCQkBZ9Z4LWqrKyEsnJyYiLi8Ply5fxxhtv2LokIiIiotsOA4gV/PHHH0hKSkJcXBz69u1r63KoHhMmTEC3bt3w3//+F7Nnz0abNm1sXRIRERHRbYXnmFjB8ePH4ePj02KfRkmmRo4cCSEETpw4YetSiIiIiG47DCBWUFpaCpVKBZlMZutSyAJeXl4Arv+9EREREVHzYgCxkvrCR0hICPr161dr24wZM+Dm5oaioqI63y+EwMKFCxEUFASFQoGgoCC8+OKLUnteXh5GjRoFuVyOsLAw7N271+T9S5cuha+vL7y9vfHqq6+aXCCfmJiIqKgoyOVyDB48GBcvXpTaysrKEBMTA09PTwQGBmLdunUm465evRoBAQFQKpWYPn06KioqpLZRo0ahbdu2UKlU6NevH37//fc69+9GmZmZkMlkUCgUUCgU6NChA9555x2p/ejRo/D19UVeXp60buXKlYiOjkZ2djZ8fHxMZjaMRiPuuOMOfPXVV9I6BkUiIiKiW4cBxMYOHz6MvLw8nDhxAufPnzdpKy8vx/fffw+5XI5NmzbVOcY333yDzZs348CBA9Dr9fjtt98QHR0ttc+ZMwft27dHfn4+li1bhgkTJqCwsBAAsGPHDqxcuRJHjx5Famoqtm/fjq+//hoAYDAYMH78eLzwwgvQaDTo378/pkyZIo0bGxsLjUaD7OxsrF+/Hk8//TTOnj0LAEhOTsbcuXOxZcsWZGVlITMzE4sWLZLe+5///AdXrlyBVqvFG2+8gXHjxll8ZzBXV1fo9Xro9XocPHgQn3/+OXbv3g0A6NevHyZOnIiXXnoJAJCTk4M33ngDq1atQocOHbBw4UI89dRTMBqNAIAPP/wQKpUK06dPt2jbRERERNQ0DCA2Fh8fj/Hjx+Pee+9FfHy8Sdu2bdvg7e2NefPmmbXdKDExEaNGjUJQUBAAIDAwUAoKer0eW7duxYIFCyCXyzF27Fj06NED27ZtAwCsWbMGzzzzDDp37gx/f3/MmzcPa9euBQDs378fCoVCmoV56623cPz4cWkWZM2aNYiNjYVSqcSAAQMwZswYrF+/HgDw7bffYtKkSejTpw9UKhXefPNNaVwAiIiIgJOTE4QQcHBwwNWrVxt1ylNwcDAGDBiA06dPS+uWLFmCffv24eeff8Zzzz2HKVOmSIHs6aefhqurKz7++GNkZWVh8eLFWLVqFWc9iIiIiKyEAcSGKisrsXHjRkyaNAmTJk0yCxlr167FxIkT8eijj+LAgQO4fPlyreP069cPq1atwooVK3DixAlUV1dLbefOnYNKpYK/v7+0LioqCqmpqQCAtLQ0REZGWtTm4eGBkJAQpKWlobCwELm5uRa/NyoqChkZGSgrK5PWPfjgg3Bzc8ODDz6I559/Hh4eHpZ/eP+/9PR0HD582OTuYkqlEh988AEeffRRHD16FAsXLpTaZDIZVq1ahQULFmDatGl44YUX0LVr1wZvl4iIiIgahwHEhnbt2oXq6moMHToU48aNQ2ZmJhITEwEAGo0GO3fuxKRJk9C5c2f07t3b7BqLGlOnTsXy5cuRkJCAgQMHwt/fX7qmQa/XQ6lUmvRXKpXQ6/W1ttfXdmO7Xq+Ho6Mj5HK5xePWrK+xfft2FBcXY9OmTejdu7fFn5vBYICXlxeUSiW6dOmCu+66y+z2xn379oVWq8WoUaPg6elp0hYREYEnnngCWVlZ+Ne//mXxdomIiIio6RhAbGjt2rUYP348nJ2doVarMXz4cGkWZOPGjQgMDJQOzGubIbnRtGnTsH//fhQVFSE2NhZPPvkkUlNToVAooNPpTPrqdDooFAoAMGuvr+3GdoVCAaPRaHLa1M3GrVl/IxcXFzz88MN4//33TU6jqo+rqyuKioqg0+mQl5eH/Px8vPrqqyZ95syZg6lTp2LDhg1IS0szGyM8PBwhISFwcXGxaJtERERE1DwYQGykuLgYCQkJ2LBhA/z8/ODn54f9+/dj/fr1MBqNiI+PR1ZWltS2ePFiJCUlSac41cXV1RXPPPMM1Go1Tp8+jdDQUGi1WuTm5kp9kpKSEBERAeD6gXhycrJFbSUlJUhPT0d4eDjUajX8/Pwsfm9SUhKCg4Ph7u5ea91VVVXIyMiw9OOTtGnTBuPGjcOuXbukdRs3bsSZM2fwySef4PXXX8fs2bMtvsCdiIiIiG4tBhAb2bx5M9q0aYMzZ87g/2vvzuOjqu7/j7+zQMhkmMnCEkIIImAkYXFD/AIKamUVBAuuAaqlghXXipWqDSLrV7FaLS5IQRFBVFTcQMDiUqSgVZaACEFIIAkEkswwkIQs5/eHv8yXIXtIZjLwej4e8zAz99xzP/dyhHnn3OXHH3/Ujz/+qB07dqigoEALFizQhg0b9OWXX3osu/baayucBXn99de1atUqHT9+XCUlJXrzzTfldDp18cUXy2q1avjw4UpOTlZ+fr5Wrlyp7du3a9iwYZKkpKQkvfTSS/rll1+UlZWlZ599VklJSZKk/v37y+VyadGiRSosLNT06dN12WWXuS92T0pK0lNPPaVjx45p48aNWrlypW6++WZJ0m233ably5frv//9rxwOh2bMmOHud//+/fr4449VUFCgwsJCvfjiizpw4IDHnbtqKi8vTx9++KG6dOkiSXI4HHrggQf00ksvqVmzZnrwwQflcDjcd/YCAACAjxnUmcPhMJKMw+Gost3TTz9tbrrpJo/PrrvuOjN16tRybSdNmmSaNGli+vfvX27Zu+++a9q3b29KS0tNQkKCefPNN40xxrz33nvmiiuuMHa73dhsNnPJJZeY999/373e4cOHzeDBg01oaKjp3LmzWbNmjUe/M2fONFFRUSY8PNxMnjzZlJaWupdt2rTJdOvWzTRr1sxceeWVZt++fe5lJ06cMLfddpsJCwszsbGxZsmSJR79Lly40MTExBir1WrGjRtnCgoKjDHG7Nu3z1xxxRWmefPmJjw83PTt29esX7/evd6MGTPMoEGD3O8HDRpkZsyYYYwx5pdffjGSTFhYmAkLCzORkZHmxhtvNBkZGcYYYyZMmGCSkpI86tiwYYNp2bKlyc7O9qht4MCB5Y5xmUsvvdTjGAIAAPiDmn4/9aUAYzg3pa6cTqfsdrscDke5i7VP9cwzz2jz5s16++23vVgdzsRll12mxx9/XCNGjPB1KQAAADVW0++nvsQpWAAAAAC8xm8DSHZ2toYOHSqLxaL4+HitW7euwnb5+flKSkpS8+bNFRcX53Er240bN6p79+4KDw9Xq1atNG7cOI/bxAIAAACoX34bQO655x7FxMToyJEjmjNnjkaPHq3c3Nxy7ZKTk5WTk6ODBw9q2bJluvvuu/Xzzz9Lkjp16qTPPvtMeXl52rdvn0pLSz0eWldfgoODdfLkyXrvFw2j7M+qSZMmPq4EAADg7OOXAcTlcunDDz/UtGnTZLFYNGLECHXt2lUfffRRubaLFy9WcnKybDabevfureHDh2vZsmWSfr2Fa9u2bSVJxhgFBATU6Vaw1enYsaMOHDig7Ozseu8b9e+HH36QJJ1//vk+rgQAAODsE+zrAupi9+7dstvtatOmjfuzHj16lHtGRm5urrKystStWzePdps2bXK/T0tLU/fu3eVwOGS1WvXJJ59Uut3CwkIVFha635/+kL7K9OvXT2FhYfrzn/+se+65R506dVJQUFCN1oX3FBUVaevWrZo7d67OP/98XXjhhb4uCQAA4KzjlwHE5XKVu6rfZrMpLy+vXLugoCBZLBaPdqde5xEXF6e8vDwdPnxYr7zyikeoOd2sWbP05JNP1rpem82mf/zjH3rkkUc0YcKEWq8P74qPj9ezzz6rgIAAX5cCAABw1vHLAGK1WsvNPjidTlmt1nLtSkpKdOLECXcIqaidJLVq1UqDBw/W2LFj9e2331a43SlTpuihhx7y2Ga7du1qVHNiYqI++ugj/fTTT8rKylJpaWmN1oP3BAcHq3379urQoYOvSwEAADhr+WUA6dy5sxwOh7KyshQdHS1J2rJli8aPH+/RLiIiQtHR0dq2bZt69erlbpeYmFhhv6WlpUpNTa10uyEhIQoJCalz3YGBgUpISFBCQkKd+wAAAAD8mV9ehG61WjV8+HAlJycrPz9fK1eu1Pbt2zVs2LBybZOSkvTUU0/p2LFj2rhxo1auXKmbb75ZkvTpp59q165dMsYoMzNTTzzxhK6++mpv7w4AAABwzvDLACJJ8+bNU3p6uqKiovTwww9r+fLlioiI0JIlSzxmOKZNm+a+YH306NGaN2+e4uPjJUmHDh3S4MGDZbVademllyo2NlYvvfSSr3YJAAAAOOsFGGOMr4vwV/7wqHsAAACcO/zh+6nfzoAAAAAA8D8EEAAAAABeQwABAAAA4DUEEAAA6qi0tFSZmZm+LgMA/AoBBACAOrr33nsVExOjN954w9elAIDfIIAAAFAHR48e1auvvipJmjt3ro+rAQD/QQABAKAO1q1bp+LiYv3tb3/T1q1bORULAGqIAAIAQB188cUX6tKli2666SZJ0oYNG3xcEQD4BwIIAAB18OOPP6pnz56KiYlRq1attG3bNl+XBAB+gQACAEAtlZaWatu2berevbskqUuXLtq5c6ePqwIA/0AAAQCgllJTU3XixAn16NFDEgEEAGqDAAIAQC3t2LFDkpSYmChJuuCCC7Rnzx4ZY3xZFgD4BQIIAAC1tHfvXlksFkVHR0uS4uLilJ+fr5ycHB9XBgCNHwEEAIBa2rt3rzp06KCAgABJvwYQSUpLS/NlWQDgFwggAADU0t69e3X++ee737dr104SAQQAaoIAAgBALZ0eQFq1aqWmTZsqPT3dh1UBgH8ggAAAUAulpaX65ZdfPAJIYGCgYmNjtX//fh9WBgD+gQACAEAtZGZmqrCwUB06dPD4vG3btsrMzPRRVQDgPwggAADUwi+//CJJ5QJI69atdfjwYV+UBAB+hQACAEAtHDx4UJIUGxvr8XmrVq106NAhX5QEAH6FAAIAQC0cPHhQFotFdrvd4/PWrVsTQACgBgggAADUQkZGhmJiYtzPACnTunVrZWdnq7S01EeVAYB/IIAAAFALGRkZatu2bbnPW7VqpdLSUh09etQHVQGA/yCAAABQCwcPHqwwgLRu3VqSuBAdAKrhtwEkOztbQ4cOlcViUXx8vNatW1dhu/z8fCUlJal58+aKi4vT0qVL3cs+/vhj/c///I/sdrtiY2P15JNPeqt8AICfKjsF63StWrWSJK4DAYBqBPu6gLq65557FBMToyNHjujzzz/X6NGjlZqaqoiICI92ycnJysnJ0cGDB7V9+3YNGTJEl156qS644AIdO3ZM06dPV9++fXXo0CENHDhQ559/vsaMGeOjvQIANGbGGGZAAOAM+eUMiMvl0ocffqhp06bJYrFoxIgR6tq1qz766KNybRcvXqzk5GTZbDb17t1bw4cP17JlyyRJt956q6699lqFhIQoLi5ON954ozZt2uTt3QEA+AmHw6H8/PwKZ0CsVquaNWtGAAGAavhlANm9e7fsdrvatGnj/qxHjx5KSUnxaJebm6usrCx169atynZlNmzYoMTExEq3W1hYKKfT6fECAJw7yp4BUtEMSEBAgCIjI5Wbm+vtsgDAr/hlAHG5XLLZbB6f2Ww2uVyucu2CgoJksViqbCdJr7zyijIzMzVu3LhKtztr1izZ7Xb3q127dme4JwAAf5KRkSFJFc6ASFJkZKRycnK8WRIA+B2/DCBWq7Xc7IPT6ZTVai3XrqSkRCdOnKiy3ccff6xp06bp448/VmhoaKXbnTJlihwOh/uVnp5eD3sDAPAXZTMgBBAAqDu/DCCdO3eWw+FQVlaW+7MtW7aUO30qIiJC0dHR2rZtW6XtvvrqK/3+97/XypUr1alTpyq3GxISIpvN5vECAJw7MjIy1KJFC4WEhFS4nAACANXzywBitVo1fPhwJScnKz8/XytXrtT27ds1bNiwcm2TkpL01FNP6dixY9q4caNWrlypm2++WZL0448/atSoUVqyZIkuvfRSb+8GAMDPHDx4sNLZD+nXAMKDCAGgan4ZQCRp3rx5Sk9PV1RUlB5++GEtX75cERERWrJkiccMx7Rp09wXrI8ePVrz5s1TfHy8JOn555/X0aNHNWLECFmtVlmtVg0ePNhXuwQAaOQqewp6GWZAAKB6AcYY4+si/JXT6ZTdbpfD4eB0LAA4B1x++eXq3r27XnvttQqXz5o1S3PnztWRI0e8XBkA/Mofvp/67QwIAADeVpMZkNzcXJWWlnqxKgDwLwQQAABqoKSkRFlZWdVeA1JaWspzogCgCgQQAABq4PDhwyopKal2BkQS14EAQBUIIAAA1EB1zwCRCCAAUBMEEAAAaqC6p6BLBBAAqAkCCAAANZCRkaGgoCC1atWq0jYEEACoHgEEAIAayMjIUJs2bRQYWPk/nVarVcHBwQQQAKgCAQQAgBooCyBVCQgI4GnoAFANAggAADWQmZlZ5fUfZcLDw+VwOLxQEQD4JwIIAAA1kJGRUaMAUvYEYgBAxQggAADUQE0DSHh4uPLy8hq+IADwUwQQAACqUVRUpMOHDzMDAgD1gAACAEA1srKyJFX9DJAyzIAAQNUIIAAAVKPsIYTV3QVLYgYEAKpDAAEAoBo1eQp6GQIIAFSNAAIAQDUyMjLUpEkTRUVFVduWU7AAoGoEEAAAqlGTp6CXsdvtKiwsVGFhoRcqAwD/QwABAKAaNX0IofRrAJHEaVgAUAkCCAAA1SibAamJ8PBwSeI0LACoBAEEAIBq1PQhhBIzIABQHQIIAADVIIAAQP0hgAAAUIXCwkIdPXqUU7AAoJ4QQAAAqMKBAwckSe3atatRe5vNJokZEACoDAEEAIAqpKWlSZLi4uJq1D4oKEhWq5UAAgCV8NsAkp2draFDh8pisSg+Pl7r1q2rsF1+fr6SkpLUvHlzxcXFaenSpe5lO3bs0HXXXSe73a4LL7zQW6UDAPxIenq6pJrPgEg8jBAAquK3AeSee+5RTEyMjhw5ojlz5mj06NHKzc0t1y45OVk5OTk6ePCgli1bprvvvls///yzJKlJkya67bbbNHfuXG+XDwDwE2lpaWrRooVCQ0NrvI7dbmcGBAAq4ZcBxOVy6cMPP9S0adNksVg0YsQIde3aVR999FG5tosXL1ZycrJsNpt69+6t4cOHa9myZZKkzp0764477lCnTp28vQsAAD+RlpZW49OvyhBAAKBywb4uoC52794tu93ucUeSHj16KCUlxaNdbm6usrKy1K1bN492mzZtqtN2CwsLVVhY6H7vdDrr1A8AwH+kp6fXOoBwChYAVM5vZ0DK7jJSxmazyeVylWsXFBQki8VSZbuamjVrlux2u/tVm/OBAQD+KS0trdZ/3zMDAgCV88sAYrVay80+OJ1OWa3Wcu1KSkp04sSJKtvV1JQpU+RwONyvsgsTAQBnJ2MMp2ABQD3zywDSuXNnORwOZWVluT/bsmWLEhMTPdpFREQoOjpa27Ztq7JdTYWEhMhms3m8AABnL4fDIZfLxSlYAFCP/DKAWK1WDR8+XMnJycrPz9fKlSu1fft2DRs2rFzbpKQkPfXUUzp27Jg2btyolStX6uabb5b062+2CgoKdPLkSY+fAQCQ/u8ZIJyCBQD1xy8DiCTNmzdP6enpioqK0sMPP6zly5crIiJCS5Ys8ZjhmDZtmvuC9dGjR2vevHmKj4+XJO3fv1+hoaEaOHCgfv75Z4WGhmrAgAG+2iUAQCOzb98+SVL79u1rtV54eLgcDoeMMQ1QFQD4N7+8C5YktWzZUp9++mm5z2+//Xbdfvvt7vehoaFasmRJhX2cd955/OMAAKhUamqqQkNDPe66WBN2u10lJSU6fvx4na87BICzld/OgAAA0NBSU1PVsWNHBQQE1Go9u90uSVwHAgAVIIAAAFCJPXv2qGPHjrVeLzw8XJK4DgQAKkAAAQCgEnv27FGnTp1qvV7ZDAgBBADKI4AAAFCBoqIi7d+/nxkQAKhnBBAAACqQlpam4uLiM5oB4RoQACiPAAIAQAX27NkjSXWaAQkLC1NQUBAzIABQAQIIAAAV2LVrl5o2bVrrp6BLUkBAgOx2OzMgAFABAggAABXYvn27unTpouDguj0yi6ehA0DFCCAAAFQgJSVFXbt2rfP64eHhzIAAQAUIIAAAnMYYo+3bt59RAGEGBAAqRgABAOA0Bw4ckNPpPOMZEAIIAJRHAAEA4DTbtm2TJCUmJta5Dy5CB4CKEUAAADjN5s2bFRUVpfPOO6/OfXAKFgBUjAACAMBpNm7cqMsvv1wBAQF17oOL0AGgYgQQAABOYYzRpk2bdMUVV5xRP8yAAEDFCCAAAJxiz549ysnJUa9evc6on/DwcLlcLhUXF9dTZQBwdiCAAABwin/9618KCgo64wBit9slSU6nsz7KAoCzBgEEAIBTfP755+rVq5fCw8PPqJ+y9TkNCwA8EUAAAPj/iouLtXbtWg0cOPCM+yqbAeFCdADwFOzrAgAAaCzWrVsnh8Oh66+//oz7KgsgzID4j++//14rVqzQ9u3bdfz4cVmtVsXGxuqCCy5Qjx491L17d0VERPi6TMDvEUAAAPj/3nzzTcXHx+viiy8+477KTsFiBuT/HD58WAEBAWrZsqWvS/Gwa9cu/fGPf9QXX3yhli1b6tJLL1VUVJSOHTumL7/8UvPnz9fJkyclSe3atdMll1yifv36qX///rrooovO6HbNwLmIAAIAgKTs7Gy99957euyxx+rlCyUzIP9n69atmjBhgjZu3ChJuvzyy/X3v//9jC/0rw9ffvmlhg0bplatWmnFihUaNmyYgoM9vx4VFRXp559/1tatW7VlyxZt2rRJf/nLX1RQUKAOHTpozJgxuvvuuxUdHe2jvQD8C9eAAAAg6fnnn1dgYKAmTpxYL/01bdpUoaGh5/wMyH/+8x/17dtXLpdLS5cu1dKlS1VaWqo+ffpoyZIlPq1t5cqVGjhwoHr16qUff/xRI0eOLBc+JKlJkyZKTEzUrbfeqtmzZ+uLL75QXl6e1q1bp2uuuUZ/+9vfdN5552nSpEk6cOCAD/YE8C9+G0Cys7M1dOhQWSwWxcfHa926dRW2y8/PV1JSkpo3b664uDgtXbrUY/miRYsUGxsrm82mO+64wz3FCgA4d+zZs0dz587VpEmTFBUVVW/9nusPIzx06JBGjhypbt26acOGDbrlllt0yy23aMOGDRo7dqzGjBmjFStW+KS2xYsX68Ybb9TQoUP18ccfy2q11mr9kJAQXXPNNXrttdeUlpamJ554QkuXLlWnTp30wAMPKCsrq4Eq9z5jjK9LwFnGbwPIPffco5iYGB05ckRz5szR6NGjlZubW65dcnKycnJydPDgQS1btkx33323fv75Z0nStm3b9NBDD+mDDz5Qenq69u3bp+nTp3t7VwAAPpSXl6dRo0apTZs2euKJJ+q17/Dw8HN6BmTq1KkqLCzUe++9p+bNm7s/b9KkiV577TWNGjVKY8eO1bZt27xa1wsvvKCxY8dq3LhxevvttxUSEnJG/YWHh+uxxx7TL7/8oscee0yLFi1Sx44d9ec//1lHjx4943pLSkp0/PhxuVyuBg8DJSUl2rRpk5566in17dtX4eHhCgoKkt1u1+WXX65HHnlEW7ZsadAacPYLMH4Ya10ul6KiorRv3z61adNGknTVVVdp/PjxGjt2rEfbNm3a6IMPPnCfZzp27Fh16tRJf/3rXzVlyhTl5eXppZdekiR98cUXGj9+vPbu3VujOpxOp/u3WzabrR73EADQ0IwxWrNmjSZNmqTs7Gx98803SkxMrNdtXHHFFUpMTNSCBQvqtV9/sHv3bnXp0kWzZ8/Www8/XGGb48ePq3fv3iosLNR///tfWSyWBq2ptLRUU6ZM0f/+7//qT3/6k55++ukGuYA8NzdXc+fO1XPPPafAwEBNmjRJt9xyi7p161bp9kpLS3XgwAH99NNP5V6ZmZnudiEhIWrTpo0SExPVvXt39ejRQxdffLE6deqkwMC6/V45LS1NX3zxhVavXq01a9bo6NGjstlsuvbaa93PxHE4HNqyZYvWrl2rw4cPq2fPnrrvvvt00003qWnTpnXaLhqGP3w/9csA8sMPP2jgwIE6fPiw+7N7771XFotFc+bMcX+Wm5uryMhIHT9+3P2X2ty5c7Vp0ya9/fbbuuGGGzRw4ED98Y9/lCQdPXpULVq00IkTJxQaGlpuu4WFhSosLHS/dzqdateunZ577rkK21elroed9VjPG+v5Ypusx3reWE/6dcZj//792rhxozIzM3XFFVdo8eLF6tSpU537rMzAgQPVvHlzvfvuu/Xed2N366236uuvv9bu3bur/Ddy586duuSSS/SHP/xBf//732u9HWOMVqxYoYULFyo1NVURERHq27evfvOb3+jKK690b3vXrl269957tXbtWs2dO1cPPPBAg9+9Kjs7W3PmzNH8+fPldDrVsmVLde3aVW3atJHFYtGJEyeUm5urtLQ0paamqqCgQNKvISM+Pl7x8fG68MIL1b59e1ksFhljdPToUR04cEDbt2/X1q1b3decWK1WXXTRRbr44ot10UUXKTY2Vi1btpTdbldgYKACAgLkcrl09OhRZWdna9euXdqxY4c2btyo1NRUBQQE6NJLL9XAgQM1aNAg9erVS02aNCm3T0VFRfrkk080b948rVmzRtHR0br77rs1YcIEtW7dut6OXXFxsbKyspSTkyOXy+WeATpx4oSMMQoICHD/+QUEBCgwMNC9nxX9t+wVFBTk8QoODnb/fGqAO3VsGGNUUlKi0tJSlZSUuF+nvj99WVFRkU6ePKmTJ0+qsLDQ/XNxcbGKiopUXFzs8XPZ32mn79fpP1e1rOznkydP6umnn27UAcQv74LlcrnKHVCbzVZumtvlcikoKMjjNyo2m00ul6vCfsp+drlcFf5lOWvWLD355JPlPn/wwQfr9JdYXf/iYz3W88Z6vtgm67GeN9azWq2Ki4vT7bffruuvv15XXXVVg30RDQ8Pr5dTcPzNjz/+qGXLlmn+/PnV/oKuS5cumjNnju6//34NGzZM1113XY23k5+fr1tvvVUffvih+vbtq4EDB+rQoUNavHixnn76aTVr1kxdu3ZVfn6+UlJS1K5dO3322Wf18qDJmmjZsqWeeeYZzZw5U+vXr9eGDRuUkpKijIwM9y87w8PD1b9/f/3hD39Q586d1aVLF8XFxSkoKKhG2zhy5Ih++OEH92v16tV68cUXqw3p4eHhSkxM1KBBg3T11VerX79+atGiRbXba9KkiUaMGKERI0Zox44d+vvf/67Zs2drxowZuvXWW3XXXXepZ8+eFYaXMkVFRcrIyNCBAwc8Xunp6e6fMzMzVVpaWqNj0NgFBQWpadOmatq0qZo0aaLg4OBy/w0MDJQxxv3nVtHPVS079eeSkhIf7GXtMANSDzMgjTlhAgB856677tIPP/ygzZs3+7oUrxoyZIhSU1OVkpJS4V2lTldaWqqBAwdq586d2rp1qyIjI6tdp7i4WKNGjdKaNWu0dOlSDR8+3L3MGKOUlBStXr1aP/30k5o2bao+ffroxhtvVLNmzc5o3/xBfn6+Dh8+rOzsbDmdThljVFpaqrCwMEVFRalFixaKjIyst+Cdk5OjBQsW6MUXX1RaWppCQ0MVHx+vmJgYhYaGqrS0VC6XS1lZWTp06JCys7M9ApLValW7du0UGxvr/m9sbKzatm2rFi1ayGq1KiwsTFarVRaLRQEBARV+OS8tLVVpaanHz2XvK5qlKHsVFxerpKTEo8/TnTpLUtHPp78/NXDUNEzWF384BcsvZ0A6d+4sh8OhrKws9z23t2zZovHjx3u0i4iIUHR0tLZt2+a+BmTLli3uc3wTEhI8LnzbsmWLOnToUOlva0JCQs74QjUAwLnjXLwI/csvv9Rnn32md955p0bhQ5ICAwO1cOFCde/eXRMnTtTbb79d5ZdjY4zuueceffzxx1q5cqWGDBnisTwgIEBdu3ZV165dz2hf/FVoaKjat2+v9u3be2V7kZGRmjx5sh588EF999132rBhg/bs2aODBw/K5XIpMDBQdrtd8fHxat26tdq0aeMRNBrrl2Q0HL+cAZGk0aNHKzIyUs8995zWrFmj3/3ud+5zP081efJk7dy5U0uXLlVKSooGDRqk//znP4qPj9e2bdvUv39/rVmzRh07dtSNN96oPn36aNq0aTWqwR8SJgDAd2bMmKHnn3/eY8b+bGaMUZ8+fXTy5Elt3ry51r9hf+edd3TTTTfp9ddfL3dTmVNNnTpVTz75pBYuXKjf/e53Z1g1cHbxh++nfnsb3nnz5ik9PV1RUVF6+OGHtXz5ckVERGjJkiUedzGZNm2a7Ha72rRpo9GjR2vevHmKj4+XJHXr1k1z587VsGHD3NN+jz32mK92CQBwlimbAWlMv+s7fvy4PvvsMz3++OO64YYbdMEFFygyMlJhYWGKi4vTkCFD9Pzzz1d4a/vqfPTRR/r22281e/bsOp3eM3r0aI0dO1aTJk3Sjh07Kmzz0ksv6cknn9TMmTMJH4Cf8tsZkMbAHxImAMB33nzzTY0ZM6bSawu9JTMzU8uXL9cHH3ygDRs26OTJk2rdurV69OihxMREtWrVSiEhITpy5Ii+//57/etf/1KTJk30xBNP6MEHH6zRbVaLi4t10UUXKTo6WmvXrq1zrU6nU3369JHD4dDXX3/tcRrRokWLdMcdd+i+++7Tc8891+B3sQL8kT98P/XLa0AAAPAHdrtdkuRwOLweQE6ePKn33ntP8+fP1/r16xUcHKwBAwbomWee0XXXXaf4+PhKv8AfOnRIc+bM0WOPPaYPPvhA7777rtq2bVvl9hYsWKCUlBQtWrTojOq22Wz67LPPdOWVV6pnz56aOXOmunTpojfeeEOvvvqq/vCHP+hvf/sb4QPwY8yAnAF/SJgAAN/5+uuvddVVV2nnzp268MILvbJNh8OhF154QfPmzVNmZqb69++vMWPGaOTIkeWuk6zOpk2b9Nvf/lZFRUV677331KdPnwrb5ebmKj4+XoMHD9brr79eH7uh7OxsTZw4UStWrJD064XOU6dO1aRJkwgfQBX84fspMyAAADSQU2dAGlpBQYFeeOEFzZ49WydOnNC4ceN07733ntHT3S+//HJ9//33GjVqlK6++mq9/PLLuvPOOz3aGGM0ceJEFRUVadasWWe6G24tW7bUe++9p0OHDunw4cPq3LnzOXELXeBcQAABAKCBhIeHS1KD34r3yy+/1F133aW9e/dq/PjxeuKJJxQTE1Mvfbdq1Upr167Vfffdp9///vfasmWL5syZo2bNmqm0tFSPP/64li9frrfffrvetnmq1q1b1+sTtgH4HgEEAIAGUhZA6nJHqZooKirSo48+qmeffVa9e/fW+++/r4SEhHrfTtOmTfXyyy+rR48euu+++7RixQoNGDBA27dv16ZNm/T000/rpptuqvftAjg7EUAAAGggzZs3V3BwsI4ePVrvfR8+fFijRo3St99+q7lz5+qBBx5QYGDD3l3/7rvv1jXXXKNnn31W33//vWJiYrR27Vpde+21DbpdAGcXAggAAA0kICBAUVFRysnJqdd+09PT9Zvf/EZOp1Pr16+v9OLwhhAfH69XXnnFa9sDcPYhgAAA0ICioqLqdQYkIyNDV111lSTpm2++UceOHeutbwDwBgIIAAANqD4DiMvl0vXXX6/i4mJt2LBB7dq1q5d+AcCbCCAAADSgyMjIegkgxhiNHz9ee/bs0TfffEP4AOC3GvZqNQAAznH1NQPy+uuv6+2339Zrr72m7t2710NlAOAbBBAAABpQfVyEfujQId1///0aN24ct7sF4PcIIAAANKD6mAGZMmWKgoODNXfu3HqqCgB8h2tAAABoQFFRUcrLy1NJSYmCgoJqvf5///tfLVy4UC+99JKioqIaoEIA8C5mQAAAaEBRUVEyxtT5aegzZsxQp06dNH78+HquDAB8gwACAEADKpu1qMtpWDt27NCKFSv06KOPKjiYkxYAnB0IIAAANKDIyEhJdQsgc+fOVWxsrMaMGVPfZQGAzxBAAABoQGUzILW9E5bT6dSyZcs0YcIENW3atCFKAwCfIIAAANCA6noK1tKlS1VQUKA77rijIcoCAJ8hgAAA0ICaNm0qq9Va6wDy2muvaejQoWrbtm0DVQYAvsEVbQAANLDaPgtkz549+u677/Tuu+82YFUA4BvMgAAA0MBat26tw4cP17j9e++9J4vFosGDBzdgVQDgGwQQAAAaWHR0tLKysmrc/t1339WQIUNksVgasCoA8A0CCAAADaw2AWTfvn367rvvNGrUqAauCgB8wy8DyObNm9WjRw9ZLBb169dP+/fvr7Rtamqq+vTpI4vFoksuuURbtmxxL3v33XfVq1cvhYSEaOLEid4oHQBwDqpNAPnggw8UEhKiIUOGNHBVAOAbfhdACgsLdeONN+r+++9XTk6Orrjiiiof0HTrrbdqwIABysnJ0Z133qmRI0equLhY0q8Ph3rkkUc0fvx4b5UPADgHRUdH69ChQyotLa227apVq9SvXz81b97cC5UBgPf5XQBZv369rFar7rzzTjVr1kx//etf9d1331U4C7Jr1y7t2rVLU6ZMUbNmzTRp0iSVlJRow4YNkqRrrrlGv/3tb9WyZUtv7wYA4BwSHR2toqIi5ebmVtkuPz9fX375pQYMGOClygDA+/wugOzYsUPdunVzvw8LC1PHjh21Y8eOCtvGx8d7PEG2e/fuSklJqdO2CwsL5XQ6PV4AAFQnOjpakqo9Deubb75RQUGBBg4c6I2yAMAn/C6AuFwu2Ww2j89sNptcLtcZta2JWbNmyW63u1/t2rWrUz8AgHNLTQPI6tWr1bZtWyUmJnqjLADwiUYXQAYMGKBmzZpV+Jo+fbqsVmu5mQen0ymr1Vqur9q0rYkpU6bI4XC4X+np6XXqBwBwbmndurWkmgWQAQMGKCAgwBtlAYBPNLonoX/++edVLl+9erVeffVV9/vjx48rNTVVCQkJ5domJCRo165dKioqUpMmTSRJW7du1eTJk+tUW0hIiEJCQuq0LgDg3GWxWGSz2ZSZmVlpm4MHD2r79u167LHHvFgZAHhfo5sBqU7//v3lcrm0aNEiFRYWavr06brsssvUvn37cm3j4+MVHx+v2bNnq7CwUPPmzVNQUJB69+4tSSopKVFBQYGKi4s9fgYAoL7FxsZWOXO+Zs0aBQQE6De/+Y0XqwIA7/O7ABISEqIVK1bo2WefVXh4uP79739r8eLF7uUTJ070eKbHW2+9pVWrVik8PFzz58/XihUrFBz868TP4sWLFRoaqhkzZui1115TaGiopk+f7vV9AgCc/c477zzt27ev0uXr1q3TxRdfrBYtWnivKADwgQBjjPF1Ef7K6XTKbrfL4XCUu9gdAIBT3XPPPfrmm288HohbxhijmJgYjR07VnPmzPFBdQDOFv7w/dTvZkAAAPBHZTMgFf3eb+fOncrKytK1117rg8oAwLsIIAAAeMF5550np9OpvLy8csvWrVunpk2bqm/fvt4vDAC8jAACAIAXnHfeeZJU4XUga9euVe/evWWxWLxbFAD4AAEEAAAv6NChgyQpNTXV4/Pi4mKtX7+e068AnDMIIAAAeEGLFi3UqlUrpaSkeHz+3Xffyel0EkAAnDMIIAAAeEnXrl21bds2j89WrVolu92unj17+qgqAPAuAggAAF7StWtXbd++3eOzlStXasiQIe5nVAHA2Y4AAgCAl/To0UO7d++Ww+GQJKWnp+uHH37QsGHDfFwZAHgPAQQAAC/p37+/SktL9dVXX0mS3nnnHTVp0kSDBg3ycWUA4D0EEAAAvKRDhw5q37691q1bJ2OM/vnPf2rEiBGKiIjwdWkA4DWccAoAgJcEBATohhtu0JIlS9SzZ0+lpKTohRde8HVZAOBVzIAAAOBF9913n1wul5KSkjR06FBdffXVvi4JALyKGRAAALyoY8eOWr9+vTZu3Kg777zT1+UAgNcRQAAA8LJevXqpV69evi4DAHyCU7AAAAAAeA0BBAAAAIDXEEAAAAAAeA0BBAAAAIDXEEAAAAAAeA0BBAAAAIDXEEAAAAAAeA0BBAAAAIDXEEAAAAAAeA0BBAAAAIDX+GUA2bx5s3r06CGLxaJ+/fpp//79lbZNTU1Vnz59ZLFYdMkll2jLli3uZXPmzNGFF16o5s2bKyEhQStWrPBG+QAAAMA5y+8CSGFhoW688Ubdf//9ysnJ0RVXXKExY8ZU2v7WW2/VgAEDlJOTozvvvFMjR45UcXGxJCkoKEjvvPOOHA6HXn75Zd1xxx1KTU311q4AAAAA55wAY4zxdRG1sXr1aj3wwAPauXOnJOn48eNq2bKldu7cqfbt23u03bVrly6//HJlZ2eradOmkqT27dtr8eLFuuqqq8r13adPHz300EP67W9/W6NanE6n7Ha7HA6HbDbbGe4ZAAAAcGb84fup382A7NixQ926dXO/DwsLU8eOHbVjx44K28bHx7vDhyR1795dKSkp5doeO3ZMKSkpSkhIqHTbhYWFcjqdHi8AAAAANed3AcTlcpVLczabTS6X64zaTpgwQcOHD1eXLl0q3fasWbNkt9vdr3bt2tVxLwAAAIBzU6MLIAMGDFCzZs0qfE2fPl1Wq7XczIPT6ZTVai3XV03bPvroo0pLS9Mrr7xSZW1TpkyRw+Fwv9LT0+u4lwAAAMC5KdjXBZzu888/r3L56tWr9eqrr7rfHz9+XKmpqRWeOpWQkKBdu3apqKhITZo0kSRt3bpVkydPdrd5+umn9dFHH+mbb75RaGholdsOCQlRSEhIbXYHAAAAwCka3QxIdfr37y+Xy6VFixapsLBQ06dP12WXXVbuAnRJio+PV3x8vGbPnq3CwkLNmzdPQUFB6t27tyTpn//8p1588UWtXr1aERER3t4VAAAA4JzjdwEkJCREK1as0LPPPqvw8HD9+9//1uLFi93LJ06cqIkTJ7rfv/XWW1q1apXCw8M1f/58rVixQsHBv078PPXUU8rMzNSFF14oq9Uqq9WqmTNnen2fAAAAgHOF392GtzHxh9ucAQAA4NzhD99P/W4GBAAAAID/IoAAAAAA8BoCCAAAAACvIYAAAAAA8BoCCAAAAACvIYAAAAAA8BoCCAAAAACvIYAAAAAA8BoCCAAAAACvIYAAAAAA8BoCCAAAAACvIYAAAAAA8BoCCAAAAACvIYAAAAAA8JpgXxfgz4wxkiSn0+njSgAAAID/+15a9j21MSKAnIFjx45Jktq1a+fjSgAAAID/c+zYMdntdl+XUaEA05jjUSNXWlqqjIwMNW/eXAEBAb4uBzXgdDrVrl07paeny2az+bocNDKMD1SF8YGqMD5QFW+OD2OMjh07ppiYGAUGNs6rLZgBOQOBgYGKjY31dRmoA5vNxj8QqBTjA1VhfKAqjA9UxVvjo7HOfJRpnLEIAAAAwFmJAAIAAADAawggOKeEhIQoOTlZISEhvi4FjRDjA1VhfKAqjA9UhfHhiYvQAQAAAHgNMyAAAAAAvIYAAgAAAMBrCCAAAAAAvIYAAgAAAMBrCCDnsOTkZCUkJCgwMFDLli1zf37ixAlNmDBBrVq1UuvWrfXMM8+4l3399deyWq3ul8ViUWBgoLKzsyVJ+fn5SkpKUvPmzRUXF6elS5dWWcPmzZvVo0cPWSwW9evXT/v373cvmzdvni666CIFBwdr9uzZ1e5PXfqaOXOme19CQkLUpEkT9/uJEydKkj777DN16tRJYWFhuuGGG5Sbm+tePzs7W0OHDpXFYlF8fLzWrVtXZY2zZ89Wy5YtFRkZqUceeUSn3gOiqvpPV91xXrRokWJjY2Wz2XTHHXfo5MmT1R4/f1DV8a7q2J6uPsddVcc6NTVVffr0kcVi0SWXXKItW7bUcc9RU94YIw899JDOP/98NW/eXJdddpm++uqrKmtijDQe3hgfycnJateunWw2mzp37qyFCxdWWRPjo/Hwxvgos2/fPoWGhrq/a1TmrB0fBuesxYsXm88//9z06tXLLF261P35X/7yF3PdddcZh8Nh0tLSTKdOncyqVasq7OP55583ffv2db+fPHmyGTx4sHE4HObf//63sdvtZteuXRWuW1BQYGJjY82CBQtMfn6+eeSRR8yVV17pXv7++++bjz76yIwcOdLMmjWryn2pj75mzZplxo0b5/HZoUOHTHh4uPn000+Ny+Uyt99+uxkzZox7+ejRo8348ePN8ePHzfvvv28iIiJMTk5Ohf1/8sknJi4uzqSmppqMjAzTpUsXs2DBghrVf7qqjvPWrVtNRESE2bx5s8nLyzP9+/c3TzzxRJXHz19UdryrOranq89xV92x7tmzp5k6darJz883L7zwgunQoYMpKiqqn4OBCnljjEydOtXs3r3blJSUmOXLl5uIiAjjdDor7Isx0rh4Y3z8/PPPxuVyuX9u06aN2b59e4V9MT4aF2+MjzIjRowwvXv3NhMmTKi0nrN5fBBAYPr16+cRQC655BLz6aefut/PnDnT3HbbbRWue/nll5uXX37Z/T46Otps3LjR/X7MmDHmySefrHDdVatWmQsvvND93uVymdDQULNv3z6PduPGjav2i2B99FVRAHn55ZfNoEGD3O/37NljmjVrZgoKCsyxY8dM06ZNTUZGhnv5lVdeaV5//fUK+7/lllvM7Nmz3e8XLFhgrr766lrVX6aq4/zoo4+aiRMnupetW7fOdOjQocJ+/ElVx7uqY3u6+hx3VR3rn376ydhsNlNYWOheHhcXZ7788ssa7C3qwttjpExMTIz57rvvKlzGGGk8fDE+fv75Z9O6dWvzySefVNgX46Px8Ob4WLVqlbnhhhtMcnJylQHkbB4fnIKFCplTphaNMUpJSSnXZs+ePfrxxx81evRoSVJubq6ysrLUrVs3d5sePXpUuK4k7dixw6NtWFiYOnbsqB07dtS63vrsq6p+O3bsqODgYO3du1e7d++W3W5XmzZt3MtP3d9vvvlG4eHhlfZ1atvq6p89e7auv/56SdUf54q288svvyg/P/+MjoWvVXW8qzq2ktS9e3e99dZbks5srKSlpSk8PFxpaWkV9nXqsd6xY4fi4+PVtGlTjzoq+/8BZ84XY2Tfvn3KyclRp06dJDFGGjNvjo/Zs2crLCxMF1xwgdq3b69rrrlGEuOjMfPW+Dh58qQmT57scXp7mXNpfAT7ugA0PgMGDNBzzz2n3r17Ky8vT4sWLaqw3ZIlSzRo0CBFRkZKklwul4KCgmSxWNxtbDabXC5Xheu7XC7ZbDaPz6pqX5X67Ov0flu2bFlhvwUFBRVuMy8vT5LUt29f988V1XhqfdXV/+ijj3r0U9Vxrmg7ZZ+HhobWav8bk8qOUV5eXpXHVpK2bt1abT81GStxcXHV/pmWfd5QYxKV8/YYKSoq0rhx4zR58mTZ7XZJjJHGzJvj49FHH9Wf//xnbdq0SWvXrlVw8K9ftxgfjZe3xsezzz6rIUOGuH9pcapzaXwwA4JyHn/8cZ133nnq0qWLBg8erNGjR6tt27bl2r311lu6/fbb3e+tVqtKSkp04sQJ92dOp1NWq1WSlJiY6L7AOy0tTVarVU6n06PPU9tXpT77qkpV/dZ2m6e3P7Vtbfqq7jhXtJ2yz/1Zbf4savPnUF372tR06rFuqDGJynlzjBhj9Lvf/U6tWrXS1KlTa1wTY8R3vP13SEBAgHr16qXMzEwtWLCgRn0xPnzHG+Pj4MGD+uc//6nHHnusTjWdTeODAIJywsLC9OqrryozM1M7d+5UQECALrvsMo82mzdvVmZmpoYNG+b+LCIiQtHR0dq2bZv7sy1btigxMVGSlJKS4k7tcXFxSkhI8Gh7/PhxpaamKiEhodoa67Ovqpze7969e1VcXKzzzz9fnTt3lsPhUFZWVoX7W11fp7atTf3VHeeKttOhQwe/nv2QVOXxrurYnq4+x0pVxzohIUG7du1SUVGRe/nWrVsrrQtnzptj5N5771VGRobefPNNBQZW/k8pY6Tx8NXfIaWlpUpNTa1RX4wP3/HG+Ni8ebPS09PVuXNnRUdH65lnntEbb7yhQYMG1aivs2p8+PgaFPjQyZMnTX5+vrnyyivNG2+8YfLz801JSYlJT083mZmZpri42Hz++ecmJibGHDx40GPd+++/v9wF28YY8/DDD5uhQ4cap9Npvv32W2O3281PP/1U4fbL7hSxcOFCU1BQYB599FGPO0UUFRWZ/Px8k5SUZJ566imTn59viouLG6yvqu6CtWrVKnP8+HGTlJTkcResUaNGmbvuusucOHHCfPjhh1XeBevjjz827du3N3v37jWZmZkmMTGx3F2wKqu/Nsd569atJjIy0nz//fcmLy/PXHPNNWfNXbAqO95VHdvT1ee4q+5Y9+zZ00ybNs0UFBSYf/zjH351hxJ/5Y0x8te//tVcdNFFxuFwVFsPY6Rx8cb4mD9/vsnNzTUlJSVm/fr1xmazVXoROuOjcWno8VFQUGAyMzPdrz/96U9m7Nix5siRIxX2dTaPDwLIOWzcuHFGksfrX//6l/niiy9MbGysCQ0NNRdffLH56quvPNYrLi420dHR5vPPPy/X54kTJ8xtt91mwsLCTGxsrFmyZEmVNWzatMl069bNNGvWzFx55ZUed4lITk4uV9/ChQsbrK+KAogxv94+9/zzzzehoaFm2LBhHgHj8OHDZvDgwSY0NNR07tzZrFmzxr3sq6++MmFhYR59zZw500RFRZnw8HAzefJkU1paWqP6Z8yY4XE3ruqO88KFC01MTIyxWq1m3LhxpqCgoNLj5k+qOt5VHduEhATz5ptvut/Xdazs37/fhIWFmf3797vbV3Wsd+/ebXr37m2aNWtmLrroIvPDDz80wFHBqbwxRiSZkJAQExYW5n6VrcsYady8MT5GjhxpIiMjjdVqNQkJCeaVV15xL2N8NG7eGB+nOv0uWOfS+AgwpoonqQAAAABAPeIaEAAAAABeQwABAAAA4DUEEAAAAABeQwABAAAA4DUEEAAAAABeQwABAAAA4DUEEAAAAABeQwABAAAA4DUEEAAAAABeQwABAAAA4DUEEAAAAABeQwABAAAA4DUEEAAAAABeQwABAAAA4DUEEAAAAABeQwABAAAA4DUEEAAAAABeQwABAAAA4DUEEAAAAABe8/8ArMzSlt9nhJcAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# `plotst` also takes wildcards to plot multiple synthetics in a single figure\n", + "! seisflows plotst output/solver/001/syn/AA.S00000[123].BXY.semd --save AA.S000001-3.BXY.semd.png\n", + "Image(filename=\"AA.S000001-3.BXY.semd.png\")" + ] } ], "metadata": { diff --git a/seisflows/preprocess/pyaflowa.py b/seisflows/preprocess/pyaflowa.py index cdaa20fd..8866dd0d 100644 --- a/seisflows/preprocess/pyaflowa.py +++ b/seisflows/preprocess/pyaflowa.py @@ -425,6 +425,7 @@ def _run_quantify_misfit(self, config, save_adjsrcs, parallel=False): del future # Free up memory once future is completed if _misfit is not None: misfit += _misfit + if _nwin is not None: nwin += _nwin # Run processing in serial else: @@ -434,6 +435,7 @@ def _run_quantify_misfit(self, config, save_adjsrcs, parallel=False): ) if _misfit is not None: misfit += _misfit + if _nwin is not None: nwin += _nwin return misfit, nwin @@ -760,7 +762,7 @@ def _make_evaluation_composite_pdf(self): return # Strip off event name to get evaluation tag for fid, i.e.: i01_s00.pdf fid_out = "_".join(os.path.basename(event_pdfs[0]).split("_")[1:]) - path_out = os.path.join(self.path._figures, f"{fid_out}.pdf") + path_out = os.path.join(self.path._figures, f"{fid_out}") # Merge PDFs into a single PDF, delete originals merge_pdfs(fids=event_pdfs, fid_out=path_out) if os.path.exists(path_out): diff --git a/seisflows/seisflows.py b/seisflows/seisflows.py index 969e4967..323bbcce 100755 --- a/seisflows/seisflows.py +++ b/seisflows/seisflows.py @@ -1029,7 +1029,6 @@ def print(self, choice=None, **kwargs): acceptable_args[choice](*self._args.args, **kwargs) - @staticmethod def plotst(self, fids, data_format="ASCII", savefig=None, **kwargs): """ Simple stream/waveform plotter to visualize synthetic waveforms created diff --git a/seisflows/workflow/inversion.py b/seisflows/workflow/inversion.py index 91d71419..3adf1c4f 100644 --- a/seisflows/workflow/inversion.py +++ b/seisflows/workflow/inversion.py @@ -186,8 +186,8 @@ def run(self): logger.info(msg.mnr(f"COMPLETE ITERATION {self.iteration:0>2}")) self.iteration += 1 logger.info(f"setting current iteration to: {self.iteration}") - # Clear the state file for new iteration - self._states = {} + # Set the state file to pending for new iteration + self._states = {key: "pending" for key in self._states} self.checkpoint() else: break @@ -455,7 +455,6 @@ def finalize_iteration(self): self.preprocess.finalize() - def _update_thrifty_status(self): """ Determine if line search forward simulation can be carried over to the From 99702a414d3c5a5d3bf461c650473922fdf4010c Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Fri, 9 Sep 2022 11:22:03 -0800 Subject: [PATCH 164/195] updated specfem2d example notebook and docs page up to Example 3 with clear explanations of how to understand example results, with figures --- ...le_13_1.png => specfem2d_example_14_1.png} | Bin ...le_15_1.png => specfem2d_example_16_1.png} | Bin ...le_17_1.png => specfem2d_example_18_1.png} | Bin .../specfem2d_example_28_1.png | Bin 217588 -> 0 bytes .../specfem2d_example_30_1.png | Bin 79493 -> 0 bytes .../specfem2d_example_31_1.png | Bin 81881 -> 0 bytes .../specfem2d_example_32_1.png | Bin 0 -> 101255 bytes .../specfem2d_example_34_1.png | Bin 0 -> 83487 bytes .../specfem2d_example_36_1.png | Bin 0 -> 74825 bytes .../specfem2d_example_43_1.png | Bin 0 -> 88490 bytes .../specfem2d_example_55_0.png | Bin 0 -> 19420 bytes .../specfem2d_example_56_0.png | Bin 0 -> 47450 bytes docs/specfem2d_example.rst | 593 ++++++++++++++---- 13 files changed, 487 insertions(+), 106 deletions(-) rename docs/images/specfem2d_example_files/{specfem2d_example_13_1.png => specfem2d_example_14_1.png} (100%) rename docs/images/specfem2d_example_files/{specfem2d_example_15_1.png => specfem2d_example_16_1.png} (100%) rename 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I've run the exa .. code:: ipython3 - %cd ~/Work/scratch/example_1 + %cd ~/sfexamples/example_1 ! ls .. parsed-literal:: - /home/bchow/Work/scratch/example_1 + /home/bchow/Work/work/seisflows_example/example_1 logs parameters.yaml sflog.txt specfem2d output scratch sfstate.txt specfem2d_workdir -In the ``output/`` directory, we can see our starting model -(MODEL_INIT), our target model (MODEL_TRUE) and the updated model from -our first iteration (MODEL_01) alongside the gradient that was used to -create it (GRADIENT_01). +Understanding example outputs +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +In the ``output/`` directory, we can see our starting/initial model +(*MODEL_INIT*), our true/target model (*MODEL_TRUE*) and the updated +model from the first iteration (*MODEL_01*). In addition, we have saved +the gradient generated during the first iteration (*GRADIENT_01*) +because we set the parameter ``export_gradient`` to True. .. code:: ipython3 + # The output directory contains important files exported during a workflow ! ls output - ! echo - ! ls output/MODEL_01 .. parsed-literal:: GRADIENT_01 MODEL_01 MODEL_INIT MODEL_TRUE - + + +.. code:: ipython3 + + # A MODEL output directory contains model files in the chosen solver format. + # In this case, Fortran Binary from SPECFEM2D + ! ls output/MODEL_01 + + +.. parsed-literal:: + proc000000_vp.bin proc000000_vs.bin -Because we’re working with SPECFEM2D, we can plot the models and -gradients that were created during our workflow using the -``seisflows plot2d`` command. If we use the ``--savefig`` option we can -also save the output .png files to disk. Because this docs page was made -in a Jupyter Notebook, we need to use the IPython Image class to open -the resulting .png file. +Plotting results (only available w/ SPECFEM2D) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +We can plot the model and gradient files created during our workflow +using the ``seisflows plot2d`` command. The ``--savefig`` flag allows us +to save output .png files to disk. The following figure shows the +starting/initial homogeneous halfspace model in Vs. -This figure shows the starting homogeneous halfspace model in Vs. + **NOTE:** Because this docs page was made in a Jupyter Notebook, we + need to use the IPython Image class to open the resulting .png file + from inside the notebook. Users following along will need to open the + figure using the GUI or command line tool. .. code:: ipython3 + # Plot and open the initial homogeneous halfspace model ! seisflows plot2d MODEL_INIT vs --savefig m_init_vs.png Image(filename='m_init_vs.png') @@ -199,11 +199,15 @@ This figure shows the starting homogeneous halfspace model in Vs. -.. image:: images/specfem2d_example_files/specfem2d_example_13_1.png +.. image:: images/specfem2d_example_files/specfem2d_example_14_1.png -Here we see the gradient created during the adjoint simulation. +We can also plot the gradient that was created during the adjoint +simulation. In this example we only have one source and one receiver, so +the gradient shows a “banana-doughnut” style kernel, representing +volumetric sensitivity of the measurement (waveform misfit) to changes +in model values. .. code:: ipython3 @@ -218,12 +222,16 @@ Here we see the gradient created during the adjoint simulation. -.. image:: images/specfem2d_example_files/specfem2d_example_15_1.png +.. image:: images/specfem2d_example_files/specfem2d_example_16_1.png -Finally we see the updated model, which is the sum of the initial model, -and a scaled gradient. +Finally we can plot the updated model (*MODEL_01*), which is the sum of +the initial model and a scaled gradient. The gradient was scaled during +the line search, where we used a steepest-descent algorithm to reduce +the misfit between data and synthetics. Since we only have one +source-receiver pair in this workflow, the updated model shown below +almost exactly mimics the Vs kernel shown above. .. code:: ipython3 @@ -238,35 +246,47 @@ and a scaled gradient. -.. image:: images/specfem2d_example_files/specfem2d_example_17_1.png +.. image:: images/specfem2d_example_files/specfem2d_example_18_1.png + +Closing thoughts +~~~~~~~~~~~~~~~~ -Have a look at the `working directory documentation page `__ for more detailed explanations of how to navigate the SeisFlows working directory. You can also run Example \#1 with more stations (up to 131), tasks/events (up to 25) and iterations (as many as you want). Note that because this is a serial inversion, the compute time will scale with all of these values. +Have a look at the `working directory documentation page `__ for more detailed explanations of how to navigate the SeisFlows working directory that was created during this example. + +You can also run Example \#1 with more stations (up to 131), tasks/events (up to 25) and iterations (as many as you want!). Note that because this is a serial inversion, the compute time will scale with all of these values. .. code:: ipython3 + # An example call for running Example 1 with variable number of stations, events and iterations ! seisflows examples run 1 --nsta 10 --ntask 5 --niter 2 -Example #2: Pyaflowa, L-BFGS inversion --------------------------------------- +Example #2: Checkerboard inversion using Pyaflowa & L-BFGS +---------------------------------------------------------- + +Building on the foundation of the previous example, Example #2 runs a 2 +iteration inversion with misfit quantification taken care of by the +``Pyaflowa`` preprocessing module, which uses the misfit quantification +package `Pyatoa `__ under the hood. +Model updates are performed using an ```L-BFGS`` nonlinear optimization +algorithm `__. +Example #2 also includes smoothing/regularization of the gradient. This +example more closely mimics a research-grade inversion problem. -Example #2 runs a 2 iteration inversion with misfit quantification taken -care of by the ``Pyaflowa`` preprocessing module. Optimization (i.e., -model updates) are performed using the ``L-BFGS`` algorithm. This -example is more complex than the default version of Example #1, using -multiple events, stations and iterations. Example #2 also includes -smoothing/regularization of the gradient before using it to perturb the -starting velocity model. + **NOTE:** This example is computationally more intense than the + default version of Example #1 as it uses multiple events and + stations, and runs multiple iterations. .. code:: ipython3 + # Run the help message dialogue to see what Example 2 will do ! seisflows examples 2 .. parsed-literal:: - No existing SPECFEM2D repo given, default to: /home/bchow/Work/scratch/example_1/specfem2d + No existing SPECFEM2D repo given, default to: /home/bchow/Work/work/seisflows_example/example_1/specfem2d @@@@@@@@@@ .@@@@. .%&( %@. @@ -297,46 +317,30 @@ starting velocity model. iterations]. The tasks involved include: 1. (optional) Download, configure, compile SPECFEM2D - 2. Set up a SPECFEM2D working directory - 3. Generate starting model from 'Tape2007' example - 4. Generate target model w/ perturbed starting model - 5. Set up a SeisFlows working directory - 6. Run the inversion workflow + 2. [Setup] a SPECFEM2D working directory + 3. [Setup] starting model from 'Tape2007' example + 4. [Setup] target model w/ perturbed starting model + 5. [Setup] a SeisFlows working directory + 6. [Run] the inversion workflow ================================================================================ -You can run the example with the same command as shown for Example 1: +Run the example +~~~~~~~~~~~~~~~ + +You can run the example with the same command as shown for Example 1. +Users following along will need to provide a path to their own +installation of SPECFEM2D using the ``-r`` flag. .. code:: ipython3 ! seisflows examples run 2 -r ${PATH_TO_SPECFEM2D} -Succesful completion of the example problem will end with the following log message +Succesful completion of the example problem will end with a log message that looks similar to the following .. code:: bash - LINE SEARCH STEP COUNT 01 - -------------------------------------------------------------------------------- - 2022-08-29 18:07:14 (I) | evaluating objective function for source 001 - 2022-08-29 18:07:14 (D) | running forward simulation with 'Specfem2D' - 2022-08-29 18:07:20 (D) | quantifying misfit with 'Pyaflowa' - 2022-08-29 18:07:29 (I) | evaluating objective function for source 002 - 2022-08-29 18:07:29 (D) | running forward simulation with 'Specfem2D' - 2022-08-29 18:07:35 (D) | quantifying misfit with 'Pyaflowa' - 2022-08-29 18:07:43 (I) | evaluating objective function for source 003 - 2022-08-29 18:07:43 (D) | running forward simulation with 'Specfem2D' - 2022-08-29 18:07:49 (D) | quantifying misfit with 'Pyaflowa' - 2022-08-29 18:07:58 (I) | evaluating objective function for source 004 - 2022-08-29 18:07:58 (D) | running forward simulation with 'Specfem2D' - 2022-08-29 18:08:04 (D) | quantifying misfit with 'Pyaflowa' - 2022-08-29 18:08:13 (D) | misfit for trial model (f_try) == 4.73E-03 - 2022-08-29 18:08:13 (D) | step length(s) = 0.00E+00, 1.00E+00 - 2022-08-29 18:08:13 (D) | misfit val(s) = 5.30E-02, 4.73E-03 - 2022-08-29 18:08:13 (I) | pass: misfit decreased, line search successful w/ alpha=1.0 - 2022-08-29 18:08:13 (I) | line search model 'm_try' parameters: - 2022-08-29 18:08:13 (I) | 5800.00 <= vp <= 5800.00 - 2022-08-29 18:08:13 (I) | 3193.01 <= vs <= 3821.37 - 2022-08-29 18:08:13 (I) | trial step successful. finalizing line search + 2022-08-29 18:08:13 (I) | FINALIZING LINE SEARCH -------------------------------------------------------------------------------- @@ -355,25 +359,37 @@ Succesful completion of the example problem will end with the following log mess //////////////////////////////////////////////////////////////////////////////// 2022-08-29 18:08:21 (I) | setting current iteration to: 3 + ================================================================================ + EXAMPLE COMPLETED SUCCESFULLY + ================================================================================ + +Understanding example outputs +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -As with Example \#1, we can look at the output gradients and models to visualize how the inversion performed. +As with Example #1, we can look at the output gradients and models to +visualize what just happenend under the hood. Be sure to read through +the output log messages as well, to get a better idea of what steps and +tasks were performed to generate these outputs. .. code:: ipython3 - %cd ~/Work/scratch/example_2 + %cd ~/sfexamples/example_2 ! ls .. parsed-literal:: - /home/bchow/Work/scratch/example_2 + /home/bchow/Work/work/seisflows_example/example_2 logs parameters.yaml sflog.txt specfem2d output scratch sfstate.txt specfem2d_workdir +Running the ``plot2d`` command without any arguments is a useful way to +determine what model/gradient files are available for plotting. + .. code:: ipython3 - ! seisflows plot2d # to check what models/gradients/kernels are avilable for plotting + ! seisflows plot2d .. parsed-literal:: @@ -390,8 +406,18 @@ As with Example \#1, we can look at the output gradients and models to visualize MODEL_TRUE -The starting model is a homogeneous halfspace but for Example #2 the -target model is a checkerboard. +Visualizing Initial and Target models +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The starting model for this example is the same homogeneous halfspace +model shown in Example #1, with :math:`V_p`\ =5.8km/s and +:math:`V_s`\ =3.5km/s. + +For this example, however, the target model is a checkerboard model with +fast and slow perturbations roughly equal to :math:`\pm10\%` of the +initial model. We can plot the model below to get a visual +representation of these perturbations, where **red==slow** and +**blue==fast**. .. code:: ipython3 @@ -406,15 +432,26 @@ target model is a checkerboard. -.. image:: images/specfem2d_example_files/specfem2d_example_28_1.png +.. image:: images/specfem2d_example_files/specfem2d_example_32_1.png + +Visualizing the Gradient +~~~~~~~~~~~~~~~~~~~~~~~~ -In the following gradient Vs kernel, we can see how the 5km x 5km -smoothing blurs away some of the detail of the raw graident. The blue -colors here suggest that the initial model needs to be sped up to best -fit waveforms (and vice versa, red colors suggest slowing down the -initial model). +We can look at the gradients created during the adjoint simulations to +get an idea of how our inversion wanted to update the model. Gradients +tell us how to perturb our starting model (the homogeneous halfspace) to +best fit the data that was generated by our target model (the +checkerboard). + +We can see that our gradient (Vs kernel) is characterized by large red +and blue blobs. The blue colors in the kernel tell us that the initial +model is too fast, while red colors tell us that the initial model is +too slow (that is, **red==too slow** and **blue==too fast**). This makes +sense if we look at the checkerboard target model above, where the +perturbation is slow (red color) the corresponding kernel tells us the +initial model is too fast (blue color). .. code:: ipython3 @@ -429,9 +466,26 @@ initial model). -.. image:: images/specfem2d_example_files/specfem2d_example_30_1.png +.. image:: images/specfem2d_example_files/specfem2d_example_34_1.png + + +Visualizing the updated model +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +After two iterations, the updated model starts to take form. We can +clearly see tha the lack of data coverage on the outer edges of the +model mean we do not see any appreciable update here, whereas the center +of the domain shows the strongest model updates which are starting to +resemble the checkerboard pattern shown in the target model. + +With only 4 events and 2 iterations, we do not have quite enough +constraint to recover the sharp contrats between checkers shown in the +Target model. We can see that smearing and regularization leads to more +prominent slow (red) regions. + +If we were to increase the number of events and iterations, will it help +our recovery of the target model? This task is left up to the reader! .. code:: ipython3 @@ -446,6 +500,333 @@ initial model). -.. image:: images/specfem2d_example_files/specfem2d_example_31_1.png +.. image:: images/specfem2d_example_files/specfem2d_example_36_1.png + + + +Re-creating kernels from Tape et al. 2007 +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The 2D checkerboard model and source-receiver configuration that runs in +this example comes from the published work of `Tape et +al. (2007) `__. +Here, Tape et al. generate event and misfit kernels for a number of +individual events in `Figure +9 `__ +(shown below). This exercise is meant to illustrate how kernel features +change for a simple target model (the checkerboard) depending on the +chosen source-receiver geometry. + +.. figure:: attachment:tape_etal_2007_fig9.jpeg + :alt: tape_etal_2007_fig9.jpeg + + tape_etal_2007_fig9.jpeg + +*Caption: Construction of a misfit kernel. (a)–(g) Individual event +kernels, each constructed via the method shown in Fig. 8 (which shows +Event 5). The colour scale for each event kernel is shown beneath (g). +(h) The misfit kernel is simply the sum of the 25 event kernels. (i) The +source–receiver geometry and target phase‐speed model. There are a total +of N= 25 × 132 = 3300 measurements that are used in constructing the +misfit kernel (see Section 5).* + +Choosing an event +^^^^^^^^^^^^^^^^^ + +The Event ID that generated each kernel is specified in the title of +each sub plot (e.g., Panel. (a) corresponds to Event #1). We can attempt +to re-create these kernels by choosing specific event IDs to run Example +2 with. + + **NOTE:** Our choice of preprocessing module, misfit function, + gradient smoothing length, nonlinear optimization algorithm, etc. + will affect how each event kernel is produced, and consequently how + much they differ from the published kernels shown above. We do not + expect to perfectly match the event kernels above, but rather to see + that first order structure is the same. + +To specify the specific event ID, we can use the ``--event_id`` flag +when running Example 2. For this docs page we’ll choose Event #7, which +is represented by Panel (g) in the figure above. + +.. code:: ipython3 + + # Run the help message to view the description of the optional arguemnt --event_id + ! seisflows examples -h + + +.. parsed-literal:: + + usage: seisflows examples [-h] [-r [SPECFEM2D_REPO]] [--nsta [NSTA]] + [--ntask [NTASK]] [--niter [NITER]] + [--event_id [EVENT_ID]] + [method] [choice] + + Lists out available example problems and allows the user to run example + problems directly from the command line. Some example problems may have pre- + run prompts mainly involving the numerical solver + + positional arguments: + method Method for running the example problem. If + notprovided, simply prints out the list of available + example problems. If given as an integer value, will + print out the help message for the given example. If + 'run', will run the example. If 'setup' will simply + setup the example working directory but will not + execute `seisflows submit` + choice If `method` in ['setup', 'run'], integervalue + corresponding to the given example problem which can + listed using `seisflows examples` + + optional arguments: + -h, --help show this help message and exit + -r [SPECFEM2D_REPO], --specfem2d_repo [SPECFEM2D_REPO] + path to the SPECFEM2D directory which should contain + binary executables. If not given, assumes directory is + called 'specfem2d/' in the current working directory. + If that dir is not found, SPECFEM2D will be + downloaded, configured and compiled automatically in + the current working directory. + --nsta [NSTA] User-defined number of stations to use for the example + problem (1 <= NSTA <= 131). If not given, each example + has its own default. + --ntask [NTASK] User-defined number of events to use for the example + problem (1 <= NTASK <= 25). If not given, each example + has its own default. + --niter [NITER] User-defined number of iterations to run for the + example problem (1 <= NITER <= inf). If not given, + each example has its own default. + --event_id [EVENT_ID] + Allow User to choose a specific event ID from the Tape + 2007 example (1 <= EVENT_ID <= 25). If not used, + example will default to choosing sequential from 1 to + NTASK + + +.. code:: ipython3 + + # Run command with open variable to set SPECFEM2D path. Choose event_id==7 and only run 1 iteration + ! seisflows examples run 2 -r ${PATH_TO_SPECFEM2D} --event_id 7 --niter 1 + +Comparing kernels +^^^^^^^^^^^^^^^^^ + +This workflow should run faster than Example #2 proper, because we are +only using 1 event and 1 iteration. In the same vein as above, we can +visualize the output gradient to see how well it matches with those +published in Tape et al. + +.. code:: ipython3 + + %cd ~/sfexamples/example_2a + ! ls + + +.. parsed-literal:: + + /home/bchow/Work/work/seisflows_example/example_2a + logs parameters.yaml sflog.txt specfem2d + output scratch sfstate.txt specfem2d_workdir + + +.. code:: ipython3 + + ! seisflows plot2d GRADIENT_01 vs_kernel --save g_01_vs.png + Image("g_01_vs.png") + + +.. parsed-literal:: + + Figure(707.107x707.107) + + + + +.. image:: images/specfem2d_example_files/specfem2d_example_43_1.png + + + +From the above figure we can see that the first order structure of our +Vs event kernel is very similar to Panel (g) from Figure 9 of Tape et +al. (2007). Our kernel shows some additional low-amplitude sensitivity, +most prominently at the ring of alternative blue and red on the edges of +the domain. From experience this is likely due to the ``Pyaflowa`` +preprocessing module attempting to window and fit very late arriving +waves that are caused by boundary reflections from the edge of the +domain. + +Example #3: En-masse Forward Simulations +---------------------------------------- + +SeisFlows is not just an inversion tool, it can also be used to simplify +workflows to run forward simulations using external numerical solvers. +In Example #3 we use SeisFlows to run en-masse forward simulations. + +To motivate this use case, imagine a User who has a velocity model of a +specific region (at any scale). This User would like to run a number of +forward simulations for N events and S stations to generate N x S +synthetic seismograms. These synthetics may be used directly, or +compared to observed seismograms to understand how well the regional +velocity model characterizes actual Earth structure. + +Although this could be done manually, if N is large, this effort may +require a large number of manual tasks, including the creation of +working directories, editing submit calls, and providing book keeping +for the external solver. SeisFlows is here to automate all of these +tasks. + +.. code:: ipython3 + + # Run the help dialogue to see what + ! seisflows examples 3 + + +.. parsed-literal:: + + No existing SPECFEM2D repo given, default to: /home/bchow/Work/work/seisflows_example/example_2a/specfem2d + + @@@@@@@@@@ + .@@@@. .%&( %@. + @@@@ @@@@ &@@@@@@ ,%@ + @@@@ @@@, /@@ @ + @@@ @@@@ @@@ @ + @@@@ @@@@ @@@ @ @ + @@@ @@@@ ,@@@ @ @ + @@@@ @@@@ @@@@ @@ @ @ + @@@@ @@@@@ @@@@@ @@@ @@ @ + @@@@ @@@@@ @@@@@@@@@@@@@@ @@ @ + @@@@ @@@@@@ @@@& @@@ @ + @@@@@ @@@@@@@@ %@@@@# @@ + @@@@# @@@@@@@@@@@@@@@@@ @@ + &@@@@@ @@@@( @@& + @@@@@@@ /@@@@ + @@@@@@@@@@@@@@@@@ + @@@@@@@@@@ + + + ================================================================================ + SEISFLOWS EXAMPLE 3 + /////////////////// + This is a [SPECFEM2D] [WORKSTATION] example, which will run forward simulations + to generate synthetic seismograms through a homogeneous halfspace starting + model. This example uses no preprocessing or optimization modules. [10 events, + 25 stations] The tasks involved include: + + 1. (optional) Download, configure, compile SPECFEM2D + 2. [Setup] a SPECFEM2D working directory + 3. [Setup] starting model from 'Tape2007' example + 4. [Setup] a SeisFlows working directory + 5. [Run] the forward simulation workflow + ================================================================================ + + +.. code:: ipython3 + + # Run command with open variable to set SPECFEM2D path + ! seisflows examples run 3 -r ${PATH_TO_SPECFEM2D} + +You will be met with the following log message after succesful completion of the example problem + +.. code:: bash + + ================================================================================ + EXAMPLE COMPLETED SUCCESFULLY + ================================================================================ + +Understanding example outputs +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This example does not produce gradients or updated models, only +synthetic seismograms. We can view these seismograms using the +``seisflows plotst`` command, which is used to quickly plot synthetic +seismograms (using ObsPy under the hood). + +.. code:: ipython3 + + %cd ~/sfexamples/example_3 + ! ls + + +.. parsed-literal:: + + /home/bchow/Work/work/seisflows_example/example_3 + logs parameters.yaml sflog.txt specfem2d + output scratch sfstate.txt specfem2d_workdir + + +In this example, we have set the ``export_traces`` parameter to +**True**, which tells SeisFlows to store synthetic waveforms generated +during the workflow in the ``output/`` directory. Under the hood, +SeisFlows is copying all synthetic seismograms from the Solver’s +``scratch/`` directory, to a more permanent location. + +.. code:: ipython3 + + # The `export_traces` parameter tells SeisFlows to save synthetics after each round of forward simulations + ! seisflows par export_traces + + +.. parsed-literal:: + + export_traces: True + + +.. code:: ipython3 + + # Exported traces will be stored in the `output` directory + ! ls output + + +.. parsed-literal:: + + MODEL_INIT solver + + +.. code:: ipython3 + + # Synthetics will be stored on a per-event basis, and in the format that the external solver created them + ! ls output/solver/ + ! echo + ! ls output/solver/001/syn + + +.. parsed-literal:: + + 001 002 003 004 005 006 007 008 009 010 + + AA.S000000.BXY.semd AA.S000009.BXY.semd AA.S000018.BXY.semd + AA.S000001.BXY.semd AA.S000010.BXY.semd AA.S000019.BXY.semd + AA.S000002.BXY.semd AA.S000011.BXY.semd AA.S000020.BXY.semd + AA.S000003.BXY.semd AA.S000012.BXY.semd AA.S000021.BXY.semd + AA.S000004.BXY.semd AA.S000013.BXY.semd AA.S000022.BXY.semd + AA.S000005.BXY.semd AA.S000014.BXY.semd AA.S000023.BXY.semd + AA.S000006.BXY.semd AA.S000015.BXY.semd AA.S000024.BXY.semd + AA.S000007.BXY.semd AA.S000016.BXY.semd + AA.S000008.BXY.semd AA.S000017.BXY.semd + + +.. code:: ipython3 + + # The `plotst` function allows us to quickly visualize output seismograms + ! seisflows plotst output/solver/001/syn/AA.S000000.BXY.semd --save AA.S000000.BXY.semd.png + Image(filename="AA.S000000.BXY.semd.png") + + + + +.. image:: images/specfem2d_example_files/specfem2d_example_55_0.png + + + +.. code:: ipython3 + + # `plotst` also takes wildcards to plot multiple synthetics in a single figure + ! seisflows plotst output/solver/001/syn/AA.S00000[123].BXY.semd --save AA.S000001-3.BXY.semd.png + Image(filename="AA.S000001-3.BXY.semd.png") + + + + +.. image:: images/specfem2d_example_files/specfem2d_example_56_0.png From b1ba4a62f3a08b10f01cbda5ec61b62dbab4e0c7 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Fri, 9 Sep 2022 11:26:09 -0800 Subject: [PATCH 165/195] slightly modified example 2a docs page to show a better kernel match to tape et al. --- .../specfem2d_example_43_1.png | Bin 88490 -> 75677 bytes docs/notebooks/specfem2d_example.ipynb | 14 ++++++++------ docs/specfem2d_example.rst | 12 ++++++------ 3 files changed, 14 insertions(+), 12 deletions(-) diff --git a/docs/images/specfem2d_example_files/specfem2d_example_43_1.png b/docs/images/specfem2d_example_files/specfem2d_example_43_1.png index 8ef291e257962411afedaf6f226f12fa36b2375f..0ce15028ede43f21c3c3a37b1501eeacd5eccda8 100644 GIT binary patch literal 75677 zcmeFZc{G;o-#2bLWtL=CWLBAp+(`qalvK!Al6e*(l_@fY zL>V&=@8{_Ed*0{S`+eWN_gZ`Zv)6jpx?6V{uIoIH^EkfW&-A@Qj%%r}U$=Q3g+f_> z7;|zB{x%NONyrHB}ZF_OSTux_+2fXT`oA- z@08pvxkH@a`qCvwmwi%FcK`hcBpsa3ONq(yso_QF91rWeP$-P1JpTbT=i}0*kB_MF%@ctsDQih#bC*963EVw^(cb)lg&!s-@LYMcT z!f1wcB?s*_e)3-_Bb}q$>i_u1*_#|9|N8qP+r~0#WhVUR-=$)9_wUbM8#6r2u<2i4 z^`Q?j{r4+42*?~GKS-@SE3qYz?q6Rm3MiNU`|H#_Isf}b|M!|=1^$1qrKZgvx({&r z2L$vC)WwFn&rc0q@9s7+%5|>D_SmkQ5*!`P5mB;i*h>&_j zbxdN8_VP^8%Hs>4pV89OAG~yF*FM(~QNw`(&l%~ZO}a8=^bJBRXIe~q;} z6opLuYR_SmDx4LxX?b7aLw)6@?g|G>afvka5b&H~xtv~vZa%S#JF zxXzxQ9_x;`GO~7GEp9o~-QRs>d2UcA&()5ahNjFcL$uj_an|Y5aPxW@85vqeM)k75 z_2Ye!N;Ly#-`H|uO=*&wLf^jKGxhr$*I+|@&igZ1_BJ1no&fe!Gx7Yi`%V12GCls- zto6f7A9Y=-i~A-6yTq z`q_2nwy>fYe zSW2f8dnfbzcZ=p62V>n_XUmwocb~UK(6dXX|M+1YbMt1gprD{_bJ4=@JlnC0nHyaB}x$Cd56@#8je_HgfuU+3dHT1ddnPzNjq|$Qr z*BpliN;-C8ds9;KnKY~Z+B@gXt0F9wmZmnfr<<wE8lrjcBx%Q6Wn^Te=%yOjR)n(J;%TNF z4BGI)EF*oia6v2DZQ#Q_1`3z_6=}mFAB9*s2j1-`pD`RgcFaP~zJsY>6MxZ+m3=*w zRJ5{e_=bmCxs@@*R7pfhDPDNXmh|~Q!?myCcb?y{x>z^uoSjX<2^o2hDeJX<1&b54 zDe37Sqxac&720X+J<%Z?SP*rw<&{iwQ%&Fo9x;u0Y136%dg3G1Q9>`<`4qkG>Wj0< zI`yjR%-<9^sE4hV=d-jge%g0s##emLp1TkBx&&(1e)wSFgV=1r#N zJU0hLiLG0M)~sEdS?IkGT9bciXyfYQbMF1DiodY9vcEnwG@QTu^NZ-S*4TZDGst9XZ^rDl;L-tY6Ls=vHAso$1q*|s&_OK^MO`MUcR zl=Lp2g&W$nwYBEEFE(35Doo!MO&gQ%nO2K1Eej;;AZhfrIH`YdFl+N}oAinP+R6U! 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z={`T8qVV&}$zSk*LxU-v%*+Ca4*`mCv}UCq)o{ivK)sMT%jhK+itLDdk=p(or1qty zN89W$&oyv2%Z1SP{j%F^uPi@Q_u#exHghN8e!LVbb^Q3|W@YbTI#6Z0(nDZbGK%C#YvYd&*Ng0|RVAiFX4(+wiX+J!h>}1QDfeP7 z?0BCtL?a6Tk)qO>It2^k2}1}(!o>T%AS0gWc_Rce+>Gns!PY!De0aM!-5jl4`#W5) z)*rBg7LN~La5xO2bGPJ}&2MZW-V$_PP&l_2IBbl`!-nDdjz+k%bLyG%HUGrZ`2R7= z{!jMU&a3`Aq5J>7C|X$m*17e+bNcT+4gP;!Qj6PUk67;|mFf2" ] }, - "execution_count": 15, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -822,7 +822,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "From the above figure we can see that the first order structure of our Vs event kernel is very similar to Panel (g) from Figure 9 of Tape et al. (2007). Our kernel shows some additional low-amplitude sensitivity, most prominently at the ring of alternative blue and red on the edges of the domain. From experience this is likely due to the `Pyaflowa` preprocessing module attempting to window and fit very late arriving waves that are caused by boundary reflections from the edge of the domain. " + "From the above figure we can see that the first order structure of our Vs event kernel is very similar to Panel (g) from Figure 9 of Tape et al. (2007). As mentioned, any differences between the kernel will be due to the differences in the parameters available to us during the inversion. \n", + "\n", + "Creating all the other kernels shown in Figure 9 of Tape et al. is an exercise left to the reader." ] }, { diff --git a/docs/specfem2d_example.rst b/docs/specfem2d_example.rst index d18af143..ec360916 100644 --- a/docs/specfem2d_example.rst +++ b/docs/specfem2d_example.rst @@ -648,12 +648,12 @@ published in Tape et al. From the above figure we can see that the first order structure of our Vs event kernel is very similar to Panel (g) from Figure 9 of Tape et -al. (2007). Our kernel shows some additional low-amplitude sensitivity, -most prominently at the ring of alternative blue and red on the edges of -the domain. From experience this is likely due to the ``Pyaflowa`` -preprocessing module attempting to window and fit very late arriving -waves that are caused by boundary reflections from the edge of the -domain. +al. (2007). As mentioned, any differences between the kernel will be due +to the differences in the parameters available to us during the +inversion. + +Creating all the other kernels shown in Figure 9 of Tape et al. is an +exercise left to the reader. Example #3: En-masse Forward Simulations ---------------------------------------- From 780ed7bb387aca5731908c7861f002c7a8071bf1 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Fri, 9 Sep 2022 13:24:47 -0800 Subject: [PATCH 166/195] updated specfem2d examples after final proofread --- docs/containers.rst | 14 +- .../tape_etal_2007_fig9.jpeg | Bin 0 -> 585756 bytes ...le_14_1.png => specfem2d_example_15_1.png} | Bin ...le_16_1.png => specfem2d_example_17_1.png} | Bin ...le_18_1.png => specfem2d_example_19_1.png} | Bin .../specfem2d_example_32_1.png | Bin 101255 -> 0 bytes .../specfem2d_example_34_1.png | Bin 83487 -> 0 bytes .../specfem2d_example_36_1.png | Bin 74825 -> 101255 bytes .../specfem2d_example_38_1.png | Bin 0 -> 79890 bytes .../specfem2d_example_40_1.png | Bin 0 -> 69093 bytes ...le_43_1.png => specfem2d_example_50_1.png} | Bin ...le_55_0.png => specfem2d_example_62_0.png} | Bin ...le_56_0.png => specfem2d_example_63_0.png} | Bin docs/notebooks/specfem2d_example.ipynb | 163 ++++++++++----- docs/specfem2d_example.rst | 195 ++++++++++-------- seisflows/examples/ex2_hh_w_pyatoa.py | 1 + 16 files changed, 228 insertions(+), 145 deletions(-) create mode 100644 docs/images/reference_figures/tape_etal_2007_fig9.jpeg rename docs/images/specfem2d_example_files/{specfem2d_example_14_1.png => specfem2d_example_15_1.png} (100%) rename docs/images/specfem2d_example_files/{specfem2d_example_16_1.png => specfem2d_example_17_1.png} (100%) rename docs/images/specfem2d_example_files/{specfem2d_example_18_1.png => specfem2d_example_19_1.png} (100%) delete mode 100644 docs/images/specfem2d_example_files/specfem2d_example_32_1.png delete mode 100644 docs/images/specfem2d_example_files/specfem2d_example_34_1.png create mode 100644 docs/images/specfem2d_example_files/specfem2d_example_38_1.png create mode 100644 docs/images/specfem2d_example_files/specfem2d_example_40_1.png rename docs/images/specfem2d_example_files/{specfem2d_example_43_1.png => specfem2d_example_50_1.png} (100%) rename docs/images/specfem2d_example_files/{specfem2d_example_55_0.png => specfem2d_example_62_0.png} (100%) rename docs/images/specfem2d_example_files/{specfem2d_example_56_0.png => specfem2d_example_63_0.png} (100%) diff --git a/docs/containers.rst b/docs/containers.rst index 4def8e76..b609d941 100644 --- a/docs/containers.rst +++ b/docs/containers.rst @@ -11,10 +11,10 @@ on high performance computers using .. note:: The SeisFlows Docker image can be found here: - https://github.com/SeisSCOPED/pyatoa + https://github.com/SeisSCOPED/adjtomo .. note:: - The `Pyatoa` container is shipped with the latest versions of + The `adjTomo` container is shipped with the latest versions of `SeisFlows `__, `Pyatoa `__, and `PySEP `__. @@ -38,7 +38,7 @@ First we need to get the latest version of the `Pyatoa` Docker image: .. code-block:: bash - docker pull ghcr.io/seisscoped/pyatoa:latest + docker pull ghcr.io/seisscoped/adjtomo:latest .. note:: These docs were run using Docker image ID ``c57883926aae`` (last accessed @@ -110,7 +110,7 @@ SeisFlows help message, we simply have to run the following: .. code-block:: bash - docker run ghcr.io/seisscoped/pyatoa:latest seisflows -h + docker run ghcr.io/seisscoped/adjtomo:latest seisflows -h The following code snippet will run a SeisFlows-Pyatoa-Specfem2D example. The extra fluff in the command allows the container to save files to your @@ -123,7 +123,7 @@ computer while it runs the example. docker run \ --workdir $(pwd) \ --mount type=bind,source=$(pwd),target=$(pwd), - ghcr.io/seisscoped/pyatoa:nightly \ + ghcr.io/seisscoped/adjtomo:nightly \ seisflows examples run 2 In the above example, we set the working directory (-w/--workdir) to the @@ -172,7 +172,7 @@ singularity module, and then use a familiar ``pull`` command. module load tacc-singularity # on TACC Frontera # module load singularity # on UAF Chinook - singularity pull seisflows.sif docker://ghcr.io/seisscoped/pyatoa:nightly + singularity pull seisflows.sif docker://ghcr.io/seisscoped/adjtomo:nightly We have now downloaded our image as a `.sif` file. 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docs/images/specfem2d_example_files/specfem2d_example_56_0.png rename to docs/images/specfem2d_example_files/specfem2d_example_63_0.png diff --git a/docs/notebooks/specfem2d_example.ipynb b/docs/notebooks/specfem2d_example.ipynb index f56bb226..6e72c9fd 100644 --- a/docs/notebooks/specfem2d_example.ipynb +++ b/docs/notebooks/specfem2d_example.ipynb @@ -4,11 +4,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Specfem2D Workstation Example\n", + "# Specfem2D Workstation Examples\n", "\n", - "SeisFlows comes with some __Specfem2D synthetic examples__ to showcase the package. These examples are meant to be run on a __local machine__ (tested on a Linux workstation running CentOS 7, and an Apple Laptop running macOS 10.14.6).\n", + "SeisFlows comes with some __Specfem2D synthetic examples__ to showcase the software in action. These examples are meant to be run on a __local machine__ (tested on a Linux workstation running CentOS 7, and an Apple Laptop running macOS 10.14.6).\n", "\n", - "The numerical solver we will use is: [SPECFEM2D](https://geodynamics.org/cig/software/specfem2d/). We'll also be working in our `seisflows` [Conda](https://docs.conda.io/en/latest/) environment, see the installation documentation page for instructions on how to install and activate the required Conda environment. \n", + "The numerical solver we will use is: [SPECFEM2D](https://geodynamics.org/cig/software/specfem2d/). We'll also be working in our `seisflows` [Conda](https://docs.conda.io/en/latest/) environment, see the installation section on the home page for instructions on how to install and activate the required Conda environment. \n", "\n", "-----------------------------------" ] @@ -34,12 +34,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Example \\#1: Simple, default inversion\n", - "Example \\#1 runs a 1-iteration synthetic inversion with 1 event and 1 station, used to illustrate misfit kernels in adjoint tomography.\n", + "## Example \\#1: Homogenous Halfspace Inversion\n", + "Example \\#1 runs a 1-iteration synthetic inversion with 1 event and 1 station, used to illustrate misfit kernels and updated models in adjoint tomography.\n", "\n", - "The starting model (MODEL_INIT) and target model (MODEL_TRUE) are used to generate synthetics and data, respectively. Both models are homogeneous halfspace models with slightly varying P- and S-wave velocity values. Only Vp and Vs are updated during the example.\n", + "The starting/initial model (*MODEL_INIT*) and target/true model (*MODEL_TRUE*) are used to generate synthetics and (synthetic) data, respectively. Both models are homogeneous halfspace models defined by velocity (Vp, Vs) and density ($\\rho$) with slightly varying P- and S-wave velocity values (**INIT**: $V_p$=5.8km/s, $V_s$=3.5km/s; **TRUE**: $V_p$=5.9km/s, $V_s$=3.55km/s). Only Vp and Vs are updated during the example.\n", "\n", - "Misfit during Example \\#1 is defined by a 'traveltime' misfit using the default preprocessing module. It also uses a gradient-descent optimization algorithm paired with a bracketing line search. No smoothing/regularization is applied to the gradient." + "Misfit during Example \\#1 is defined by a 'traveltime' misfit using the `Default` preprocessing module. It also uses a `gradient-descent` optimization algorithm paired with a bracketing line search. No smoothing/regularization is applied to the gradient." ] }, { @@ -220,11 +220,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Plotting results (only available w/ SPECFEM2D)\n", + "### Plotting results\n", "\n", - "We can plot the model and gradient files created during our workflow using the `seisflows plot2d` command. The `--savefig` flag allows us to save output .png files to disk. The following figure shows the starting/initial homogeneous halfspace model in Vs.\n", + "We can plot the model and gradient files created during our workflow using the `seisflows plot2d` command. The `--savefig` flag allows us to save output .png files to disk. The following figure shows the starting/initial homogeneous halfspace model in Vs." + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + ".. note::\n", + " Models and gradients can only be plotted when using `SPECFEM2D` as the chosen solver. Other solvers (e.g., SPECFEM3D and 3D_GLOBE) require external software (e.g., ParaView) to visualize volumetric quantities like models and gradients.\n", "\n", - ">__NOTE:__ Because this docs page was made in a Jupyter Notebook, we need to use the IPython Image class to open the resulting .png file from inside the notebook. Users following along will need to open the figure using the GUI or command line tool." + ".. note::\n", + " Because this docs page was made in a Jupyter Notebook, we need to use the IPython Image class to open the resulting .png file from inside the notebook. Users following along will need to open the figure using the GUI or command line tool." ] }, { @@ -361,11 +370,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Example \\#2: Checkerboard inversion using Pyaflowa \\& L-BFGS \n", + "## Example \\#2: Checkerboard Inversion (w/ Pyaflowa \\& L-BFGS)\n", "\n", - "Building on the foundation of the previous example, Example \\#2 runs a 2 iteration inversion with misfit quantification taken care of by the `Pyaflowa` preprocessing module, which uses the misfit quantification package [Pyatoa](https://github.com/adjtomo/pyatoa) under the hood. Model updates are performed using an [`L-BFGS` nonlinear optimization algorithm](https://en.wikipedia.org/wiki/Limited-memory_BFGS). Example \\#2 also includes smoothing/regularization of the gradient. This example more closely mimics a research-grade inversion problem.\n", + "Building on the foundation of the previous example, Example \\#2 runs a 2 iteration inversion with misfit quantification taken care of by the `Pyaflowa` preprocessing module, which uses the misfit quantification package [Pyatoa](https://github.com/adjtomo/pyatoa) under the hood. \n", "\n", - ">__NOTE:__ This example is computationally more intense than the default version of Example \\#1 as it uses multiple events and stations, and runs multiple iterations. " + "Model updates are performed using an [L-BFGS nonlinear optimization algorithm](https://en.wikipedia.org/wiki/Limited-memory_BFGS). Example \\#2 also includes smoothing/regularization of the gradient. This example more closely mimics a research-grade inversion problem." + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + ".. note::\n", + " This example is computationally more intense than the default version of Example \\#1 as it uses multiple events and stations, and runs multiple iterations. " ] }, { @@ -450,15 +467,6 @@ "\n", ".. code:: bash\n", "\n", - "\n", - " 2022-08-29 18:08:13 (I) | \n", - " FINALIZING LINE SEARCH\n", - " --------------------------------------------------------------------------------\n", - " 2022-08-29 18:08:13 (I) | writing optimization stats\n", - " 2022-08-29 18:08:13 (I) | renaming current (new) optimization vectors as previous model (old)\n", - " 2022-08-29 18:08:13 (I) | setting accepted trial model (try) as current model (new)\n", - " 2022-08-29 18:08:13 (I) | misfit of accepted trial model is f=4.727E-03\n", - " 2022-08-29 18:08:13 (I) | resetting line search step count to 0\n", " 2022-08-29 18:08:13 (I) | \n", " CLEANING WORKDIR FOR NEXT ITERATION\n", " --------------------------------------------------------------------------------\n", @@ -485,7 +493,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -512,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -536,6 +544,41 @@ "! seisflows plot2d" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, running `plot2d` with 1 argument will help determine what quantities are available to plot" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\r\n", + " File \"/home/bchow/miniconda3/envs/docs/bin/seisflows\", line 33, in \r\n", + " sys.exit(load_entry_point('seisflows', 'console_scripts', 'seisflows')())\r\n", + " File \"/home/bchow/REPOSITORIES/seisflows/seisflows/seisflows.py\", line 1383, in main\r\n", + " sf()\r\n", + " File \"/home/bchow/REPOSITORIES/seisflows/seisflows/seisflows.py\", line 438, in __call__\r\n", + " getattr(self, self._args.command)(**vars(self._args))\r\n", + " File \"/home/bchow/REPOSITORIES/seisflows/seisflows/seisflows.py\", line 1106, in plot2d\r\n", + " save=savefig)\r\n", + " File \"/home/bchow/REPOSITORIES/seisflows/seisflows/tools/specfem.py\", line 428, in plot2d\r\n", + " f\"chosen `parameter` must be in {self._parameters}\"\r\n", + "AssertionError: chosen `parameter` must be in ['vp', 'vs']\r\n" + ] + } + ], + "source": [ + "! seisflows plot2d MODEL_TRUE" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -584,12 +627,12 @@ "\n", "We can look at the gradients created during the adjoint simulations to get an idea of how our inversion wanted to update the model. Gradients tell us how to perturb our starting model (the homogeneous halfspace) to best fit the data that was generated by our target model (the checkerboard).\n", "\n", - "We can see that our gradient (Vs kernel) is characterized by large red and blue blobs. The blue colors in the kernel tell us that the initial model is too fast, while red colors tell us that the initial model is too slow (that is, **red==too slow** and **blue==too fast**). This makes sense if we look at the checkerboard target model above, where the perturbation is slow (red color) the corresponding kernel tells us the initial model is too fast (blue color)." + "We can see that our gradient (Vs kernel) is characterized by large red and blue blobs. The blue colors in the kernel tell us that the initial model is too fast, while red colors tell us that the initial model is too slow (that is, **red==too slow** and **blue==too fast**). This makes sense if we look at the checkerboard target model above, where the perturbation is slow (red color) the corresponding kernel tells us the initial (homogeneous halfspace) model is too fast (blue color)." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -601,12 +644,12 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "" ] }, - "execution_count": 10, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -622,16 +665,16 @@ "source": [ "### Visualizing the updated model\n", "\n", - "After two iterations, the updated model starts to take form. We can clearly see tha the lack of data coverage on the outer edges of the model mean we do not see any appreciable update here, whereas the center of the domain shows the strongest model updates which are starting to resemble the checkerboard pattern shown in the target model.\n", + "After two iterations, the updated model starts to take form. We can clearly see that the lack of data coverage on the outer edges of the model mean we do not see any appreciable update here, whereas the center of the domain shows the strongest model updates which are starting to resemble the checkerboard pattern shown in the target model.\n", "\n", - "With only 4 events and 2 iterations, we do not have quite enough constraint to recover the sharp contrats between checkers shown in the Target model. We can see that smearing and regularization leads to more prominent slow (red) regions. \n", + "With only 4 events and 2 iterations, we do not have quite enough constraint to recover the sharp contrasts between checkers shown in the Target model. We can see that data coverage, smearing and regularization leads to more prominent slow (red) regions. \n", "\n", "If we were to increase the number of events and iterations, will it help our recovery of the target model? This task is left up to the reader!" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -643,12 +686,12 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "" ] }, - "execution_count": 11, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -659,21 +702,29 @@ ] }, { - "attachments": { - "tape_etal_2007_fig9.jpeg": { - "image/jpeg": 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" - } - }, + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Re-creating kernels from Tape et al. 2007\n", "\n", - "The 2D checkerboard model and source-receiver configuration that runs in this example comes from the published work of [Tape et al. (2007)](https://academic.oup.com/gji/article/168/3/1105/929373). Here, Tape et al. generate event and misfit kernels for a number of individual events in [Figure 9](https://academic.oup.com/view-large/figure/31726687/168-3-1105-fig009.jpeg) (shown below). This exercise is meant to illustrate how kernel features change for a simple target model (the checkerboard) depending on the chosen source-receiver geometry. \n", - "\n", - "![tape_etal_2007_fig9.jpeg](attachment:tape_etal_2007_fig9.jpeg)\n", + "The 2D checkerboard model and source-receiver configuration that runs in this example come from the published work of [Tape et al. (2007)](https://academic.oup.com/gji/article/168/3/1105/929373). Here, Tape et al. generate event kernels for a number of individual events ([Figure 9](https://academic.oup.com/view-large/figure/31726687/168-3-1105-fig009.jpeg), shown below). This exercise illustrates how kernel features change for a simple target model (the checkerboard) depending on the chosen source-receiver geometry. \n", "\n", - "*Caption: Construction of a misfit kernel. (a)–(g) Individual event kernels, each constructed via the method shown in Fig. 8 (which shows Event 5). The colour scale for each event kernel is shown beneath (g). (h) The misfit kernel is simply the sum of the 25 event kernels. (i) The source–receiver geometry and target phase‐speed model. There are a total of N= 25 × 132 = 3300 measurements that are used in constructing the misfit kernel (see Section 5).*" + "An attentive reader will notice that the misfit kernel we generated above looks very similar to Panel (h) in the figure below." + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + ".. image:: reference_figures/tape_etal_2007_fig9.jpeg" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Caption from publication: *Construction of a misfit kernel. (a)–(g) Individual event kernels, each constructed via the method shown in Fig. 8 (which shows Event 5). The colour scale for each event kernel is shown beneath (g). (h) The misfit kernel is simply the sum of the 25 event kernels. (i) The source–receiver geometry and target phase‐speed model. There are a total of N= 25 × 132 = 3300 measurements that are used in constructing the misfit kernel (see Section 5).*" ] }, { @@ -684,11 +735,17 @@ "\n", "The Event ID that generated each kernel is specified in the title of each sub plot (e.g., Panel. (a) corresponds to Event \\#1). We can attempt to re-create these kernels by choosing specific event IDs to run Example 2 with. \n", "\n", - ">__NOTE:__ Our choice of preprocessing module, misfit function, gradient smoothing length, nonlinear optimization algorithm, etc. will affect how each event kernel is produced, and consequently how much they differ from the published kernels shown above. We do not expect to perfectly match the event kernels above, but rather to see that first order structure is the same.\n", - "\n", "To specify the specific event ID, we can use the `--event_id` flag when running Example 2. For this docs page we'll choose Event \\#7, which is represented by Panel (g) in the figure above. " ] }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + ".. note::\n", + " Our choice of preprocessing module, misfit function, gradient smoothing length, nonlinear optimization algorithm, etc. will affect how each event kernel is produced, and consequently how much they differ from the published kernels shown above. We do not expect to perfectly match the event kernels above, but rather to see that first order structure is the same." + ] + }, { "cell_type": "code", "execution_count": 13, @@ -771,7 +828,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -835,21 +892,25 @@ "\n", "SeisFlows is not just an inversion tool, it can also be used to simplify workflows to run forward simulations using external numerical solvers. In Example \\#3 we use SeisFlows to run en-masse forward simulations. \n", "\n", - "To motivate this use case, imagine a User who has a velocity model of a specific region (at any scale). This User would like to run a number of forward simulations for N events and S stations to generate N x S synthetic seismograms. These synthetics may be used directly, or compared to observed seismograms to understand how well the regional velocity model characterizes actual Earth structure. \n", + "### Motivation\n", "\n", - "Although this could be done manually, if N is large, this effort may require a large number of manual tasks, including the creation of working directories, editing submit calls, and providing book keeping for the external solver. SeisFlows is here to automate all of these tasks." + "Imagine a User who has a velocity model of a specific region (at any scale). This User would like to run a number of forward simulations for **N** events and **S** stations to generate **N** $\\times$ **S** synthetic seismograms. These synthetics may be used directly, or compared to observed seismograms to understand how well the regional velocity model characterizes actual Earth structure. \n", + "\n", + "If **N** is large this effort may require a large number of manual tasks, including the creation of working directories, editing submit calls (if working on a cluster), and book keeping for files generated by the external solver. SeisFlows is here to automate all of these tasks.\n", + "\n", + "### Running the example" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "No existing SPECFEM2D repo given, default to: /home/bchow/Work/work/seisflows_example/example_2a/specfem2d\r\n", + "No existing SPECFEM2D repo given, default to: /home/bchow/Work/work/seisflows_example/example_2/specfem2d\r\n", "\r\n", " @@@@@@@@@@ \r\n", " .@@@@. .%&( %@. \r\n", @@ -888,7 +949,7 @@ } ], "source": [ - "# Run the help dialogue to see what \n", + "# Run the help dialogue to see what occurs in Example 3\n", "! seisflows examples 3" ] }, @@ -926,7 +987,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 12, "metadata": {}, "outputs": [ { diff --git a/docs/specfem2d_example.rst b/docs/specfem2d_example.rst index ec360916..5a632346 100644 --- a/docs/specfem2d_example.rst +++ b/docs/specfem2d_example.rst @@ -1,17 +1,17 @@ -Specfem2D Workstation Example -============================= +Specfem2D Workstation Examples +============================== SeisFlows comes with some **Specfem2D synthetic examples** to showcase -the package. These examples are meant to be run on a **local machine** -(tested on a Linux workstation running CentOS 7, and an Apple Laptop -running macOS 10.14.6). +the software in action. These examples are meant to be run on a **local +machine** (tested on a Linux workstation running CentOS 7, and an Apple +Laptop running macOS 10.14.6). The numerical solver we will use is: `SPECFEM2D `__. We’ll also be working in our ``seisflows`` `Conda `__ environment, see the -installation documentation page for instructions on how to install and -activate the required Conda environment. +installation section on the home page for instructions on how to install +and activate the required Conda environment. -------------- @@ -22,19 +22,23 @@ activate the required Conda environment. from IPython.display import Image # Used to display .png files in the notebook/docs -Example #1: Simple, default inversion -------------------------------------- +Example #1: Homogenous Halfspace Inversion +------------------------------------------ Example #1 runs a 1-iteration synthetic inversion with 1 event and 1 -station, used to illustrate misfit kernels in adjoint tomography. +station, used to illustrate misfit kernels and updated models in adjoint +tomography. -The starting model (MODEL_INIT) and target model (MODEL_TRUE) are used -to generate synthetics and data, respectively. Both models are -homogeneous halfspace models with slightly varying P- and S-wave -velocity values. Only Vp and Vs are updated during the example. +The starting/initial model (*MODEL_INIT*) and target/true model +(*MODEL_TRUE*) are used to generate synthetics and (synthetic) data, +respectively. Both models are homogeneous halfspace models defined by +velocity (Vp, Vs) and density (:math:`\rho`) with slightly varying P- +and S-wave velocity values (**INIT**: :math:`V_p`\ =5.8km/s, +:math:`V_s`\ =3.5km/s; **TRUE**: :math:`V_p`\ =5.9km/s, +:math:`V_s`\ =3.55km/s). Only Vp and Vs are updated during the example. Misfit during Example #1 is defined by a ‘traveltime’ misfit using the -default preprocessing module. It also uses a gradient-descent +``Default`` preprocessing module. It also uses a ``gradient-descent`` optimization algorithm paired with a bracketing line search. No smoothing/regularization is applied to the gradient. @@ -172,18 +176,19 @@ because we set the parameter ``export_gradient`` to True. proc000000_vp.bin proc000000_vs.bin -Plotting results (only available w/ SPECFEM2D) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Plotting results +~~~~~~~~~~~~~~~~ We can plot the model and gradient files created during our workflow using the ``seisflows plot2d`` command. The ``--savefig`` flag allows us to save output .png files to disk. The following figure shows the starting/initial homogeneous halfspace model in Vs. - **NOTE:** Because this docs page was made in a Jupyter Notebook, we - need to use the IPython Image class to open the resulting .png file - from inside the notebook. Users following along will need to open the - figure using the GUI or command line tool. +.. note:: + Models and gradients can only be plotted when using `SPECFEM2D` as the chosen solver. Other solvers (e.g., SPECFEM3D and 3D_GLOBE) require external software (e.g., ParaView) to visualize volumetric quantities like models and gradients. + +.. note:: + Because this docs page was made in a Jupyter Notebook, we need to use the IPython Image class to open the resulting .png file from inside the notebook. Users following along will need to open the figure using the GUI or command line tool. .. code:: ipython3 @@ -199,7 +204,7 @@ starting/initial homogeneous halfspace model in Vs. -.. image:: images/specfem2d_example_files/specfem2d_example_14_1.png +.. image:: images/specfem2d_example_files/specfem2d_example_15_1.png @@ -222,7 +227,7 @@ in model values. -.. image:: images/specfem2d_example_files/specfem2d_example_16_1.png +.. image:: images/specfem2d_example_files/specfem2d_example_17_1.png @@ -246,7 +251,7 @@ almost exactly mimics the Vs kernel shown above. -.. image:: images/specfem2d_example_files/specfem2d_example_18_1.png +.. image:: images/specfem2d_example_files/specfem2d_example_19_1.png @@ -262,21 +267,21 @@ You can also run Example \#1 with more stations (up to 131), tasks/events (up to # An example call for running Example 1 with variable number of stations, events and iterations ! seisflows examples run 1 --nsta 10 --ntask 5 --niter 2 -Example #2: Checkerboard inversion using Pyaflowa & L-BFGS ----------------------------------------------------------- +Example #2: Checkerboard Inversion (w/ Pyaflowa & L-BFGS) +--------------------------------------------------------- Building on the foundation of the previous example, Example #2 runs a 2 iteration inversion with misfit quantification taken care of by the ``Pyaflowa`` preprocessing module, which uses the misfit quantification package `Pyatoa `__ under the hood. -Model updates are performed using an ```L-BFGS`` nonlinear optimization + +Model updates are performed using an `L-BFGS nonlinear optimization algorithm `__. Example #2 also includes smoothing/regularization of the gradient. This example more closely mimics a research-grade inversion problem. - **NOTE:** This example is computationally more intense than the - default version of Example #1 as it uses multiple events and - stations, and runs multiple iterations. +.. note:: + This example is computationally more intense than the default version of Example \#1 as it uses multiple events and stations, and runs multiple iterations. .. code:: ipython3 @@ -340,15 +345,6 @@ Succesful completion of the example problem will end with a log message that loo .. code:: bash - - 2022-08-29 18:08:13 (I) | - FINALIZING LINE SEARCH - -------------------------------------------------------------------------------- - 2022-08-29 18:08:13 (I) | writing optimization stats - 2022-08-29 18:08:13 (I) | renaming current (new) optimization vectors as previous model (old) - 2022-08-29 18:08:13 (I) | setting accepted trial model (try) as current model (new) - 2022-08-29 18:08:13 (I) | misfit of accepted trial model is f=4.727E-03 - 2022-08-29 18:08:13 (I) | resetting line search step count to 0 2022-08-29 18:08:13 (I) | CLEANING WORKDIR FOR NEXT ITERATION -------------------------------------------------------------------------------- @@ -406,6 +402,30 @@ determine what model/gradient files are available for plotting. MODEL_TRUE +Similarly, running ``plot2d`` with 1 argument will help determine what +quantities are available to plot + +.. code:: ipython3 + + ! seisflows plot2d MODEL_TRUE + + +.. parsed-literal:: + + Traceback (most recent call last): + File "/home/bchow/miniconda3/envs/docs/bin/seisflows", line 33, in + sys.exit(load_entry_point('seisflows', 'console_scripts', 'seisflows')()) + File "/home/bchow/REPOSITORIES/seisflows/seisflows/seisflows.py", line 1383, in main + sf() + File "/home/bchow/REPOSITORIES/seisflows/seisflows/seisflows.py", line 438, in __call__ + getattr(self, self._args.command)(**vars(self._args)) + File "/home/bchow/REPOSITORIES/seisflows/seisflows/seisflows.py", line 1106, in plot2d + save=savefig) + File "/home/bchow/REPOSITORIES/seisflows/seisflows/tools/specfem.py", line 428, in plot2d + f"chosen `parameter` must be in {self._parameters}" + AssertionError: chosen `parameter` must be in ['vp', 'vs'] + + Visualizing Initial and Target models ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -432,7 +452,7 @@ representation of these perturbations, where **red==slow** and -.. image:: images/specfem2d_example_files/specfem2d_example_32_1.png +.. image:: images/specfem2d_example_files/specfem2d_example_36_1.png @@ -451,7 +471,7 @@ model is too fast, while red colors tell us that the initial model is too slow (that is, **red==too slow** and **blue==too fast**). This makes sense if we look at the checkerboard target model above, where the perturbation is slow (red color) the corresponding kernel tells us the -initial model is too fast (blue color). +initial (homogeneous halfspace) model is too fast (blue color). .. code:: ipython3 @@ -466,7 +486,7 @@ initial model is too fast (blue color). -.. image:: images/specfem2d_example_files/specfem2d_example_34_1.png +.. image:: images/specfem2d_example_files/specfem2d_example_38_1.png @@ -474,15 +494,15 @@ Visualizing the updated model ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ After two iterations, the updated model starts to take form. We can -clearly see tha the lack of data coverage on the outer edges of the +clearly see that the lack of data coverage on the outer edges of the model mean we do not see any appreciable update here, whereas the center of the domain shows the strongest model updates which are starting to resemble the checkerboard pattern shown in the target model. With only 4 events and 2 iterations, we do not have quite enough -constraint to recover the sharp contrats between checkers shown in the -Target model. We can see that smearing and regularization leads to more -prominent slow (red) regions. +constraint to recover the sharp contrasts between checkers shown in the +Target model. We can see that data coverage, smearing and regularization +leads to more prominent slow (red) regions. If we were to increase the number of events and iterations, will it help our recovery of the target model? This task is left up to the reader! @@ -500,7 +520,7 @@ our recovery of the target model? This task is left up to the reader! -.. image:: images/specfem2d_example_files/specfem2d_example_36_1.png +.. image:: images/specfem2d_example_files/specfem2d_example_40_1.png @@ -508,27 +528,27 @@ Re-creating kernels from Tape et al. 2007 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The 2D checkerboard model and source-receiver configuration that runs in -this example comes from the published work of `Tape et +this example come from the published work of `Tape et al. (2007) `__. -Here, Tape et al. generate event and misfit kernels for a number of -individual events in `Figure -9 `__ -(shown below). This exercise is meant to illustrate how kernel features -change for a simple target model (the checkerboard) depending on the -chosen source-receiver geometry. - -.. figure:: attachment:tape_etal_2007_fig9.jpeg - :alt: tape_etal_2007_fig9.jpeg - - tape_etal_2007_fig9.jpeg - -*Caption: Construction of a misfit kernel. (a)–(g) Individual event -kernels, each constructed via the method shown in Fig. 8 (which shows -Event 5). The colour scale for each event kernel is shown beneath (g). -(h) The misfit kernel is simply the sum of the 25 event kernels. (i) The -source–receiver geometry and target phase‐speed model. There are a total -of N= 25 × 132 = 3300 measurements that are used in constructing the -misfit kernel (see Section 5).* +Here, Tape et al. generate event kernels for a number of individual +events (`Figure +9 `__, +shown below). This exercise illustrates how kernel features change for a +simple target model (the checkerboard) depending on the chosen +source-receiver geometry. + +An attentive reader will notice that the misfit kernel we generated +above looks very similar to Panel (h) in the figure below. + +.. image:: images/reference_figures/tape_etal_2007_fig9.jpeg + +Caption from publication: *Construction of a misfit kernel. (a)–(g) +Individual event kernels, each constructed via the method shown in Fig. +8 (which shows Event 5). The colour scale for each event kernel is shown +beneath (g). (h) The misfit kernel is simply the sum of the 25 event +kernels. (i) The source–receiver geometry and target phase‐speed model. +There are a total of N= 25 × 132 = 3300 measurements that are used in +constructing the misfit kernel (see Section 5).* Choosing an event ^^^^^^^^^^^^^^^^^ @@ -538,17 +558,13 @@ each sub plot (e.g., Panel. (a) corresponds to Event #1). We can attempt to re-create these kernels by choosing specific event IDs to run Example 2 with. - **NOTE:** Our choice of preprocessing module, misfit function, - gradient smoothing length, nonlinear optimization algorithm, etc. - will affect how each event kernel is produced, and consequently how - much they differ from the published kernels shown above. We do not - expect to perfectly match the event kernels above, but rather to see - that first order structure is the same. - To specify the specific event ID, we can use the ``--event_id`` flag when running Example 2. For this docs page we’ll choose Event #7, which is represented by Panel (g) in the figure above. +.. note:: + Our choice of preprocessing module, misfit function, gradient smoothing length, nonlinear optimization algorithm, etc. will affect how each event kernel is produced, and consequently how much they differ from the published kernels shown above. We do not expect to perfectly match the event kernels above, but rather to see that first order structure is the same. + .. code:: ipython3 # Run the help message to view the description of the optional arguemnt --event_id @@ -642,7 +658,7 @@ published in Tape et al. -.. image:: images/specfem2d_example_files/specfem2d_example_43_1.png +.. image:: images/specfem2d_example_files/specfem2d_example_50_1.png @@ -662,28 +678,33 @@ SeisFlows is not just an inversion tool, it can also be used to simplify workflows to run forward simulations using external numerical solvers. In Example #3 we use SeisFlows to run en-masse forward simulations. -To motivate this use case, imagine a User who has a velocity model of a -specific region (at any scale). This User would like to run a number of -forward simulations for N events and S stations to generate N x S +Motivation +~~~~~~~~~~ + +Imagine a User who has a velocity model of a specific region (at any +scale). This User would like to run a number of forward simulations for +**N** events and **S** stations to generate **N** :math:`\times` **S** synthetic seismograms. These synthetics may be used directly, or compared to observed seismograms to understand how well the regional velocity model characterizes actual Earth structure. -Although this could be done manually, if N is large, this effort may -require a large number of manual tasks, including the creation of -working directories, editing submit calls, and providing book keeping -for the external solver. SeisFlows is here to automate all of these -tasks. +If **N** is large this effort may require a large number of manual +tasks, including the creation of working directories, editing submit +calls (if working on a cluster), and book keeping for files generated by +the external solver. SeisFlows is here to automate all of these tasks. + +Running the example +~~~~~~~~~~~~~~~~~~~ .. code:: ipython3 - # Run the help dialogue to see what + # Run the help dialogue to see what occurs in Example 3 ! seisflows examples 3 .. parsed-literal:: - No existing SPECFEM2D repo given, default to: /home/bchow/Work/work/seisflows_example/example_2a/specfem2d + No existing SPECFEM2D repo given, default to: /home/bchow/Work/work/seisflows_example/example_2/specfem2d @@@@@@@@@@ .@@@@. .%&( %@. @@ -814,7 +835,7 @@ SeisFlows is copying all synthetic seismograms from the Solver’s -.. image:: images/specfem2d_example_files/specfem2d_example_55_0.png +.. image:: images/specfem2d_example_files/specfem2d_example_62_0.png @@ -827,6 +848,6 @@ SeisFlows is copying all synthetic seismograms from the Solver’s -.. image:: images/specfem2d_example_files/specfem2d_example_56_0.png +.. image:: images/specfem2d_example_files/specfem2d_example_63_0.png diff --git a/seisflows/examples/ex2_hh_w_pyatoa.py b/seisflows/examples/ex2_hh_w_pyatoa.py index 9f446af4..274d6d44 100644 --- a/seisflows/examples/ex2_hh_w_pyatoa.py +++ b/seisflows/examples/ex2_hh_w_pyatoa.py @@ -76,6 +76,7 @@ def __init__(self, ntask=None, niter=None, nsta=None, nproc=None, # Adjust the existing parameter list self._parameters["smooth_h"] = 5000. self._parameters["smooth_v"] = 5000. + self._parameters["pyflex_preset"] = "null" # no windowing in Pyaflowa # Pyaflowa preprocessing parameters self._parameters["unit_output"] = "DISP" From 44e617f7b8a57672d1ad983a30f644e9278dd690 Mon Sep 17 00:00:00 2001 From: Bryant Chow Date: Fri, 9 Sep 2022 14:09:49 -0800 Subject: [PATCH 167/195] added plotst to command line tool docs page --- docs/command_line_tool.rst | 496 +++++++++++------- .../command_line_tool_39_0.png | Bin 66983 -> 0 bytes .../command_line_tool_40_1.png | Bin 0 -> 79890 bytes .../command_line_tool_44_0.png | Bin 0 -> 19420 bytes .../command_line_tool_45_0.png | Bin 0 -> 143089 bytes docs/notebooks/command_line_tool.ipynb | 158 +++++- 6 files changed, 434 insertions(+), 220 deletions(-) delete mode 100644 docs/images/command_line_tool_files/command_line_tool_39_0.png create mode 100644 docs/images/command_line_tool_files/command_line_tool_40_1.png create mode 100644 docs/images/command_line_tool_files/command_line_tool_44_0.png create mode 100644 docs/images/command_line_tool_files/command_line_tool_45_0.png diff --git a/docs/command_line_tool.rst b/docs/command_line_tool.rst index 7afc0894..98b6a2f2 100644 --- a/docs/command_line_tool.rst +++ b/docs/command_line_tool.rst @@ -19,43 +19,43 @@ help dialogue, you can type ``seisflows`` or ``seisflows -h`` .. parsed-literal:: - usage: seisflows [-h] [-w [WORKDIR]] [-p [PARAMETER_FILE]] - {setup,configure,swap,init,submit,resume,restart,clean,par,sempar,check,print,reset,debug,examples} - ... - - ================================================================================ - - SeisFlows: Waveform Inversion Package - - ================================================================================ - - optional arguments: - -h, --help show this help message and exit - -w [WORKDIR], --workdir [WORKDIR] - The SeisFlows working directory, default: cwd - -p [PARAMETER_FILE], --parameter_file [PARAMETER_FILE] - Parameters file, default: 'parameters.yaml' - - command: - Available SeisFlows arguments and their intended usages - - setup Setup working directory from scratch - configure Fill parameter file with defaults - swap Swap module parameters in an existing parameter file - init Initiate working environment - submit Submit initial workflow to system - resume Re-submit previous workflow to system - restart Remove current environment and submit new workflow - clean Remove files relating to an active working environment - par View and edit SeisFlows parameter file - sempar View and edit SPECFEM parameter file - check Check state of an active environment - print Print information related to an active environment - reset Reset modules within an active state - debug Start interactive debug environment - examples Look at and run pre-configured example problems - - 'seisflows [command] -h' for more detailed descriptions of each command. + usage: seisflows [-h] [-w [WORKDIR]] [-p [PARAMETER_FILE]] + {setup,configure,swap,init,submit,resume,restart,clean,par,sempar,check,print,reset,debug,examples} + ... + + ================================================================================ + + SeisFlows: Waveform Inversion Package + + ================================================================================ + + optional arguments: + -h, --help show this help message and exit + -w [WORKDIR], --workdir [WORKDIR] + The SeisFlows working directory, default: cwd + -p [PARAMETER_FILE], --parameter_file [PARAMETER_FILE] + Parameters file, default: 'parameters.yaml' + + command: + Available SeisFlows arguments and their intended usages + + setup Setup working directory from scratch + configure Fill parameter file with defaults + swap Swap module parameters in an existing parameter file + init Initiate working environment + submit Submit initial workflow to system + resume Re-submit previous workflow to system + restart Remove current environment and submit new workflow + clean Remove files relating to an active working environment + par View and edit SeisFlows parameter file + sempar View and edit SPECFEM parameter file + check Check state of an active environment + print Print information related to an active environment + reset Reset modules within an active state + debug Start interactive debug environment + examples Look at and run pre-configured example problems + + 'seisflows [command] -h' for more detailed descriptions of each command. Setting up @@ -87,17 +87,17 @@ file. .. parsed-literal:: - usage: seisflows setup [-h] [-f] - - In the specified working directory, copy template parameter file containing - only module choices, and symlink source code for both the base and super - repositories for easy edit access. If a parameter file matching the provided - name exists in the working directory, a prompt will appear asking the user if - they want to overwrite. - - optional arguments: - -h, --help show this help message and exit - -f, --force automatically overwrites existing parameter file + usage: seisflows setup [-h] [-f] + + In the specified working directory, copy template parameter file containing + only module choices, and symlink source code for both the base and super + repositories for easy edit access. If a parameter file matching the provided + name exists in the working directory, a prompt will appear asking the user if + they want to overwrite. + + optional arguments: + -h, --help show this help message and exit + -f, --force automatically overwrites existing parameter file .. code:: ipython3 @@ -108,7 +108,7 @@ file. .. parsed-literal:: - creating parameter file: parameters.yaml + creating parameter file: parameters.yaml Having a look at the template ``parameters.yaml`` file that was just @@ -135,36 +135,36 @@ set of unique parameters which make up a workflow. .. parsed-literal:: - # ////////////////////////////////////////////////////////////////////////////// - # - # SeisFlows YAML Parameter File - # - # ////////////////////////////////////////////////////////////////////////////// - # - # Modules correspond to the structure of the source code, and determine - # SeisFlows' behavior at runtime. Each module requires its own sub-parameters. - # - # .. rubric:: - # - To determine available options for modules listed below, run: - # > seisflows print modules - # - To auto-fill with docstrings and default values (recommended), run: - # > seisflows configure - # - To set values as NoneType, use: null - # - To set values as infinity, use: inf - # - # MODULES - # /////// - # workflow (str): The types and order of functions for running SeisFlows - # system (str): Computer architecture of the system being used - # solver (str): External numerical solver to use for waveform simulations - # preprocess (str): Preprocessing schema for waveform data - # optimize (str): Optimization algorithm for the inverse problem - # ============================================================================== - workflow: forward - system: workstation - solver: specfem2d - preprocess: default - optimize: gradient + # ////////////////////////////////////////////////////////////////////////////// + # + # SeisFlows YAML Parameter File + # + # ////////////////////////////////////////////////////////////////////////////// + # + # Modules correspond to the structure of the source code, and determine + # SeisFlows' behavior at runtime. Each module requires its own sub-parameters. + # + # .. rubric:: + # - To determine available options for modules listed below, run: + # > seisflows print modules + # - To auto-fill with docstrings and default values (recommended), run: + # > seisflows configure + # - To set values as NoneType, use: null + # - To set values as infinity, use: inf + # + # MODULES + # /////// + # workflow (str): The types and order of functions for running SeisFlows + # system (str): Computer architecture of the system being used + # solver (str): External numerical solver to use for waveform simulations + # preprocess (str): Preprocessing schema for waveform data + # optimize (str): Optimization algorithm for the inverse problem + # ============================================================================== + workflow: forward + system: workstation + solver: specfem2d + preprocess: default + optimize: gradient seisflows configure @@ -181,19 +181,19 @@ file. .. parsed-literal:: - usage: seisflows configure [-h] [-a] - - SeisFlows parameter files will vary depending on chosen modules and their - respective required parameters. This function will dynamically traverse the - source code and generate a template parameter file based on module choices. - The resulting file incldues docstrings and type hints for each parameter. - Optional parameters will be set with default values and required parameters - and paths will be marked appropriately. Required parameters must be set before - a workflow can be submitted. - - optional arguments: - -h, --help show this help message and exit - -a, --absolute_paths Set default paths relative to cwd + usage: seisflows configure [-h] [-a] + + SeisFlows parameter files will vary depending on chosen modules and their + respective required parameters. This function will dynamically traverse the + source code and generate a template parameter file based on module choices. + The resulting file incldues docstrings and type hints for each parameter. + Optional parameters will be set with default values and required parameters + and paths will be marked appropriately. Required parameters must be set before + a workflow can be submitted. + + optional arguments: + -h, --help show this help message and exit + -a, --absolute_paths Set default paths relative to cwd .. code:: ipython3 @@ -328,23 +328,23 @@ script your parameter file setup for improved reproducibility. .. parsed-literal:: - usage: seisflows par [-h] [-p] [parameter] [value] - - Directly edit values in the parameter file by providing the parameter and - corresponding value. If no value is provided, will simply print out the - current value of the given parameter. Works also with path names. - - positional arguments: - parameter Parameter to edit or view, (case independent). - value Optional value to set parameter to. If not given, will - print out current parameter. If given, will replace - current parameter with new value. Set as 'null' for - NoneType and set '' for empty string - - optional arguments: - -h, --help show this help message and exit - -p, --skip_print Skip the print statement which is typically sent to stdout - after changing parameters. + usage: seisflows par [-h] [-p] [parameter] [value] + + Directly edit values in the parameter file by providing the parameter and + corresponding value. If no value is provided, will simply print out the + current value of the given parameter. Works also with path names. + + positional arguments: + parameter Parameter to edit or view, (case independent). + value Optional value to set parameter to. If not given, will + print out current parameter. If given, will replace + current parameter with new value. Set as 'null' for + NoneType and set '' for empty string + + optional arguments: + -h, --help show this help message and exit + -p, --skip_print Skip the print statement which is typically sent to stdout + after changing parameters. The call structure of the ``par`` command is provided in the help @@ -362,7 +362,7 @@ We can view parameters by providing a single ‘parameter’ argument to the .. parsed-literal:: - ntask: 1 + ntask: 1 We can change a given parameter from it’s original value by providing a @@ -375,7 +375,7 @@ second ‘value’ argument .. parsed-literal:: - ntask: 1 -> 3 + ntask: 1 -> 3 seisflows sempar @@ -401,13 +401,13 @@ having to wait on queue times or waste computational resources. .. parsed-literal:: - - ================================================================================ - PARAMETER ERRROR - //////////////// - `path_specfem_bin` must exist and must point to directory containing SPECFEM - executables - ================================================================================ + + ================================================================================ + PARAMETER ERRROR + //////////////// + `path_specfem_bin` must exist and must point to directory containing SPECFEM + executables + ================================================================================ Here we can see that a given path has not been set correctly in the @@ -429,19 +429,19 @@ cases for when this might be useful include switching from a .. parsed-literal:: - usage: seisflows swap [-h] [module] [classname] - - During workflow development, it may be necessary to swap between different - sub-modules (e.g., system.workstation -> system.cluster). However this would - typically involving re-generating and re-filling a parameter file. The 'swap' - function makes it easier to swap parameters between modules. - - positional arguments: - module Module name to swap - classname Classname to swap to - - optional arguments: - -h, --help show this help message and exit + usage: seisflows swap [-h] [module] [classname] + + During workflow development, it may be necessary to swap between different + sub-modules (e.g., system.workstation -> system.cluster). However this would + typically involving re-generating and re-filling a parameter file. The 'swap' + function makes it easier to swap parameters between modules. + + positional arguments: + module Module name to swap + classname Classname to swap to + + optional arguments: + -h, --help show this help message and exit Running workflows @@ -468,18 +468,18 @@ the task list, ensuring that no processing is run on login nodes. .. parsed-literal:: - usage: seisflows submit [-h] [-s STOP_AFTER] - - The main SeisFlows execution command. Submit a SeisFlows workflow to the - chosen system, equal to executing seisflows.workflow.main(). This function - will create and fill the working directory with required paths, perform path - and parameter error checking, and establish the active working environment - before executing the workflow. - - optional arguments: - -h, --help show this help message and exit - -s STOP_AFTER, --stop_after STOP_AFTER - Optional override of the 'STOP_AFTER' parameter + usage: seisflows submit [-h] [-s STOP_AFTER] + + The main SeisFlows execution command. Submit a SeisFlows workflow to the + chosen system, equal to executing seisflows.workflow.main(). This function + will create and fill the working directory with required paths, perform path + and parameter error checking, and establish the active working environment + before executing the workflow. + + optional arguments: + -h, --help show this help message and exit + -s STOP_AFTER, --stop_after STOP_AFTER + Optional override of the 'STOP_AFTER' parameter seisflows clean @@ -498,14 +498,14 @@ statement. .. parsed-literal:: - usage: seisflows clean [-h] [-f] - - Delete all SeisFlows related files in the working directory, except for the - parameter file. - - optional arguments: - -h, --help show this help message and exit - -f, --force Skip the warning check that precedes the clean function + usage: seisflows clean [-h] [-f] + + Delete all SeisFlows related files in the working directory, except for the + parameter file. + + optional arguments: + -h, --help show this help message and exit + -f, --force Skip the warning check that precedes the clean function seisflows restart @@ -523,19 +523,23 @@ defines. .. parsed-literal:: - usage: seisflows restart [-h] [-f] - - Akin to running seisflows clean; seisflows submit. Restarts the workflow by - removing the current state and submitting a fresh workflow. - - optional arguments: - -h, --help show this help message and exit - -f, --force Skip the clean warning check statement + usage: seisflows restart [-h] [-f] + + Akin to running seisflows clean; seisflows submit. Restarts the workflow by + removing the current state and submitting a fresh workflow. + + optional arguments: + -h, --help show this help message and exit + -f, --force Skip the clean warning check statement Plotting ~~~~~~~~ +.. code:: ipython3 + + from IPython.display import Image # Required for showing inline figures in notebook/docs + seisflows plot2d ^^^^^^^^^^^^^^^^ @@ -546,16 +550,16 @@ provided in the help message. .. code:: ipython3 - # a directory where we have run example #2 - %cd ~/Work/scratch/ + # a directory where we have run an example problem + %cd ~/sfexamples/example_2 ! ls .. parsed-literal:: - /home/bchow/Work/scratch - logs parameters.yaml sflog.txt specfem2d - output scratch sfstate.txt specfem2d_workdir + /home/bchow/Work/work/seisflows_example/example_2 + logs parameters.yaml sflog.txt specfem2d + output scratch sfstate.txt specfem2d_workdir .. code:: ipython3 @@ -565,22 +569,22 @@ provided in the help message. .. parsed-literal:: - usage: seisflows plot2d [-h] [-c [CMAP]] [-s [SAVEFIG]] [name] [parameter] - - Plots model/kernels/gradient files located in the output/ - directory. ONLY available for SPECFEM2D models. - - positional arguments: - name Name of directory in the output/ directory - parameter Name of parameter to plot from `name`. E.g., 'vs', - 'vp' etc. - - optional arguments: - -h, --help show this help message and exit - -c [CMAP], --cmap [CMAP] - colormap to be passed to PyPlot - -s [SAVEFIG], --savefig [SAVEFIG] - optional name and path to save figure + usage: seisflows plot2d [-h] [-c [CMAP]] [-s [SAVEFIG]] [name] [parameter] + + Plots model/kernels/gradient files located in the output/ + directory. ONLY available for SPECFEM2D models. + + positional arguments: + name Name of directory in the output/ directory + parameter Name of parameter to plot from `name`. E.g., 'vs', + 'vp' etc. + + optional arguments: + -h, --help show this help message and exit + -c [CMAP], --cmap [CMAP] + colormap to be passed to PyPlot + -s [SAVEFIG], --savefig [SAVEFIG] + optional name and path to save figure Running ``plot2d`` without any arguments will print out a list of @@ -593,14 +597,16 @@ available directories you can plot .. parsed-literal:: - PLOT2D - ////// - Available models/gradients/kernels - - GRADIENT_01 - MODEL_01 - MODEL_INIT - MODEL_TRUE + PLOT2D + ////// + Available models/gradients/kernels + + GRADIENT_01 + GRADIENT_02 + MODEL_01 + MODEL_02 + MODEL_INIT + MODEL_TRUE Users will also have to choose which parameter they would like to plot, @@ -615,38 +621,120 @@ parameters are available to plot. .. parsed-literal:: - Traceback (most recent call last): - File "/home/bchow/miniconda3/envs/docs/bin/seisflows", line 33, in - sys.exit(load_entry_point('seisflows', 'console_scripts', 'seisflows')()) - File "/home/bchow/REPOSITORIES/seisflows/seisflows/seisflows.py", line 1298, in main - sf() - File "/home/bchow/REPOSITORIES/seisflows/seisflows/seisflows.py", line 410, in __call__ - getattr(self, self._args.command)(**vars(self._args)) - File "/home/bchow/REPOSITORIES/seisflows/seisflows/seisflows.py", line 1021, in plot2d - save=savefig) - File "/home/bchow/REPOSITORIES/seisflows/seisflows/tools/specfem.py", line 428, in plot2d - f"chosen `parameter` must be in {self._parameters}" - AssertionError: chosen `parameter` must be in ['vp_kernel', 'vs_kernel'] + Traceback (most recent call last): + File "/home/bchow/miniconda3/envs/docs/bin/seisflows", line 33, in + sys.exit(load_entry_point('seisflows', 'console_scripts', 'seisflows')()) + File "/home/bchow/REPOSITORIES/seisflows/seisflows/seisflows.py", line 1383, in main + sf() + File "/home/bchow/REPOSITORIES/seisflows/seisflows/seisflows.py", line 438, in __call__ + getattr(self, self._args.command)(**vars(self._args)) + File "/home/bchow/REPOSITORIES/seisflows/seisflows/seisflows.py", line 1106, in plot2d + save=savefig) + File "/home/bchow/REPOSITORIES/seisflows/seisflows/tools/specfem.py", line 428, in plot2d + f"chosen `parameter` must be in {self._parameters}" + AssertionError: chosen `parameter` must be in ['vp_kernel', 'vs_kernel'] .. code:: ipython3 ! seisflows plot2d GRADIENT_01 vs_kernel --savefig gradient_01_vs_kernel.png + Image(filename='gradient_01_vs_kernel.png') .. parsed-literal:: - Figure(707.107x707.107) + Figure(707.107x707.107) + + +.. image:: images/command_line_tool_files/command_line_tool_40_1.png + + + +seisflows plotst +^^^^^^^^^^^^^^^^ + +``plotst`` (i.e., *plot st*\ ream) is a wrapper for ObsPy’s +Stream.plot() which plots waveforms generated by the external numerical +solver. In this case we have generated waveforms in the ASCII format +during one of our example problems. Using the ``export_traces`` +parameter, our workflow has saved these waveforms to the ``output/`` +directory of our SeisFlows working directory. + .. code:: ipython3 - from IPython.display import Image - Image(filename='gradient_01_vs_kernel.png') + %cd ~/sfexamples/example_3/output/solver/001/syn + ! ls + + +.. parsed-literal:: + + /home/bchow/Work/work/seisflows_example/example_3/output/solver/001/syn + AA.S000000.BXY.semd AA.S000009.BXY.semd AA.S000018.BXY.semd + AA.S000001.BXY.semd AA.S000010.BXY.semd AA.S000019.BXY.semd + AA.S000002.BXY.semd AA.S000011.BXY.semd AA.S000020.BXY.semd + AA.S000003.BXY.semd AA.S000012.BXY.semd AA.S000021.BXY.semd + AA.S000004.BXY.semd AA.S000013.BXY.semd AA.S000022.BXY.semd + AA.S000005.BXY.semd AA.S000014.BXY.semd AA.S000023.BXY.semd + AA.S000006.BXY.semd AA.S000015.BXY.semd AA.S000024.BXY.semd + AA.S000007.BXY.semd AA.S000016.BXY.semd + AA.S000008.BXY.semd AA.S000017.BXY.semd + + +.. code:: ipython3 + + # Run the help message + ! seisflows plotst -h + + +.. parsed-literal:: + + usage: seisflows plotst [-h] [--data_format [DATA_FORMAT]] [-s [SAVEFIG]] + [fids [fids ...]] + + Plots waveforms output by the solver. Uses ObsPy's + Stream.plot() function under the hood. Example call would be + `seisflows plotst scratch/solver/mainsolver/traces/syn/*` + + + positional arguments: + fids File IDs to be passed to plotting. Wildcards + acceptable + + optional arguments: + -h, --help show this help message and exit + --data_format [DATA_FORMAT] + Data format of the files. Must match file type that + SeisFlows can read. These include:['SU', 'ASCII']. + Defaults to 'ASCII'. See + SeisFlows.preprocess.default.read() for all options. + -s [SAVEFIG], --savefig [SAVEFIG] + optional name and path to save figure + + +.. code:: ipython3 + + # Plot a single synthetic seismogram + ! seisflows plotst AA.S000000.BXY.semd --savefig AA.S000000.BXY.semd.png + Image("AA.S000000.BXY.semd.png") + + + + +.. image:: images/command_line_tool_files/command_line_tool_44_0.png + + + +.. code:: ipython3 + + # Use wild cards to plot multiple stations at once + ! seisflows plotst AA.S00000?.BXY.semd --savefig AA.S00000X.BXY.semd.png + Image("AA.S00000X.BXY.semd.png") -.. image:: images/command_line_tool_files/command_line_tool_39_0.png +.. image:: images/command_line_tool_files/command_line_tool_45_0.png diff --git a/docs/images/command_line_tool_files/command_line_tool_39_0.png b/docs/images/command_line_tool_files/command_line_tool_39_0.png deleted file mode 100644 index 8e903d5175323442efd8795d79dbe635b234da61..0000000000000000000000000000000000000000 GIT binary patch literal 0 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