Feature/analysis combined visualize

autofit/__init__.py

@@ -62,6 +62,7 @@
 from .non_linear.abstract_search import NonLinearSearch
 from .non_linear.abstract_search import PriorPasser
 from .non_linear.analysis.analysis import Analysis
+from .non_linear.analysis.combined import CombinedAnalysis
 from .non_linear.grid.grid_search import GridSearchResult
 from .non_linear.initializer import InitializerBall
 from .non_linear.initializer import InitializerPrior
@@ -109,4 +110,4 @@ def save_abc(pickler, obj):
 
 conf.instance.register(__file__)
 
-__version__ = "2023.3.21.3"
+__version__ = "2023.3.27.1"

autofit/config/general.yaml

@@ -3,6 +3,9 @@ analysis:
 hpc:
   hpc_mode: false                   # If True, use HPC mode, which disables GUI visualization, logging to screen and other settings which are not suited to running on a super computer.
   iterations_per_update: 5000       # The number of iterations between every update (visualization, results output, etc) in HPC mode.
+inversion:
+  check_reconstruction: true        # If True, the inversion's reconstruction is checked to ensure the solution of a meshs's mapper is not an invalid solution where the values are all the same.
+  reconstruction_vmax_factor: 0.5   # Plots of an Inversion's reconstruction use the reconstructed data's bright value multiplied by this factor.  
 model:
   ignore_prior_limits: false        # If ``True`` the limits applied to priors will be ignored, where limits set upper / lower limits. This stops PriorLimitException's from being raised.
 output:

autofit/graphical/declarative/abstract.py

@@ -253,6 +253,11 @@ def visualize(
                 instance,
                 during_analysis
             )
+            model_factor.visualize_combined(
+                paths,
+                instance,
+                during_analysis
+            )
 
     @property
     def global_prior_model(self) -> Collection:

autofit/graphical/declarative/factor/analysis.py

@@ -129,6 +129,11 @@ def visualize(
             instance,
             during_analysis
         )
+        self.analysis.visualize_combined(
+            paths,
+            instance,
+            during_analysis
+        )
 
     def log_likelihood_function(
             self,

autofit/non_linear/abstract_search.py

@@ -22,6 +22,7 @@
     FactorApproximation,
 )
 from autofit.graphical.utils import Status
+from autofit.graphical.utils import Status
 from autofit.mapper.prior_model.collection import Collection
 from autofit.non_linear.initializer import Initializer
 from autofit.non_linear.parallel import SneakyPool
@@ -768,6 +769,9 @@ def perform_update(self, model, analysis, during_analysis):
         analysis.visualize(
             paths=self.paths, instance=instance, during_analysis=during_analysis
         )
+        analysis.visualize_combined(
+            analyses=None, paths=self.paths, instance=instance, during_analysis=during_analysis
+        )
 
         if self.should_profile:
             self.logger.debug("Profiling Maximum Likelihood Model")

autofit/non_linear/analysis/analysis.py

@@ -44,6 +44,9 @@ def log_likelihood_function(self, instance):
     def visualize(self, paths: AbstractPaths, instance, during_analysis):
         pass
 
+    def visualize_combined(self, analyses, paths: AbstractPaths, instance, during_analysis):
+        pass
+
     def save_attributes_for_aggregator(self, paths: AbstractPaths):
         pass
 

autofit/non_linear/analysis/combined.py

@@ -206,6 +206,28 @@ def func(child_paths, analysis):
 
         self._for_each_analysis(func, paths)
 
+    def visualize_combined(self, analyses : List["Analysis"], paths: AbstractPaths, instance, during_analysis):
+        """
+        Visualise the instance using images and quantities which are shared across all analyses.
+
+        For example, each Analysis may have a different dataset, where the fit to each dataset is intended to all
+        be plotted on the same matplotlib subplot. This function can be overwritten to allow the visualization of such
+        a plot.
+
+        Only the first analysis is used to visualize the combined results, where it is assumed that it uses the
+        `analyses` property to access the other analyses and perform visualization.
+
+        Parameters
+        ----------
+        paths
+            An object describing the paths for saving data (e.g. hard-disk directories or entries in sqlite database).
+        instance
+            The maximum likelihood instance of the model so far in the non-linear search.
+        during_analysis
+            Is this visualisation during analysis?
+        """
+        self.analyses[0].visualize_combined(analyses=self.analyses, paths=paths, instance=instance, during_analysis=during_analysis)
+
     def profile_log_likelihood_function(
         self, paths: AbstractPaths, instance,
     ):

autofit/non_linear/analysis/indexed.py

@@ -36,6 +36,9 @@ def log_likelihood_function(self, instance):
     def visualize(self, paths: AbstractPaths, instance, during_analysis):
         return self.analysis.visualize(paths, instance[self.index], during_analysis)
 
+    def visualize_combined(self, analyses, paths: AbstractPaths, instance, during_analysis):
+        return self.analysis.visualize_combined(paths, instance[self.index], during_analysis)
+
     def profile_log_likelihood_function(self, paths: AbstractPaths, instance):
         return self.profile_log_likelihood_function(paths, instance[self.index])
 

requirements.txt

@@ -10,6 +10,6 @@ pyprojroot==0.2.0
 pyswarms==1.3.0
 h5py>=2.10.0
 SQLAlchemy==1.3.20
-scipy>=1.5.1
+scipy>=1.5.4,<=1.8.1
 astunparse==1.6.3
 xxhash==3.0.0

test_autofit/config/general.yaml

@@ -3,6 +3,9 @@ analysis:
 hpc:
   hpc_mode: false                   # If True, use HPC mode, which disables GUI visualization, logging to screen and other settings which are not suited to running on a super computer.
   iterations_per_update: 5000       # The number of iterations between every update (visualization, results output, etc) in HPC mode.
+inversion:
+  check_reconstruction: true        # If True, the inversion's reconstruction is checked to ensure the solution of a meshs's mapper is not an invalid solution where the values are all the same.
+  reconstruction_vmax_factor: 0.5   # Plots of an Inversion's reconstruction use the reconstructed data's bright value multiplied by this factor.  
 model:
   ignore_prior_limits: false        # If ``True`` the limits applied to priors will be ignored, where limits set upper / lower limits. This stops PriorLimitException's from being raised.
 output:
@@ -21,6 +24,7 @@ profiling:
   should_profile: false             # If True, the ``profile_log_likelihood_function()`` function of an analysis class is called throughout a model-fit, profiling run times.
   repeats: 1                        # The number of repeat function calls used to measure run-times when profiling.
 test:
-  check_preloads: false             # If True, perform a sanity check that the likelihood using preloads is identical to the likelihood not using preloads.
+  check_preloads: false
+  preloads_check_threshold: 0.1     # If the figure of merit of a fit with and without preloads is greater than this threshold, the check preload test fails and an exception raised for a model-fit.              # If True, perform a sanity check that the likelihood using preloads is identical to the likelihood not using preloads.
   exception_override: false
   parallel_profile: false

test_autofit/non_linear/test_analysis.py

@@ -13,6 +13,7 @@
 class Analysis(af.Analysis):
     def __init__(self):
         self.did_visualise = False
+        self.did_visualise_combined = False
         self.did_profile = False
 
     def log_likelihood_function(self, instance):
@@ -23,6 +24,10 @@ def visualize(self, paths: AbstractPaths, instance, during_analysis):
         os.makedirs(paths.image_path)
         open(f"{paths.image_path}/image.png", "w+").close()
 
+    def visualize_combined(self, analyses, paths: AbstractPaths, instance, during_analysis):
+
+        self.did_visualise_combined = True
+
     def profile_log_likelihood_function(self, paths: AbstractPaths, instance):
         self.did_profile = True
 
@@ -37,6 +42,17 @@ def test_visualise():
     assert analysis_2.did_visualise is True
 
 
+def test_visualise_combined():
+    analysis_1 = Analysis()
+    analysis_2 = Analysis()
+
+    (analysis_1 + analysis_2).visualize_combined(af.DirectoryPaths(), None, None)
+
+    assert analysis_1.did_visualise_combined is True
+    assert analysis_2.did_visualise_combined is False
+
+
+
 def test__profile_log_likelihood():
     analysis_1 = Analysis()
     analysis_2 = Analysis()