From edf856500d496f36f7a2aef86f043780e380aef1 Mon Sep 17 00:00:00 2001 From: Oliver Ruebel Date: Wed, 7 Jul 2021 14:37:03 -0700 Subject: [PATCH] Add icephys meta pt 2 (#1349) Use extension in docs to simplify linking to common targets Move advanced data I/O tutorials to their own section and move parallel I/O tutorial Update Changelog for tutorials Updated deprecation warning in SweepTable tutorial Fix minor spelling error in make_test_files Several corrections and enhancments for the icephys tutorial Moved functions to create icephys test file from tests to pynwb Moved functions to create icephys test file from tests to pynwb Start for new tutorial to show conversion of icephys tables to pandas dataframes Test to_hierarchical_dataframe and to_denormalized_dataframe functions Updated tutorial to convert icephys tabels to pandas Fix minor spelling error in NWBFile docstring Add TimeseriesReferenceVectorData.get method to mask missing values on load Update IntracellularRecordingsTable. Update add_recording defaults for startindex and count to None and update to_dataframe method to handle missing values in TimeSeriesReferenceVectorData Update create_icephys_testfile to create testdata with missing stimuli Update existing tests to match the new behavior Update icephys tutorial for match new behavior Update icephys pandas tutorial to demonstrate new behavior Remove debug print statements Add table augmentation and queries to the icephys_pandas tutorial Fix flake8 Update to use keyword args Clarify text in icephys tutorial Fix flake8 on tutorial Fix flake8 Update to use dev branch of nwb-schema Set default name and default description for TimeSeriesReferenceVectorData added icephys_testutils to pynwb.testing.__init__ Added test for IntracellularRecordingsTable.to_dataframe with options Always use MaskedArrya in TimeSeriesReferenceVectorData instead of MaskedConstant Add tests for TimeSeriesReferenceVectorData.get Updated change log Fix minor docstring issue in TimeSeriesReferenceData.get Clarify docstring for TimeSeriesReferenceData.get Fix build warnings in docs (#1380) * Remove unused and outdated convert.rst from the docs Fix #1378 * Fix extra numbered footnote reference in ophys tutorial * Change code block highlighting from c to bash to avoid build warning * Fix duplicate target here warning in 3_spec_api.rst * Fix missing section label for crossreferencing between the extension tutorial and gallery * Updated Changelog Update CHANGELOG.md Update icephys.py Update docs/gallery/domain/plot_icephys.py Update plot_icephys.py Minor text fixes Update plot_icephys_pandas.py Minor text edits Update icephys_testutils.py Minor docstring edits Update file.py Fix typos Update docs/gallery/domain/plot_icephys.py Replace master with main Mention use of create_icephys_testfile function Co-authored-by: Ryan Ly Remove old comment in TimeSeriesReferenceVectorData Co-authored-by: Ryan Ly Fix typo in changelog Fix broken link target Fix flake8 in docs/gallery Minor text fixes Minor text and formatting fixes Update tests/unit/test_icephys_metadata_tables.py Co-authored-by: Ryan Ly Use namedtuple instead of numpy masked structed array to represent values of TimeSeriesReferenceVectorData Update tests/unit/test_icephys_metadata_tables.py Co-authored-by: Ryan Ly Update tests/unit/test_icephys_metadata_tables.py Co-authored-by: Ryan Ly Update tests/unit/test_icephys_metadata_tables.py Co-authored-by: Ryan Ly Update tests/unit/test_icephys_metadata_tables.py Co-authored-by: Ryan Ly Update tests/unit/test_icephys_metadata_tables.py Co-authored-by: Ryan Ly Update tests/unit/test_icephys_metadata_tables.py Co-authored-by: Ryan Ly Update tests/unit/test_icephys_metadata_tables.py Co-authored-by: Ryan Ly Update tests/unit/test_icephys_metadata_tables.py Co-authored-by: Ryan Ly Fix bad indent in test Update base.py - minor text edits Use hdmf 3.1.1 Enhance introspection, slicing, and data addition for TimeSeriesReference and TimeSeriesReferenceVectorData Updated icephys query tutorial to use latest get_linked_tables behavior and improve rendering Fix flake8 in gallery Fix spelling in error message Update src/pynwb/base.py Co-authored-by: Ryan Ly Update src/pynwb/base.py Co-authored-by: Ryan Ly Update src/pynwb/base.py Co-authored-by: Ryan Ly Update src/pynwb/base.py Co-authored-by: Ryan Ly Update src/pynwb/base.py Co-authored-by: Ryan Ly Update src/pynwb/base.py Co-authored-by: Ryan Ly Fix bug in TimeSeriesReference.timestamps and add comments Fix gallery tests to handle allensdk pinning pynwb/hdmf Fix rebase --- CHANGELOG.md | 47 +- docs/gallery/advanced_io/README.txt | 7 + .../h5dataio.py} | 72 +- .../iterative_write.py | 0 .../{general => advanced_io}/linking_data.py | 0 docs/gallery/advanced_io/parallelio.py | 81 ++ docs/gallery/domain/brain_observatory.py | 2 + docs/gallery/domain/icephys.py | 7 +- docs/gallery/domain/ophys.py | 2 +- docs/gallery/domain/plot_icephys.py | 509 +++++----- docs/gallery/domain/plot_icephys_pandas.py | 441 +++++++++ docs/source/conf.py | 11 +- .../2_create_extension_spec_walkthrough.rst | 14 +- .../source/extensions_tutorial/3_spec_api.rst | 33 +- .../extensions_tutorial_home.rst | 2 + docs/source/figures/gallery_thumbnails.pptx | Bin 2258651 -> 2353616 bytes .../gallery_thumbnails_advnaced_hdf5_io.png | Bin 49409 -> 0 bytes .../figures/gallery_thumbnails_h5dataio.png | Bin 0 -> 45517 bytes .../gallery_thumbnails_icephys_pandas.png | Bin 0 -> 182881 bytes .../gallery_thumbnails_linking_data.png | Bin 72354 -> 89246 bytes .../figures/gallery_thumbnails_parallelio.png | Bin 0 -> 103501 bytes docs/source/figures/plot_icephys_figures.pptx | Bin 521005 -> 541967 bytes .../figures/plot_icephys_table_hierarchy.png | Bin 258505 -> 476000 bytes docs/source/make_a_release.rst | 2 +- docs/source/tutorial_source/convert.rst | 36 - requirements-doc.txt | 2 +- requirements-min.txt | 2 +- requirements.txt | 2 +- setup.py | 2 +- src/pynwb/base.py | 215 ++++- src/pynwb/file.py | 8 +- src/pynwb/icephys.py | 75 +- src/pynwb/nwb-schema | 2 +- src/pynwb/testing/__init__.py | 1 + src/pynwb/testing/icephys_testutils.py | 177 ++++ src/pynwb/testing/make_test_files.py | 2 +- tests/unit/test_base.py | 219 ++++- tests/unit/test_core.py | 1 - tests/unit/test_icephys_metadata_tables.py | 899 +++++++++--------- tox.ini | 31 +- 40 files changed, 2010 insertions(+), 894 deletions(-) create mode 100644 docs/gallery/advanced_io/README.txt rename docs/gallery/{general/advanced_hdf5_io.py => advanced_io/h5dataio.py} (77%) rename docs/gallery/{general => advanced_io}/iterative_write.py (100%) rename docs/gallery/{general => advanced_io}/linking_data.py (100%) create mode 100644 docs/gallery/advanced_io/parallelio.py create mode 100644 docs/gallery/domain/plot_icephys_pandas.py delete mode 100644 docs/source/figures/gallery_thumbnails_advnaced_hdf5_io.png create mode 100644 docs/source/figures/gallery_thumbnails_h5dataio.png create mode 100644 docs/source/figures/gallery_thumbnails_icephys_pandas.png create mode 100644 docs/source/figures/gallery_thumbnails_parallelio.png delete mode 100644 docs/source/tutorial_source/convert.rst create mode 100644 src/pynwb/testing/icephys_testutils.py diff --git a/CHANGELOG.md b/CHANGELOG.md index c50c53f53..1e99bde3a 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,31 +2,46 @@ ## PyNWB 2.0.0 (Upcoming) -### New features -- -- -- -- Drop Python 3.6 support, add Python 3.9 support. @rly (#1377) -- Update requirements to allow compatibility with HDMF 3 and h5py 3. @rly (#1377) +### Breaking changes: +- ``SweepTable`` has been deprecated in favor of the new icephys metadata tables. Use of ``SweepTable`` + is still possible but no longer recommended. @oruebel (#1349) + +### New features: +- Added new intracellular electrophysiology hierarchical table structure from ndx-icephys-meta to NWB core. + This includes the new types ``TimeSeriesReferenceVectorData``, ``IntracellularRecordingsTable``, + ``SimultaneousRecordingsTable``, ``SequentialRecordingsTable``, ``RepetitionsTable`` and + ``ExperimentalConditionsTable`` as well as corresponding updates to ``NWBFile`` to support interaction + with the new tables. @oruebel (#1349) +- Added support for nwb-schema 2.4.0. See [Release Notes](https://nwb-schema.readthedocs.io/en/latest/format_release_notes.html) + for more details. @oruebel (#1349) +- Dropped Python 3.6 support, added Python 3.9 support. @rly (#1377) +- Updated requirements to allow compatibility with HDMF 3 and h5py 3. @rly (#1377) + +### Tutorial enhancements: +- Added new tutorial for intracellular electrophysiology to describe the use of the new metadata tables + and declared the previous tutoral using ``SweepTable`` as deprecated. @oruebel (#1349) +- Added new tutorial for querying intracellular electrophysiology metadata + (``docs/gallery/domain/plot_icephys_pandas.py``). @oruebel (#1349, #1383) +- Added thumbnails for tutorials to improve presentation of online docs. @oruebel (#1349) +- Used `sphinx.ext.extlinks` extension in docs to simplify linking to common targets. @oruebel (#1349) +- Created new section for advanced I/O tutorials and moved parallel I/O tutorial to its own file. @oruebel (#1349) ### Minor new features: - Add RRID for citing PyNWB to the docs. @oruebel (#1372) - Update CI and tests to handle deprecations in libraries. @rly (#1377) +- Add test utilities for icephys (``pynwb.testing.icephys_testutils``) to ease creation of test data + for tests and tutorials. @oruebel (#1349, #1383) ### Bug fixes: -- Enforce electrode ID uniqueness during insertion into table. @CodyCBakerPhD (#1344) -- Fix integration tests with invalid test data that will be caught by future hdmf validator version. +- Updated behavior of ``make clean`` command for docs to ensure tutorial files are cleaned up. @oruebel (#1349) +- Enforced electrode ID uniqueness during insertion into table. @CodyCBakerPhD (#1344) +- Fixed integration tests with invalid test data that will be caught by future hdmf validator version. @dsleiter, @rly (#1366, #1376) - -### Bug fixes -- -- -- -- +- Fixed build warnings in docs @oruebel (#1380) ## PyNWB 1.5.1 (May 24, 2021) -## Bug fix: +### Bug fixes: - Raise minimum version of pandas from 0.23 to 1.0.5 to be compatible with numpy 1.20, and raise minimum version of HDMF to use the corresponding change in HDMF. @rly (#1363) - Update documentation and update structure of requirements files. @rly (#1363) @@ -69,7 +84,7 @@ - Add capability to add a row to a column after IO. - Add method `AbstractContainer.get_fields_conf`. - Add functionality for storing external resource references. - - Add method `hdmf.utils.get_docval_macro` to get a tuple of the current values for a docval_macro, e.g., 'array_data' + - Add method `hdmf.utils.get_docval_macro` to get a tuple of the current values for a docval_macro, e.g., 'array_data' and 'scalar_data'. - `DynamicTable` can be automatically generated using `get_class`. Now the HDMF API can read files with extensions that contain a DynamicTable without needing to import the extension first. diff --git a/docs/gallery/advanced_io/README.txt b/docs/gallery/advanced_io/README.txt new file mode 100644 index 000000000..134df19b3 --- /dev/null +++ b/docs/gallery/advanced_io/README.txt @@ -0,0 +1,7 @@ + + +.. _general-tutorials: + + +Advanced I/O +------------ diff --git a/docs/gallery/general/advanced_hdf5_io.py b/docs/gallery/advanced_io/h5dataio.py similarity index 77% rename from docs/gallery/general/advanced_hdf5_io.py rename to docs/gallery/advanced_io/h5dataio.py index c4092b416..f184e0719 100644 --- a/docs/gallery/general/advanced_hdf5_io.py +++ b/docs/gallery/advanced_io/h5dataio.py @@ -1,6 +1,6 @@ ''' -Advanced HDF5 I/O -===================== +Defining HDF5 Dataset I/O Settings (chunking, compression, etc.) +================================================================ The HDF5 storage backend supports a broad range of advanced dataset I/O options, such as, chunking and compression. Here we demonstrate how to use these features @@ -18,7 +18,7 @@ # Before we get started, lets create an NWBFile for testing so that we can add our data to it. # -# sphinx_gallery_thumbnail_path = 'figures/gallery_thumbnails_advnaced_hdf5_io.png' +# sphinx_gallery_thumbnail_path = 'figures/gallery_thumbnails_h5dataio.png' from datetime import datetime from dateutil.tz import tzlocal from pynwb import NWBFile @@ -217,72 +217,6 @@ # will be ignored as the h5py.Dataset will either be linked to or copied as on write. # -#################### -# Parallel I/O using MPI -# ---------------------- -# -# The HDF5 storage backend supports parallel I/O using the Message Passing Interface (MPI). -# Using this feature requires that you install ``hdf5`` and ``h5py`` against an MPI driver, and you -# install ``mpi4py``. The basic installation of pynwb will not work. Setup can be tricky, and -# is outside the scope of this tutorial (for now), and the following assumes that you have -# HDF5 installed in a MPI configuration. Here we: -# -# 1. **Instantiate a dataset for parallel write**: We create TimeSeries with 4 timestamps that we -# will write in parallel -# -# 2. **Write to that file in parallel using MPI**: Here we assume 4 MPI ranks while each rank writes -# the data for a different timestamp. -# -# 3. **Read from the file in parallel using MPI**: Here each of the 4 MPI ranks reads one time -# step from the file -# -# .. code-block:: python -# -# from mpi4py import MPI -# import numpy as np -# from dateutil import tz -# from pynwb import NWBHDF5IO, NWBFile, TimeSeries -# from datetime import datetime -# from hdmf.data_utils import DataChunkIterator -# -# start_time = datetime(2018, 4, 25, 2, 30, 3, tzinfo=tz.gettz('US/Pacific')) -# fname = 'test_parallel_pynwb.nwb' -# rank = MPI.COMM_WORLD.rank # The process ID (integer 0-3 for 4-process run) -# -# # Create file on one rank. Here we only instantiate the dataset we want to -# # write in parallel but we do not write any data -# if rank == 0: -# nwbfile = NWBFile('aa', 'aa', start_time) -# data = DataChunkIterator(data=None, maxshape=(4,), dtype=np.dtype('int')) -# -# nwbfile.add_acquisition(TimeSeries('ts_name', description='desc', data=data, -# rate=100., unit='m')) -# with NWBHDF5IO(fname, 'w') as io: -# io.write(nwbfile) -# -# # write to dataset in parallel -# with NWBHDF5IO(fname, 'a', comm=MPI.COMM_WORLD) as io: -# nwbfile = io.read() -# print(rank) -# nwbfile.acquisition['ts_name'].data[rank] = rank -# -# # read from dataset in parallel -# with NWBHDF5IO(fname, 'r', comm=MPI.COMM_WORLD) as io: -# print(io.read().acquisition['ts_name'].data[rank]) - -#################### -# To specify details about chunking, compression and other HDF5-specific I/O options, -# we can wrap data via ``H5DataIO``, e.g, -# -# .. code-block:: python -# -# data = H5DataIO(DataChunkIterator(data=None, maxshape=(100000, 100), -# dtype=np.dtype('float')), -# chunks=(10, 10), maxshape=(None, None)) -# -# would initialize your dataset with a shape of (100000, 100) and maxshape of (None, None) -# and your own custom chunking of (10, 10). - #################### # Disclaimer # ---------------- diff --git a/docs/gallery/general/iterative_write.py b/docs/gallery/advanced_io/iterative_write.py similarity index 100% rename from docs/gallery/general/iterative_write.py rename to docs/gallery/advanced_io/iterative_write.py diff --git a/docs/gallery/general/linking_data.py b/docs/gallery/advanced_io/linking_data.py similarity index 100% rename from docs/gallery/general/linking_data.py rename to docs/gallery/advanced_io/linking_data.py diff --git a/docs/gallery/advanced_io/parallelio.py b/docs/gallery/advanced_io/parallelio.py new file mode 100644 index 000000000..a91e567d5 --- /dev/null +++ b/docs/gallery/advanced_io/parallelio.py @@ -0,0 +1,81 @@ +''' +Parallel I/O using MPI +====================== + +The HDF5 storage backend supports parallel I/O using the Message Passing Interface (MPI). +Using this feature requires that you install ``hdf5`` and ``h5py`` against an MPI driver, and you +install ``mpi4py``. The basic installation of pynwb will not work. Setup can be tricky, and +is outside the scope of this tutorial (for now), and the following assumes that you have +HDF5 installed in a MPI configuration. +''' + +# sphinx_gallery_thumbnail_path = 'figures/gallery_thumbnails_parallelio.png' + +#################### +# Here we: +# +# 1. **Instantiate a dataset for parallel write**: We create TimeSeries with 4 timestamps that we +# will write in parallel +# +# 2. **Write to that file in parallel using MPI**: Here we assume 4 MPI ranks while each rank writes +# the data for a different timestamp. +# +# 3. **Read from the file in parallel using MPI**: Here each of the 4 MPI ranks reads one time +# step from the file +# +# .. code-block:: python +# +# from mpi4py import MPI +# import numpy as np +# from dateutil import tz +# from pynwb import NWBHDF5IO, NWBFile, TimeSeries +# from datetime import datetime +# from hdmf.data_utils import DataChunkIterator +# +# start_time = datetime(2018, 4, 25, 2, 30, 3, tzinfo=tz.gettz('US/Pacific')) +# fname = 'test_parallel_pynwb.nwb' +# rank = MPI.COMM_WORLD.rank # The process ID (integer 0-3 for 4-process run) +# +# # Create file on one rank. Here we only instantiate the dataset we want to +# # write in parallel but we do not write any data +# if rank == 0: +# nwbfile = NWBFile('aa', 'aa', start_time) +# data = DataChunkIterator(data=None, maxshape=(4,), dtype=np.dtype('int')) +# +# nwbfile.add_acquisition(TimeSeries('ts_name', description='desc', data=data, +# rate=100., unit='m')) +# with NWBHDF5IO(fname, 'w') as io: +# io.write(nwbfile) +# +# # write to dataset in parallel +# with NWBHDF5IO(fname, 'a', comm=MPI.COMM_WORLD) as io: +# nwbfile = io.read() +# print(rank) +# nwbfile.acquisition['ts_name'].data[rank] = rank +# +# # read from dataset in parallel +# with NWBHDF5IO(fname, 'r', comm=MPI.COMM_WORLD) as io: +# print(io.read().acquisition['ts_name'].data[rank]) + +#################### +# To specify details about chunking, compression and other HDF5-specific I/O options, +# we can wrap data via ``H5DataIO``, e.g, +# +# .. code-block:: python +# +# data = H5DataIO(DataChunkIterator(data=None, maxshape=(100000, 100), +# dtype=np.dtype('float')), +# chunks=(10, 10), maxshape=(None, None)) +# +# would initialize your dataset with a shape of (100000, 100) and maxshape of (None, None) +# and your own custom chunking of (10, 10). + +#################### +# Disclaimer +# ---------------- +# +# External links included in the tutorial are being provided as a convenience and for informational purposes only; +# they do not constitute an endorsement or an approval by the authors of any of the products, services or opinions of +# the corporation or organization or individual. The authors bear no responsibility for the accuracy, legality or +# content of the external site or for that of subsequent links. Contact the external site for answers to questions +# regarding its content. diff --git a/docs/gallery/domain/brain_observatory.py b/docs/gallery/domain/brain_observatory.py index 07cbfc9b4..11e490251 100644 --- a/docs/gallery/domain/brain_observatory.py +++ b/docs/gallery/domain/brain_observatory.py @@ -10,6 +10,8 @@ # physiology submodule (pynwb.ophys). We will use the allensdk as a read API, while leveraging the pynwb data model and # write api to transform and write the data back to disk. # +# .. note: Using the latest allensdk package requires Python 3.6 or higher. + ######################################## # .. raw:: html # :url: https://gist.githubusercontent.com/nicain/82e6b3d8f9ff5b85ef01a582e41e2389/raw/ diff --git a/docs/gallery/domain/icephys.py b/docs/gallery/domain/icephys.py index e096edac8..e1c4907e5 100644 --- a/docs/gallery/domain/icephys.py +++ b/docs/gallery/domain/icephys.py @@ -8,8 +8,8 @@ The following tutorial describes storage of intracellular electrophysiology data in NWB using the SweepTable to manage recordings. -.. note:: - The use of SweepTable has been deprecated as of PyNWB >v1.4 in favor of the new hierarchical +.. warning:: + The use of SweepTable has been deprecated as of PyNWB >v2.0 in favor of the new hierarchical intracellular electrophysiology metadata tables to allow for a more complete description of intracellular electrophysiology experiments. See the :doc:`Intracellular electrophysiology ` tutorial for details. @@ -190,8 +190,7 @@ # PatchClampSeries which belongs to a certain sweep number via # :py:meth:`~pynwb.icephys.SweepTable.get_series`. # -# The following call will return the voltage clamp data, of two timeseries +# The following call will return the voltage clamp data of two timeseries # consisting of acquisition and stimulus, from sweep 1. series = nwbfile.sweep_table.get_series(1) -print("hello world") diff --git a/docs/gallery/domain/ophys.py b/docs/gallery/domain/ophys.py index ed1a1aecb..f457eb8d9 100644 --- a/docs/gallery/domain/ophys.py +++ b/docs/gallery/domain/ophys.py @@ -120,7 +120,7 @@ # Storing fluorescence measurements # --------------------------------- # -# Now that ROIs are stored, you can store fluorescence (or dF/F [#]_) data for these regions of interest. +# Now that ROIs are stored, you can store fluorescence (or dF/F) data for these regions of interest. # This type of data is stored using the :py:class:`~pynwb.ophys.RoiResponseSeries` class. You will not need # to instantiate this class directly to create objects of this type, but it is worth noting that this is the # class you will work with after you read data back in. diff --git a/docs/gallery/domain/plot_icephys.py b/docs/gallery/domain/plot_icephys.py index b1adcc247..4e9c8f774 100644 --- a/docs/gallery/domain/plot_icephys.py +++ b/docs/gallery/domain/plot_icephys.py @@ -3,7 +3,7 @@ .. _icephys_tutorial_new: Intracellular electrophysiology -================================================================== +=============================== The following tutorial describes storage of intracellular electrophysiology data in NWB. NWB supports storage of the time series describing the stimulus and response, information @@ -11,14 +11,10 @@ experiment. .. note:: For a video tutorial on intracellular electrophysiology in NWB see also the - `Intracellular electrophysiology basics in NWB `_ - and `Intracellular electrophysiology structured metadata in NWB `_ - tutorials as part of the `NWB Course at the INCF Training Space `_. - - -To describe the organization of intracellular experiments the metadata is organized -hierarchically in a sequence of tables. All of the tables are so-called DynamicTables -enabling users to add columns for custom metadata. + :incf_lesson:`Intracellular electrophysiology basics in NWB ` and + :incf_lesson:`Intracellular ephys metadata ` + tutorials as part of the :incf_collection:`NWB Course ` + at the INCF Training Space. .. figure:: ../../figures/plot_icephys_table_hierarchy.png :width: 700 @@ -27,6 +23,15 @@ Illustration of the hierarchy of metadata tables used to describe the organization of intracellular electrophysiology experiments. +Recordings of intracellular electrophysiology stimuli and responses are represented +with subclasses of :py:class:`~pynwb.icephys.PatchClampSeries` using the +:py:class:`~pynwb.icephys.IntracellularElectrode` and :py:class:`~pynwb.device.Device` +type to describe the electrode and device used. + +To describe the organization of intracellular experiments, the metadata is organized +hierarchically in a sequence of tables. All of the tables are so-called DynamicTables +enabling users to add columns for custom metadata. + - :py:class:`~pynwb.icephys.IntracellularRecordingsTable` relates electrode, stimulus and response pairs and describes metadata specific to individual recordings. - :py:class:`~pynwb.icephys.SimultaneousRecordingsTable` groups intracellular @@ -51,9 +56,25 @@ - :py:class:`~pynwb.icephys.ExperimentalConditionsTable` groups repetitions of intracellular recording from the :py:class:`~pynwb.icephys.RepetitionsTable` together that belong to the same experimental conditions. + +Storing data in hierarchical tables has the advantage that it allows us to avoid duplication of +metadata. E.g., for a single experiment we only need to describe the metadata that is constant +across an experimental condition as a single row in the :py:class:`~pynwb.icephys.ExperimentalConditionsTable` +without having to replicate the same information across all repetitions and sequential-, simultaneous-, and +individual intracellular recordings. For analysis, this means that we can easily focus on individual +aspects of an experiment while still being able to easily access information about information from +related tables. + +.. note:: All of the above mentioned metadata tables are optional and are created automatically + by the :py:class:`~pynwb.file.NWBFile` class the first time data is being + added to a table via the corresponding add functions. However, as tables at higher + levels of the hierarchy link to the other tables that are lower in the hierarchy, + we may only exclude tables from the top of the hierarchy. This means, for example, + a file containing a :py:class:`~pynwb.icephys.SimultaneousRecordingsTable` then + must also always contain a corresponding :py:class:`~pynwb.icephys.IntracellularRecordingsTable`. ''' -####################### +##################################################################### # Imports used in the tutorial # ------------------------------ @@ -63,7 +84,9 @@ from dateutil.tz import tzlocal import numpy as np import pandas -pandas.set_option("display.max_colwidth", 30) # Set Pandas rendering option to avoid very wide cells +# Set pandas rendering option to avoid very wide tables in the html docs +pandas.set_option("display.max_colwidth", 30) +pandas.set_option("display.max_rows", 10) # Import main NWB file class from pynwb import NWBFile @@ -74,20 +97,25 @@ # Import additional core datatypes used in the example from pynwb.core import DynamicTable, VectorData -####################### -# A brief example script -# ------------------------------ +##################################################################### +# A brief example +# --------------- +# +# The following brief example provides a quick overview of the main steps +# to create an NWBFile for intracelluar electrophysiology data. We then +# discuss the individual steps in more detail afterwards. # -# The following shows a brief example script, providing a brief overview of the -# main steps to create and NWB icephys file. A more detailed dicussion of the -# various steps is then provided afterwards. To avoid collisions between this -# example script and the more detailed discussion we prefix -# all variables in the example script with ``ex_``. +# .. note: +# To avoid collisions between this example script and the more +# detailed discussion we prefix all variables in the example +# script with ``ex_``. # Create an ICEphysFile -ex_nwbfile = NWBFile(session_description='my first recording', - identifier='EXAMPLE_ID', - session_start_time=datetime.now(tzlocal())) +ex_nwbfile = NWBFile( + session_description='my first recording', + identifier='EXAMPLE_ID', + session_start_time=datetime.now(tzlocal()) +) # Add a device ex_device = ex_nwbfile.create_device(name='Heka ITC-1600') @@ -96,44 +124,52 @@ ex_electrode = ex_nwbfile.create_icephys_electrode( name="elec0", description='a mock intracellular electrode', - device=ex_device) + device=ex_device +) # Create an ic-ephys stimulus -ex_stimulus = VoltageClampStimulusSeries(name="stimulus", - data=[1, 2, 3, 4, 5], - starting_time=123.6, - rate=10e3, - electrode=ex_electrode, - gain=0.02) +ex_stimulus = VoltageClampStimulusSeries( + name="stimulus", + data=[1, 2, 3, 4, 5], + starting_time=123.6, + rate=10e3, + electrode=ex_electrode, + gain=0.02 +) # Create an ic-response -ex_response = VoltageClampSeries(name='response', - data=[0.1, 0.2, 0.3, 0.4, 0.5], - conversion=1e-12, - resolution=np.nan, - starting_time=123.6, - rate=20e3, - electrode=ex_electrode, - gain=0.02, - capacitance_slow=100e-12, - resistance_comp_correction=70.0) +ex_response = VoltageClampSeries( + name='response', + data=[0.1, 0.2, 0.3, 0.4, 0.5], + conversion=1e-12, + resolution=np.nan, + starting_time=123.6, + rate=20e3, + electrode=ex_electrode, + gain=0.02, + capacitance_slow=100e-12, + resistance_comp_correction=70.0 +) # (A) Add an intracellular recording to the file # NOTE: We can optionally define time-ranges for the stimulus/response via -# the corresponding option _start_index and _index_count parameters. +# the corresponding optional _start_index and _index_count parameters. # NOTE: It is allowed to add a recording with just a stimulus or a response # NOTE: We can add custom columns to any of our tables in steps (A)-(E) -ex_ir_index = ex_nwbfile.add_intracellular_recording(electrode=ex_electrode, - stimulus=ex_stimulus, - response=ex_response) +ex_ir_index = ex_nwbfile.add_intracellular_recording( + electrode=ex_electrode, + stimulus=ex_stimulus, + response=ex_response +) -# (B) Add a list of sweeps to the sweeps table +# (B) Add a list of sweeps to the simultaneous recordings table ex_sweep_index = ex_nwbfile.add_icephys_simultaneous_recording(recordings=[ex_ir_index, ]) # (C) Add a list of simultaneous recordings table indices as a sequential recording ex_sequence_index = ex_nwbfile.add_icephys_sequential_recording( simultaneous_recordings=[ex_sweep_index, ], - stimulus_type='square') + stimulus_type='square' +) # (D) Add a list of sequential recordings table indices as a repetition run_index = ex_nwbfile.add_icephys_repetition(sequential_recordings=[ex_sequence_index, ]) @@ -142,7 +178,7 @@ ex_nwbfile.add_icephys_experimental_condition(repetitions=[run_index, ]) # Write our test file -ex_testpath = "ex_test_icephys_file.h5" +ex_testpath = "ex_test_icephys_file.nwb" with NWBHDF5IO(ex_testpath, 'w') as io: io.write(ex_nwbfile) @@ -161,17 +197,17 @@ except ImportError: # ignore in case hdmf_docutils is not installed pass -####################### +##################################################################### # Now that we have seen a brief example, we are going to start from the beginning and # go through each of the steps in more detail in the following sections. -####################### -# Creating an icephys NWB file -# ------------------------------ +##################################################################### +# Creating an NWB file for Intracellular electrophysiology +# -------------------------------------------------------- # # When creating an NWB file, the first step is to create the :py:class:`~pynwb.file.NWBFile`. The first -# argument is is a brief description of the dataset. +# argument is a brief description of the dataset. # Create the file nwbfile = NWBFile( @@ -182,19 +218,20 @@ lab='Bag End Laboratory', institution='University of Middle Earth at the Shire', experiment_description='I went on an adventure with thirteen dwarves to reclaim vast treasures.', - session_id='LONELYMTN') + session_id='LONELYMTN' +) -####################### +##################################################################### # Device metadata # ^^^^^^^^^^^^^^^ # # Device metadata is represented by :py:class:`~pynwb.device.Device` objects. -# To create a device, you can use the :py:class:`~pynwb.device.Device` instance method +# To create a device, you can use the :py:class:`~pynwb.file.NWBFile` instance method # :py:meth:`~pynwb.file.NWBFile.create_device`. device = nwbfile.create_device(name='Heka ITC-1600') -####################### +##################################################################### # Electrode metadata # ^^^^^^^^^^^^^^^^^^ # @@ -202,11 +239,13 @@ # To create an electrode group, you can use the :py:class:`~pynwb.file.NWBFile` instance method # :py:meth:`~pynwb.file.NWBFile.create_icephys_electrode`. -electrode = nwbfile.create_icephys_electrode(name="elec0", - description='a mock intracellular electrode', - device=device) +electrode = nwbfile.create_icephys_electrode( + name="elec0", + description='a mock intracellular electrode', + device=device +) -####################### +##################################################################### # Stimulus and response data # ^^^^^^^^^^^^^^^^^^^^^^^^^^ # @@ -228,29 +267,33 @@ # # Below we create a simple example stimulus/response recording data pair. -# Create an example icephys stimulus. -stimulus = VoltageClampStimulusSeries(name="ccss", - data=[1, 2, 3, 4, 5], - starting_time=123.6, - rate=10e3, - electrode=electrode, - gain=0.02, - sweep_number=np.uint64(15)) +# Create an example icephys stimulus. +stimulus = VoltageClampStimulusSeries( + name="ccss", + data=[1, 2, 3, 4, 5], + starting_time=123.6, + rate=10e3, + electrode=electrode, + gain=0.02, + sweep_number=np.uint64(15) +) # Create and icephys response -response = VoltageClampSeries(name='vcs', - data=[0.1, 0.2, 0.3, 0.4, 0.5], - conversion=1e-12, - resolution=np.nan, - starting_time=123.6, - rate=20e3, - electrode=electrode, - gain=0.02, - capacitance_slow=100e-12, - resistance_comp_correction=70.0, - sweep_number=np.uint64(15)) - -####################### +response = VoltageClampSeries( + name='vcs', + data=[0.1, 0.2, 0.3, 0.4, 0.5], + conversion=1e-12, + resolution=np.nan, + starting_time=123.6, + rate=20e3, + electrode=electrode, + gain=0.02, + capacitance_slow=100e-12, + resistance_comp_correction=70.0, + sweep_number=np.uint64(15) +) + +##################################################################### # Adding an intracellular recording # --------------------------------- # @@ -260,7 +303,7 @@ # # .. figure:: ../../figures/plot_icephys_intracellular_recordings_table.png # :width: 700 -# :alt: IntracellularRecordingsTabl +# :alt: IntracellularRecordingsTable # # Illustration of the structure of the IntracellularRecordingsTable # @@ -271,22 +314,20 @@ # :py:meth:`~pynwb.file.NWBFile.add_intracellular_recording`) returns the integer index of the # newly created row. The ``rowindex`` is used in subsequent tables that reference rows in our table. -rowindex = nwbfile.add_intracellular_recording(electrode=electrode, - stimulus=stimulus, - response=response, - id=10) +rowindex = nwbfile.add_intracellular_recording( + electrode=electrode, + stimulus=stimulus, + response=response, + id=10 +) -####################### -# .. note:: All of the above mentioned metadata tables are optional and are created automatically -# by the :py:class:`~pynwb.file.NWBFile` class the first time data is being -# added to a table via the corresponding add functions. -# +##################################################################### # .. note:: Since :py:meth:`~pynwb.file.NWBFile.add_intracellular_recording` can automatically add -# the objects to the NWBFile we do not need to separately call +# the objects to the NWBFile we do not need to separately call # :py:meth:`~pynwb.file.NWBFile.add_stimulus` and :py:meth:`~pynwb.file.NWBFile.add_acquistion` # to add our stimulus and response, but it is still fine to do so. # -# .. note:: The ``id`` parameter in the call is optional and if the ``id`` is omitted then PyNWB will +# .. note:: The ``id`` parameter in the call is optional and if the ``id`` is omitted then PyNWB will # automatically number recordings in sequences (i.e., id is the same as the rowindex) # # .. note:: The IntracellularRecordigns, SimultaneousRecordings, SequentialRecordingsTable, @@ -295,93 +336,105 @@ # results in a ValueError. # -####################### +##################################################################### # .. note:: We may optionally also specify the relevant time range for a stimulus and/or response as part of # the intracellular_recording. This is useful, e.g., in case where the recording of the stimulus -# and response do not align (e.g., in case that recording of the response started before +# and response do not align (e.g., in case the recording of the response started before # the recording of the stimulus). -rowindex2 = nwbfile.add_intracellular_recording(electrode=electrode, - stimulus=stimulus, - stimulus_start_index=1, - stimulus_index_count=3, - response=response, - response_start_index=2, - response_index_count=3, - id=11) - -####################### +rowindex2 = nwbfile.add_intracellular_recording( + electrode=electrode, + stimulus=stimulus, + stimulus_start_index=1, + stimulus_index_count=3, + response=response, + response_start_index=2, + response_index_count=3, + id=11 +) + +##################################################################### # .. note:: A recording may optionally also consist of just an electrode and stimulus or electrode -# and response, but at least one of stimulus or response are required. If either stimulus or response -# are missing, then the stimulus and response are internally set ot the same TimeSeries and the -# start_index and index_count for the missing parameter are set to -1 - -rowindex3 = nwbfile.add_intracellular_recording(electrode=electrode, - response=response, - id=12) - -####################### +# and response, but at least one of stimulus or response is required. If either stimulus or response +# is missing, then the stimulus and response are internally set to the same TimeSeries and the +# start_index and index_count for the missing parameter are set to -1. When retrieving +# data from the :py:class:`~pynwb.base.TimeSeriesReferenceVectorData`, the missing values +# will be represented via masked numpy arrays, i.e., as masked values in a +# ``numpy.ma.masked_array`` or as a ``np.ma.core.MaskedConstant``. + +rowindex3 = nwbfile.add_intracellular_recording( + electrode=electrode, + response=response, + id=12 +) + +##################################################################### # .. warning:: For brevity we reused in the above example the same response and stimulus in # all rows of the intracellular_recordings. While this is allowed, in most practical # cases the stimulus and response will change between intracellular_recordings. -####################### +##################################################################### # Adding custom columns to the intracellular recordings table # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # -# We can add a column to the main intracellular recordings table as follows +# We can add a column to the main intracellular recordings table as follows. nwbfile.intracellular_recordings.add_column( name='recording_tag', data=['A1', 'A2', 'A3'], - description='String with a recording tag') - -####################### -# Similarly, we can also add custom columns to any of the subcategory tables, i.e., -# the electrodes, stimuli, and responses tables. All we need to do is indicate -# the name of the category we want to add the column to. -# + description='String with a recording tag' +) -nwbfile.intracellular_recordings.add_column( - name='voltage_threshold', - data=[0.1, 0.12, 0.13], - description='Just and example filter on the electrode', - category='electrodes') - -####################### +##################################################################### # The :py:class:`~pynwb.icephys.IntracellularRecordingsTable` table is not just a ``DynamicTable`` # but an ``AlignedDynamicTable`. The ``AlignedDynamicTable` type is itself a ``DynamicTable`` # that may contain an arbitrary number of additional ``DynamicTable``, each of which defines # a "category". This is similar to a table with "sub-headings". In the case of the -# :py:class:`~pynwb.icephys.IntracellularRecordingsTable` we have three predefined categories, -# i.e., electrodes, stimuli, and responses. We can now also dynamically add new categories to -# the table. As each category corresponds to a DynamicTable this means we have to create a +# :py:class:`~pynwb.icephys.IntracellularRecordingsTable`, we have three predefined categories, +# i.e., electrodes, stimuli, and responses. We can also dynamically add new categories to +# the table. As each category corresponds to a DynamicTable, this means we have to create a # new DynamicTable and add it to our table. -# Create a new DynamicTable for our category +# Create a new DynamicTable for our category that contains a location column of type VectorData +location_column = VectorData( + name='location', + data=['Mordor', 'Gondor', 'Rohan'], + description='Recording location in Middle Earth' +) + lab_category = DynamicTable( name='recording_lab_data', description='category table for lab-specific recording metadata', colnames=['location', ], - columns=[VectorData(name='location', - data=['Mordor', 'Gondor', 'Rohan'], - description='Recording location in Middle Earth')]) + columns=[location_column, ] +) # Add the table as a new category to our intracellular_recordings nwbfile.intracellular_recordings.add_category(category=lab_category) -# Note, the name of the category is name of the table, i.e. recording_lab_data +# Note, the name of the category is name of the table, i.e., 'recording_lab_data' -####################### -# .. note:: In an ``AlignedDynamicTable`` all category tables MUST align with the master table, +##################################################################### +# .. note:: In an ``AlignedDynamicTable`` all category tables MUST align with the main table, # i.e., all tables must have the same number of rows and rows are expected to # correspond to each other by index +##################################################################### +# We can also add custom columns to any of the subcategory tables, i.e., +# the electrodes, stimuli, and responses tables, and any custom subcategory tables. +# All we need to do is indicate the name of the category we want to add the column to. + +nwbfile.intracellular_recordings.add_column( + name='voltage_threshold', + data=[0.1, 0.12, 0.13], + description='Just an example column on the electrodes category table', + category='electrodes' +) -####################### +##################################################################### # Add a simultaneous recording # --------------------------------- # # Before adding a simultaneous recording, we will take a brief discourse to illustrate -# how we can add custom columns to tables before and after we have populated the table with data +# how we can add custom columns to tables before and after we have populated the table with data # # Define a custom column for a simultaneous recording before populating the table # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -394,31 +447,33 @@ print(nwbfile.icephys_simultaneous_recordings) -####################### +##################################################################### # The :py:class:`~pynwb.icephys.SimultaneousRecordingsTable` is optional, and since we have # not populated it with any data yet, we can see that the table does not actually exist yet. # In order to make sure the table is being created we can use -# :py:meth:`~pynwb.file.NWBFile.get_icephys_simultaneous_recordings` instead, which ensures +# :py:meth:`~pynwb.file.NWBFile.get_icephys_simultaneous_recordings`, which ensures # that the table is being created if it does not exist yet. icephys_simultaneous_recordings = nwbfile.get_icephys_simultaneous_recordings() -icephys_simultaneous_recordings.add_column(name='simultaneous_recording_tag', - description='A custom tag for simultaneous_recordings') +icephys_simultaneous_recordings.add_column( + name='simultaneous_recording_tag', + description='A custom tag for simultaneous_recordings' +) print(icephys_simultaneous_recordings.colnames) -####################### -# As we can see, we now have succesfully created a new custom column +##################################################################### +# As we can see, we now have succesfully created a new custom column. # -# .. note:: The same process applies to all our other tables as well. Here we use the -# corresponding :py:meth:`~pynwb.file.NWBFile.get_intracelluar_recordings`, +# .. note:: The same process applies to all our other tables as well. We can use the +# corresponding :py:meth:`~pynwb.file.NWBFile.get_intracellular_recordings`, # :py:meth:`~pynwb.file.NWBFile.get_icephys_sequential_recordings`, # :py:meth:`~pynwb.file.NWBFile.get_icephys_repetitions`, and -# :py:meth:`~pynwb.file.NWBFile.get_icephys_conditions` functions intead. +# :py:meth:`~pynwb.file.NWBFile.get_icephys_conditions` functions instead. # In general, we can always use the get functions instead of accessing the property # of the file. # -####################### +##################################################################### # Add a simultaneous recording # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # @@ -428,34 +483,36 @@ # here is simply a list of ints with the indices of the corresponding rows in # the :py:class:`~pynwb.icephys.IntracellularRecordingsTable` # -# .. note:: Since we created our custom ``simultaneous_recording_tag column`` earlier +# .. note:: Since we created our custom ``simultaneous_recording_tag column`` earlier, # we now also need to populate this custom field for every row we add to # the :py:class:`~pynwb.icephys.SimultaneousRecordingsTable`. # -rowindex = nwbfile.add_icephys_simultaneous_recording(recordings=[rowindex, rowindex2, rowindex3], - id=12, - simultaneous_recording_tag='LabTag1') +rowindex = nwbfile.add_icephys_simultaneous_recording( + recordings=[rowindex, rowindex2, rowindex3], + id=12, + simultaneous_recording_tag='LabTag1' +) -####################### +##################################################################### # .. note:: The ``recordings`` argument is the list of indices of the rows in the # :py:class:`~pynwb.icephys.IntracellularRecordingsTable` that we want # to reference. The indices are determined by the order in which we # added the elements to the table. If we don't know the row indicies, # but only the ids of the relevant intracellular recordings, then -# we can search for them as follows: +# we can search for them as follows: -temp_row_indicies = (nwbfile.intracellular_recordings.id == [10, 11]) -print(temp_row_indicies) +temp_row_indices = (nwbfile.intracellular_recordings.id == [10, 11]) +print(temp_row_indices) -####################### +##################################################################### # .. note:: The same is true for our other tables as well, i.e., referencing is # done always by indices of rows (NOT ids). If we only know ids then we # can search for them in the same manner on the other tables as well, # e.g,. via ``nwbfile.simultaneous_recordings.id == 15``. In the search # we can use a list of integer ids or a single int. -####################### +##################################################################### # Define a custom column for a simultaneous recording after adding rows # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # @@ -467,9 +524,10 @@ nwbfile.icephys_simultaneous_recordings.add_column( name='simultaneous_recording_type', description='Description of the type of simultaneous_recording', - data=['SimultaneousRecordingType1', ]) + data=['SimultaneousRecordingType1', ] +) -####################### +##################################################################### # Add a sequential recording # -------------------------- # @@ -483,9 +541,10 @@ rowindex = nwbfile.add_icephys_sequential_recording( simultaneous_recordings=[0], stimulus_type='square', - id=15) + id=15 +) -####################### +##################################################################### # Add a repetition # ---------------- # @@ -498,8 +557,7 @@ rowindex = nwbfile.add_icephys_repetition(sequential_recordings=[0], id=17) - -####################### +##################################################################### # Add an experimental condition # ----------------------------- # @@ -512,7 +570,7 @@ rowindex = nwbfile.add_icephys_experimental_condition(repetitions=[0], id=19) -####################### +##################################################################### # As mentioned earlier, to add additonal columns to any of the tables, we can # use the ``.add_column`` function on the corresponding table after they have # been created. @@ -520,23 +578,25 @@ nwbfile.icephys_experimental_conditions.add_column( name='tag', data=np.arange(1), - description='integer tag for a experimental condition') + description='integer tag for a experimental condition' +) -####################### -# When we add new items then we now also need to set the values for the new column, e.g.: +##################################################################### +# When we add new items, then we now also need to set the values for the new column, e.g.: rowindex = nwbfile.add_icephys_experimental_condition( repetitions=[0], id=21, - tag=3) + tag=3 +) -####################### +##################################################################### # Read/write the NWBFile # ----------------------------- # # Write our test file -testpath = "test_icephys_file.h5" +testpath = "test_icephys_file.nwb" with NWBHDF5IO(testpath, 'w') as io: io.write(nwbfile) @@ -545,58 +605,49 @@ infile = io.read() -####################### +##################################################################### # Accessing the tables # ----------------------------- # # All of the icephys metadata tables are available as attributes on the NWBFile object. -# Below we simply convert the tables to Pandas dataframes to show their content. - +# For display purposes, we convert the tables to pandas DataFrames to show their content. +# For a more in-depth discussion of how to access and use the tables, +# see the tutorial on :ref:`icephys_pandas_tutorial`. +pandas.set_option("display.max_columns", 6) # avoid oversize table in the html docs nwbfile.intracellular_recordings.to_dataframe() -####################### +##################################################################### # # optionally we can ignore the id columns of the category subtables +pandas.set_option("display.max_columns", 5) # avoid oversize table in the html docs nwbfile.intracellular_recordings.to_dataframe(ignore_category_ids=True) -####################### +##################################################################### # nwbfile.icephys_simultaneous_recordings.to_dataframe() -####################### +##################################################################### # nwbfile.icephys_sequential_recordings.to_dataframe() -####################### +##################################################################### # nwbfile.icephys_repetitions.to_dataframe() -####################### +##################################################################### # nwbfile.icephys_experimental_conditions.to_dataframe() -####################### -# .. note:: As the tables in the hierarchy are optional, a given NWBFile may -# contain, e.g., a ``experimental_conditions`` table. To provide a consistent -# interface for users, and to avoid having to manually check which tables exist -# in a given file in order to find the "top-most" table in the hierarchy, -# NWBFile provides the ``:py:meth:`~pynwb.file.NWBFile.get_icephys_meta_parent_table`` -# function. With this we can retrieve the highest table in the icephys metadata -# hierarchy that actually exists in the file. For example: -top_table = nwbfile.get_icephys_meta_parent_table() -print(top_table.neurodata_type) - - -####################### +##################################################################### # Validate data # ^^^^^^^^^^^^^ # -# This section is primarily for internal testing purpose to validate that the roundtrip +# This section is for internal testing purposes only to validate that the roundtrip # of the data (i.e., generate --> write --> read) produces the correct results. # Read the data back in @@ -606,27 +657,47 @@ # assert intracellular_recordings assert np.all(infile.intracellular_recordings.id[:] == nwbfile.intracellular_recordings.id[:]) - # Assert that the ids and the VectorData, VectorIndex, and table target of the recordings column of the Sweeps table are correct - assert np.all(infile.icephys_simultaneous_recordings.id[:] == nwbfile.icephys_simultaneous_recordings.id[:]) - assert np.all(infile.icephys_simultaneous_recordings['recordings'].target.data[:] == nwbfile.icephys_simultaneous_recordings['recordings'].target.data[:]) - assert np.all(infile.icephys_simultaneous_recordings['recordings'].data[:] == nwbfile.icephys_simultaneous_recordings['recordings'].data[:]) - assert infile.icephys_simultaneous_recordings['recordings'].target.table.name == nwbfile.icephys_simultaneous_recordings['recordings'].target.table.name - - # Assert that the ids and the VectorData, VectorIndex, and table target of the simultaneous recordings column of the SweepSequences table are correct - assert np.all(infile.icephys_sequential_recordings.id[:] == nwbfile.icephys_sequential_recordings.id[:]) - assert np.all(infile.icephys_sequential_recordings['simultaneous_recordings'].target.data[:] == nwbfile.icephys_sequential_recordings['simultaneous_recordings'].target.data[:]) - assert np.all(infile.icephys_sequential_recordings['simultaneous_recordings'].data[:] == nwbfile.icephys_sequential_recordings['simultaneous_recordings'].data[:]) - assert infile.icephys_sequential_recordings['simultaneous_recordings'].target.table.name == nwbfile.icephys_sequential_recordings['simultaneous_recordings'].target.table.name - - # Assert that the ids and the VectorData, VectorIndex, and table target of the sequential_recordings column of the Repetitions table are correct + # Assert that the ids and the VectorData, VectorIndex, and table target of the + # recordings column of the Sweeps table are correct + assert np.all(infile.icephys_simultaneous_recordings.id[:] == + nwbfile.icephys_simultaneous_recordings.id[:]) + assert np.all(infile.icephys_simultaneous_recordings['recordings'].target.data[:] == + nwbfile.icephys_simultaneous_recordings['recordings'].target.data[:]) + assert np.all(infile.icephys_simultaneous_recordings['recordings'].data[:] == + nwbfile.icephys_simultaneous_recordings['recordings'].data[:]) + assert (infile.icephys_simultaneous_recordings['recordings'].target.table.name == + nwbfile.icephys_simultaneous_recordings['recordings'].target.table.name) + + # Assert that the ids and the VectorData, VectorIndex, and table target of the simultaneous + # recordings column of the SweepSequences table are correct + assert np.all(infile.icephys_sequential_recordings.id[:] == + nwbfile.icephys_sequential_recordings.id[:]) + assert np.all(infile.icephys_sequential_recordings['simultaneous_recordings'].target.data[:] == + nwbfile.icephys_sequential_recordings['simultaneous_recordings'].target.data[:]) + assert np.all(infile.icephys_sequential_recordings['simultaneous_recordings'].data[:] == + nwbfile.icephys_sequential_recordings['simultaneous_recordings'].data[:]) + assert (infile.icephys_sequential_recordings['simultaneous_recordings'].target.table.name == + nwbfile.icephys_sequential_recordings['simultaneous_recordings'].target.table.name) + + # Assert that the ids and the VectorData, VectorIndex, and table target of the + # sequential_recordings column of the Repetitions table are correct assert np.all(infile.icephys_repetitions.id[:] == nwbfile.icephys_repetitions.id[:]) - assert np.all(infile.icephys_repetitions['sequential_recordings'].target.data[:] == nwbfile.icephys_repetitions['sequential_recordings'].target.data[:]) - assert np.all(infile.icephys_repetitions['sequential_recordings'] .data[:] == nwbfile.icephys_repetitions['sequential_recordings'].data[:]) - assert infile.icephys_repetitions['sequential_recordings'].target.table.name == nwbfile.icephys_repetitions['sequential_recordings'].target.table.name - - # Assert that the ids and the VectorData, VectorIndex, and table target of the repetitions column of the Conditions table are correct - assert np.all(infile.icephys_experimental_conditions.id[:] == nwbfile.icephys_experimental_conditions.id[:]) - assert np.all(infile.icephys_experimental_conditions['repetitions'].target.data[:] == nwbfile.icephys_experimental_conditions['repetitions'].target.data[:]) - assert np.all(infile.icephys_experimental_conditions['repetitions'] .data[:] == nwbfile.icephys_experimental_conditions['repetitions'].data[:]) - assert infile.icephys_experimental_conditions['repetitions'].target.table.name == nwbfile.icephys_experimental_conditions['repetitions'].target.table.name - assert np.all(infile.icephys_experimental_conditions['tag'][:] == nwbfile.icephys_experimental_conditions['tag'][:]) + assert np.all(infile.icephys_repetitions['sequential_recordings'].target.data[:] == + nwbfile.icephys_repetitions['sequential_recordings'].target.data[:]) + assert np.all(infile.icephys_repetitions['sequential_recordings'] .data[:] == + nwbfile.icephys_repetitions['sequential_recordings'].data[:]) + assert (infile.icephys_repetitions['sequential_recordings'].target.table.name == + nwbfile.icephys_repetitions['sequential_recordings'].target.table.name) + + # Assert that the ids and the VectorData, VectorIndex, and table target of the + # repetitions column of the Conditions table are correct + assert np.all(infile.icephys_experimental_conditions.id[:] == + nwbfile.icephys_experimental_conditions.id[:]) + assert np.all(infile.icephys_experimental_conditions['repetitions'].target.data[:] == + nwbfile.icephys_experimental_conditions['repetitions'].target.data[:]) + assert np.all(infile.icephys_experimental_conditions['repetitions'] .data[:] == + nwbfile.icephys_experimental_conditions['repetitions'].data[:]) + assert (infile.icephys_experimental_conditions['repetitions'].target.table.name == + nwbfile.icephys_experimental_conditions['repetitions'].target.table.name) + assert np.all(infile.icephys_experimental_conditions['tag'][:] == + nwbfile.icephys_experimental_conditions['tag'][:]) diff --git a/docs/gallery/domain/plot_icephys_pandas.py b/docs/gallery/domain/plot_icephys_pandas.py new file mode 100644 index 000000000..39ee2ec0b --- /dev/null +++ b/docs/gallery/domain/plot_icephys_pandas.py @@ -0,0 +1,441 @@ +# -*- coding: utf-8 -*- +''' +.. _icephys_pandas_tutorial: + +Query intracellular electrophysiology metadata +============================================== + +This tutorial focuses on using pandas to query experiment metadata for +intracellular electrophysiology experiments using the metadata tables +from the :py:meth:`~pynwb.icephys` module. See the :ref:`icephys_tutorial_new` +tutorial for an introduction to the intracellular electrophysiology metadata +tables and how to create an NWBFile for intracellular electrophysiology data. +''' + +##################################################################### +# Imports used in the tutorial +# ------------------------------ + +# sphinx_gallery_thumbnail_path = 'figures/gallery_thumbnails_icephys_pandas.png' +# Standard Python imports +import numpy as np +import pandas +# Set pandas rendering option to avoid very wide tables in the html docs +pandas.set_option("display.max_colwidth", 30) +pandas.set_option("display.max_rows", 10) +pandas.set_option("display.max_columns", 6) + +##################################################################### +# Example setup +# --------------- +# +# Generate a simple example NWBFile with dummy intracellular electrophysiology data. +# This example uses a utility function :py:meth:`~pynwb.testing.icephys_testutils.create_icephys_testfile` +# to create a dummy NWB file with random icephys data. + +from pynwb.testing.icephys_testutils import create_icephys_testfile +test_filename = "icephys_pandas_testfile.nwb" +nwbfile = create_icephys_testfile( + filename=test_filename, # Write the file to disk for testing + add_custom_columns=True, # Add a custom column to each metadata table + randomize_data=True, # Randomize the data in the simulus and response + with_missing_stimulus=True # Don't include the stimulus for row 0 and 10 +) + +##################################################################### +# Accessing the ICEphys metadata tables +# ------------------------------------- +# +# Get the parent metadata table +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +# +# The intracellular electrophysiology metadata consists of a hierarchy of DynamicTables, i.e., +# :py:class:`~pynwb.icephys.ExperimentalConditionsTable` --> +# :py:class:`~pynwb.icephys.RepetitionsTable` --> +# :py:class:`~pynwb.icephys.SequentialRecordingsTable` --> +# :py:class:`~pynwb.icephys.SimultaneousRecordingsTable` --> +# :py:class:`~pynwb.icephys.IntracellularRecordingsTable`. +# However, in a given :py:class:`~pynwb.file.NWBFile`, not all tables may exist - a user may choose +# to exclude tables from the top of the hierarchy (e.g., a file may only contain +# :py:class:`~pynwb.icephys.SimultaneousRecordingsTable` and :py:class:`~pynwb.icephys.IntracellularRecordingsTable` +# while omitting all of the other tables that are higher in the hierarchy). +# To provide a consistent interface for users, PyNWB allows us to easily locate the table +# that defines the root of the table hierarchy via the function +# :py:meth:`~pynwb.file.NWBFile.get_icephys_meta_parent_table`. + +root_table = nwbfile.get_icephys_meta_parent_table() +print(root_table.neurodata_type) + +##################################################################### +# Getting a specific ICEphys metadata table +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +# +# We can retrieve any of the ICEphys metadata tables via the corresponding properties of NWBFile, i.e., +# :py:meth:`~pynwb.file.NWBFile.intracellular_recordings`, +# :py:meth:`~pynwb.file.NWBFile.icephys_simultaneous_recordings`, +# :py:meth:`~pynwb.file.NWBFile.icephys_sequential_recordings`, +# :py:meth:`~pynwb.file.NWBFile.icephys_repetitions`, +# :py:meth:`~pynwb.file.NWBFile.icephys_experimental_conditions`. +# The property will be ``None`` if the file does not contain the corresponding table. +# As such we can also easily check if a NWBFile contains a particular ICEphys metadata table via, e.g.: + +nwbfile.icephys_sequential_recordings is not None + +##################################################################### +# +# .. warning:: Always use the :py:class:`~pynwb.file.NWBFile` properties rather than the +# corresponding get methods if you only want to retrieve the ICEphys metadata tables. +# The get methods (e.g., :py:meth:`~pynwb.file.NWBFile.get_icephys_simultaneous_recordings`) +# are designed to always return a corresponding ICEphys metadata table for the file and will +# automatically add the missing table (and all required tables that are lower in the hierarchy) +# to the file. This behavior is to ease populating the ICEphys metadata tables when creating +# or updating an :py:class:`~pynwb.file.NWBFile`. +# + +##################################################################### +# Inspecting the table hierarchy +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +# For any given table we can further check if and which columns are foreign +# :py:class:`~hdmf.common.table.DynamicTableRegion` columns pointing to other tables +# via the the :py:meth:`~hdmf.common.table.DynamicTable.has_foreign_columns` and +# :py:meth:`~hdmf.common.table.DynamicTable.get_foreign_columns`, respectively. +# + +print("Has Foreign Columns:", root_table.has_foreign_columns()) +print("Foreign Columns:", root_table.get_foreign_columns()) + +##################################################################### +# Using :py:meth:`~hdmf.common.table.DynamicTable.get_linked_tables` we can then also +# look at all links defined directly or indirectly from a given table to other tables. +# The result is a ``list`` of ``typing.NamedTuple`` objects containing, for each found link, the: +# +# * *"source_table"* :py:class:`~hdmf.common.table.DynamicTable` object, +# * *"source_column"* :py:class:`~hdmf.common.table.DynamicTableRegion` column from the source table, and +# * *"target_table"* :py:class:`~hdmf.common.table.DynamicTable` (which is the same as *source_column.table*). + +linked_tables = root_table.get_linked_tables() + +# Print the links +for i, link in enumerate(linked_tables): + print("%s (%s, %s) ----> %s" % (" " * i, + link.source_table.name, + link.source_column.name, + link.target_table.name)) + +##################################################################### +# Converting ICEphys metadata tables to pandas DataFrames +# ------------------------------------------------------- +# + +##################################################################### +# Using nested DataFrames +# ^^^^^^^^^^^^^^^^^^^^^^^ +# Using the :py:meth:`~hdmf.common.table.DynamicTable.to_dataframe` method we can easily convert tables +# to pandas `DataFrames `_. + +exp_cond_df = root_table.to_dataframe() +exp_cond_df + +##################################################################### +# By default, the method will resolve :py:class:`~hdmf.common.table.DynamicTableRegion` +# references and include the rows that are referenced in related tables as +# `DataFrame `_ objects, +# resulting in a hierarchically nested `DataFrame`_. For example, looking at a single cell of the +# ``repetitions`` column of our :py:class:`~pynwb.icephys.ExperimentalConditionsTable` table, +# we get the corresponding subset of repetitions from the py:class:`~pynwb.icephys.RepetitionsTable`. + +exp_cond_df.iloc[0]['repetitions'] + +##################################################################### +# In contrast to the other ICEphys metadata tables, the +# :py:class:`~pynwb.icephys.IntracellularRecordingsTable` does not contain any +# :py:class:`~hdmf.common.table.DynamicTableRegion` columns, but it is a +# :py:class:`~hdmf.common.alignedtable.AlignedDynamicTable` which contains sub-tables for +# ``electrodes``, ``stimuli``, and ``responses``. For convenience, the +# :py:meth:`~pynwb.icephys.IntracellularRecordingsTable.to_dataframe` of the +# :py:class:`~pynwb.icephys.IntracellularRecordingsTable` provides a few +# additonal optional parameters to optionally ignore the ids of the category tables +# (via ``ignore_category_ids=True``) or to convert the electrode, stimulus, and +# response references to ObjectIds. For example: +# + +nwbfile.intracellular_recordings.to_dataframe( + ignore_category_ids=True, + electrode_refs_as_objectids=True, + stimulus_refs_as_objectids=True, + response_refs_as_objectids=True +) + +##################################################################### +# Using indexed DataFrames +# ^^^^^^^^^^^^^^^^^^^^^^^^ +# +# Depending on the particular analysis, we may be interested only in a particular table and do not +# want to recursively load and resolve all the linked tables. By setting ``index=True`` when +# converting the table :py:meth:`~hdmf.common.table.DynamicTable.to_dataframe` the +# :py:class:`~hdmf.common.table.DynamicTableRegion` links will be represented as +# lists of integers indicating the rows in the target table (without loading data from +# the referenced table). + +root_table.to_dataframe(index=True) + +##################################################################### +# To resolve links related to a set of rows, we can then simply use the corresponding +# :py:class:`~hdmf.common.table.DynamicTableRegion` column from our original table, e.g.: + +root_table['repetitions'][0] # Look-up the repetitions for the first experimental condition + +##################################################################### +# We can also naturally resolve links ourselves by looking up the relevant table and +# then accessing elements of the table directly. + +# All DynamicTableRegion columns in the ICEphys table are indexed so we first need to +# follow the ".target" to the VectorData and then look up the table via ".table" +target_table = root_table['repetitions'].target.table +target_table[[0, 1]] + +##################################################################### +# .. note:: We can also explicitly exclude the :py:class:`~hdmf.common.table.DynamicTableRegion` columns +# (or any other column) from the `DataFrame`_ using e.g., ``root_table.to_dataframe(exclude={'repetitions', })``. + +##################################################################### +# Using a single, hierarchical DataFrame +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +# To gain a more direct overview of all metadata at once and avoid iterating across levels of nested +# DataFrames during analysis, it can be useful to flatten (or unnest) nested DataFrames, expanding the +# nested DataFrames by adding their columns to the main table, and expanding the corresponding rows in +# the parent table by duplicating the data from the existing columns across the new rows. +# For example, an experimental condition represented by a single row in the +# :py:class:`~pynwb.icephys.ExperimentalConditionsTable` containing 5 repetitions would be expanded +# to 5 rows, each containing a copy of the metadata from the experimental condition along with the +# metadata of one of the repetitions. Repeating this process recursively, a single row in the +# :py:class:`~pynwb.icephys.ExperimentalConditionsTable` will then ultimately expand to the total +# number of intracellular recordings from the :py:class:`~pynwb.icephys.IntracellularRecordingsTable` +# that belong to the experimental conditions table. +# +# HDMF povides several convenience functions to help with this process. Using the +# :py:func:`~hdmf.common.hierarchicaltable.to_hierarchical_dataframe` method, we can transform +# our hierarchical table into a single pandas `DataFrame`_. +# To avoid duplication of data in the display, the hierarchy is represented as a pandas +# `MultiIndex `_ on +# the rows so that only the data from the last table in our hierarchy (i.e. here the +# :py:class:`~pynwb.icephys.IntracellularRecordingsTable`) is represented as columns. + +from hdmf.common.hierarchicaltable import to_hierarchical_dataframe +icephys_meta_df = to_hierarchical_dataframe(root_table) + +##################################################################### +# + +# To avoid a too wide display in the online docs we here only show 4 select rows of the +# table and transpose the table to show the large MultiIndex as columns instead of rows +icephys_meta_df.iloc[[0, 1, 18, 19]].transpose() + +##################################################################### +# Depending on the analysis, it can be useful to further process our `DataFrame`_. Using the standard +# `reset_index `_ +# function, we can turn the data from the `MultiIndex`_ to columns of the table itself, +# effectively denormalizing the display by repeating all data across rows. HDMF then also +# provides: 1) :py:func:`~hdmf.common.hierarchicaltable.drop_id_columns` to remove all "id" columns +# and 2) :py:func:`~hdmf.common.hierarchicaltable.flatten_column_index` to turn the +# `MultiIndex`_ on the columns of the table into a regular +# `Index `_ of tuples. +# +# .. note:: Dropping ``id`` columns is often useful for visualization purposes while for +# query and analysis it is often useful to maintain the ``id`` columns to facilitate +# lookups and correlation of information. + +from hdmf.common.hierarchicaltable import drop_id_columns, flatten_column_index +# Reset the index of the dataframe and turn the values into columns instead +icephys_meta_df.reset_index(inplace=True) +# Flatten the column-index, turning the pandas.MultiIndex into a pandas.Index of tuples +flatten_column_index(dataframe=icephys_meta_df, max_levels=2, inplace=True) +# Remove the id columns. By setting inplace=False allows us to visualize the result of this +# action while keeping the id columns in our main icephys_meta_df table +drop_id_columns(dataframe=icephys_meta_df, inplace=False) + +##################################################################### +# Useful additional data preparations +# ----------------------------------- +# +# Expanding TimeSeriesReference columns +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +# +# For query purposes it can be useful to expand the stimulus and response columns to separate the +# ``(start, count, timeseries)`` values in separate columns. This is primarily useful if we want to +# perform queries on these components directly, otherwise it is usually best to keep the stimulus/response +# references around as `:py:class:`~pynwb.base.TimeSeriesReference`, which provides additional features +# to inspect and validate the references and load data. We, therefore, here keep the data in both forms +# in the table + +# Expand the ('stimuli', 'stimulus') to a DataFrame with 3 columns +stimulus_df = pandas.DataFrame( + icephys_meta_df[('stimuli', 'stimulus')].tolist(), + columns=[('stimuli', 'idx_start'), ('stimuli', 'count'), ('stimuli', 'timeseries')], + index=icephys_meta_df.index +) +# If we want to remove the original ('stimuli', 'stimulus') from the dataframe we can call +# icephys_meta_df.drop(labels=[('stimuli', 'stimulus'), ], axis=1, inplace=True) +# Add our expanded columns to the icephys_meta_df dataframe +icephys_meta_df = pandas.concat([icephys_meta_df, stimulus_df], axis=1) +# display the table for the HTML docs +icephys_meta_df + +##################################################################### +# We can then easily expand also the ``(responses, response)`` column in the same way + +response_df = pandas.DataFrame( + icephys_meta_df[('responses', 'response')].tolist(), + columns=[('responses', 'idx_start'), ('responses', 'count'), ('responses', 'timeseries')], + index=icephys_meta_df.index +) +icephys_meta_df = pandas.concat([icephys_meta_df, response_df], axis=1) + + +##################################################################### +# Adding Stimulus/Response Metadata +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +# +# With all TimeSeries stimuli and responses listed in the table, we can easily iterate over the +# TimeSeries to expand our table with additional columns with information from the TimeSeries, e.g., +# the ``neurodata_type`` or ``name`` or any other properties we may wish to extract from our +# stimulus and response TimeSeries (e.g., ``rate``, ``starting_time``, ``gain`` etc.). +# Here we show a few examples. + +# Add a column with the name of the stimulus TimeSeries object. +# Note: We use getattr here to easily deal with missing values, i.e., cases where no stimulus is present +col = ('stimuli', 'name') +icephys_meta_df[col] = [getattr(s, 'name', None) + for s in icephys_meta_df[('stimuli', 'timeseries')]] + +# Often we can easily do this in bulk-fashion by specifing the collection of fields of interest +for field in ['neurodata_type', 'gain', 'rate', 'starting_time', 'object_id']: + col = ('stimuli', field) + icephys_meta_df[col] = [getattr(s, field, None) + for s in icephys_meta_df[('stimuli', 'timeseries')]] +icephys_meta_df + +##################################################################### +# Naturally we can again do the same also for our response columns +for field in ['name', 'neurodata_type', 'gain', 'rate', 'starting_time', 'object_id']: + col = ('responses', field) + icephys_meta_df[col] = [getattr(s, field, None) + for s in icephys_meta_df[('responses', 'timeseries')]] + +##################################################################### +# And we can use the same process to also gather additional metadata about the +# :py:class:`~pynwb.icephys.IntracellularElectrode`, :py:class:`~pynwb.device.Device` and others +for field in ['name', 'device', 'object_id']: + col = ('electrodes', field) + icephys_meta_df[col] = [getattr(s, field, None) + for s in icephys_meta_df[('electrodes', 'electrode')]] + +##################################################################### +# This basic approach allows us to easily collect all data needed for query in a convenient +# spreadsheet for display, query, and analysis. + +##################################################################### +# Performing common metadata queries +# ---------------------------------- +# +# With regard to the experiment metadata tables, many of the queries we identified based on +# feedback from the community follow the model of: *"Given X return Y"*, e.g.: +# +# * Given a particular stimulus return: +# * the corresponding response +# * the corresponding electrode +# * the stimulus type +# * all stimuli/responses recorded at the same time (i.e., during the same simultaneous recording) +# * all stimuli/responses recorded during the same sequential recording +# * Given a particular response return: +# * the corresponding stimulus +# * the corresponding electrode +# * all stimuli/responses recorded at the same time (i.e., during the same simultaneous recording) +# * all stimuli/responses recorded during the same sequential recording +# * Given an electrode return: +# * all responses (and stimuli) related to the electrode +# * all sequential recordings (a.k.a., sweeps) recorded with the electrode +# * Given a stimulus type return: +# * all related stimulus/response recordings +# * all the repetitions in which it is present +# * Given a stimulus type and a repetition return: +# * all the responses +# * Given a simultaneous recording (a.k.a., sweep) return: +# * the repetition/condition/sequential recording it belongs to +# * all other simultaneous recordings that are part of the same repetition +# * the experimental condition the simultaneous recording is part of +# * Given a repetition return: +# * the experimental condition the simultaneous recording is part of +# * all sequential- and/or simultaneous recordings within that repetition +# * Given an experimental condition return: +# * All corresponding repetitions or sequential/simultaneous/intracellular recordings +# * Get the list of all stimulus types +# +# More complex analytics will then commonly combine multiple such query constraints to further process +# the corresponding data, e.g., +# +# * Given a stimulus and a condition, return all simultaneous recordings (a.k.a., sweeps) across repetitions +# and average the responses +# +# Generally, many of the queries involve looking up a piece of information in on table (e.g., finding +# a stimulus type in :py:class:`~pynwb.icephys.SequentialRecordingsTable`) and then querying for +# related information in child tables (by following the :py:class:`~hdmf.common.table.DynamicTableRegion` links +# included in the corresponding rows) to look up more specific information (e.g., all recordings related to +# the stimulus type) or alternatively querying for related information in parent tables (by finding rows in the +# parent table that link to our rows) and then looking up more general information (e.g., information about the +# experimental condition). Using this approach, we can resolve the above queries using the individual +# :py:class:`~hdmf.common.table.DynamicTable` objects directly, while loading only the data that is +# absolutely necessary into memory. +# +# With the bulk data stored usually in some form of :py:class:`~pynwb.icephys.PatchClampSeries`, the +# ICEphys metadata tables will usually be comparatively small (in terms of total memory). Once we have created +# our integrated `DataFrame`_ as shown above, performing the queries described above becomes quite simple +# as all links between tables have already been resolved and all data has been expanded across all rows. +# In general, resolving queries on our "denormalized" table amounts to evaluating one or more conditions +# on one or more columns and then retrieving the rows that match our conditions form the table. +# +# Once we have all metadata in a single table, we can also easily sort the rows of our table based on +# a flexible set of conditions or even cluster rows to compute more advanced groupings of intracellular recordings. +# +# Below we show just a few simple examples: +# +# Given a response, get the stimulus +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +# Get a response 'vcs_9' from the file +response = nwbfile.get_acquisition('vcs_9') +# Return all data related to that response, including the stimulus as part of ('stimuli', 'stimulus') column +icephys_meta_df[icephys_meta_df[('responses', 'object_id')] == response.object_id] + + +##################################################################### +# Given a response load the associated data +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +# +# References to timeseries are stored in the :py:class:`~pynwb.icephys.IntracellularRecordingsTable` via +# :py:class:`~pynwb.base.TimeSeriesReferenceVectorData` columns which return the references to the stimulus/response +# via :py:class:`~pynwb.base.TimeSeriesReference` objects. Using :py:class:`~pynwb.base.TimeSeriesReference` we can +# easily inspect the selected data. + +ref = icephys_meta_df[('responses', 'response')][0] # Get the TimeSeriesReference +_ = ref.isvalid() # Is the reference valid +_ = ref.idx_start # Get the start index +_ = ref.count # Get the count +_ = ref.timeseries.name # Get the timeseries +_ = ref.timestamps # Get the selected timestamps +ref_data = ref.data # Get the selected recorded response data values +# Print the data values just as an example +print("data = " + str(ref_data)) + +##################################################################### +# Get a list of all stimulus types +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +unique_stimulus_types = np.unique(icephys_meta_df[('sequential_recordings', 'stimulus_type')]) +print(unique_stimulus_types) + +##################################################################### +# Given a stimulus type, get all corresponding intracellular recordings +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +icephys_meta_df[icephys_meta_df[('sequential_recordings', 'stimulus_type')] == 'StimType_1'] diff --git a/docs/source/conf.py b/docs/source/conf.py index 63923cff0..f0ab5d4a6 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -53,6 +53,7 @@ 'sphinx.ext.napoleon', 'sphinx.ext.intersphinx', 'sphinx.ext.viewcode', + 'sphinx.ext.extlinks', 'sphinx_gallery.gen_gallery' ] @@ -63,9 +64,10 @@ 'examples_dirs': ['../gallery'], # path where to save gallery generated examples 'gallery_dirs': ['tutorials'], - 'subsection_order': ExplicitOrder(['../gallery/general', '../gallery/domain']), + 'subsection_order': ExplicitOrder(['../gallery/general', '../gallery/domain', '../gallery/advanced_io']), 'backreferences_dir': 'gen_modules/backreferences', - 'min_reported_time': 5 + 'min_reported_time': 5, + 'remove_config_comments': True } intersphinx_mapping = { @@ -77,6 +79,11 @@ 'pandas': ('https://pandas.pydata.org/pandas-docs/stable/', None) } +extlinks = {'incf_lesson': ('https://training.incf.org/lesson/%s', ''), + 'incf_collection': ('https://training.incf.org/collection/%s', ''), + 'nwb_extension': ('https://github.com/nwb-extensions/%s', ''), + 'pynwb': ('https://github.com/NeurodataWithoutBorders/pynwb/%s', '')} + # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] diff --git a/docs/source/extensions_tutorial/2_create_extension_spec_walkthrough.rst b/docs/source/extensions_tutorial/2_create_extension_spec_walkthrough.rst index a158ecdec..ad6fcf48e 100644 --- a/docs/source/extensions_tutorial/2_create_extension_spec_walkthrough.rst +++ b/docs/source/extensions_tutorial/2_create_extension_spec_walkthrough.rst @@ -5,13 +5,13 @@ Using ndx-template ~~~~~~~~~~~~~~~~~~ Extensions should be created in their own repository, not alongside data conversion code. This facilitates sharing and editing of the extension separately from the code that uses it. When starting a new extension, we highly -recommend using the `ndx-template `_ repository, which automatically -generates a repository with the appropriate directory structure. +recommend using the :nwb_extension:`ndx-template` repository, which automatically generates a repository with +the appropriate directory structure. -After you finish the instructions `here `_, +After you finish the instructions :nwb_extension:`here `, you should have a directory structure that looks like this -.. code-block:: c +.. code-block:: bash ├── LICENSE.txt ├── MANIFEST.in @@ -51,8 +51,8 @@ you should have a directory structure that looks like this └── create_extension_spec.py At its core, an NWB extension consists of YAML text files, such as those generated in the `spec` -folder. While you can write these YAML extension files by hand, PyNWB provides a convenient API -via the :py:mod:`~pynwb.spec` module for creating extensions. +folder. While you can write these YAML extension files by hand, PyNWB provides a convenient API +via the :py:mod:`~pynwb.spec` module for creating extensions. Open ``src/spec/create_extension_spec.py``. You will be modifying this script to create your own NWB extension. Let's first walk through each piece. @@ -60,7 +60,7 @@ modifying this script to create your own NWB extension. Let's first walk through Creating a namespace ~~~~~~~~~~~~~~~~~~~~ NWB organizes types into namespaces. You must define a new namespace before creating any new types. After following -the instructions from `ndx-template `_, you should have a file +the instructions from the :nwb_extension:`ndx-template`, you should have a file ``ndx-my-ext/src/spec/create_extension_spec.py``. The beginning of this file should look like .. code-block:: python diff --git a/docs/source/extensions_tutorial/3_spec_api.rst b/docs/source/extensions_tutorial/3_spec_api.rst index 54df0cca0..d4b42864c 100644 --- a/docs/source/extensions_tutorial/3_spec_api.rst +++ b/docs/source/extensions_tutorial/3_spec_api.rst @@ -34,8 +34,8 @@ within it Datasets, Attributes, Links, and/or other Groups. Groups are specified - ``neurodata_type_def`` declares the name of the neurodata type. - ``neurodata_type_inc`` indicates what data type you are extending (Groups must extend Groups, and Datasets must extend Datasets). -- To define a new neurodata type that does not extend an existing type, use - ``neurodata_type_inc=NWBContainer`` for a group or ``neurodata_type_inc=NWBData`` for a dataset. +- To define a new neurodata type that does not extend an existing type, use + ``neurodata_type_inc=NWBContainer`` for a group or ``neurodata_type_inc=NWBData`` for a dataset. ``NWBContainer`` and ``NWBData`` are base types for NWB. - To use a type that has already been defined, use ``neurodata_type_inc`` and not ``neurodata_type_def``. - You can define a group that is not a neurodata type by omitting both ``neurodata_type_def`` and ``neurodata_type_inc``. @@ -79,17 +79,20 @@ All larger blocks of numeric or text data should be stored in Datasets. Specifyi ``neurodata_type_def``, ``neurodata_type_inc``, ``doc``, ``name``, ``default_name``, ``linkable``, ``quantity``, and ``attributes`` all work the same as they do in :py:class:`~pynwb.spec.NWBGroupSpec`, described in the previous section. -``dtype`` defines the type of the data, which can be a basic type, compound type, or reference type. -See a list of options `here `_. -Basic types can be defined as string objects and more complex types via :py:class:`~pynwb.spec.NWBDtypeSpec` and `RefSpec `_. +``dtype`` defines the type of the data, which can be a basic type, compound type, or reference type. +See a list of `dtype options `_ +as part of the specification language docs. Basic types can be defined as string objects and more complex +types via :py:class:`~pynwb.spec.NWBDtypeSpec` and +`RefSpec `_. -``shape`` is a specification defining the allowable shapes for the dataset. See the shape specification -`here `_. ``None`` is -mapped to ``null``. Is no shape is provided, it is assumed that the dataset is only a single element. +``shape`` is a specification defining the allowable shapes for the dataset. See the +`shape specification `_ +as part of the specification language docs. ``None`` is mapped to ``null``. Is no shape is provided, it is +assumed that the dataset is only a single element. -If the dataset is a single element (scalar) that represents meta-data, consider using an Attribute (see -below) to store the data more efficiently instead. However, note that a Dataset can have Attributes, +If the dataset is a single element (scalar) that represents meta-data, consider using an Attribute (see +below) to store the data more efficiently instead. However, note that a Dataset can have Attributes, whereas an Attribute cannot have Attributes of its own. ``dims`` provides labels for each dimension of ``shape``. @@ -139,16 +142,16 @@ defined in the ``attributes`` field of a :py:class:`~pynwb.spec.NWBGroupSpec` or neurodata type, i.e., the ``neurodata_type_def`` and ``neurodata_type_inc`` keys are not allowed. The only way to match an object with a spec is through the name of the attribute so ``name`` is required. You cannot have multiple attributes on a single group/dataset that correspond to the same :py:class:`~pynwb.spec.NWBAttributeSpec`, since these would have to have the same name. Therefore, instead of -specifying number of ``quantity``, you have a ``required`` field which takes a boolean value. Another +specifying number of ``quantity``, you have a ``required`` field which takes a boolean value. Another key difference between datasets and attributes is that attributes cannot have attributes of their own. .. tip:: Dataset or Attribute? It is often possible to store data as either a Dataset or an Attribute. Our best advice is - to keep Attributes small. In HDF5 the typical size limit for attributes is 64Kbytes. If an attribute is going to - store more than 64Kbyte, then make it a Dataset. Attributes are also more efficient for storing very - small data, such as scalars. However, attributes cannot have attributes of their own, and in HDF5, + to keep Attributes small. In HDF5 the typical size limit for attributes is 64Kbytes. If an attribute is going to + store more than 64Kbyte, then make it a Dataset. Attributes are also more efficient for storing very + small data, such as scalars. However, attributes cannot have attributes of their own, and in HDF5, I/O filters, such as compression and chunking, cannot apply to attributes. - + Link Specifications ^^^^^^^^^^^^^^^^^^^ diff --git a/docs/source/extensions_tutorial/extensions_tutorial_home.rst b/docs/source/extensions_tutorial/extensions_tutorial_home.rst index 6fec63b01..988794c4a 100644 --- a/docs/source/extensions_tutorial/extensions_tutorial_home.rst +++ b/docs/source/extensions_tutorial/extensions_tutorial_home.rst @@ -1,3 +1,5 @@ +.. _extending-nwb: + Extending NWB ============= diff --git a/docs/source/figures/gallery_thumbnails.pptx b/docs/source/figures/gallery_thumbnails.pptx index d94f17ac5b6bbc876e3fd1dde094e469e770a7e9..e16e5e0eb147e857d45492919066c0d2dc5b6a28 100644 GIT binary patch delta 107899 zcmY(qV{|6Z7cCsywr$(CF|mz_ojk!LnHW!O+n(6AF|qAr;y1tlz3+$nVOMw6s_t4{ z>s0kQd!O34i~yZohy%q@kq3vs0D%I50RaIa1;ITp?coOl0ntaR$DsfNxMZ@R1YT#K z^0TyiL@TV9*bMW8e`dV`aZ8c&de^nU5~UJ1|6O(%k67c_MhDFe{ph_4=%8nupT07@ 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b/docs/source/make_a_release.rst index 955a261ca..18d4fe075 100644 --- a/docs/source/make_a_release.rst +++ b/docs/source/make_a_release.rst @@ -122,7 +122,7 @@ PyPI: Step-by-step 8. Once the builds are completed, check that the distributions are available on `PyPI`_ and that - a new `GitHub release `_ was created. + a new :pynwb:`GitHub release ` was created. 9. Create a clean testing environment to test the installation diff --git a/docs/source/tutorial_source/convert.rst b/docs/source/tutorial_source/convert.rst deleted file mode 100644 index 6ed3030ad..000000000 --- a/docs/source/tutorial_source/convert.rst +++ /dev/null @@ -1,36 +0,0 @@ -Converting data to NWB -====================== - -The following are example Jupyter notebooks for converting custom lab data to NWB: - -crcns-ret-1: Meister lab retina data ------------------------------------- - -* **Notebook:** https://github.com/NeurodataWithoutBorders/pynwb/blob/dev/docs/notebooks/convert-crcns-ret-1-meisterlab.ipynb -* **Example:** This example shows: - - * Use of ``UnitTimes``, ``SpikeUnit``, ``ImageSeries``, ``ElectrodeGroup``, ``EpochTimeSeries``, ``Device`` - * Creation and use of custom namespace and extension to extend ``ImageSeries`` to add custom metadata attributes - * Create external link for ``ImageSeries.data`` - * Read of crcns-ret-1 dataset - * Convert of the data to NWB - * Comparison of H5Gate and PyNWB - -* **Data:** Convert single-unit neural responses recorded from - isolated retina from lab mice (Mus Musculus) using - a 61-electrode array in response to various visual - stimuli. Recordings were done by Yifeng Zhang in - Markus Meister's lab at Harvard University in 2008. - Further description of the data are available here: - https://crcns.org/data-sets/retina/ret-1/about-ret-1 - -* **Data Terms of use** The data used on the notebook and, therefore also the NWB files generated by the notebook are governed - by the terms-of-use of CRCNS.org described here https://crcns.org/terms . - -* **Comparison to NWB 1.0.x`:** - - * **Notebook:** https://github.com/NeurodataWithoutBorders/pynwb/blob/dev/docs/notebooks/convert-crcns-ret-1-meisterlab-compare-nwb-1.0.6.ipynb - * **Description:** This notebook shows the convert of the same data using the original NWB 1.0.x API to allow for comparison of the - NWB 1.0.x and NWB 2.x file. - - diff --git a/requirements-doc.txt b/requirements-doc.txt index 5f7ceff81..7ae6261cb 100644 --- a/requirements-doc.txt +++ b/requirements-doc.txt @@ -3,4 +3,4 @@ sphinx matplotlib sphinx_rtd_theme sphinx-gallery -allensdk # note that as of allensdk 2.10.0, python 3.8 is not supported +allensdk>=2.11.0 # python 3.8 is not supported in allensdk<2.11 diff --git a/requirements-min.txt b/requirements-min.txt index 9b9d14bd3..f7adc6641 100644 --- a/requirements-min.txt +++ b/requirements-min.txt @@ -1,6 +1,6 @@ # minimum versions of package dependencies for installing PyNWB h5py==2.10 # support for selection of datasets with list of indices added in 2.10 -hdmf==3.0.1 +hdmf==3.1.1 numpy==1.16 pandas==1.0.5 python-dateutil==2.7 diff --git a/requirements.txt b/requirements.txt index ba1493ad7..31b8d102e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ # pinned dependencies to reproduce an entire development environment to use PyNWB h5py==3.3.0 -hdmf==3.0.1 +hdmf==3.1.1 numpy==1.21.0 pandas==1.3.0 python-dateutil==2.8.1 diff --git a/setup.py b/setup.py index f862c3446..f6a05bee6 100755 --- a/setup.py +++ b/setup.py @@ -14,7 +14,7 @@ reqs = [ 'h5py>=2.9,<4', - 'hdmf>=3.0.1,<4', + 'hdmf>=3.1.1,<4', 'numpy>=1.16,<1.22', 'pandas>=1.0.5,<2', 'python-dateutil>=2.7,<3', diff --git a/src/pynwb/base.py b/src/pynwb/base.py index 81e71fdcd..1a5ced830 100644 --- a/src/pynwb/base.py +++ b/src/pynwb/base.py @@ -1,8 +1,10 @@ from warnings import warn from collections.abc import Iterable +from typing import NamedTuple from hdmf.utils import docval, getargs, popargs, call_docval_func, get_docval from hdmf.common import DynamicTable, VectorData +import numpy as np from . import register_class, CORE_NAMESPACE @@ -284,14 +286,225 @@ def __init__(self, **kwargs): self.images = images +class TimeSeriesReference(NamedTuple): + """ + Class used to represent data values of a :py:class:`~pynwb.base.TimeSeriesReferenceVectorData` + This is a ``typing.NamedTuple`` type with predefined tuple components + :py:meth:`~pynwb.base.TimeSeriesReference.idx_start`, :py:meth:`~pynwb.base.TimeSeriesReference.count`, and + :py:meth:`~pynwb.base.TimeSeriesReference.timeseries`. + + :cvar idx_start: + :cvar count: + :cvar timeseries: + """ + idx_start: int + """Start index in time for the timeseries""" + + count: int + """Number of timesteps to be selected starting from :py:meth:`~pynwb.base.TimeSeriesReference.idx_start`""" + + timeseries: TimeSeries + """The :py:class:`~pynwb.base.TimeSeries` object the TimeSeriesReference applies to""" + + def check_types(self): + """ + Helper function to check correct types for :py:meth:`~pynwb.base.TimeSeriesReference.idx_start`, + :py:meth:`~pynwb.base.TimeSeriesReference.count`, and :py:meth:`~pynwb.base.TimeSeriesReference.timeseries`. + + This function is usually used in the try/except block to check for `TypeError` raised by the function. + + See also :py:meth:`~pynwb.base.TimeSeriesReference.isvalid` to check both validity of types and the + reference itself. + + :returns: True if successful. If unsuccessful `TypeError` is raised. + + :raises TypeError: If one of the fields does not match the expected type + """ + if not isinstance(self.idx_start, (int, np.integer)): + raise TypeError("idx_start must be an integer not %s" % str(type(self.idx_start))) + if not isinstance(self.count, (int, np.integer)): + raise TypeError("count must be an integer %s" % str(type(self.count))) + if not isinstance(self.timeseries, TimeSeries): + raise TypeError("timeseries must be of type TimeSeries. %s" % str(type(self.timeseries))) + return True + + def isvalid(self): + """ + Check whether the reference is valid. Setting :py:meth:`~pynwb.base.TimeSeriesReference.idx_start` and + :py:meth:`~pynwb.base.TimeSeriesReference.count` to -1 is used to indicate invalid references. This is + useful to allow for missing data in :py:class:`~pynwb.base.TimeSeriesReferenceVectorData` + + :returns: True if the selection is valid. Returns False if both + :py:meth:`~pynwb.base.TimeSeriesReference.idx_start` and :py:meth:`~pynwb.base.TimeSeriesReference.count` + are negative. Raises `IndexError` in case the indices are bad. + + :raises IndexError: If the combination of :py:meth:`~pynwb.base.TimeSeriesReference.idx_start` and + :py:meth:`~pynwb.base.TimeSeriesReference.count` are not valid for the given timeseries. + + :raises TypeError: If one of the fields does not match the expected type + """ + # Check types first + self.check_types() + # Check for none-type selection + if self.idx_start < 0 and self.count < 0: + return False + num_samples = self.timeseries.num_samples + if num_samples is not None: + if self.idx_start >= num_samples or self.idx_start < 0: + raise IndexError("'idx_start' %i out of range for timeseries '%s'" % + (self.idx_start, self.timeseries.name)) + if self.count < 0: + raise IndexError("'count' %i invalid. 'count' must be positive" % self.count) + if (self.idx_start + self.count) > num_samples: + raise IndexError("'idx_start + count' out of range for timeseries '%s'" % self.timeseries.name) + return True + + @property + def timestamps(self): + """ + Get the floating point timestamp offsets in seconds from the timeseries that correspond to the array. + These are either loaded directly from the :py:meth:`~pynwb.base.TimeSeriesReference.timeseries` + timestamps or calculated from the starting time and sampling rate. + + + :raises IndexError: If the combination of :py:meth:`~pynwb.base.TimeSeriesReference.idx_start` and + :py:meth:`~pynwb.base.TimeSeriesReference.count` are not valid for the given timeseries. + + :raises TypeError: If one of the fields does not match the expected type + + :returns: Array with the timestamps. + """ + # isvalid will be False only if both idx_start and count are negative. Otherwise well get errors or be True. + if not self.isvalid(): + return None + # load the data from the timestamps + elif self.timeseries.timestamps is not None: + return self.timeseries.timestamps[self.idx_start: (self.idx_start + self.count)] + # construct the timestamps from the starting_time and rate + else: + start_time = self.timeseries.rate * self.idx_start + self.timeseries.starting_time + return np.arange(0, self.count) * self.timeseries.rate + start_time + + @property + def data(self): + """ + Get the selected data values. This is a convenience function to slice data from the + :py:meth:`~pynwb.base.TimeSeriesReference.timeseries` based on the given + :py:meth:`~pynwb.base.TimeSeriesReference.idx_start` and + :py:meth:`~pynwb.base.TimeSeriesReference.count` + + :raises IndexError: If the combination of :py:meth:`~pynwb.base.TimeSeriesReference.idx_start` and + :py:meth:`~pynwb.base.TimeSeriesReference.count` are not valid for the given timeseries. + + :raises TypeError: If one of the fields does not match the expected type + + :returns: Result of ``self.timeseries.data[self.idx_start: (self.idx_start + self.count)]``. Returns + None in case the reference is invalid (i.e., if both :py:meth:`~pynwb.base.TimeSeriesReference.idx_start` + and :py:meth:`~pynwb.base.TimeSeriesReference.count` are negative. + """ + # isvalid will be False only if both idx_start and count are negative. Otherwise well get errors or be True. + if not self.isvalid(): + return None + # load the data from the timeseries + return self.timeseries.data[self.idx_start: (self.idx_start + self.count)] + + @register_class('TimeSeriesReferenceVectorData', CORE_NAMESPACE) class TimeSeriesReferenceVectorData(VectorData): """ Column storing references to a TimeSeries (rows). For each TimeSeries this VectorData column stores the start_index and count to indicate the range in time to be selected as well as an object reference to the TimeSeries. + + **Representing missing values** In practice we sometimes need to be able to represent missing values, + e.g., in the :py:class:`~pynwb.icephys.IntracellularRecordingsTable` we have + :py:class:`~pynwb.base.TimeSeriesReferenceVectorData` to link to stimulus and + response recordings, but a user can specify either only one of them or both. Since there is no + ``None`` value for a complex types like ``(idx_start, count, TimeSeries)``, NWB defines + ``None`` as ``(-1, -1, TimeSeries)`` for storage, i.e., if the ``idx_start`` (and ``count``) is negative + then this indicates an invalid link (in practice both ``idx_start`` and ``count`` must always + either both be positive or both be negative). When selecting data via the + :py:meth:`~pynwb.base.TimeSeriesReferenceVectorData.get` or + :py:meth:`~pynwb.base. TimeSeriesReferenceVectorData.__getitem__` + functions, ``(-1, -1, TimeSeries)`` values are replaced by the corresponding + :py:class:`~pynwb.base.TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_NONE_TYPE` tuple + to avoid exposing NWB storage internals to the user and simplifying the use of and checking + for missing values. **NOTE:** We can still inspect the raw data values by looking at ``self.data`` + directly instead. + + :cvar TIME_SERIES_REFERENCE_TUPLE: + :cvar TIME_SERIES_REFERENCE_NONE_TYPE: """ - @docval(*get_docval(VectorData.__init__)) + TIME_SERIES_REFERENCE_TUPLE = TimeSeriesReference + """Return type when calling :py:meth:`~pynwb.base.TimeSeriesReferenceVectorData.get` or + :py:meth:`~pynwb.base. TimeSeriesReferenceVectorData.__getitem__`.""" + + TIME_SERIES_REFERENCE_NONE_TYPE = TIME_SERIES_REFERENCE_TUPLE(None, None, None) + """Tuple used to represent None values when calling :py:meth:`~pynwb.base.TimeSeriesReferenceVectorData.get` or + :py:meth:`~pynwb.base. TimeSeriesReferenceVectorData.__getitem__`. See also + :py:class:`~pynwb.base.TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE`""" + + @docval({'name': 'name', 'type': str, 'doc': 'the name of this VectorData', 'default': 'timeseries'}, + {'name': 'description', 'type': str, 'doc': 'a description for this column', + 'default': "Column storing references to a TimeSeries (rows). For each TimeSeries this " + "VectorData column stores the start_index and count to indicate the range in time " + "to be selected as well as an object reference to the TimeSeries."}, + *get_docval(VectorData.__init__, 'data')) def __init__(self, **kwargs): call_docval_func(super().__init__, kwargs) + + @docval({'name': 'val', 'type': TIME_SERIES_REFERENCE_TUPLE, 'doc': 'the value to add to this column'}) + def add_row(self, **kwargs): + """Append a data value to this column.""" + val = getargs('val', kwargs) + val.check_types() + super().append(val) + + @docval({'name': 'arg', 'type': TIME_SERIES_REFERENCE_TUPLE, 'doc': 'the value to append to this column'}) + def append(self, **kwargs): + """Append a data value to this column.""" + arg = getargs('arg', kwargs) + arg.check_types() + super().append(arg) + + def get(self, key, **kwargs): + """ + Retrieve elements from this object. + + The function uses :py:class:`~pynwb.base.TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE` + to describe individual records in the dataset. This allows the code to avoid exposing internal + details of the schema to the user and simplifies handling of missing values by explictly + representing missing values via + :py:class:`~pynwb.base.TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_NONE_TYPE` + rather than the internal representation used for storage of ``(-1, -1, TimeSeries)``. + + :param key: Selection of the elements + :param kwargs: Ignored + + :returns: :py:class:`~pynwb.base.TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE` if a single + element is being selected. Otherwise return a list of + :py:class:`~pynwb.base.TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE` objects. + Missing values are represented by + :py:class:`~pynwb.base.TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_NONE_TYPE` + in which all values (i.e., idx_start, count, timeseries) are set to None. + """ + vals = super().get(key) + # we only selected one row. + if isinstance(key, (int, np.integer)): + # NOTE: If we never wrote the data to disk, then vals will be a single tuple. + # If the data is loaded from an h5py.Dataset then vals will be a single + # np.void object. I.e., an alternative check would be + # if isinstance(vals, tuple) or isinstance(vals, np.void): + # ... + if vals[0] < 0 or vals[1] < 0: + return self.TIME_SERIES_REFERENCE_NONE_TYPE + else: + return self.TIME_SERIES_REFERENCE_TUPLE(*vals) + else: # key selected multiple rows + # When loading from HDF5 we get an np.ndarray otherwise we get list-of-list. This + # makes the values consistent and tranforms the data to use our namedtuple type + re = [self.TIME_SERIES_REFERENCE_NONE_TYPE + if (v[0] < 0 or v[1] < 0) else self.TIME_SERIES_REFERENCE_TUPLE(*v) + for v in vals] + return re diff --git a/src/pynwb/file.py b/src/pynwb/file.py index 107c71710..25bfdd86d 100644 --- a/src/pynwb/file.py +++ b/src/pynwb/file.py @@ -782,14 +782,14 @@ def get_intracellular_recordings(self): def add_intracellular_recording(self, **kwargs): """ Add a intracellular recording to the intracellular_recordings table. If the - electrode, stimulus, and/or response do not exsist yet in the NWBFile, then + electrode, stimulus, and/or response do not exist yet in the NWBFile, then they will be added to this NWBFile before adding them to the table. Note: For more complex organization of intracellular recordings you may also be interested in the related SimultaneousRecordingsTable, SequentialRecordingsTable, - RepetitionsTable, and ExperimentalConditionsTable tables an the related function - of NWBFile: add_icephsy_simultaneous_recording, add_icephys_sequential_recording, - add_icephys_repetition, and add_icephys_condition. + RepetitionsTable, and ExperimentalConditionsTable tables and the related functions + of NWBFile: add_icephys_simultaneous_recording, add_icephys_sequential_recording, + add_icephys_repetition, and add_icephys_experimental_condition. """ # Add the stimulus, response, and electrode to the file if they don't exist yet stimulus, response, electrode = getargs('stimulus', 'response', 'electrode', kwargs) diff --git a/src/pynwb/icephys.py b/src/pynwb/icephys.py index f76c72d00..215e7d9d5 100644 --- a/src/pynwb/icephys.py +++ b/src/pynwb/icephys.py @@ -464,13 +464,13 @@ def __init__(self, **kwargs): call_docval_func(super().__init__, kwargs) @docval({'name': 'electrode', 'type': IntracellularElectrode, 'doc': 'The intracellular electrode used'}, - {'name': 'stimulus_start_index', 'type': 'int', 'doc': 'Start index of the stimulus', 'default': -1}, - {'name': 'stimulus_index_count', 'type': 'int', 'doc': 'Stop index of the stimulus', 'default': -1}, + {'name': 'stimulus_start_index', 'type': 'int', 'doc': 'Start index of the stimulus', 'default': None}, + {'name': 'stimulus_index_count', 'type': 'int', 'doc': 'Stop index of the stimulus', 'default': None}, {'name': 'stimulus', 'type': TimeSeries, 'doc': 'The TimeSeries (usually a PatchClampSeries) with the stimulus', 'default': None}, - {'name': 'response_start_index', 'type': 'int', 'doc': 'Start index of the response', 'default': -1}, - {'name': 'response_index_count', 'type': 'int', 'doc': 'Stop index of the response', 'default': -1}, + {'name': 'response_start_index', 'type': 'int', 'doc': 'Start index of the response', 'default': None}, + {'name': 'response_index_count', 'type': 'int', 'doc': 'Stop index of the response', 'default': None}, {'name': 'response', 'type': TimeSeries, 'doc': 'The TimeSeries (usually a PatchClampSeries) with the response', 'default': None}, @@ -488,9 +488,9 @@ def add_recording(self, **kwargs): Add a single recording to the IntracellularRecordingsTable table. Typically, both stimulus and response are expected. However, in some cases only a stimulus - or a resposne may be recodred as part of a recording. In this case, None, may be given + or a response may be recodred as part of a recording. In this case, None may be given for either stimulus or response, but not both. Internally, this results in both stimulus - and response pointing to the same timeseries, while the start_index and index_count for + and response pointing to the same TimeSeries, while the start_index and index_count for the invalid series will both be set to -1. """ # Get the input data @@ -549,13 +549,15 @@ def add_recording(self, **kwargs): stimuli = copy(popargs('stimulus_metadata', kwargs)) if stimuli is None: stimuli = {} - stimuli['stimulus'] = (stimulus_start_index, stimulus_index_count, stimulus) + stimuli['stimulus'] = TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE( + stimulus_start_index, stimulus_index_count, stimulus) - # Compile the reponses table data + # Compile the responses table data responses = copy(popargs('response_metadata', kwargs)) if responses is None: responses = {} - responses['response'] = (response_start_index, response_index_count, response) + responses['response'] = TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE( + response_start_index, response_index_count, response) _ = super().add_row(enforce_unique_id=True, electrodes=electrodes, @@ -578,17 +580,21 @@ def __compute_index(start_index, index_count, time_series, name): :raises IndexError: If index_count cannot be determined or start_index+index_count are outside of the range of the timeseries. - :returns: A tuple of integers with the start_index and index_count to use + :returns: A tuple of integers with the start_index and index_count to use. """ + # If times_series is not valid then return -1, -1 to indicate invalid times if time_series is None: return -1, -1 - start_index = start_index if start_index >= 0 else 0 + # Since time_series is valid, negative or None start_index means the user did not specify a start_index + # so we now need to set it to 0 + if start_index is None or start_index < 0: + start_index = 0 + # If index_count has not been set yet (i.e., it is -1 or None) then attempt to set it to the + # full range of the timeseries starting from start_index num_samples = time_series.num_samples - index_count = (index_count - if index_count >= 0 - else ((num_samples - start_index) - if num_samples is not None - else None)) + if index_count is None or index_count < 0: + index_count = (num_samples - start_index) if num_samples is not None else None + # Check that the start_index and index_count are valid and raise IndexError if they are invalid if index_count is None: raise IndexError("Invalid %s_index_count cannot be determined from %s data." % (name, name)) if num_samples is not None: @@ -596,6 +602,7 @@ def __compute_index(start_index, index_count, time_series, name): raise IndexError("%s_start_index out of range" % name) if (start_index + index_count) > num_samples: raise IndexError("%s_start_index + %s_index_count out of range" % (name, name)) + # Return the values return start_index, index_count @docval(*get_docval(AlignedDynamicTable.to_dataframe, 'ignore_category_ids'), @@ -615,9 +622,15 @@ def to_dataframe(self, **kwargs): if getargs('electrode_refs_as_objectids', kwargs): res[('electrodes', 'electrode')] = [e.object_id for e in res[('electrodes', 'electrode')]] if getargs('stimulus_refs_as_objectids', kwargs): - res[('stimuli', 'stimulus')] = [(e[0], e[1], e[2].object_id) for e in res[('stimuli', 'stimulus')]] + res[('stimuli', 'stimulus')] = \ + [e if e[2] is None + else TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE(e[0], e[1], e[2].object_id) + for e in res[('stimuli', 'stimulus')]] if getargs('response_refs_as_objectids', kwargs): - res[('responses', 'response')] = [(e[0], e[1], e[2].object_id) for e in res[('responses', 'response')]] + res[('responses', 'response')] = \ + [e if e[2] is None else + TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE(e[0], e[1], e[2].object_id) + for e in res[('responses', 'response')]] return res @@ -631,7 +644,7 @@ class SimultaneousRecordingsTable(DynamicTable): __columns__ = ( {'name': 'recordings', - 'description': 'Column with a references to one or more rows in the IntracellularRecordingsTable table', + 'description': 'Column with references to one or more rows in the IntracellularRecordingsTable table', 'required': True, 'index': True, 'table': True}, @@ -667,8 +680,8 @@ def __init__(self, **kwargs): allow_extra=True) def add_simultaneous_recording(self, **kwargs): """ - Add a single Sweep consisting of one-or-more recordings and associated custom - SimultaneousRecordingsTable metadata to the table. + Add a single simultaneous recording (i.e., one sweep, or one row) consisting of one or more + recordings and associated custom simultaneous recording metadata to the table. """ _ = super().add_row(enforce_unique_id=True, **kwargs) return len(self.id) - 1 @@ -685,7 +698,7 @@ class SequentialRecordingsTable(DynamicTable): __columns__ = ( {'name': 'simultaneous_recordings', - 'description': 'Column with a references to one or more rows in the SimultaneousRecordingsTable table', + 'description': 'Column with references to one or more rows in the SimultaneousRecordingsTable table', 'required': True, 'index': True, 'table': True}, @@ -730,8 +743,8 @@ def __init__(self, **kwargs): allow_extra=True) def add_sequential_recording(self, **kwargs): """ - Add a sequential recording (i.e., one row) consisting of one-or-more recording simultaneous_recordings - and associated custom sequential recording metadata to the table. + Add a sequential recording (i.e., one row) consisting of one or more simultaneous recordings + and associated custom sequential recording metadata to the table. """ _ = super().add_row(enforce_unique_id=True, **kwargs) return len(self.id) - 1 @@ -747,7 +760,7 @@ class RepetitionsTable(DynamicTable): __columns__ = ( {'name': 'sequential_recordings', - 'description': 'Column with a references to one or more rows in the SequentialRecordingsTable table', + 'description': 'Column with references to one or more rows in the SequentialRecordingsTable table', 'required': True, 'index': True, 'table': True}, @@ -785,8 +798,8 @@ def __init__(self, **kwargs): allow_extra=True) def add_repetition(self, **kwargs): """ - Add a repetition (i.e., one row) consisting of one-or-more recording sequential recordings - and associated custom repetition metadata to the table. + Add a repetition (i.e., one row) consisting of one or more sequential recordings + and associated custom repetition metadata to the table. """ _ = super().add_row(enforce_unique_id=True, **kwargs) return len(self.id) - 1 @@ -801,7 +814,7 @@ class ExperimentalConditionsTable(DynamicTable): __columns__ = ( {'name': 'repetitions', - 'description': 'Column with a references to one or more rows in the RepetitionsTable table', + 'description': 'Column with references to one or more rows in the RepetitionsTable table', 'required': True, 'index': True, 'table': True}, @@ -828,15 +841,15 @@ def __init__(self, **kwargs): @docval({'name': 'repetitions', 'type': 'array_data', - 'doc': 'the indices of the repetitions belonging to this condition', + 'doc': 'the indices of the repetitions belonging to this condition', 'default': None}, returns='Integer index of the row that was added to this table', rtype=int, allow_extra=True) def add_experimental_condition(self, **kwargs): """ - Add a condition (i.e., one row) consisting of one-or-more recording repetitions of sequential recordings - and associated custom experimental_conditions metadata to the table. + Add a condition (i.e., one row) consisting of one or more repetitions of sequential recordings + and associated custom experimental_conditions metadata to the table. """ _ = super().add_row(enforce_unique_id=True, **kwargs) return len(self.id) - 1 diff --git a/src/pynwb/nwb-schema b/src/pynwb/nwb-schema index 72dcc7245..55f264d19 160000 --- a/src/pynwb/nwb-schema +++ b/src/pynwb/nwb-schema @@ -1 +1 @@ -Subproject commit 72dcc72452f7828c89cd1dd71bc36ed0072b1b36 +Subproject commit 55f264d19f11aa2bdbbeef98eab3ace3e2e9fcf8 diff --git a/src/pynwb/testing/__init__.py b/src/pynwb/testing/__init__.py index fa8eb5c7c..e017f4a16 100644 --- a/src/pynwb/testing/__init__.py +++ b/src/pynwb/testing/__init__.py @@ -1,5 +1,6 @@ from hdmf.testing import TestCase, H5RoundTripMixin from .testh5io import NWBH5IOMixin, AcquisitionH5IOMixin from .utils import remove_test_file +from .icephys_testutils import create_icephys_stimulus_and_response, create_icephys_testfile CORE_NAMESPACE = 'core' diff --git a/src/pynwb/testing/icephys_testutils.py b/src/pynwb/testing/icephys_testutils.py new file mode 100644 index 000000000..732f312e6 --- /dev/null +++ b/src/pynwb/testing/icephys_testutils.py @@ -0,0 +1,177 @@ +""" +Module with helper functions to facilitate testing +""" +import numpy as np +from datetime import datetime +from dateutil.tz import tzlocal +from pynwb.file import NWBFile +from pynwb.icephys import (VoltageClampStimulusSeries, VoltageClampSeries) +from pynwb import NWBHDF5IO + + +def create_icephys_stimulus_and_response(sweep_number, electrode, randomize_data): + """ + Internal helper function to construct a dummy stimulus and response pair representing an + intracellular recording: + + :param sweep_number: Integer sweep number of the recording + :type sweep_number: int + :param electrode: Intracellular electrode used + :type electrode: pynwb.icephys.IntracellularElectrode + :param randomize_data: Randomize data values in the stimulus and response + :type randomize_data: bool + + :returns: Tuple of VoltageClampStimulusSeries with the stimulus and VoltageClampSeries with the response. + """ + stimulus = VoltageClampStimulusSeries( + name="ccss_"+str(sweep_number), + data=[1, 2, 3, 4, 5] if not randomize_data else np.random.rand(10), + starting_time=123.6 if not randomize_data else (np.random.rand() * 100), + rate=10e3 if not randomize_data else int(np.random.rand()*10) * 1000 + 1000., + electrode=electrode, + gain=0.1 if not randomize_data else np.random.rand(), + sweep_number=sweep_number) + # Create and ic-response + response = VoltageClampSeries( + name='vcs_'+str(sweep_number), + data=[0.1, 0.2, 0.3, 0.4, 0.5] if not randomize_data else np.random.rand(10), + conversion=1e-12, + resolution=np.nan, + starting_time=123.6 if not randomize_data else (np.random.rand() * 100), + rate=20e3 if not randomize_data else int(np.random.rand() * 20) * 1000. + 1000., + electrode=electrode, + gain=0.02 if not randomize_data else np.random.rand(), + capacitance_slow=100e-12, + resistance_comp_correction=70.0 if not randomize_data else 70.0 + np.random.rand(), + sweep_number=sweep_number) + return stimulus, response + + +def create_icephys_testfile(filename=None, add_custom_columns=True, randomize_data=True, with_missing_stimulus=True): + """ + Create a small but relatively complex icephys test file that + we can use for testing of queries. + + :param filename: The name of the output file to be generated. If set to None then the file is not written + but only created in memory + :type filename: str, None + :param add_custom_colums: Add custom metadata columns to each table + :type add_custom_colums: bool + :param randomize_data: Randomize data values in the stimulus and response + :type randomize_data: bool + + :returns: ICEphysFile NWBFile object created for writing. NOTE: If filename is provided then + the file is written to disk, but the function does not read the file back. If + you want to use the file from disk then you will need to read it with NWBHDF5IO. + :rtype: ICEphysFile + """ + nwbfile = NWBFile( + session_description='my first synthetic recording', + identifier='EXAMPLE_ID', + session_start_time=datetime.now(tzlocal()), + experimenter='Dr. Bilbo Baggins', + lab='Bag End Laboratory', + institution='University of Middle Earth at the Shire', + experiment_description='I went on an adventure with thirteen dwarves to reclaim vast treasures.', + session_id='LONELYMTN') + # Add a device + device = nwbfile.create_device(name='Heka ITC-1600') + # Add an intracellular electrode + electrode0 = nwbfile.create_icephys_electrode( + name="elec0", + description='a mock intracellular electrode', + device=device) + # Add an intracellular electrode + electrode1 = nwbfile.create_icephys_electrode( + name="elec1", + description='another mock intracellular electrode', + device=device) + # Add the intracelluar recordings + for sweep_number in range(20): + elec = (electrode0 if (sweep_number % 2 == 0) else electrode1) + stim, resp = create_icephys_stimulus_and_response(sweep_number=np.uint64(sweep_number), + electrode=elec, + randomize_data=randomize_data) + if with_missing_stimulus and sweep_number in [0, 10]: + stim = None + nwbfile.add_intracellular_recording(electrode=elec, + stimulus=stim, + response=resp, + id=sweep_number) + nwbfile.intracellular_recordings.add_column(name='recording_tags', + data=['A1', 'A2', + 'B1', 'B2', + 'C1', 'C2', 'C3', + 'D1', 'D2', 'D3', + 'A1', 'A2', + 'B1', 'B2', + 'C1', 'C2', 'C3', + 'D1', 'D2', 'D3'], + description='String with a set of recording tags') + # Add simultaneous_recordings + nwbfile.add_icephys_simultaneous_recording(recordings=[0, 1], id=np.int64(100)) + nwbfile.add_icephys_simultaneous_recording(recordings=[2, 3], id=np.int64(101)) + nwbfile.add_icephys_simultaneous_recording(recordings=[4, 5, 6], id=np.int64(102)) + nwbfile.add_icephys_simultaneous_recording(recordings=[7, 8, 9], id=np.int64(103)) + nwbfile.add_icephys_simultaneous_recording(recordings=[10, 11], id=np.int64(104)) + nwbfile.add_icephys_simultaneous_recording(recordings=[12, 13], id=np.int64(105)) + nwbfile.add_icephys_simultaneous_recording(recordings=[14, 15, 16], id=np.int64(106)) + nwbfile.add_icephys_simultaneous_recording(recordings=[17, 18, 19], id=np.int64(107)) + if add_custom_columns: + nwbfile.icephys_simultaneous_recordings.add_column( + name='tag', + data=np.arange(8), + description='some integer tag for a sweep') + + # Add sequential recordings + nwbfile.add_icephys_sequential_recording(simultaneous_recordings=[0, 1], + id=np.int64(1000), + stimulus_type="StimType_1") + nwbfile.add_icephys_sequential_recording(simultaneous_recordings=[2, ], + id=np.int64(1001), + stimulus_type="StimType_2") + nwbfile.add_icephys_sequential_recording(simultaneous_recordings=[3, ], + id=np.int64(1002), + stimulus_type="StimType_3") + nwbfile.add_icephys_sequential_recording(simultaneous_recordings=[4, 5], + id=np.int64(1003), + stimulus_type="StimType_1") + nwbfile.add_icephys_sequential_recording(simultaneous_recordings=[6, ], + id=np.int64(1004), + stimulus_type="StimType_2") + nwbfile.add_icephys_sequential_recording(simultaneous_recordings=[7, ], + id=np.int64(1005), + stimulus_type="StimType_3") + if add_custom_columns: + nwbfile.icephys_sequential_recordings.add_column( + name='type', + data=['T1', 'T2', 'T3', 'T1', 'T2', 'T3'], + description='type of the sequential recording') + + # Add repetitions + nwbfile.add_icephys_repetition(sequential_recordings=[0, ], id=np.int64(10000)) + nwbfile.add_icephys_repetition(sequential_recordings=[1, 2], id=np.int64(10001)) + nwbfile.add_icephys_repetition(sequential_recordings=[3, ], id=np.int64(10002)) + nwbfile.add_icephys_repetition(sequential_recordings=[4, 5], id=np.int64(10003)) + if add_custom_columns: + nwbfile.icephys_repetitions.add_column( + name='type', + data=['R1', 'R2', 'R1', 'R2'], + description='some repetition type indicator') + + # Add experimental_conditions + nwbfile.add_icephys_experimental_condition(repetitions=[0, 1], id=np.int64(100000)) + nwbfile.add_icephys_experimental_condition(repetitions=[2, 3], id=np.int64(100001)) + if add_custom_columns: + nwbfile.icephys_experimental_conditions.add_column( + name='temperature', + data=[32., 24.], + description='Temperatur in C') + + # Write our test file + if filename is not None: + with NWBHDF5IO(filename, 'w') as io: + io.write(nwbfile) + + # Return our in-memory NWBFile + return nwbfile diff --git a/src/pynwb/testing/make_test_files.py b/src/pynwb/testing/make_test_files.py index 161db19d0..b61af37b5 100644 --- a/src/pynwb/testing/make_test_files.py +++ b/src/pynwb/testing/make_test_files.py @@ -4,7 +4,7 @@ # pynwb 1.0.2 should be installed with hdmf 1.0.3 # pynwb 1.0.3 should be installed with hdmf 1.0.5 # pynwb 1.1.0 should be installed with hdmf 1.2.0 -# pynwb 1.1.1+ should be installed with an appopriate version of hdmf +# pynwb 1.1.1+ should be installed with an appropriate version of hdmf def _write(test_name, nwbfile): diff --git a/tests/unit/test_base.py b/tests/unit/test_base.py index a67d5b2f8..b2579e8f1 100644 --- a/tests/unit/test_base.py +++ b/tests/unit/test_base.py @@ -1,6 +1,6 @@ import numpy as np -from pynwb.base import ProcessingModule, TimeSeries, Images, Image, TimeSeriesReferenceVectorData +from pynwb.base import ProcessingModule, TimeSeries, Images, Image, TimeSeriesReferenceVectorData, TimeSeriesReference from pynwb.testing import TestCase from hdmf.data_utils import DataChunkIterator from hdmf.backends.hdf5 import H5DataIO @@ -226,4 +226,219 @@ def test_images(self): class TestTimeSeriesReferenceVectorData(TestCase): def test_init(self): - TimeSeriesReferenceVectorData(name='test', description='test') + temp = TimeSeriesReferenceVectorData() + self.assertEqual(temp.name, 'timeseries') + self.assertEqual(temp.description, + "Column storing references to a TimeSeries (rows). For each TimeSeries this " + "VectorData column stores the start_index and count to indicate the range in time " + "to be selected as well as an object reference to the TimeSeries.") + self.assertListEqual(temp.data, []) + temp = TimeSeriesReferenceVectorData(name='test', description='test') + self.assertEqual(temp.name, 'test') + self.assertEqual(temp.description, 'test') + + def test_get_empty(self): + """Get data from an empty TimeSeriesReferenceVectorData""" + temp = TimeSeriesReferenceVectorData() + self.assertListEqual(temp[:], []) + with self.assertRaises(IndexError): + temp[0] + + def test_get_length1_valid_data(self): + """Get data from a TimeSeriesReferenceVectorData with one element and valid data""" + temp = TimeSeriesReferenceVectorData() + value = TimeSeriesReference(0, 5, TimeSeries(name='test', description='test', + data=np.arange(10), starting_time=5.0, rate=0.1)) + temp.append(value) + self.assertTupleEqual(temp[0], value) + self.assertListEqual(temp[:], [TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE(*value), ]) + + def test_get_length1_invalid_data(self): + """Get data from a TimeSeriesReferenceVectorData with one element and invalid data""" + temp = TimeSeriesReferenceVectorData() + value = TimeSeriesReference(-1, -1, TimeSeries(name='test', description='test', + data=np.arange(10), starting_time=5.0, rate=0.1)) + temp.append(value) + # test index slicing + re = temp[0] + self.assertTrue(isinstance(re, TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE)) + self.assertTupleEqual(re, TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_NONE_TYPE) + # test array slicing and list slicing + selection = [slice(None), [0, ]] + for s in selection: + re = temp[s] + self.assertTrue(isinstance(re, list)) + self.assertTrue(len(re), 1) + self.assertTrue(isinstance(re[0], TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE)) + self.assertTupleEqual(re[0], TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_NONE_TYPE) + + def test_get_length5_valid_data(self): + """Get data from a TimeSeriesReferenceVectorData with 5 elements""" + temp = TimeSeriesReferenceVectorData() + num_values = 5 + values = [TimeSeriesReference(0, 5, TimeSeries(name='test'+str(i), description='test', + data=np.arange(10), starting_time=5.0, rate=0.1)) + for i in range(num_values)] + for v in values: + temp.append(v) + # Test single element selection + for i in range(num_values): + # test index slicing + re = temp[i] + self.assertTupleEqual(re, values[i]) + # test slicing + re = temp[i:i+1] + self.assertTupleEqual(re[0], TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE(*values[i])) + # Test multi element selection + re = temp[0:2] + self.assertTupleEqual(re[0], TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE(*values[0])) + self.assertTupleEqual(re[1], TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE(*values[1])) + + def test_get_length5_with_invalid_data(self): + """Get data from a TimeSeriesReferenceVectorData with 5 elements""" + temp = TimeSeriesReferenceVectorData() + num_values = 5 + values = [TimeSeriesReference(0, 5, TimeSeries(name='test'+str(i+1), description='test', + data=np.arange(10), starting_time=5.0, rate=0.1)) + for i in range(num_values-2)] + values = ([TimeSeriesReference(-1, -1, TimeSeries(name='test'+str(0), description='test', + data=np.arange(10), starting_time=5.0, rate=0.1)), ] + + values + + [TimeSeriesReference(-1, -1, TimeSeries(name='test'+str(5), description='test', + data=np.arange(10), starting_time=5.0, rate=0.1)), ]) + for v in values: + temp.append(v) + # Test single element selection + for i in range(num_values): + # test index slicing + re = temp[i] + if i in [0, 4]: + self.assertTrue(isinstance(re, TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE)) + self.assertTupleEqual(re, TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_NONE_TYPE) + else: + self.assertTupleEqual(re, values[i]) + # test slicing + re = temp[i:i+1] + if i in [0, 4]: + self.assertTrue(isinstance(re[0], TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE)) + self.assertTupleEqual(re[0], TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_NONE_TYPE) + else: + self.assertTupleEqual(re[0], TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE(*values[i])) + # Test multi element selection + re = temp[0:2] + self.assertTupleEqual(re[0], TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_NONE_TYPE) + self.assertTupleEqual(re[1], TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE(*values[1])) + + def test_add_row_restricted_type(self): + v = TimeSeriesReferenceVectorData(name='a', description='a') + with self.assertRaisesWith(TypeError, "TimeSeriesReferenceVectorData.add_row: incorrect type for " + "'val' (got 'int', expected 'TimeSeriesReference')"): + v.add_row(1) + + def test_append_restricted_type(self): + v = TimeSeriesReferenceVectorData(name='a', description='a') + with self.assertRaisesWith(TypeError, "TimeSeriesReferenceVectorData.append: incorrect type for " + "'arg' (got 'float', expected 'TimeSeriesReference')"): + v.append(2.0) + + +class TestTimeSeriesReference(TestCase): + + def test_check_types(self): + # invalid selection but with correct types + tsr = TimeSeriesReference(-1, -1, TimeSeries(name='test'+str(0), description='test', + data=np.arange(10), starting_time=5.0, rate=0.1)) + self.assertTrue(tsr.check_types()) + # invalid types, use float instead of int for both idx_start and count + tsr = TimeSeriesReference(1.0, 5.0, TimeSeries(name='test'+str(0), description='test', + data=np.arange(10), starting_time=5.0, rate=0.1)) + with self.assertRaisesWith(TypeError, "idx_start must be an integer not "): + tsr.check_types() + # invalid types, use float instead of int for idx_start only + tsr = TimeSeriesReference(1.0, 5, TimeSeries(name='test'+str(0), description='test', + data=np.arange(10), starting_time=5.0, rate=0.1)) + with self.assertRaisesWith(TypeError, "idx_start must be an integer not "): + tsr.check_types() + # invalid types, use float instead of int for count only + tsr = TimeSeriesReference(1, 5.0, TimeSeries(name='test'+str(0), description='test', + data=np.arange(10), starting_time=5.0, rate=0.1)) + with self.assertRaisesWith(TypeError, "count must be an integer "): + tsr.check_types() + # invalid type for TimeSeries but valid idx_start and count + tsr = TimeSeriesReference(1, 5, None) + with self.assertRaisesWith(TypeError, "timeseries must be of type TimeSeries. "): + tsr.check_types() + + def test_is_invalid(self): + tsr = TimeSeriesReference(-1, -1, TimeSeries(name='test'+str(0), description='test', + data=np.arange(10), starting_time=5.0, rate=0.1)) + self.assertFalse(tsr.isvalid()) + + def test_is_valid(self): + tsr = TimeSeriesReference(0, 10, TimeSeries(name='test'+str(0), description='test', + data=np.arange(10), starting_time=5.0, rate=0.1)) + self.assertTrue(tsr.isvalid()) + + def test_is_valid_bad_index(self): + # Error: negative start_index but positive count + tsr = TimeSeriesReference(-1, 10, TimeSeries(name='test0', description='test0', + data=np.arange(10), starting_time=5.0, rate=0.1)) + with self.assertRaisesWith(IndexError, "'idx_start' -1 out of range for timeseries 'test0'"): + tsr.isvalid() + # Error: start_index too large + tsr = TimeSeriesReference(10, 0, TimeSeries(name='test0', description='test0', + data=np.arange(10), starting_time=5.0, rate=0.1)) + with self.assertRaisesWith(IndexError, "'idx_start' 10 out of range for timeseries 'test0'"): + tsr.isvalid() + # Error: positive start_index but negative count + tsr = TimeSeriesReference(0, -3, TimeSeries(name='test0', description='test0', + data=np.arange(10), starting_time=5.0, rate=0.1)) + with self.assertRaisesWith(IndexError, "'count' -3 invalid. 'count' must be positive"): + tsr.isvalid() + # Error: start_index + count too large + tsr = TimeSeriesReference(3, 10, TimeSeries(name='test0', description='test0', + data=np.arange(10), starting_time=5.0, rate=0.1)) + with self.assertRaisesWith(IndexError, "'idx_start + count' out of range for timeseries 'test0'"): + tsr.isvalid() + + def test_timestamps_property(self): + # Timestamps from starting_time and rate + tsr = TimeSeriesReference(5, 4, TimeSeries(name='test0', description='test0', + data=np.arange(10), starting_time=5.0, rate=0.1)) + np.testing.assert_array_equal(tsr.timestamps, np.array([5.5, 5.6, 5.7, 5.8])) + # Timestamps from timestamps directly + tsr = TimeSeriesReference(5, 4, TimeSeries(name='test0', description='test0', + data=np.arange(10), timestamps=np.arange(10).astype(float))) + np.testing.assert_array_equal(tsr.timestamps, np.array([5., 6., 7., 8.])) + + def test_timestamps_property_invalid_reference(self): + # Timestamps from starting_time and rate + tsr = TimeSeriesReference(-1, -1, TimeSeries(name='test0', description='test0', + data=np.arange(10), starting_time=5.0, rate=0.1)) + self.assertIsNone(tsr.timestamps) + + def test_timestamps_property_bad_reference(self): + tsr = TimeSeriesReference(0, 12, TimeSeries(name='test0', description='test0', + data=np.arange(10), timestamps=np.arange(10).astype(float))) + with self.assertRaisesWith(IndexError, "'idx_start + count' out of range for timeseries 'test0'"): + tsr.timestamps + tsr = TimeSeriesReference(0, 12, TimeSeries(name='test0', description='test0', + data=np.arange(10), starting_time=5.0, rate=0.1)) + with self.assertRaisesWith(IndexError, "'idx_start + count' out of range for timeseries 'test0'"): + tsr.timestamps + + def test_data_property(self): + tsr = TimeSeriesReference(5, 4, TimeSeries(name='test0', description='test0', + data=np.arange(10), starting_time=5.0, rate=0.1)) + np.testing.assert_array_equal(tsr.data, np.array([5., 6., 7., 8.])) + + def test_data_property_invalid_reference(self): + tsr = TimeSeriesReference(-1, -1, TimeSeries(name='test0', description='test0', + data=np.arange(10), starting_time=5.0, rate=0.1)) + self.assertIsNone(tsr.data) + + def test_data_property_bad_reference(self): + tsr = TimeSeriesReference(0, 12, TimeSeries(name='test0', description='test0', + data=np.arange(10), starting_time=5.0, rate=0.1)) + with self.assertRaisesWith(IndexError, "'idx_start + count' out of range for timeseries 'test0'"): + tsr.data diff --git a/tests/unit/test_core.py b/tests/unit/test_core.py index 0a9d74f85..ee599b00e 100644 --- a/tests/unit/test_core.py +++ b/tests/unit/test_core.py @@ -2,7 +2,6 @@ from dateutil.tz import tzlocal from hdmf.utils import docval, call_docval_func -from hdmf import __version__ as hdmfversion from pynwb import NWBFile, TimeSeries, available_namespaces from pynwb.core import NWBContainer diff --git a/tests/unit/test_icephys_metadata_tables.py b/tests/unit/test_icephys_metadata_tables.py index 1fd67cc33..d63e1ffb8 100644 --- a/tests/unit/test_icephys_metadata_tables.py +++ b/tests/unit/test_icephys_metadata_tables.py @@ -1,6 +1,6 @@ """ Module for testing of the intracellular electrophysiology experiment metadata -tables originally created as part of the ndx-icephy-meta extension. These +tables originally created as part of the ndx-icephys-meta extension. These are tested in this separate module to avoid crowding of the main test_icephys test module and to allow us to test the main experiment metadata structures separately. @@ -14,11 +14,12 @@ import h5py -from pynwb.testing import TestCase, remove_test_file +from pynwb.testing import TestCase, remove_test_file, create_icephys_stimulus_and_response from pynwb.file import NWBFile from pynwb.icephys import (VoltageClampStimulusSeries, VoltageClampSeries, CurrentClampStimulusSeries, IZeroClampSeries, IntracellularRecordingsTable, SimultaneousRecordingsTable, SequentialRecordingsTable, RepetitionsTable, ExperimentalConditionsTable) +from pynwb.base import TimeSeriesReferenceVectorData from pynwb import NWBHDF5IO from hdmf.utils import docval, popargs @@ -26,171 +27,7 @@ class ICEphysMetaTestBase(TestCase): """ Base helper class for setting up tests for the ndx-icephys-meta extension. - - Here we use the base NWBFile class for read/write (rather than our custom ICEphysFile) - to make sure tests of the individual tables do not depend on our custom class. We are testing - ICEphysFile separately. """ - @classmethod - def create_stimulus_and_response(cls, sweep_number, electrode, randomize_data): - """ - Internal helper function to construct a dummy stimulus and reponse pair representing an - instracellular recording: - - :param sweep_number: Integer sweep number of the recording - :param electrode: Intracellular electrode used - :param randomize_data: Randomize data values in the stimulus and response - - :returns: Tuple of VoltageClampStimulusSeries with the stimulus and VoltageClampSeries with the response. - """ - stimulus = VoltageClampStimulusSeries( - name="ccss_"+str(sweep_number), - data=[1, 2, 3, 4, 5] if not randomize_data else np.random.rand(10), - starting_time=123.6 if not randomize_data else (np.random.rand() * 100), - rate=10e3 if not randomize_data else int(np.random.rand()*10) * 1000 + 1000., - electrode=electrode, - gain=0.1 if not randomize_data else np.random.rand(), - sweep_number=sweep_number) - # Create and ic-response - response = VoltageClampSeries( - name='vcs_'+str(sweep_number), - data=[0.1, 0.2, 0.3, 0.4, 0.5] if not randomize_data else np.random.rand(10), - conversion=1e-12, - resolution=np.nan, - starting_time=123.6 if not randomize_data else (np.random.rand() * 100), - rate=20e3 if not randomize_data else int(np.random.rand() * 20) * 1000. + 1000., - electrode=electrode, - gain=0.02 if not randomize_data else np.random.rand(), - capacitance_slow=100e-12, - resistance_comp_correction=70.0 if not randomize_data else 70.0 + np.random.rand(), - sweep_number=sweep_number) - return stimulus, response - - @classmethod - def create_icephs_meta_testfile(cls, filename=None, add_custom_columns=True, randomize_data=True): - """ - Create a small but relatively complex icephys test file that - we can use for testing of queries. - - :param filename: The name of the output file to be generated. If set to None then the file is not written - but only created in memory - :type filename: str, None - :param add_custom_colums: Add custom metadata columns to each table - :type add_custom_colums: bool - :param randomize_data: Randomize data values in the stimulus and response - :type randomize_data: bool - - :returns: ICEphysFile NWBFile object - :rtype: ICEphysFile - """ - nwbfile = NWBFile( - session_description='my first synthetic recording', - identifier='EXAMPLE_ID', - session_start_time=datetime.now(tzlocal()), - experimenter='Dr. Bilbo Baggins', - lab='Bag End Laboratory', - institution='University of Middle Earth at the Shire', - experiment_description='I went on an adventure with thirteen dwarves to reclaim vast treasures.', - session_id='LONELYMTN') - # Add a device - device = nwbfile.create_device(name='Heka ITC-1600') - # Add an intracellular electrode - electrode0 = nwbfile.create_icephys_electrode( - name="elec0", - description='a mock intracellular electrode', - device=device) - # Add an intracellular electrode - electrode1 = nwbfile.create_icephys_electrode( - name="elec1", - description='another mock intracellular electrode', - device=device) - # Add the intracelluar recordings - for sweep_number in range(20): - elec = (electrode0 if (sweep_number % 2 == 0) else electrode1) - stim, resp = cls.create_stimulus_and_response(sweep_number=np.uint64(sweep_number), - electrode=elec, - randomize_data=randomize_data) - nwbfile.add_intracellular_recording(electrode=elec, - stimulus=stim, - response=resp, - id=sweep_number) - nwbfile.intracellular_recordings.add_column(name='recording_tags', - data=['A1', 'A2', - 'B1', 'B2', - 'C1', 'C2', 'C3', - 'D1', 'D2', 'D3', - 'A1', 'A2', - 'B1', 'B2', - 'C1', 'C2', 'C3', - 'D1', 'D2', 'D3'], - description='String with a set of recording tags') - # Add simultaneous_recordings - nwbfile.add_icephys_simultaneous_recording(recordings=[0, 1], id=np.int64(100)) - nwbfile.add_icephys_simultaneous_recording(recordings=[2, 3], id=np.int64(101)) - nwbfile.add_icephys_simultaneous_recording(recordings=[4, 5, 6], id=np.int64(102)) - nwbfile.add_icephys_simultaneous_recording(recordings=[7, 8, 9], id=np.int64(103)) - nwbfile.add_icephys_simultaneous_recording(recordings=[10, 11], id=np.int64(104)) - nwbfile.add_icephys_simultaneous_recording(recordings=[12, 13], id=np.int64(105)) - nwbfile.add_icephys_simultaneous_recording(recordings=[14, 15, 16], id=np.int64(106)) - nwbfile.add_icephys_simultaneous_recording(recordings=[17, 18, 19], id=np.int64(107)) - if add_custom_columns: - nwbfile.icephys_simultaneous_recordings.add_column( - name='tag', - data=np.arange(8), - description='some integer tag for a sweep') - - # Add sequential recordings - nwbfile.add_icephys_sequential_recording(simultaneous_recordings=[0, 1], - id=np.int64(1000), - stimulus_type="StimType_1") - nwbfile.add_icephys_sequential_recording(simultaneous_recordings=[2, ], - id=np.int64(1001), - stimulus_type="StimType_2") - nwbfile.add_icephys_sequential_recording(simultaneous_recordings=[3, ], - id=np.int64(1002), - stimulus_type="StimType_3") - nwbfile.add_icephys_sequential_recording(simultaneous_recordings=[4, 5], - id=np.int64(1003), - stimulus_type="StimType_1") - nwbfile.add_icephys_sequential_recording(simultaneous_recordings=[6, ], - id=np.int64(1004), - stimulus_type="StimType_2") - nwbfile.add_icephys_sequential_recording(simultaneous_recordings=[7, ], - id=np.int64(1005), - stimulus_type="StimType_3") - if add_custom_columns: - nwbfile.icephys_sequential_recordings.add_column( - name='type', - data=['T1', 'T2', 'T3', 'T1', 'T2', 'T3'], - description='type of the sequential recording') - - # Add repetitions - nwbfile.add_icephys_repetition(sequential_recordings=[0, ], id=np.int64(10000)) - nwbfile.add_icephys_repetition(sequential_recordings=[1, 2], id=np.int64(10001)) - nwbfile.add_icephys_repetition(sequential_recordings=[3, ], id=np.int64(10002)) - nwbfile.add_icephys_repetition(sequential_recordings=[4, 5], id=np.int64(10003)) - if add_custom_columns: - nwbfile.icephys_repetitions.add_column( - name='type', - data=['R1', 'R2', 'R1', 'R2'], - description='some repetition type indicator') - - # Add experimental_conditions - nwbfile.add_icephys_experimental_condition(repetitions=[0, 1], id=np.int64(100000)) - nwbfile.add_icephys_experimental_condition(repetitions=[2, 3], id=np.int64(100001)) - if add_custom_columns: - nwbfile.icephys_experimental_conditions.add_column( - name='temperature', - data=[32., 24.], - description='Temperatur in C') - - # Write our test file - if filename is not None: - with NWBHDF5IO(filename, 'w') as io: - io.write(nwbfile) - - # Return our in-memory NWBFile - return nwbfile def setUp(self): # Create an example nwbfile with a device, intracellular electrode, stimulus, and response @@ -202,30 +39,37 @@ def setUp(self): lab='Bag End Laboratory', institution='University of Middle Earth at the Shire', experiment_description='I went on an adventure with thirteen dwarves to reclaim vast treasures.', - session_id='LONELYMTN') + session_id='LONELYMTN' + ) self.device = self.nwbfile.create_device(name='Heka ITC-1600') - self.electrode = self.nwbfile.create_icephys_electrode(name="elec0", - description='a mock intracellular electrode', - device=self.device) - self.stimulus = VoltageClampStimulusSeries(name="ccss", - data=[1, 2, 3, 4, 5], - starting_time=123.6, - rate=10e3, - electrode=self.electrode, - gain=0.02, - sweep_number=np.uint64(15)) + self.electrode = self.nwbfile.create_icephys_electrode( + name="elec0", + description='a mock intracellular electrode', + device=self.device + ) + self.stimulus = VoltageClampStimulusSeries( + name="ccss", + data=[1, 2, 3, 4, 5], + starting_time=123.6, + rate=10e3, + electrode=self.electrode, + gain=0.02, + sweep_number=np.uint64(15) + ) self.nwbfile.add_stimulus(self.stimulus) - self.response = VoltageClampSeries(name='vcs', - data=[0.1, 0.2, 0.3, 0.4, 0.5], - conversion=1e-12, - resolution=np.nan, - starting_time=123.6, - rate=20e3, - electrode=self.electrode, - gain=0.02, - capacitance_slow=100e-12, - resistance_comp_correction=70.0, - sweep_number=np.uint64(15)) + self.response = VoltageClampSeries( + name='vcs', + data=[0.1, 0.2, 0.3, 0.4, 0.5], + conversion=1e-12, + resolution=np.nan, + starting_time=123.6, + rate=20e3, + electrode=self.electrode, + gain=0.02, + capacitance_slow=100e-12, + resistance_comp_correction=70.0, + sweep_number=np.uint64(15) + ) self.nwbfile.add_acquisition(self.response) self.path = 'test_icephys_meta_intracellularrecording.h5' @@ -316,11 +160,7 @@ class IntracellularElectrodesTableTests(TestCase): The IntracellularElectrodesTable is covered by the IntracellularRecordingsTableTests as this table is part of that table. """ - def setUp(self): - pass - - def tearDown(self): - pass + pass class IntracellularStimuliTableTests(TestCase): @@ -328,11 +168,7 @@ class IntracellularStimuliTableTests(TestCase): The IntracellularStimuliTable is covered by the IntracellularRecordingsTableTests as this table is part of that table. """ - def setUp(self): - pass - - def tearDown(self): - pass + pass class IntracellularResponsesTableTests(TestCase): @@ -340,11 +176,7 @@ class IntracellularResponsesTableTests(TestCase): The IntracellularResponsesTable is covered by the IntracellularRecordingsTableTests as this table is part of that table. """ - def setUp(self): - pass - - def tearDown(self): - pass + pass class IntracellularRecordingsTableTests(ICEphysMetaTestBase): @@ -353,20 +185,22 @@ class IntracellularRecordingsTableTests(ICEphysMetaTestBase): """ def test_init(self): - _ = IntracellularRecordingsTable() - self.assertTrue(True) + ret = IntracellularRecordingsTable() + self.assertEqual(ret.name, 'intracellular_recordings') def test_add_row(self): # Add a row to our IR table ir = IntracellularRecordingsTable() - row_index = ir.add_recording(electrode=self.electrode, - stimulus=self.stimulus, - response=self.response, - id=np.int64(10)) - # test that we get the correct row index back + row_index = ir.add_recording( + electrode=self.electrode, + stimulus=self.stimulus, + response=self.response, + id=np.int64(10) + ) + # Test that we get the correct row index back self.assertEqual(row_index, 0) - # read our first (and only) row and assert that it is correct + # Read our first (and only) row and assert that it is correct res = ir[0] # Confirm that slicing one row give the same result as converting the whole table, which has only one row assert_frame_equal(ir.to_dataframe(), res) @@ -378,7 +212,7 @@ def test_add_row(self): self.assertTupleEqual(res[('stimuli', 'stimulus')].iloc[0], (0, 5, self.stimulus)) # Check the response self.assertTupleEqual(res[('responses', 'response')].iloc[0], (0, 5, self.response)) - # test writing out ir table + # Test writing out ir table self.write_test_helper(ir) def test_add_row_incompatible_types(self): @@ -391,13 +225,16 @@ def test_add_row_incompatible_types(self): rate=10e3, electrode=self.electrode, gain=0.1, - sweep_number=np.uint64(sweep_number)) + sweep_number=np.uint64(sweep_number) + ) ir = IntracellularRecordingsTable() with self.assertRaises(ValueError): - _ = ir.add_recording(electrode=self.electrode, - stimulus=local_stimulus, - response=self.response, - id=np.int64(10)) + _ = ir.add_recording( + electrode=self.electrode, + stimulus=local_stimulus, + response=self.response, + id=np.int64(10) + ) def test_warn_if_IZeroClampSeries_with_stimulus(self): local_response = IZeroClampSeries( @@ -407,38 +244,49 @@ def test_warn_if_IZeroClampSeries_with_stimulus(self): rate=10e3, electrode=self.electrode, gain=0.02, - sweep_number=np.uint64(100000)) + sweep_number=np.uint64(100000) + ) ir = IntracellularRecordingsTable() with self.assertRaises(ValueError): - _ = ir.add_recording(electrode=self.electrode, - stimulus=self.stimulus, - response=local_response, - id=np.int64(10)) + _ = ir.add_recording( + electrode=self.electrode, + stimulus=self.stimulus, + response=local_response, + id=np.int64(10) + ) def test_inconsistent_PatchClampSeries(self): - local_electrode = self.nwbfile.create_icephys_electrode(name="elec1", - description='a mock intracellular electrode', - device=self.device) - local_stimulus = VoltageClampStimulusSeries(name="ccss", - data=[1, 2, 3, 4, 5], - starting_time=123.6, - rate=10e3, - electrode=local_electrode, - gain=0.02, - sweep_number=np.uint64(100000)) + local_electrode = self.nwbfile.create_icephys_electrode( + name="elec1", + description='a mock intracellular electrode', + device=self.device + ) + local_stimulus = VoltageClampStimulusSeries( + name="ccss", + data=[1, 2, 3, 4, 5], + starting_time=123.6, + rate=10e3, + electrode=local_electrode, + gain=0.02, + sweep_number=np.uint64(100000) + ) ir = IntracellularRecordingsTable() with self.assertRaises(ValueError): - _ = ir.add_recording(electrode=self.electrode, - stimulus=local_stimulus, - response=self.response, - id=np.int64(10)) + _ = ir.add_recording( + electrode=self.electrode, + stimulus=local_stimulus, + response=self.response, + id=np.int64(10) + ) def test_add_row_no_response(self): ir = IntracellularRecordingsTable() - row_index = ir.add_recording(electrode=self.electrode, - stimulus=self.stimulus, - response=None, - id=np.int64(10)) + row_index = ir.add_recording( + electrode=self.electrode, + stimulus=self.stimulus, + response=None, + id=np.int64(10) + ) res = ir[0] # Check the ID self.assertEqual(row_index, 0) @@ -450,16 +298,19 @@ def test_add_row_no_response(self): # Check the stimulus self.assertTupleEqual(res[('stimuli', 'stimulus')].iloc[0], (0, 5, self.stimulus)) # Check the response - self.assertTupleEqual(res[('responses', 'response')].iloc[0], (-1, -1, self.stimulus)) - # test writing out ir table + self.assertTrue(isinstance(res[('responses', 'response')].iloc[0], + TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE)) + # Test writing out ir table self.write_test_helper(ir) def test_add_row_no_stimulus(self): ir = IntracellularRecordingsTable() - row_index = ir.add_recording(electrode=self.electrode, - stimulus=None, - response=self.response, - id=np.int64(10)) + row_index = ir.add_recording( + electrode=self.electrode, + stimulus=None, + response=self.response, + id=np.int64(10) + ) res = ir[0] # Check the ID self.assertEqual(row_index, 0) @@ -469,110 +320,132 @@ def test_add_row_no_stimulus(self): # Check electrodes self.assertIs(res[('electrodes', 'electrode')].iloc[0], self.electrode) # Check the stimulus - self.assertTupleEqual(res[('stimuli', 'stimulus')].iloc[0], (-1, -1, self.response)) + self.assertTrue(isinstance(res[('stimuli', 'stimulus')].iloc[0], + TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE)) # Check the response self.assertTupleEqual(res[('responses', 'response')].iloc[0], (0, 5, self.response)) - # test writing out ir table + # Test writing out ir table self.write_test_helper(ir) def test_add_row_check_start_index_and_index_count_are_fixed(self): # Make sure -1 values are converted ir = IntracellularRecordingsTable() - ir.add_recording(electrode=self.electrode, - stimulus=self.stimulus, - stimulus_start_index=-1, # assert this is fixed to 0 - stimulus_index_count=-1, # assert this is fixed to len(stimulus) - response=None, - response_start_index=0, # assert this is fixed to -1 - response_index_count=10, # assert this is fixed to -1 - id=np.int64(10)) + ir.add_recording( + electrode=self.electrode, + stimulus=self.stimulus, + stimulus_start_index=-1, # assert this is fixed to 0 + stimulus_index_count=-1, # assert this is fixed to len(stimulus) + response=None, + response_start_index=0, # assert this is fixed to -1 + response_index_count=10, # assert this is fixed to -1 + id=np.int64(10) + ) res = ir[0] self.assertTupleEqual(res[('stimuli', 'stimulus')].iloc[0], (0, len(self.stimulus.data), self.stimulus)) - self.assertTupleEqual(res[('responses', 'response')].iloc[0], - (-1, -1, self.stimulus)) + self.assertTrue(isinstance(res[('responses', 'response')].iloc[0], + TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE)) # Make sure single -1 values are converted ir = IntracellularRecordingsTable() - ir.add_recording(electrode=self.electrode, - stimulus=self.stimulus, - stimulus_start_index=2, - id=np.int64(10)) + ir.add_recording( + electrode=self.electrode, + stimulus=self.stimulus, + stimulus_start_index=2, + id=np.int64(10) + ) res = ir[0] self.assertTupleEqual(res[('stimuli', 'stimulus')].iloc[0], (2, len(self.stimulus.data)-2, self.stimulus)) # Make sure single -1 values are converted ir = IntracellularRecordingsTable() - ir.add_recording(electrode=self.electrode, - stimulus=self.stimulus, - stimulus_index_count=2, - id=np.int64(10)) + ir.add_recording( + electrode=self.electrode, + stimulus=self.stimulus, + stimulus_index_count=2, + id=np.int64(10) + ) res = ir[0] self.assertTupleEqual(res[('stimuli', 'stimulus')].iloc[0], (0, 2, self.stimulus)) def test_add_row_index_out_of_range(self): - # Stimulus/Response start_index to large with self.assertRaises(IndexError): ir = IntracellularRecordingsTable() - ir.add_recording(electrode=self.electrode, - stimulus=self.stimulus, - stimulus_start_index=10, - response=self.response, - id=np.int64(10)) + ir.add_recording( + electrode=self.electrode, + stimulus=self.stimulus, + stimulus_start_index=10, + response=self.response, + id=np.int64(10) + ) with self.assertRaises(IndexError): ir = IntracellularRecordingsTable() - ir.add_recording(electrode=self.electrode, - stimulus=self.stimulus, - response_start_index=10, - response=self.response, - id=np.int64(10)) + ir.add_recording( + electrode=self.electrode, + stimulus=self.stimulus, + response_start_index=10, + response=self.response, + id=np.int64(10) + ) # Stimulus/Reponse index count too large with self.assertRaises(IndexError): ir = IntracellularRecordingsTable() - ir.add_recording(electrode=self.electrode, - stimulus=self.stimulus, - stimulus_index_count=10, - response=self.response, - id=np.int64(10)) + ir.add_recording( + electrode=self.electrode, + stimulus=self.stimulus, + stimulus_index_count=10, + response=self.response, + id=np.int64(10) + ) with self.assertRaises(IndexError): ir = IntracellularRecordingsTable() - ir.add_recording(electrode=self.electrode, - stimulus=self.stimulus, - response_index_count=10, - response=self.response, - id=np.int64(10)) + ir.add_recording( + electrode=self.electrode, + stimulus=self.stimulus, + response_index_count=10, + response=self.response, + id=np.int64(10) + ) # Stimulus/Reponse start+count combination too large with self.assertRaises(IndexError): ir = IntracellularRecordingsTable() - ir.add_recording(electrode=self.electrode, - stimulus=self.stimulus, - stimulus_start_index=3, - stimulus_index_count=4, - response=self.response, - id=np.int64(10)) + ir.add_recording( + electrode=self.electrode, + stimulus=self.stimulus, + stimulus_start_index=3, + stimulus_index_count=4, + response=self.response, + id=np.int64(10) + ) with self.assertRaises(IndexError): ir = IntracellularRecordingsTable() - ir.add_recording(electrode=self.electrode, - stimulus=self.stimulus, - response_start_index=3, - response_index_count=4, - response=self.response, - id=np.int64(10)) + ir.add_recording( + electrode=self.electrode, + stimulus=self.stimulus, + response_start_index=3, + response_index_count=4, + response=self.response, + id=np.int64(10) + ) def test_add_row_no_stimulus_and_response(self): with self.assertRaises(ValueError): ir = IntracellularRecordingsTable() - ir.add_recording(electrode=self.electrode, - stimulus=None, - response=None) + ir.add_recording( + electrode=self.electrode, + stimulus=None, + response=None + ) def test_add_column(self): ir = IntracellularRecordingsTable() - ir.add_recording(electrode=self.electrode, - stimulus=self.stimulus, - response=self.response, - id=np.int64(10)) + ir.add_recording( + electrode=self.electrode, + stimulus=self.stimulus, + response=self.response, + id=np.int64(10) + ) ir.add_column(name='test', description='test column', data=np.arange(1)) self.assertTupleEqual(ir.colnames, ('test',)) @@ -581,29 +454,94 @@ def test_enforce_unique_id(self): Test to ensure that unique ids are enforced on RepetitionsTable table """ ir = IntracellularRecordingsTable() - ir.add_recording(electrode=self.electrode, - stimulus=self.stimulus, - response=self.response, - id=np.int64(10)) + ir.add_recording( + electrode=self.electrode, + stimulus=self.stimulus, + response=self.response, + id=np.int64(10) + ) with self.assertRaises(ValueError): - ir.add_recording(electrode=self.electrode, - stimulus=self.stimulus, - response=self.response, - id=np.int64(10)) + ir.add_recording( + electrode=self.electrode, + stimulus=self.stimulus, + response=self.response, + id=np.int64(10) + ) def test_basic_write(self): """ Populate, write, and read the SimultaneousRecordingsTable container and other required containers """ ir = IntracellularRecordingsTable() - row_index = ir.add_recording(electrode=self.electrode, - stimulus=self.stimulus, - response=self.response, - id=np.int64(10)) + row_index = ir.add_recording( + electrode=self.electrode, + stimulus=self.stimulus, + response=self.response, + id=np.int64(10) + ) self.assertEqual(row_index, 0) ir.add_column(name='test', description='test column', data=np.arange(1)) self.write_test_helper(ir=ir) + def test_to_dataframe(self): + # Add the intracelluar recordings + # Create a table setup for testing a number of conditions + electrode0 = self.electrode + electrode1 = self.nwbfile.create_icephys_electrode( + name="elec1", + description='another mock intracellular electrode', + device=self.device + ) + for sweep_number in range(20): + elec = (electrode0 if (sweep_number % 2 == 0) else electrode1) + stim, resp = create_icephys_stimulus_and_response(sweep_number=np.uint64(sweep_number), + electrode=elec, + randomize_data=False) + if sweep_number in [0, 10]: # include missing stimuli + stim = None + self.nwbfile.add_intracellular_recording(electrode=elec, + stimulus=stim, + response=resp, + id=sweep_number) + tags_data = ['A1', 'A2', 'B1', 'B2', 'C1', 'C2', 'C3', + 'D1', 'D2', 'D3', 'A1', 'A2', 'B1', 'B2', + 'C1', 'C2', 'C3', 'D1', 'D2', 'D3'] + self.nwbfile.intracellular_recordings.add_column(name='recording_tags', + data=tags_data, + description='String with a set of recording tags') + # Test normal conversion to a dataframe + df = self.nwbfile.intracellular_recordings.to_dataframe() + expected_cols = [('intracellular_recordings', 'recording_tags'), ('electrodes', 'id'), + ('electrodes', 'electrode'), ('stimuli', 'id'), ('stimuli', 'stimulus'), + ('responses', 'id'), ('responses', 'response')] + self.assertListEqual(df.columns.to_list(), expected_cols) + self.assertListEqual(df[('intracellular_recordings', 'recording_tags')].to_list(), tags_data) + # Test conversion with ignore category ids set + df = self.nwbfile.intracellular_recordings.to_dataframe(ignore_category_ids=True) + expected_cols_no_ids = [('intracellular_recordings', 'recording_tags'), + ('electrodes', 'electrode'), ('stimuli', 'stimulus'), + ('responses', 'response')] + self.assertListEqual(df.columns.to_list(), expected_cols_no_ids) + # Test conversion with stimulus_refs_as_objectids + df = self.nwbfile.intracellular_recordings.to_dataframe(stimulus_refs_as_objectids=True) + self.assertListEqual(df.columns.to_list(), expected_cols) + expects_stim_col = [e if e[2] is None else (e[0], e[1], e[2].object_id) + for e in self.nwbfile.intracellular_recordings[('stimuli', 'stimulus')][:]] + self.assertListEqual(df[('stimuli', 'stimulus')].tolist(), expects_stim_col) + # Test conversion with response_refs_as_objectids + df = self.nwbfile.intracellular_recordings.to_dataframe(response_refs_as_objectids=True) + self.assertListEqual(df.columns.to_list(), expected_cols) + expects_resp_col = [e if e[2] is None else (e[0], e[1], e[2].object_id) + for e in self.nwbfile.intracellular_recordings[('responses', 'response')][:]] + self.assertListEqual(df[('responses', 'response')].tolist(), expects_resp_col) + # Test conversion with all options enabled + df = self.nwbfile.intracellular_recordings.to_dataframe(ignore_category_ids=True, + stimulus_refs_as_objectids=True, + response_refs_as_objectids=True) + self.assertListEqual(df.columns.to_list(), expected_cols_no_ids) + self.assertListEqual(df[('stimuli', 'stimulus')].tolist(), expects_stim_col) + self.assertListEqual(df[('responses', 'response')].tolist(), expects_resp_col) + def test_round_trip_container_no_data(self): """Test read and write the container by itself""" curr = IntracellularRecordingsTable() @@ -621,43 +559,51 @@ def test_write_with_stimulus_template(self): Populate, write, and read the SimultaneousRecordingsTable container and other required containers """ local_nwbfile = NWBFile( - session_description='my first synthetic recording', - identifier='EXAMPLE_ID', - session_start_time=datetime.now(tzlocal()), - experimenter='Dr. Bilbo Baggins', - lab='Bag End Laboratory', - institution='University of Middle Earth at the Shire', - experiment_description='I went on an adventure with thirteen dwarves to reclaim vast treasures.', - session_id='LONELYMTN') + session_description='my first synthetic recording', + identifier='EXAMPLE_ID', + session_start_time=datetime.now(tzlocal()), + experimenter='Dr. Bilbo Baggins', + lab='Bag End Laboratory', + institution='University of Middle Earth at the Shire', + experiment_description='I went on an adventure with thirteen dwarves to reclaim vast treasures.', + session_id='LONELYMTN' + ) # Add a device local_device = local_nwbfile.create_device(name='Heka ITC-1600') local_electrode = local_nwbfile.create_icephys_electrode( name="elec0", description='a mock intracellular electrode', - device=local_device) - local_stimulus = VoltageClampStimulusSeries(name="ccss", - data=[1, 2, 3, 4, 5], - starting_time=123.6, - rate=10e3, - electrode=local_electrode, - gain=0.02, - sweep_number=np.uint64(15)) - local_response = VoltageClampSeries(name='vcs', - data=[0.1, 0.2, 0.3, 0.4, 0.5], - conversion=1e-12, - resolution=np.nan, - starting_time=123.6, - rate=20e3, - electrode=local_electrode, - gain=0.02, - capacitance_slow=100e-12, - resistance_comp_correction=70.0, - sweep_number=np.uint64(15)) + device=local_device + ) + local_stimulus = VoltageClampStimulusSeries( + name="ccss", + data=[1, 2, 3, 4, 5], + starting_time=123.6, + rate=10e3, + electrode=local_electrode, + gain=0.02, + sweep_number=np.uint64(15) + ) + local_response = VoltageClampSeries( + name='vcs', + data=[0.1, 0.2, 0.3, 0.4, 0.5], + conversion=1e-12, + resolution=np.nan, + starting_time=123.6, + rate=20e3, + electrode=local_electrode, + gain=0.02, + capacitance_slow=100e-12, + resistance_comp_correction=70.0, + sweep_number=np.uint64(15) + ) local_nwbfile.add_stimulus_template(local_stimulus) - row_index = local_nwbfile.add_intracellular_recording(electrode=local_electrode, - stimulus=local_stimulus, - response=local_response, - id=np.int64(10)) + row_index = local_nwbfile.add_intracellular_recording( + electrode=local_electrode, + stimulus=local_stimulus, + response=local_response, + id=np.int64(10) + ) self.assertEqual(row_index, 0) # Write our test file with NWBHDF5IO(self.path, 'w') as io: @@ -671,11 +617,12 @@ class SimultaneousRecordingsTableTests(ICEphysMetaTestBase): def test_init(self): """ - Test __init__ to make sure we can instantiate the SimultaneousRecordingsTable container + Test __init__ to make sure we can instantiate the SimultaneousRecordingsTable container """ ir = IntracellularRecordingsTable() - _ = SimultaneousRecordingsTable(intracellular_recordings_table=ir) - self.assertTrue(True) + ret = SimultaneousRecordingsTable(intracellular_recordings_table=ir) + self.assertIs(ret.recordings.table, ir) + self.assertEqual(ret.name, 'simultaneous_recordings') def test_missing_intracellular_recordings_on_init(self): """ @@ -691,10 +638,12 @@ def test_add_simultaneous_recording(self): Populate, write, and read the SimultaneousRecordingsTable container and other required containers """ ir = IntracellularRecordingsTable() - row_index = ir.add_recording(electrode=self.electrode, - stimulus=self.stimulus, - response=self.response, - id=np.int64(10)) + row_index = ir.add_recording( + electrode=self.electrode, + stimulus=self.stimulus, + response=self.response, + id=np.int64(10) + ) self.assertEqual(row_index, 0) self.assertEqual(len(ir), 1) sw = SimultaneousRecordingsTable(intracellular_recordings_table=ir) @@ -709,10 +658,12 @@ def test_basic_write(self): Populate, write, and read the SimultaneousRecordingsTable container and other required containers """ ir = IntracellularRecordingsTable() - row_index = ir.add_recording(electrode=self.electrode, - stimulus=self.stimulus, - response=self.response, - id=np.int64(10)) + row_index = ir.add_recording( + electrode=self.electrode, + stimulus=self.stimulus, + response=self.response, + id=np.int64(10) + ) self.assertEqual(row_index, 0) self.assertEqual(len(ir), 1) sw = SimultaneousRecordingsTable(intracellular_recordings_table=ir) @@ -725,10 +676,12 @@ def test_enforce_unique_id(self): Test to ensure that unique ids are enforced on RepetitionsTable table """ ir = IntracellularRecordingsTable() - ir.add_recording(electrode=self.electrode, - stimulus=self.stimulus, - response=self.response, - id=np.int64(10)) + ir.add_recording( + electrode=self.electrode, + stimulus=self.stimulus, + response=self.response, + id=np.int64(10) + ) sw = SimultaneousRecordingsTable(intracellular_recordings_table=ir) sw.add_simultaneous_recording(recordings=[0], id=np.int64(10)) with self.assertRaises(ValueError): @@ -746,8 +699,9 @@ def test_init(self): """ ir = IntracellularRecordingsTable() sw = SimultaneousRecordingsTable(intracellular_recordings_table=ir) - _ = SequentialRecordingsTable(simultaneous_recordings_table=sw) - self.assertTrue(True) + ret = SequentialRecordingsTable(simultaneous_recordings_table=sw) + self.assertIs(ret.simultaneous_recordings.table, sw) + self.assertEqual(ret.name, 'sequential_recordings') def test_missing_simultaneous_recordings_on_init(self): """ @@ -763,10 +717,12 @@ def test_basic_write(self): Populate, write, and read the SequentialRecordingsTable container and other required containers """ ir = IntracellularRecordingsTable() - row_index = ir.add_recording(electrode=self.electrode, - stimulus=self.stimulus, - response=self.response, - id=np.int64(10)) + row_index = ir.add_recording( + electrode=self.electrode, + stimulus=self.stimulus, + response=self.response, + id=np.int64(10) + ) self.assertEqual(row_index, 0) sw = SimultaneousRecordingsTable(intracellular_recordings_table=ir) row_index = sw.add_simultaneous_recording(recordings=[0]) @@ -781,10 +737,12 @@ def test_enforce_unique_id(self): Test to ensure that unique ids are enforced on RepetitionsTable table """ ir = IntracellularRecordingsTable() - ir.add_recording(electrode=self.electrode, - stimulus=self.stimulus, - response=self.response, - id=np.int64(10)) + ir.add_recording( + electrode=self.electrode, + stimulus=self.stimulus, + response=self.response, + id=np.int64(10) + ) sw = SimultaneousRecordingsTable(intracellular_recordings_table=ir) sw.add_simultaneous_recording(recordings=[0]) sws = SequentialRecordingsTable(sw) @@ -805,8 +763,9 @@ def test_init(self): ir = IntracellularRecordingsTable() sw = SimultaneousRecordingsTable(intracellular_recordings_table=ir) sws = SequentialRecordingsTable(simultaneous_recordings_table=sw) - _ = RepetitionsTable(sequential_recordings_table=sws) - self.assertTrue(True) + ret = RepetitionsTable(sequential_recordings_table=sws) + self.assertIs(ret.sequential_recordings.table, sws) + self.assertEqual(ret.name, 'repetitions') def test_missing_sequential_recordings_on_init(self): """ @@ -822,10 +781,12 @@ def test_basic_write(self): Populate, write, and read the RepetitionsTable container and other required containers """ ir = IntracellularRecordingsTable() - row_index = ir.add_recording(electrode=self.electrode, - stimulus=self.stimulus, - response=self.response, - id=np.int64(10)) + row_index = ir.add_recording( + electrode=self.electrode, + stimulus=self.stimulus, + response=self.response, + id=np.int64(10) + ) self.assertEqual(row_index, 0) sw = SimultaneousRecordingsTable(intracellular_recordings_table=ir) row_index = sw.add_simultaneous_recording(recordings=[0]) @@ -842,10 +803,12 @@ def test_enforce_unique_id(self): Test to ensure that unique ids are enforced on RepetitionsTable table """ ir = IntracellularRecordingsTable() - ir.add_recording(electrode=self.electrode, - stimulus=self.stimulus, - response=self.response, - id=np.int64(10)) + ir.add_recording( + electrode=self.electrode, + stimulus=self.stimulus, + response=self.response, + id=np.int64(10) + ) sw = SimultaneousRecordingsTable(intracellular_recordings_table=ir) sw.add_simultaneous_recording(recordings=[0]) sws = SequentialRecordingsTable(sw) @@ -869,8 +832,9 @@ def test_init(self): sw = SimultaneousRecordingsTable(intracellular_recordings_table=ir) sws = SequentialRecordingsTable(simultaneous_recordings_table=sw) repetitions = RepetitionsTable(sequential_recordings_table=sws) - _ = ExperimentalConditionsTable(repetitions_table=repetitions) - self.assertTrue(True) + ret = ExperimentalConditionsTable(repetitions_table=repetitions) + self.assertIs(ret.repetitions.table, repetitions) + self.assertEqual(ret.name, 'experimental_conditions') def test_missing_repetitions_on_init(self): """ @@ -926,7 +890,6 @@ def test_enforce_unique_id(self): cond.add_experimental_condition(repetitions=[0, ], id=np.int64(10)) -# TODO: Move these tests from ICEPhys to NWBFile proper class NWBFileTests(TestCase): """ Test class for testing the NWBFileTests Container class @@ -945,12 +908,8 @@ def __get_icephysfile(self): icefile = NWBFile( session_description='my first synthetic recording', identifier='EXAMPLE_ID', - session_start_time=datetime.now(tzlocal()), - experimenter='Dr. Bilbo Baggins', - lab='Bag End Laboratory', - institution='University of Middle Earth at the Shire', - experiment_description='I went on an adventure with thirteen dwarves to reclaim vast treasures.', - session_id='LONELYMTN') + session_start_time=datetime.now(tzlocal()) + ) return icefile def __add_device(self, icefile): @@ -961,7 +920,7 @@ def __add_electrode(self, icefile, device): description='a mock intracellular electrode', device=device) - def __get_stimuls(self, electrode): + def __get_stimulus(self, electrode): """ Create a dummy VoltageClampStimulusSeries """ @@ -972,7 +931,8 @@ def __get_stimuls(self, electrode): rate=10e3, electrode=electrode, gain=0.02, - sweep_number=np.uint64(15)) + sweep_number=np.uint64(15) + ) def __get_response(self, electrode): """ @@ -989,13 +949,8 @@ def __get_response(self, electrode): gain=0.02, capacitance_slow=100e-12, resistance_comp_correction=70.0, - sweep_number=np.uint64(15)) - - def test_init(self): - """ - Test __init__ to make sure we can instantiate the NWBFileTests container - """ - _ = self.__get_icephysfile() + sweep_number=np.uint64(15) + ) def test_deprecate_simultaneous_recordings_on_add_stimulus(self): """ @@ -1004,8 +959,8 @@ def test_deprecate_simultaneous_recordings_on_add_stimulus(self): nwbfile = self.__get_icephysfile() device = self.__add_device(nwbfile) electrode = self.__add_electrode(nwbfile, device) - stimulus = self.__get_stimuls(electrode=electrode) - responce = self.__get_response(electrode=electrode) + stimulus = self.__get_stimulus(electrode=electrode) + response = self.__get_response(electrode=electrode) # Make sure we warn if sweeptable is added on add_stimulus with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") # Trigger all warnings @@ -1013,7 +968,7 @@ def test_deprecate_simultaneous_recordings_on_add_stimulus(self): self.assertEqual(len(w), 1) assert issubclass(w[-1].category, DeprecationWarning) # make sure we don't trigger the same deprecation warning twice - nwbfile.add_acquisition(responce, use_sweep_table=True) + nwbfile.add_acquisition(response, use_sweep_table=True) self.assertEqual(len(w), 1) def test_deprecate_sweeptable_on_add_stimulus_template(self): @@ -1024,7 +979,8 @@ def test_deprecate_sweeptable_on_add_stimulus_template(self): local_electrode = nwbfile.create_icephys_electrode( name="elec0", description='a mock intracellular electrode', - device=nwbfile.create_device(name='Heka ITC-1600')) + device=nwbfile.create_device(name='Heka ITC-1600') + ) local_stimulus = VoltageClampStimulusSeries( name="ccss", data=[1, 2, 3, 4, 5], @@ -1032,7 +988,8 @@ def test_deprecate_sweeptable_on_add_stimulus_template(self): rate=10e3, electrode=local_electrode, gain=0.02, - sweep_number=np.uint64(15)) + sweep_number=np.uint64(15) + ) local_stimulus2 = VoltageClampStimulusSeries( name="ccss2", data=[1, 2, 3, 4, 5], @@ -1040,7 +997,8 @@ def test_deprecate_sweeptable_on_add_stimulus_template(self): rate=10e3, electrode=local_electrode, gain=0.02, - sweep_number=np.uint64(15)) + sweep_number=np.uint64(15) + ) with warnings.catch_warnings(record=True) as w: nwbfile.add_stimulus_template(local_stimulus, use_sweep_table=True) self.assertEqual(len(w), 1) @@ -1059,11 +1017,11 @@ def test_deprecate_sweepstable_on_add_acquistion(self): nwbfile = self.__get_icephysfile() device = self.__add_device(nwbfile) electrode = self.__add_electrode(nwbfile, device) - stimulus = self.__get_stimuls(electrode=electrode) - responce = self.__get_response(electrode=electrode) + stimulus = self.__get_stimulus(electrode=electrode) + response = self.__get_response(electrode=electrode) # Make sure we warn if sweeptable is added on add_stimulus with warnings.catch_warnings(record=True) as w: - nwbfile.add_acquisition(responce, use_sweep_table=True) + nwbfile.add_acquisition(response, use_sweep_table=True) self.assertEqual(len(w), 1) assert issubclass(w[-1].category, DeprecationWarning) self.assertEqual(str(w[-1].message), @@ -1083,15 +1041,11 @@ def test_deprecate_sweepstable_on_init(self): session_description='my first synthetic recording', identifier='EXAMPLE_ID', session_start_time=datetime.now(tzlocal()), - experimenter='Dr. Bilbo Baggins', - lab='Bag End Laboratory', - institution='University of Middle Earth at the Shire', - experiment_description='I went on an adventure with thirteen dwarves to reclaim vast treasures.', - session_id='LONELYMTN', - sweep_table=SweepTable()) + sweep_table=SweepTable() + ) device = self.__add_device(nwbfile) electrode = self.__add_electrode(nwbfile, device) - stimulus = self.__get_stimuls(electrode=electrode) + stimulus = self.__get_stimulus(electrode=electrode) self.assertEqual(len(w), 1) assert issubclass(w[-1].category, DeprecationWarning) # make sure we don't trigger the same deprecation warning twice @@ -1104,26 +1058,18 @@ def test_deprectation_icephys_filtering_on_init(self): session_description='my first synthetic recording', identifier='EXAMPLE_ID', session_start_time=datetime.now(tzlocal()), - experimenter='Dr. Bilbo Baggins', - lab='Bag End Laboratory', - institution='University of Middle Earth at the Shire', - experiment_description='I went on an adventure with thirteen dwarves to reclaim vast treasures.', - session_id='LONELYMTN', - icephys_filtering='test filtering') + icephys_filtering='test filtering' + ) assert issubclass(w[-1].category, DeprecationWarning) self.assertEqual(nwbfile.icephys_filtering, 'test filtering') def test_icephys_filtering_roundtrip(self): # create the base file nwbfile = NWBFile( - session_description='my first synthetic recording', - identifier='EXAMPLE_ID', - session_start_time=datetime.now(tzlocal()), - experimenter='Dr. Bilbo Baggins', - lab='Bag End Laboratory', - institution='University of Middle Earth at the Shire', - experiment_description='I went on an adventure with thirteen dwarves to reclaim vast treasures.', - session_id='LONELYMTN') + session_description='my first synthetic recording', + identifier='EXAMPLE_ID', + session_start_time=datetime.now(tzlocal()) + ) # set the icephys_filtering attribute and make sure we get a deprectation warning with warnings.catch_warnings(record=True) as w: nwbfile.icephys_filtering = 'test filtering' @@ -1145,46 +1091,49 @@ def test_get_icephys_meta_parent_table(self): returns the expected top table """ local_nwbfile = NWBFile( - session_description='my first synthetic recording', - identifier='EXAMPLE_ID', - session_start_time=datetime.now(tzlocal()), - experimenter='Dr. Bilbo Baggins', - lab='Bag End Laboratory', - institution='University of Middle Earth at the Shire', - experiment_description='I went on an adventure with thirteen dwarves to reclaim vast treasures.', - session_id='LONELYMTN') + session_description='my first synthetic recording', + identifier='EXAMPLE_ID', + session_start_time=datetime.now(tzlocal()) + ) # Add a device local_device = local_nwbfile.create_device(name='Heka ITC-1600') local_electrode = local_nwbfile.create_icephys_electrode( name="elec0", description='a mock intracellular electrode', - device=local_device) - local_stimulus = VoltageClampStimulusSeries(name="ccss", - data=[1, 2, 3, 4, 5], - starting_time=123.6, - rate=10e3, - electrode=local_electrode, - gain=0.02, - sweep_number=np.uint64(15)) - local_response = VoltageClampSeries(name='vcs', - data=[0.1, 0.2, 0.3, 0.4, 0.5], - conversion=1e-12, - resolution=np.nan, - starting_time=123.6, - rate=20e3, - electrode=local_electrode, - gain=0.02, - capacitance_slow=100e-12, - resistance_comp_correction=70.0, - sweep_number=np.uint64(15)) + device=local_device + ) + local_stimulus = VoltageClampStimulusSeries( + name="ccss", + data=[1, 2, 3, 4, 5], + starting_time=123.6, + rate=10e3, + electrode=local_electrode, + gain=0.02, + sweep_number=np.uint64(15) + ) + local_response = VoltageClampSeries( + name='vcs', + data=[0.1, 0.2, 0.3, 0.4, 0.5], + conversion=1e-12, + resolution=np.nan, + starting_time=123.6, + rate=20e3, + electrode=local_electrode, + gain=0.02, + capacitance_slow=100e-12, + resistance_comp_correction=70.0, + sweep_number=np.uint64(15) + ) local_nwbfile.add_stimulus_template(local_stimulus) # Check that none of the table exist yet self.assertIsNone(local_nwbfile.get_icephys_meta_parent_table()) # Add a recording and confirm that intracellular_recordings is the top table - _ = local_nwbfile.add_intracellular_recording(electrode=local_electrode, - stimulus=local_stimulus, - response=local_response, - id=np.int64(10)) + _ = local_nwbfile.add_intracellular_recording( + electrode=local_electrode, + stimulus=local_stimulus, + response=local_response, + id=np.int64(10) + ) self.assertIsInstance(local_nwbfile.get_icephys_meta_parent_table(), IntracellularRecordingsTable) # Add a sweep and check that the simultaneous_recordings table is the top table @@ -1217,7 +1166,7 @@ def test_add_icephys_meta_full_roundtrip(self): device = self.__add_device(nwbfile) electrode = self.__add_electrode(nwbfile, device) # Add the data using standard methods from NWBFile - stimulus = self.__get_stimuls(electrode=electrode) + stimulus = self.__get_stimulus(electrode=electrode) nwbfile.add_stimulus(stimulus) # Check that the deprecated sweep table has indeed not been created self.assertIsNone(nwbfile.sweep_table) @@ -1229,18 +1178,22 @@ def test_add_icephys_meta_full_roundtrip(self): ############################################# # Test adding IntracellularRecordingsTable ############################################# - # Check that our IntracellularRecordingsTable table does not yet exists + # Check that our IntracellularRecordingsTable table does not yet exist self.assertIsNone(nwbfile.intracellular_recordings) # Add an intracellular recording intracellular_recording_ids = [np.int64(10), np.int64(11)] - nwbfile.add_intracellular_recording(electrode=electrode, - stimulus=stimulus, - response=response, - id=intracellular_recording_ids[0]) - nwbfile.add_intracellular_recording(electrode=electrode, - stimulus=stimulus, - response=response, - id=intracellular_recording_ids[1]) + nwbfile.add_intracellular_recording( + electrode=electrode, + stimulus=stimulus, + response=response, + id=intracellular_recording_ids[0] + ) + nwbfile.add_intracellular_recording( + electrode=electrode, + stimulus=stimulus, + response=response, + id=intracellular_recording_ids[1] + ) # Check that the table has been created self.assertIsNotNone(nwbfile.intracellular_recordings) # Check that the values in our row are correct diff --git a/tox.ini b/tox.ini index 81ed4fcd2..390204ad6 100644 --- a/tox.ini +++ b/tox.ini @@ -107,9 +107,13 @@ install_command = deps = -rrequirements-dev.txt -rrequirements.txt - -rrequirements-doc.txt commands = + pip install -r requirements-doc.txt + # installing allensdk may downgrade certain requirements so + # reinstall the repo with its requirements + pip install -r requirements-dev.txt -r requirements.txt + pip install -U -e . python test.py --example [testenv:gallery-py37] @@ -130,22 +134,38 @@ commands = {[testenv:gallery]commands} # Test with python 3.9, pinned dev and doc reqs, and upgraded run requirements [testenv:gallery-py39-upgrade-dev] basepython = python3.9 + install_command = pip install -U -e . {opts} {packages} + deps = -rrequirements-dev.txt - -rrequirements-doc.txt -commands = {[testenv:gallery]commands} + +commands = + pip install -r requirements-doc.txt + # installing allensdk may downgrade certain requirements so + # reinstall the repo with its requirements + pip install -r requirements-dev.txt -r requirements.txt + pip install -U -e . + python test.py --example # Test with python 3.9, pinned dev and doc reqs, and pre-release run requirements [testenv:gallery-py39-upgrade-dev-pre] basepython = python3.9 + install_command = pip install -U --pre -e . {opts} {packages} + deps = -rrequirements-dev.txt - -rrequirements-doc.txt -commands = {[testenv:gallery]commands} + +commands = + pip install -r requirements-doc.txt + # installing allensdk may downgrade certain requirements so + # reinstall the repo with its requirements + pip install -r requirements-dev.txt -r requirements.txt + pip install -U --pre -e . + python test.py --example # Test with python 3.7, pinned dev reqs, and minimum run requirements [testenv:gallery-py37-min-req] @@ -153,7 +173,6 @@ basepython = python3.7 deps = -rrequirements-dev.txt -rrequirements-min.txt - -rrequirements-doc.txt commands = {[testenv:gallery]commands} [testenv:validation-py39]